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IJCHM
28,1
Customer satisfaction and its
measurement in hospitality
enterprises: a revisit and update
2
Received 2 April 2015
Revised 12 May 2015
Accepted 13 June 2015
Abraham Pizam
Rosen College of Hospitality Management, University of Central Florida,
Orlando, Florida, USA
Valeriya Shapoval
Department of Tourism, Events and Attractions, Rosen College of Hospitality
Management, University of Central Florida, Orlando, Florida, USA, and
Taylor Ellis
College of Business Administration, University of Central Florida,
Orlando, Florida, USA
Abstract
Purpose – This paper aims to review and discuss customer satisfaction and its application to the
hospitality and tourism industries. This paper defines the concept and analyzes its importance to
services in general and to hospitality/tourism services in particular. This paper is a revision and update
of an article previously published by Pizam and Ellis (1999) on customer satisfaction measurements.
Design/methodology/approach – The most recent research on customer satisfaction
measurements and scales is summarized and presented in the paper.
Findings – Following a discussion on the dimensions and attributes of satisfaction, the main methods
of measuring satisfaction are listed, and cross-cultural issues that affect satisfaction are reviewed.
Finally, the paper concludes with a comprehensive review of the current online tools and techniques
available for measuring customer satisfaction.
Research limitations/implications – This summary gives a good overview to researchers who
require a comprehensive review of the available research measurements and scales for customer satisfaction.
Originality/value – For the past decade, a considerable amount of research has been conducted in
customer satisfaction. Finding the appropriate measurements and scales for customer satisfaction can
be time-consuming and confusing. This paper provides a comprehensive overview of the best-known
measurements and scales in customer satisfaction research. The paper also provides innovative online
tools and techniques available for research.
Keywords Tourism, Service quality, Customer satisfaction, Hospitality, Online surveys,
Measurement scales
Paper type General review
International Journal of
Contemporary Hospitality
Management
Vol. 28 No. 1, 2016
pp. 2-35
© Emerald Group Publishing Limited
0959-6119
DOI 10.1108/IJCHM-04-2015-0167
An earlier version of this article was published by Pizam and Ellis (1999) in the International
Journal of Contemporary Hospitality Management (IJCHM). This current version is a revision and
update of the authors’ previous work on customer satisfaction measurement.
Measuring service quality via customer satisfaction
Customer satisfaction is the leading criterion for determining the quality that is actually
delivered to customers through the product/service and by the accompanying servicing
(Vavra, 1997, 2002; Ganguli and Roy, 2011; Gil et al., 2008). Simply stated, customer
satisfaction is essential for corporate survival. Several studies have found that it costs
about five times more time, money and resources to attract a new customer than to retain
an existing customer (Naumann, 1995; Helm et al., 2010; Xu and Goedegebuure, 2005).
This creates the challenge of maintaining high levels of service, awareness of customer
expectations and improvement in services and products.
Knowledge of customer expectations and requirements, Hayes states, is essential for
two reasons: “it provides understanding of how the customer defines quality of service
and products, and facilitates the development of customer satisfaction questionnaires”
(Hayes, 1997, p. 7). Furthermore, customer satisfaction is important to all commercial
firms because of its influence on repeat purchases and word-of-mouth recommendations
(Berkman and Gilson, 1986; Tsao and Hsieh, 2012; Abubakar and Mavondo, 2014; Kwun
et al., 2013; Ha and Hyunjoo, 2012):
Satisfaction reinforces positive attitudes toward the brand, leading to a greater likelihood that
the same brand will be purchased again […] dissatisfaction leads to negative brand attitudes
and lessens the likelihood of buying the same brand again (Assael, 1987, p. 47).
Or as others suggested:
[…] if consumers are satisfied with a product or brand, they will be more likely to continue to
purchase and use it and to tell others of their favorable experience with it […] if they are
dissatisfied, they will be more likely to switch brands and complain to manufacturers,
retailers, and other consumers about the product (Peter and Olson, 1987, p. 512).
Customer satisfaction is also the cheapest means of promotion (Kozinets et al., 2010; Ha
and Hyunjoo, 2012). Various researchers have found that this ratio ranges from about 10
to 1 (Knutson, 1988, p. 17) to 5 to 1 (Naumann, 1995, p. 22).
There are several ways to assess the quality of services and customer satisfaction
through subjective, or soft, measures of quality, which focus on customer perceptions
and attitudes instead of more concrete objective criteria. These soft measures include
customer satisfaction surveys and questionnaires to determine customer attitudes and
perceptions of the quality of the service (Hayes, 1997, p. 2; Vavra, 2002). Because the
extent to which goods or services meet customers’ needs and requirements is the index
by which quality is determined, customers’ perceptions of service are vital in identifying
customer needs and satisfaction.
To be successful, a customer satisfaction measurement (CSM) program must come
from and be incorporated into the firm’s corporate culture (Naumann, 1995, p. 12). In
today’s competitive environment, one of the most important goals of corporate cultures
is retaining and satisfying current and past customers. Experience shows that only
“consumer-oriented” corporations can achieve this goal. These companies focus on the
needs and wants of specific target groups and then work hard to maximize satisfaction
with the product or service being offered (Vavra, 1997, p. 12, 2002). Instead of waiting for
customer complaints to let the company know when something is not satisfactory or is
wrong, a “consumer-oriented” corporate culture seeks continual feedback from
customers through repeated CSMs (Vavra, 1997, p. 13, 2002).
Customer
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4
In reality, application of CSM often does not accomplish the objectives of the
researcher or company. The reasons for this shortfall are numerous. First, organizations
often set customer satisfaction goals without any clear understanding of the current
customers’ satisfaction levels (Dutka, 1994; Ograjenšek and Gal, