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Abstract

Purpose – The purpose is to investigate, the difference between SERVQUAL and SERVPERF's predictive validity of service quality. Design/methodology/approach – Data from 17 studies containing 42 effect sizes of the relationships between SERVQUAL or SERVPERF with overall service quality (OSQ) are meta-analyzed. Findings – Overall, SERVQUAL and SERVPERF are equally valid predictors of OSQ. Adapting the SERVQUAL scale to the measurement context improves its predictive validity; conversely, the predictive validity of SERVPERF is not improved by context adjustments. In addition, measures of services quality gain predictive validity when used in: less individualistic cultures, non-English speaking countries, and industries with an intermediate level of customization (hotels, rental cars, or banks). Research limitations/implications – No study, that were using non-adapted scales were conducted outside of the USA making it impossible to disentangle the impact of scale adaptation vs contextual differences on the moderating effect of language and culture. More comparative studies on the usage of adapted vs non-adapted scales outside the USA are needed before settling this issue meta-analytically. Practical implications – SERVQUAL scales require to be adapted to the study context more so than SERVPERF. Owing to their equivalent predictive validity the choice between SERVQUAL or SERVPERF should be dictated by diagnostic purpose (SERVQUAL) vs a shorter instrument (SERVPERF). Originality/value – Because of the high statistical power of meta-analysis, these findings could be considered as a major step toward ending the debate whether SERVPERF is superior to SERVQUAL as an indicator of OSQ.
The validity of the SERVQUAL
and SERVPERF scales
A meta-analytic view of 17 years of research
across five continents
Franc¸ois A. Carrillat
HEC Montre
´al, Montre
´al, Canada
Fernando Jaramillo
Department of Marketing, University of Texas at Arlington, Arlington,
Texas, USA, and
Jay P. Mulki
Marketing Group, Northeastern University, Boston, Massachusetts, USA
Abstract
Purpose – The purpose is to investigate, the difference between SERVQUAL and SERVPERF’s
predictive validity of service quality.
Design/methodology/approach Data from 17 studies containing 42 effect sizes of the
relationships between SERVQUAL or SERVPERF with overall service quality (OSQ) are
meta-analyzed.
Findings Overall, SERVQUAL and SERVPERF are equally valid predictors of OSQ. Adapting the
SERVQUAL scale to the measurement context improves its predictive validity; conversely, the
predictive validity of SERVPERF is not improved by context adjustments. In addition, measures of
services quality gain predictive validity when used in: less individualistic cultures, non-English
speaking countries, and industries with an intermediate level of customization (hotels, rental cars, or
banks).
Research limitations/implications – No study, that were using non-adapted scales were
conducted outside of the USA making it impossible to disentangle the impact of scale adaptation vs
contextual differences on the moderating effect of language and culture. More comparative studies on
the usage of adapted vs non-adapted scales outside the USA are needed before settling this issue
meta-analytically.
Practical implications – SERVQUAL scales require to be adapted to the study context more so
than SERVPERF. Owing to their equivalent predictive validity the choice between SERVQUAL or
SERVPERF should be dictated by diagnostic purpose (SERVQUAL) vs a shorter instrument
(SERVPERF).
Originality/value – Because of the high statistical power of meta-analysis, these findings could be
considered as a major step toward ending the debate whether SERVPERF is superior to SERVQUAL
as an indicator of OSQ.
Keywords Services, SERVQUAL, Culture, Quality
Paper type Research paper
Over the years, marketing researchers have reached consensuses on several issues related
to the domain of services. First, as the economy has become mostly service-based,
researchers now consider the marketing discipline as being service dominated.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/0956-4233.htm
IJSIM
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Received 9 January 2006
Revised 4 February 2007
Accepted 17 May 2007
International Journal of Service
Industry Management
Vol. 18 No. 5, 2007
pp. 472-490
qEmerald Group Publishing Limited
0956-4233
DOI 10.1108/09564230710826250
Consumers in OECD countries spend more on services than for tangible goods (Martin,
1999). Indeed, service activities constitute about 70 percent of OECD (2005) countries GDP,
and this trend is expected to continue in the coming decade. The globalization of services
marketing has presented both academics and practitioners challenges and opportunities
in this area (Javalgi et al., 2006). Reflecting this changing emphasis services marketing
has become a wellestablished field of academic inquiry and now represents an alternative
paradigm to the marketing of goods (Lovelock and Gummesson, 2004).
Researchers also agree that a central topic in service research is service quality (SQ),
which is a critical determinant of business performance as well as firms’ long-term
viability (Bolton and Drew, 1991; Gale, 1994). This is because SQ leads to customer
satisfaction which in turn has a positive impact on customer word-of-mouth,
attitudinal loyalty, and purchase intentions (Gremler and Gwinner, 2000). The view
that SQ results from customers’ evaluation of the service encounter prevails in the
literature (Cronin and Taylor, 1992; Parasuraman et al., 1985). Under this perspective,
researchers further agree that SQ is best represented as an aggregate of the discrete
elements from the service encounter such as reliability, responsiveness, competence,
access, courtesy, communication, credibility, security, understanding, and tangible
elements of the service offer (Cronin and Taylor, 1992; Dabholkar et al., 2000;
Parasuraman et al., 1985).
On the other hand, the question of the operationalization of SQ has continued to evoke
discussion. This discussion has been primarily centered on two important issues. The
first relates to the debate of whether SERVQUAL or SERVPERF should be used for
measuring SQ (Cui et al., 2003; Hudson et al., 2004; Jain and Gupta, 2004; Kettinger and
Lee, 1997; Mukherje and Nath, 2005; Quester and Romaniuk, 1997). SERVQUAL,
grounded in the Gap model, measures SQ as the calculated difference between customer
expectations and performance perceptions of a service encounter (Parasuraman et al.,
1988, 1991). Cronin and Taylor (1992) challenged this approach and developed the
SERVPERF scale which directly captures customers’ performance perceptions in
comparison to their expectations of the service encounter. In spite of recent attempts in
the literature toward settling this issue, the SERVQUAL-SERVPERF debate has never
been so relevant. In fact, numerous authors have supported the view that SERVPERF is
a better alternative than SERVQUAL (Babakus and Boller, 1992; Brady et al., 2002;
Brown et al., 1993; Zhou, 2004) while, on the other hand, SERVQUAL has enjoyed and
continues to enjoy widespread acceptance as a measure of SQ (Chebat et al., 1995; Furrer
et al., 2000; Zeithaml and Bitner, 2003). In addition, the web of science reveals that the
original SERVQUAL paper published in 1988, as well as the following 1991 scale
refinement paper has both received more than 46 percent of their total citations within
the last five years. The same is true of SERVPERF, which also received more than
46 percent of its citations within the last five years. This indicates that Cronin and
Taylor’s (1994) conceptual arguments in favor of SERVPERF, while it may have
contributed to SERVPERF popularity, have not reduced SERVQUAL’s usage among
scholars. In addition, it suggests that the multilevel scale, offered by Brady and Cronin
(2001) as a reconciling perspective, has not moved researchers away from either
SERVQUAL to SERVPERF. Therefore, shedding light on whether one scale is better
than the other remains a very important question to be answered.
The second issue centers on the trade-off between the generalizability and
specificity level of the SERVQUAL and SERVPERF scales (Asubonteng et al., 1996).
The validity of
the SERVQUAL
and SERVPERF
473
A scale can be applied in more diversified contexts as its items become more abstract
(Babakus and Boller, 1992; Dabholkar et al., 2000). However, this limits the scale’s
ability to capture specific context elements (Babakus and Boller, 1992; Dabholkar et al.,
2000). There is a general acceptance of the need to modify scale items to suit study
context. However, empirical investigation regarding the impact of item adaptation on
scale validity (i.e. when original SERVQUAL/SERVPERF items versus modified items
are used) has not been undertaken. In addition, research is needed to assess the
appropriateness of the SERVQUAL/SERVPERF scales when they are used outside
the USA. This is because differences in national culture or language require not only
modification of items but also create distortions in how respondents perceive the
construct under investigation (Herk et al., 2005).
The above discussion raises several important research questions. First, are
SERVQUAL and SERVPERF adequate predictors of SQ? And, as proposed by Cronin
and Taylor (1992), is SERVPERF a better predictor of SQ than SERVQUAL? Second, is
there an improvement in the predictive validity of the SERVQUAL and SERVPERF
measures when the scale items are adapted to the study context? Third, does the
predictive power of SERVQUAL and SERVPERF depend on national culture or scale
language? Finally, is the predictive validity of SERVQUAL and SERVPERF influenced
by the type of industry in which the study is conducted?
The current study addresses these research questions by meta-analyzing empirical SQ
research. Meta-analysis is appropriate for addressing these research questions because it
systematically integrates findings across studies, controls for statistical artifacts, and
provides very robust answers about relationships among variables (Arthur et al.,2001;
Hunter and Schmidt, 2004). Our meta-analytic framework relies on 42 effect sizes from 17
empirical studies conducted across five continents spanning 17 years.
Previous research has already attempted to compare SERVQUAL and SERVPERF
(Brady et al., 2002; Cronin and Taylor, 1992; Cui et al., 2003; Hudson et al., 2004; Jain and
Gupta, 2004; Kettinger and Lee, 1997; Quester and Romaniuk, 1997). However,
considering these studies individually provide dispersed evidence that might add, rather
than subtract, ambiguity surrounding the measurement debate. For instance, Jain and
Gupta (2004) as well as Kettinger and Lee (1997) found that SERVPERF was more
strongly correlated to overall service quality (OSQ) than SERVQUAL whereas Quester
and Romaniuk (1997) reported that SERVQUAL exhibited a stronger relationship with
OSQ than SERVPERF. In some cases, studies comparing SERVQUAL and SERVPERF
focus on dimensionality issues without considering predictive validity (Cui et al., 2003;
Hudson et al., 2004). Furthermore, the aforementioned studies rely on one or two samples
at most which prevent them from drawing robust conclusions and from testing the
impact of contingency factors such as country, language, or industry. Therefore,
the current research constitutes a significant contribution to the service literature
because it provides answers to the SERVQUAL/SERVPERF validity debate tackled by
Cronin and Taylor (1992, 1994), Brady et al. (2002), and Parasuraman et al. (1994).
In addition, because meta-analysis is based on the accumulation of empirical evidence
over the years, it allows investigating moderating factors by comparing sub-groups of
studies that share a similar characteristic e.g. the country where the sample was
drawn (Lipsey and Wilson, 2001).
This paper is organized as follows. First, a review of the literature is presented
and hypotheses are developed. Second, a description of the meta-analytic
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procedure is provided. Third, results, as well as implications and suggestions for
further research, are discussed.
Conceptual background
Both SERVQUAL and SERVPERF’s operationalizations relied on the conceptual
definition that SQ is an attitude toward the service offered by a firm resulting from a
comparison of expectations with performance (Parasuraman et al., 1985, 1988; Cronin
and Taylor, 1992). However, SERVQUAL directly measures both expectations and
performance perceptions whereas SERVPERF only measures performance
perceptions. SERVPERF uses only performance data because it assumes that
respondents provide their ratings by automatically comparing performance
perceptions with performance expectations. Thus, SERVPERF assumes that directly
measuring performance expectations is unnecessary.
Research comparing the predictive validity of SERVQUAL with SERVPERF has
been based on assessing which of the two measures is a better predictor of OSQ. OSQ
has been used as the criterion because it is a global representation of the quality of the
service offered by an organization (Cronin and Taylor, 1992, 1994; Jain and Gupta,
2004; Kettinger and Lee, 1997; Quester and Romaniuk, 1997). In their comparison of
SERVQUAL with SERVPERF, Cronin and Taylor (1992) built their argument for the
superiority of SERVPERF over SERVQUAL by empirically showing that SERVPERF
is a better predictor of OSQ than SERVQUAL. Also, Parasuraman et al. (1988) assessed
the construct validity of SERVQUAL by evaluating whether the scale was an adequate
predictor of OSQ. In view of this, the predictive validity of SERVQUAL and
SERVPERF is assessed by meta-analyzing extant empirical research on the strength of
the relationship between each scale and OSQ.
The predictive validity of SERVQUAL and SERVPERF
SERVQUAL and SERVPERF are based on rigorous scale development procedures
(Parasuraman et al., 1988, 1991) and have been widely used by researchers. Therefore,
it is expected that both the SERVQUAL and SERVPERF measures of SQ will be
strongly related to OSQ. The literature on scale development does not specifically point
to a particular correlation value with a criterion against which the predictive validity of
a scale can be assessed. However, it is possible to turn to less formal guidelines
formulated by researchers. According to Cohen’s (1992) rule of thumb, a “small” effect
size is observed when the correlation is 0.10, a “medium” effect size is obtained when
the correlation is 0.30, and a “large” effect size corresponds to a correlation of 0.50.
These guidelines have been previously used to qualify the strength of meta-analytic
correlations (Jaramillo et al., 2005). Therefore, the following is hypothesized:
H1. The correlation between SERVQUAL or SERVPERF and OSQ will be strong
and above 0.50.
The disconfirmation vs performance-only debate
In Parasuraman et al.’s (1985) “disconfirmation” perspective, the SQ construct is seen as
an attitude resulting from customers’ comparison of their expectations about the service
encounter with their perceptions of the service encounter. The SERVQUAL instrument
operationalizes this construct as the difference between expected and actual (perceived)
performance (Parasuraman et al., 1988, 1991). Alternatively, SERVPERF is based on the
The validity of
the SERVQUAL
and SERVPERF
475
“performance only” perspective and operationalizes SQ as customers’ evaluations of
the service encounter. As a result, SERVPERF uses only the performance items of the
SERVQUAL scale (Brady et al., 2002; Cronin and Taylor, 1992, 1994).
In discussing the relative merits of each scale, the debate has been primarily
centered on predictive validity and specifically on whether SERVQUAL or SERVPERF
better captures SQ. First, some researchers have argued that SERVPERF is a better
measure because it does not depend on ambiguous customers’ expectations.
Arguments in favor of SERVPERF are based on the notion that performance
perceptions are already the result of customers’ comparison of the expected and actual
service (Babakus and Boller, 1992; Oliver and DeSarbo, 1988). Therefore, performance
only measures should be preferred to avoid redundancy. Second, as Teas (1993)
points out, Parasuraman et al.’s (1991) conceptualization of SQ is inconsistent with its
operationalization. Teas (1993) argues that, since Parasuraman et al. (1991) define
expectations as a type of attitudes, customer expectations must be considered as ideal
points. Hence, the Gap model implication that superior perceptions of SQ occur when
performance increasingly exceeds expectations is theoretically inconsistent. The
classical attitudinal perspective suggests that positive attitudes are formed when
evaluations of an object are close to an expected ideal point. Therefore, SQ should peak
when perceptions equal expectations (Teas, 1993).
Parasuraman et al. (1994) defended SERVQUAL by demonstrating that there was
virtually no difference in predictive power between SERVQUAL and SERVPERF.
Although, discussions have continued on whether disconfirmation-based measures are
superior to performance-only based measures (Dabholkar et al., 2000; Hudson et al.,
2004; Jain and Gupta, 2004), the above discussed arguments point toward the
superiority of SERVPERF over SERVQUAL. Thus:
H2. The relationship between SQ and OSQ is stronger when SQ is measured with
SERVPERF than with SERVQUAL.
Contextual factors
Any scale represents a compromise between relevance and the extent to which it can be
applied in a wide array of contexts (Babakus and Boller, 1992). Scale modification is
done by adding, deleting or rewording items to ensure suitability for a particular
research context. SERVQUAL and SERVPERF scale modifications have led to
discussions about:
.the universal versus context specific character of the scales; and
.whether changes to fit a specific context result in better predictive validity.
It is important to mention that in their original development, SERVQUAL and
SERVPERF were purported to be universal measures of SQ because the scale
development process relied on samples from multiple industries (Cronin and Taylor,
1992; Parasuraman et al., 1988). However, Parasuraman et al. (1988) recognize that
SERVQUAL can be adapted to the specific research needs of a particular organization.
As Rossiter (2002) indicates, the specificities of the measurement context play an
important role in construct validity.
Researchers are particularly concerned about the effect of environmental factors on
the validity of SQ scales (Babin et al., 2004). In fact, researchers have failed to replicate
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the five original dimensions of the SERVQUAL/SERVPERF scales, namely tangibility,
reliability, responsiveness, assurance, and empathy (White and Schneider, 2000).
Based on this, researchers have noted that SQ scales need to be adapted to the study
context (Carman, 1990). For instance, tangibility might not be relevant for a cable
company because the customer might never see the facilities of the service provider,
whereas it may be critical for a healthcare facility customer. In their study on the
photography industry, Dabholkar et al. (2000) dropped items related to physical
facilities (tangibility) from the original SERVQUAL because customers did not have to
visit the company’s site; however, they added items related to “salespeople pressure”
that are absent from SERVQUAL. The above discussion suggests that context adapted
versions of SERVQUAL and SERVPERF, hereinafter referred to as MQUAL and
MPERF, will have a better predictive validity than non-modified versions (QUAL or
PERF, respectively). Thus:
H3a. The relationship between SQ and OSQ will be stronger when SQ is measured
with MQUAL rather than with QUAL.
H3b. The relationship between SQ and OSQ will be stronger when SQ is measured
with MPERF rather than with PERF.
Country culture
Studies using SERVQUAL and SERVPERF have been conducted across more than 17
countries and on each and every continent. The use of these scales in an international
context raises a legitimate concern about validity across borders because research has
shown that cultural values influence customer responses on measures of SQ (Laroche
et al., 2004; Zhou, 2004). According to Herk et al. (2005), research conducted
internationally can be affected both by construct bias (i.e. the construct studied differs
across countries) and item bias (i.e. items are distorted when used internationally). For
instance, Sultan et al. (2000) found significant differences across US and European
passengers on their expectations and performance perceptions of airlines SQ. In
addition, Mattila (1999) found that Western customers are more likely than their Asian
counterparts to rely on tangible cues from the physical environment, which evidences
that the tangibility dimension of SERVQUAL is more important for them.
Researchers have found that cultural differences can also create item bias.
Steenkamp and Baumgartner (1998) show that both:
(1) the metric invariance (i.e. the interpretation of the distance between the scale
points); and
(2) the scalar invariance (i.e. whether scale latent means have systematic biases) of
items become uncertain when scales are used across cultures.
In fact, Diamantopoulos et al. (2006) found that international differences in response
styles (i.e. item wording, type of scale, etc.) generate item bias. Therefore, we propose
that SERVQUAL and SERVPERF are likely to be affected by construct and item biases
when used in international settings.
In order to account for cultural differences, it was decided to rely on Hofstede’s (1997)
individualism/collectivism (IDV) measure of national culture. IDV is useful and
parsimonious for explaining cross-cultural differences in attitudes and behaviours. Also,
IDV has satisfactory reliability and uni-dimensionality (Cano et al., 2004; Triandis, 1995).
The validity of
the SERVQUAL
and SERVPERF
477
Research indicates that IDV may affect perceptions of OSQ and its dimensions. For
instance, Furrer et al. (2000) argue that, in high individualistic cultures, consumers tend
to be independent, have an ethic of self-responsibility and demand a higher level of SQ.
Furrer et al. (2000) also note that individualistic consumers prefer to maintain a
significant distance between themselves and the service provider. In addition, their
study results show that consumers with a high degree of individualism considered
“responsiveness” and “tangibles” dimensions as more important compared to
consumers from collectivistic cultures. Individualistic customers tend focus on their
own benefits and interests, and expect the service providers to do the best in catering to
their needs (Donthu and Yoo, 1998). Thus, individualistic customers pay careful
attention to the service provided and are not likely to accept lower SQ. Donthu and Yoo’s
(1998) study showed that individualistic customers have higher OSQ expectations,
higher empathy and assurance expectations from their service providers compared to
customers from collectivistic societies. SERVQUAL and SERVPERF were developed in
the USA, a country with the highest IDV level (Hofstede, 1997). In view of this, the
existing dimensions of the SERVQUAL and SERVPERF scales should match more
closely with the expectations of consumers from individualistic countries. As a result, it
is expected that the predictive validity of SERVQUAL will be diminished in countries
with a lower IDV level:
H4a. The strength of the relationship between SERVQUAL or SERVPERF and
OSQ decreases as the degree of individualism of the country decreases.
Country language
It is generally known that language translation can be a worsening factor of cultural
bias. Even when scales are carefully translated and closely checked by experts
(Witkowski and Wolfinbarger, 2002; Zhou, 2004), the absence of a concept in a language
does not permit a perfect accuracy in scale translation (Herk et al., 2005). Thus, scale
translation can result in higher measurement error which attenuates relationships
among constructs (Hunter and Schmidt, 2004). Therefore, the following is hypothesized:
H4b. The strength of the relationship between SERVQUAL or SERVPERF and
OSQ is stronger when SERVQUAL or SERVPERF is administered in English
than when translated.
Type of services
It is expected that SERVQUAL or SERVPERF will perform differently depending on
the industry in which they are used. This is because the relevance of the scale
dimensions depends on the study setting (White and Schneider, 2000). Many
categorizations of services have been proposed in the literature (Bitner, 1992; Lovelock,
1983; Silvestro et al., 1992). Among the numerous service classifications, Silvestro
et al.’s (1992) production perspective has emerged as integrative of other service
typologies. Silvestro et al. (1992) divide service providers into the following three
groups that range from lower to higher intensity of customer processing:
(1) Professional services (PS) (i.e. low customer processing intensity) include services
provided by lawyers, business consultants, or field engineering. Some
characteristics of this group are: few transactions, highly customized,
process-oriented and long customer contact times. Value is added by front office
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service employees who rely extensively on their own judgment to perform
the service.
(2) Service shops (SS) (i.e. intermediate customer processing intensity) such as
hotels, rental cars, or banks. This group has an intermediate level of
customization and judgment from service employees. Value added is generated
in both the back and front offices.
(3) Mass services (MS) (i.e. high customer processing intensity) such as provided
by retailer, transportation, or confectionery. The group has many customer
transactions, few contact opportunities, and limited customization. Value added
comes from the back office and service employees use little judgment.
According to Silvestro et al. (1992), as the intensity of custome r-processing decreases, the
emphasis on process rather than product intensifies. The process elements of a service
are by nature intangible while the product elements are more tangible (Zeithaml and
Bitner, 2003). Therefore, less customer-processing-oriented service industries will have
more intangible service offers. Because, SERVQUAL is purp orted to measure the service
aspects of the quality of customer experience, it is expected to perform better when
customer-processing intensity decreases while intangibility increases. Thus:
H5. The strength of the relationship between SERVQUAL and OSQ decreases as
the service category moves from PS to services shop and to MS.
Methodology
All studies containing an effect size (
r
) that measures the strength of the relationship
between SQ (SERVQUAL, SERVPERF) and OSQ were eligible for inclusion.
Valid statistics included Pearson’s correlation coefficients (r) or any other statistics
that could be converted to r, such as F-value, t-value, p-value, and
x
2
. Empirical
studies published in 1988 or after and available before May 30, 2005 were included in this
meta-analysis. This timeframe is used since SERVQUAL was first published in 1988.
Study search
The following procedure was used to obtain an ample collection of studies reporting
the desired effect sizes. First, an electronic search of the following databases was
conducted: Direct Science,Emerald,ProQuest (ABI/INFORM Global and dissertation
abstracts). Second, a manual examination of the articles identified from the
computer-based searches was carried out. Third, manual searches of leading
marketing and service journals were conducted. To contact marketing researchers, a
call for working papers, forthcoming articles, conference papers, and unpublished
research was posted on ELMAR-AMA (,5,000 subscribers). The search process
yielded a total of 17 studies containing 42 effect sizes resulting from studying 9,880
respondents (Table I).
Meta-analytic model
Meta-analyses can be conducted using either a fixed-effect (FE) or a random-effect (RE)
model (Hunter and Schmidt, 2004). A FE model assumes that the same
r
value
underlies the observed effect sizes in all the studies, whereas the RE model allows for
The validity of
the SERVQUAL
and SERVPERF
479
Authors Scale
a
Country IDV score
b
Language Services
c
n
d
r
e
Angur et al. (1999) QUAL USA 91 English Mass 143 0.70
Angur et al. (1999) PERF USA 91 English Mass 143 0.72
Babakus and Boller (1992) PERF USA 91 English Shop 520 0.66
Bojanic (1991) PERF USA 91 English Pro 32 0.57
Brady et al. (2002) MPERF USA 91 English *1548 0.62
Cronin and Taylor (1992) PERF USA 91 English *660 0.60
Cronin and Taylor (1992) QUAL USA 91 English *660 0.54
Dabholkar et al. (2000) MQUAL USA 91 English Mass 397 0.78
Dabholkar et al. (2000) MPERF USA 91 English Mass 397 0.65
Freeman and Dart (1993) MQUAL Canada 80 English Pro 217 0.63
Jabnoun and Al-Tamimi (2003) MQUAL UAI 38 Non-English Mass 462 0.82
Lam (1995) PERF Hong Kong 25 English Mass 214 0.82
Lam (1995) QUAL Hong Kong 25 English Mass 214 0.69
Lam (1997) PERF Hong Kong 25 English Pro 82 0.71
Lee et al. (2000) MQUAL USA 91 English Shop 196 0.75
Lee et al. (2000) MQUAL USA 91 English Pro 128 0.59
Lee et al. (2000) MPERF USA 91 English Mass 197 0.72
Lee et al. (2000) MPERF USA 91 English Pro 128 0.71
Lee et al. (2000) MPERF USA 91 English Shop 196 0.81
Lee et al. (2000) MQUAL USA 91 English Mass 197 0.47
Mehta et al. (2000) MPERF Singapore 20 Non-English Shop 161 0.63
Mehta et al. (2000) MPERF Singapore 20 Non-English Shop 161 0.75
Mittal and Lassar (1996) MQUAL USA 91 English Pro 123 0.79
Mittal and Lassar (1996) QUAL USA 91 English Pro 123 0.77
Mittal and Lassar (1996) MQUAL USA 91 English Pro 110 0.86
Mittal and Lassar (1996) QUAL USA 91 English Pro 110 0.85
(continued)
Table I.
Coding of effect sizes
included in the
meta-analysis
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Authors Scale
a
Country IDV score
b
Language Services
c
n
d
r
e
Pariseau and McDaniel (1997) MQUAL USA 91 English Mass 39 0.71
Quester and Romaniuk (1997) PERF Australia 90 English Pro 182 0.55
Quester and Romaniuk (1997) QUAL Australia 90 English Pro 182 0.51
Smith (1999) MQUAL UK 89 English Pro 177 0.38
Smith (1999) MPERF UK 89 English Pro 177 0.36
Wal et al. (2002) QUAL South Africa 65 English Shop 583 0.08
Witkowski and Wolfinbarger (2002) MQUAL Germany 67 Non-English Mass 101 0.63
Witkowski and Wolfinbarger (2002) MQUAL USA 91 English Shop 86 0.62
Witkowski and Wolfinbarger (2002) MQUAL USA 91 English Shop 75 0.62
Witkowski and Wolfinbarger (2002) MQUAL Germany 67 Non-English Shop 114 0.56
Witkowski and Wolfinbarger (2002) MQUAL Germany 67 Non-English Mass 132 0.54
Witkowski and Wolfinbarger (2002) MQUAL USA 91 English Mass 81 0.59
Witkowski and Wolfinbarger (2002) MQUAL USA 91 English Pro 103 0.59
Witkowski and Wolfinbarger (2002) MQUAL Germany 67 Non-English Pro 105 0.58
Witkowski and Wolfinbarger (2002) MQUAL USA 91 English Shop 105 0.57
Witkowski and Wolfinbarger (2002) MQUAL Germany 67 Non-English Shop 119 0.57
Notes:
a
QUAL ¼original SERVQUAL, MQUAL ¼modified SERVQUAL, PERF ¼original SERVPERF, MPERF ¼modified SERVPERF;
b
Hofstede’s
individualism score;
c
type of service industry based on Silvestro et al. (1992);
d
sample size;
e
observed effect size; *these studies relied on multiple
industries spanning across service types and were not included in this moderator analysis
Table I.
The validity of
the SERVQUAL
and SERVPERF
481
variation of the population parameter
r
across studies. Credibility intervals (i.e. the
distribution of population parameter values) were computed in addition to confidence
intervals (i.e. the range of the true population value) (Hunter and Schmidt, 2004).
Hunter and Schmidt’s (2004) RE model was used as it accounts for both random and
systematic variance and has been shown to yield very accurate credibility intervals in
simulation studies (Hall and Brannick, 2002). Also, both the observed mean
correlations (r) and the corrected mean correlations (r
c
) were estimated by following
Arthur et al.’s (2001) procedure to account for measurement error.
Test of moderators
When estimating the significance of nominal moderator variables with two categories,
we relied on the “standard method” as advised by Schenker and Gentleman (2001) and
implemented in a recent marketing meta-analysis (Jaramillo et al., 2005). The “standard
method” consists of building only one interval around the difference between the
two-point estimates by adding and subtracting the appropriate z-value multiplied by
the square root of the sum of the squared SE of each point estimate. If that interval does
not include zero, the difference between the two point estimates is statistically
significant. The standard method is preferred to comparisons of confidence intervals
since testing of moderating hypotheses has greater statistical power (Jaramillo et al.,
2005; Schenker and Gentleman, 2001). Note that since all the moderator hypotheses
were directional, the z-value used for computing the interval around the difference
between the point estimates corresponded to a 90 percent confidence level to generate
an
a
level of 0.05 as in a one-tailed test (Jaramillo et al., 2005).
When testing for continuous moderators, or nominal moderators with more than two
categories, the weighted regression approach of Lipsey and Wilson (2001) was adopted.
This procedure consists in regressing the disattenuated effect sizes on independent
variables (continuous or dummy coded) with w
i
(the inverse variance component which
gives more weight to effect sizes coming from homogeneous distributions) as the weight
for each observation. The moderation effect of IDV is tested using weighted regression
analysis. Weighted regression analysis is adequate to test the moderating effect of IDV
since it is a continuous variable (Cano et al., 2004; Lipsey and Wilson, 2001).
Results
Table II presents the results of the meta-analysis. The overall strength of the relationship
between SERVQUAL and OSQ is larger than 0.50 (r¼0.58; r
c
¼0.68;
CI90percent ¼0:50 20:66). The average SERVPERF and OSQ correlation is also larger
than 0.50 (r¼0.64; r
c
¼0.75; CI90percent ¼0:52 20:77). Since, the lower bound values
of the 90 percent confidence intervals for both SERVQUAL and SERVPERF are above
0.50, the grand mean correlations can be interpreted as large (Cohen, 1992). This indicates
that both SERVQUAL and SERVPERF are valid measures of SQ, thus bringing support
for H1. The presence of moderators of the SERVQUAL-OSQ and SERVPERF-OSQ
relationships is evidenced in statistically significant Q-statistics (Table II). The Q-statistic
is distributed as a
x
2
with k21 degrees of freedom and is compared to the corresponding
critical
x
2
statistic. A significant Q-statistic demonstrates that the effect size distribution
is heterogeneous and indicates that the population varies systematically according to
some factors other than subject level sampling and measurement errors (Lipsey and
Wilson, 2001).
IJSIM
18,5
482
k
a
n
b
r
c
r
c
d
Q-statistic
e
Confidence
interval
f
Credibility
interval
g
Percentage of variance
explained
h
FS N
i
Overall 42 9.880 0.61 0.71 288 0.56-0.66 0.44-0.98 67.9 2.982
SERVQUAL 27 5.082 0.58 0.68 241 0.50-0.66 0.33-1.03 70.9 1.836
QUAL 7 2.015 0.46 0.54 56 0.27-0.66 0.11-0.96 71.6 378
MQUAL 20 3.067 0.66 0.77 57 0.60-0.72 0.57-0.97 64.4 1.540
SERVPERF 15 4.798 0.64 0.75 38 0.52-0.77 0.34-1.15 68.7 1.125
PERF 7 1.751 0.65 0.73 11 0.59-0.71 0.62-0.84 64.2 511
MPERF 8 2.965 0.64 0.75 29 0.57-0.70 0.64-0.87 42.9 600
English speaking 34 8.525 0.60 0.70 260 0.54-0.66 0.42-0.97 67.8 2.380
Non English speaking 8 1.355 0.69 0.79 19 0.60-0.77 0.63-0.96 61.6 632
Notes:
a
Number of effect sizes;
b
sample size;
c
attenuated mean effect size;
d
disattenuated (i.e. corrected) mean effect size;
e
critical values range from 12.59
to 56.93;
f
at the 95 percent level;
g
at the 90 percent level;
h
variance explained by sample and measurement artifact;
i
fail-safe N: number of studies with an
effect size of zero (r
i
¼0) needed to reduce the mean effect size (r
c
) to 0.01
Table II.
Overall meta-analytic
results and categorical
moderators for the
relationship between SQ
and OSQ
The validity of
the SERVQUAL
and SERVPERF
483
H2 posited that the relationship between SERVPERF and OSQ is stronger than
the SERVQUAL-OSQ relationship. However, a comparison of the strength of these
relationships reveals no significant difference. As shown in Table II, although the
mean SERVPERF-OSQ correlation (r
c
¼0.75) is larger than the SERVQUAL-OSQ
correlation (r
c
¼0.68), the difference is not statistically significant. In effect, the
90 percent confidence interval for the difference between the two point estimates
(r
c
¼0.75 and r
c
¼0.68) includes zero (CI90percent ¼20:06 to 0:19), indicating that
there is no significant difference between the predictive validity of SERVQUAL versus
SERVPERF (Schenker and Gentleman, 2001).
H3a and H3b stated that the modified SERVQUAL or SERVPERF scales would be
more strongly related to OSQ than the original scales. The observed difference between
the predictive validity of the original SERVQUAL and its modified version was
statistically significant (QUAL r
c
¼0.54 vs MQUAL r
c
¼0.77;
D
r
c
¼0.23,
CI90percent ¼0:06 20:40). This suggests that the predictive validity of SERVQUAL
increases when it is adapted to the study context. However, the observed difference
between the predictive validity of the original version of SERVPERF and its modified
version was not statistically significant (PERF r
c
¼0.73 vs MPERF r
c
¼0.75;
D
r
c
¼0.02, CI90percent ¼20:04 20:09). This suggests that the predictive validity of
SERVPERF does not change when the scale is modified.
According to H4a and H4b, the predictive validity of SERVQUAL on OSQ
decreases:
.as the individualism of the country sample decreases; and
.when the study is conducted in a non-English speaking country.
A weighted regression with the disattenuated correlations between SQ and OSQ as the
dependent variable, and IDV as the independent variable, revealed that a country’s
individualism negatively impacts the predictive validity of SERVQUAL (B¼20.001,
p,0.05), which is contrary to what was hypothesized in H4a (Table III). In addition, the
mean effect size for English speaking countries was smaller than the mean effect size for
non-English speaking countries (non-English speaking r
c
¼0.79 vs English speaking
r
c
¼0.70; CI90percent ¼0:04 20:16); thus, not providing support for H4b (Table II).
According to H5, when moving from lower to higher levels of customer processing
intensity, the predictive validity of SERVQUAL on OSQ should decrease. H5 implied
that the SERVQUAL-OSQ relationships should be strongest for PS followed by SS, and
weakest for MS. As shown in Table III, the strongest SERVQUAL-OSQ relationships
Model
b
Adjusted SE
a
z-value
Individualism-collectivism y¼b1x1þ1B
1
¼20.001 0.0004 22.68 *
Industry type
b
y¼b1x1þb2x2þ1B
1
¼0.096 0.035 2.78 *
B
2
¼20.12 0.037 23.28 *
Notes: *Significant at 1¼0.05;
a
when applied in a meta-analytic study, although the
b
coefficient
estimates are accurate, their standard errors need to be adjusted; Lipsey and Wilson (2001) indicate
that the standard errors of the
b
coefficients need be divided by the square root of the mean square
residuals of the regression model in order to yield z-value used for significance testing;
b
professional
services is the base level; B
1
corresponds to service shops and B
2
to mass services
Table III.
Results for continuous
moderators and
multimodal categorical
moderators
IJSIM
18,5
484
are for SS (B
1
¼0.096, p,0.05), followed by PS (base line), and then MS (B
2
¼20.12,
p,0.05). Hence, H5 is not supported.
Discussion
The study results have important implications because they question isolated findings
from earlier studies. In spite of the discussions and several arguments provided by
researchers about the superiority of SERVPERF over SERVQUAL (Cronin and Taylor,
1992, 1994), the results of this meta-analysis suggest that both scales are adequate and
equally valid predictors of OSQ. Because of the high statistical power of meta-analysis
(Cohn and Becker, 2003), these findings could be considered as a major step toward ending
the debate whether SERVPERF is superior to SERVQUAL as an indicator of OSQ.
As Parasuraman et al. (1994) pointed out, the use of performance-only (SERVPERF)
vs the expectation/performance difference scale (SERVQUAL) should be governed by
whether the scale is used for a diagnostic purpose or for establishing theoretically
sound models. We believe that the SERVQUAL scale would have greater interest for
practitioners because of its richer diagnostic value. By comparing customer
expectations of service versus perceived service across dimensions, managers can
identify service shortfalls and use this information to allocate resources to improve SQ
(Parasuraman et al., 1994).
Our findings also reveal that the need to adapt the measure to the context of the
study is greater when SERVQUAL rather than SERVPERF is used. In effect, the
original versions of SERVQUAL had a significantly lower OSQ predictive validity
than the modified versions. However, both the original and modified versions of
SERVPERF had the same level of OSQ predictive validity. This has important
implications for both practitioners and academics. Practitioners using SERVQUAL for
OSQ diagnostic purposes need to spend greater effort in modifying the scale for
context than SERVPERF users.
Our results also show an interesting pattern. Since, SERVQUAL and SERVPERF
were originally developed in the USA, we expected that the predictive validity of these
instruments would be higher when used in countries with national cultures and
languages similar to the US. However, results show that the predictive validity of
SERVQUAL and SERVPERF on OSQ was higher for non-English speaking countries
and for countries with lower levels of individualism. A closer examination of the
sample used in our study revealed that all studies conducted in non-English speaking
countries as well as those conducted in less individualistic countries relied on modified
versions of the SERVQUAL scale. Hence, scale modification rather than cultural
context could be driving the results. Since, there were no studies conducted outside the
US using non-modified scales, it was not possible to isolate the effect of national culture
and language. Further, research is needed to address this important issue. An
interesting avenue would be an experimental design where respondents outside the US,
would be given a modified scale (i.e. adapted to the industry context) and others would
be given the original items; this would allow teasing apart the effects of culture and
scale adaptation on the scale’s validity.
Finally, results suggest the predictive validity of SERVQUAL on OSQ is highest in
medium customer processing intensity contexts with an intermediate degree of
intangibility (SS) followed by low customer processing intensity (PS) and high
customer processing intensity (MS).
The validity of
the SERVQUAL
and SERVPERF
485
A plausible explanation for this finding is that SERVQUAL was developed as a
scale generalizable across service contexts. Hence, predictive validity peaks in the
category that represents a compromise between the emphasis on process and product
(i.e. service shop). Another reason could be the varying degree of importance of the
service used in the analysis to the customer. Additional research is needed for a better
understanding of this result.
With the growing proliferation of technology based self-service (SST) encounters,
factors that contribute to satisfaction and dissatisfaction in the SST customer
interaction have drawn considerable interest from researchers and practitioners
(Meuter et al., 2000). Further, research could explore the degree of predictive validity of
SERVQUAL on OSQ in SST customer interactions.
Like any other meta-analysis, this study is subject to the file drawer problem which
prevents the true effect size from being uncovered (Lipsey and Wilson, 2001). However,
as shown in Table II, the fail-safe Nstatistic reveals that several hundred studies
unaccounted for, with an effect size of zero, would be necessary to nullify the effect
sizes computed. This strengthens the confidence in the results obtained. Finally, in this
study, SERVQUAL and SERVPERF were only assessed through their predictive
validity of OSQ. A future meta-analysis could employ additional validation techniques.
For example, meta-analysis can be used to construct a broader nomological network
that includes constructs related to SQ such as customer satisfaction, customer loyalty,
purchase intention, and word-of-mouth (Zeithaml, 2000). Researchers could then assess
whether using SERVQUAL or SERVPERF affects the effect of SQ on the above
referred constructs.
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Corresponding author
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... A review of the literature reveals that the most popular scales used to measure service quality in higher education are: (a) SERVQUAL-Service Quality (6); (b) SERVPERF-Service Performance (7); (c) HedPERF-Higher Education Performance (5) and (d) a merged SERVPERF-HedPERF (5,7). These scales were used to assess services in higher education in different parts of the world (8)(9)(10)(11)(12). ...
... While SERVQUAL considers both the expectations and perceptions of customers' evaluation, SERVPERF merely considers the customers' perceptions. The review was shown that both SERVQUAL and SERVPERF are equally valid predictors of overall service quality (8,13). Depending on the purpose of study, type of services, and level of involvement, the appropriate tool could be selected. ...
... SERVQUAL is considered helpful for diagnostic purpose, and SERVPERF is recommended for the sound theoretical model. Despite the common usage of SERVQUAL, there seems to be solid support for the performance-based model, SERVPERF, even in the context of higher education (8,14). The practitioners will have less effort in modifying tools for specific contexts with SERVPERF than SERVQUAL. ...
Article
Full-text available
Purpose This article aims to assess the validity and reliability of the SERVPERF scale used for evaluating the quality of training services at the Hanoi University of Public Health. Methodology The research team used the SERVPERF scale, and translated and standardized this instrument. The self-structured questionnaire based on the SERVPERF scale was administered to 350 students currently attending formal courses at the University. Factor analysis was performed with Cronbach’ Alpha as a measure of internal consistency of the instrument items to assess the scale's reliability. Confirmatory factor analysis (CFA) was used to validate the relevance of the scale. Findings A total of 350 students were studied. All the interscale correlations were positive and significant. The overall statistical value for Cronbach's alpha was equal to 0.91 (95% CI: 0.91-0.94), and in all domains, this value ranged from 0.7 to 0.92. The factor analysis identified eight factors that explain 66.6% of the variance, 5 of which consisted of the same structure as the theoretical model's five factors (domains). Value The University should use SERVPERF to assess the quality of training services yearly so that proper adjustments can be made to better the quality of training, thereby enhancing students’ satisfaction and confidence in service quality.
... Then they were able to avoid many of the defects of the previous scale and designed the famous (SERVQUAL) scale, which consists of five dimensions (Parasuraman et al., 1988), also known as the gap model and as the gap scale, and its validity test to measure what is known as the five gaps of service quality. Carlett and his colleagues indicate that service is measured (Carrillat et al., 2007)-usually-in five main dimensions: ...
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This study aims to reveal the gap between the perceptions and expectations of the quality of services delivered to participants at scientific conferences held at emerging Saudi universities (using University of Hafr Al Batin as a model). The study also reveals if there are differences, with statistical significance, in the sample response due to several variants such as gender, nationality, and the attendance rate of conferences per year. The research adopts the descriptive approach and uses SERVQUAL instruments. It has been applied to a random sample of 155 persons. The study outcomes show that the expectations are higher than the perceptions in terms of all dimensions and also demonstrate the differences that exist due to the attendance rate of conferences per year in favor of those who attended three or more conferences. The study provides suggestions to raise the quality of services delivered to the participants at these conferences.
... This perception may emerge especially with the evaluation of student satisfaction. However, despite the positive and significant effect of service quality in particular, academic and non-academic aspects, program issues, university reputation, quality management practices, and access to university facilities on student satisfaction [18,21,24], there are limited studies specifically focusing on the context of mediating the role of distance education practices during the COVID-19 pandemic in the educational administration and leadership (EAL) literature that examines how the services and activities offered by universities affect students' commitment, their perceptions, and satisfaction with the sustainability of the services quality of higher education [18,[25][26][27][28], the quality of service measured from the perspective of students' commitment, and decision-making processes from the perspective of organizational image [18,21,29]. ...
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In the present study, the purpose was to determine the direct and indirect effects of the crisis management skills and distance education practices of universities on student satisfaction and organizational image in the continuing Coronavirus pandemic. To conduct the study, a questionnaire was applied to 467 students who had to receive compulsory distance education at TRNC universities during the pandemic process. The relation levels between the crisis management and distance education practices of universities, corporate image, student satisfaction, and direct and indirect effects between the variables, were designed with a structural equation modeling by forming hypotheses according to the sub-dimensions of the student satisfaction scale. The findings of the study showed that as the crisis management of the university administrations in the pandemic process was perceived positively by the students, their organizational image and satisfaction increased. However, it was detected that there was a lower level of relationship between attitudes towards distance learning and crisis management, and that this had a limited effect on student satisfaction. It was concluded that the structural equation model can be used to explain the causal relationship between the variables. The study also showed that the determinants of organizational image and student satisfaction in education must be understood better and that universities must review their crisis management and distance education practices and develop new service plans.
... In this current study, the adapted SERVPERF scale had seven (7) dimensions with 42 items ("tangibles, reliability, responsiveness, assurance, empathy, reputation/image, and understanding"). Literature has supported the reliability and validity of SERVPERF due to its ability to produce a better result (Abdullah, 2006;Carrillat et al., 2007;Bayraktaroglu, & Atrek, 2010 (Ramsden, 1991;Wilson et al., 1997;Griffin et al., 2003). The original CEQ was developed and validated by Ramsden (1991) and Wilson et al. (1997) in Australia. ...
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Criticisms have been raised against the quality in Management Education Programme (MEP) for failing to produce competent graduates for the job market. This study examined the perceptions of lecturers and students on quality in the MEP in a HE. The study was rooted within TQM theory, Expectation-Confirmation Theory (ECT) and CIPP Model of programme evaluation. The study employed sequential explanatory mixed methods design within the pragmatism research philosophy. The population was Mangement lecturers and final year students in UCC. Census method was used to include 43 lecturers and 529 students and interviews were conducted among eight (8) lecturers and twelve (12) students. The data were collected using QUAMEP-Q and Follow-up Interview Guide (FIG) and processed via SPSS version 25.0, AMOS version 21.0 and PROCESS Macro version 3.3. Thematic analysis was employed for qualitative data. It was discovered that the lecturers and students perceived a moderate level of quality in the programme in terms of quality: learning environment (QLE), services (QS), teaching (QT), student engagement (QSE) and student competences acquisistion (SCA). They were, also, moderately satisfied (SAT) with the programme. These were as a result of large class size, low quality and inadequate facilities, learning resources, support systems, health and accommodations services, unfavourable learning environment, high workload and lack of practical delivery of lessons. Further, the study established that QLE and QS significantly influence QT. There was significant conditional direct and indirect influence of QLE on QSE as moderated by QT and QS. Also, SCA and SAT with the programme were significantly conditionally predicted by QLE, QS, QT and QSE. The age of students significantly influence their perceptions toward quality drivers in the programme. The study recommended that the Management of the University should continue to provide and strengthen quality culture by fostering continuous improvement in QLE, QS, QT QSE, SCA, and SAT with the programme. They should make every effort for the provision of quality instructional resources, learning climate and infrastructure facilities to help reduce the large class size. The lecturers should continue to highly engaged the students and not relent in equipping the students with the 21st century employability skills. Keywords: Context, Input, Process and Product (CIPP) model, Conditional Process Analysis, Expectation-Confirmation Theory (ECT), Management Education Programme (MEP), Moderated Mediation Analysis, Quality Learning Environment (QLE), Quality Service (QS), Quality Student Engagement (QSE), Quality Teaching (QT), Student Competence Acquisition (SCA), Student Satisfaction (SAT), Total Quality Management (TQM)
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Student's satisfaction – measurement, models, implications Some changes in the environment that contemporary societies struggle with determine the level of quality of their life, make forecasting the future more complicated, and generate unprecedented challenges. The sources of the greatest threats and challenges for contemporary universities are related to international competition in the higher education sector, student mobility and the growing popularity of non-formal and informal learning paths. These and many other conditions for the functioning of modern universities imply the need for a holistic approach to students, and focus not only on educational aspects, but also are related to their personal and social development, and provide them with a sense of care and support from the university during their studies. It should result in students' satisfaction with their studies and a sense of conviction about making the right decision related to education at the higher level as the best path of education. The main purpose of the monograph is to identify determinants and consequences of students’ satisfaction, measure its level and develop models. The theoretical part of the work presents, inter alia, the current situation of Polish higher education, controversy related to the contemporary face of Polish universities, demographic and technological determinants of forecasts for universities, the most relevant aspects related to the quality of education, with particular emphasis on modern teaching methods, as well as the features and expectations of generation Z. The attention is also paid to contemporary concepts connected with managing university relations with students as the key group of their stakeholders. Due to the significant role of students’ satisfaction as the subject of this monograph, various ways of interpreting this concept, its determinants and consequences, methods of measurement and models, as well as benefits for the university resulting from the satisfaction of this group of its stakeholders are presented. The empirical part of the work features the assumptions, results and conclusions of the author's qualitative and quantitative research. The first, exploratory one, was carried out using the in-depth individual interview (IDI) and focus group interview (FGI) methods. The purpose of the qualitative studies was to identify the factors and consequences of students’ satisfaction and dissatisfaction. The objective of quantitative research was primarily to determine the level of students’ satisfaction with their studies, determinants and successors of this phenomenon, as well as to create models of students’ satisfaction. An attempt was also made to define the causes and consequences of dissatisfaction and post-purchase dissonance among students related to their studies. The research was carried out on a sample of 1,600 students from four universities, by means of the method of an auditorium survey. In the research such specialized methods of measuring satisfaction and loyalty of respondents as CSI, NPS, and IPA analysis were applied. The author's research has confirmed many of the research results presented in the theoretical part of the monograph, especially those relating to the factors of students’ satisfaction and its consequences. The most important differences between the research results presented in the literature and those obtained by the author concern the timetable, which turned out to be the most important factor for the author's respondents, and did not appear in any of the studies presented in the theoretical part. The most significant satisfaction factors in the author's research was also the majors offer (not present in the research of other authors). The main conclusions resulting from the secondary and primary research carried out by the author focus on the need to meet the expectations of students with regard to the quality of education that will enable them to undertake satisfactory work, as well as in the scope of such organization of studies, with particular emphasis on the timetable, so that they can take up a job while studying at the same time. As the author's research shows, the precise expectations of students denote maturity of most of them, expressed in responsible planning of their education, with a view to maximize the resulting benefits in their professional and personal life. The striving to meet their expectations, necessary in the context of students’ satisfaction, is also associated with the need to develop study programs, curricula and majors very important for students, based on monitoring of current changes in the labor market and related to professional forecasts and scenarios. It is also extremely important to use appropriate forms of educating students, based on the use of activating methods (e.g. PBL, RBL), while reducing the number of hours spent on the so-called an indicative form of education, which is a classic lecture (the least effective in terms of the results of education). The effectiveness of activities aimed at meeting the expectations of students and achieving their satisfaction and loyalty is directly related to the multi-area skills and competences of academic teachers, as well as their friendly and positive attitude towards students, which is repeatedly emphasized at work, as well as their commitment which is inspiring and motivating.
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Abstract The current study illustrates the influence of restaurant ambient conditions on customers satisfaction in the tourism and hospitality industries through cluster and simple random techniques. The primary objective was to ascertain the relationship between the restaurant ambient conditions and customer satisfaction in rural restaurants. A closed-ended questionnaires with varying options were designed to collect primary data from randomly selected customers from 11 restaurants from the study settings. Primary data was analysed via the SPSS software based on statistical tools of regression analysis to determine the relationship between the dependent and independent variables. Final outcomes indicated significant relationship between the dependent and independent variables. The restaurant ambient conditions have significant relationship with customer satisfaction. Based on the findings, this study recommends that owner-managers of restaurants in rural areas need to improve the bulk of the ambient situations to attract more customers. This empirical study contributes to existing literature on the tourism and hospitality industry with specific reference to the restaurant businesses. Keywords: Ambient situation, Customer satisfaction, restaurant, environment, and customer expectations
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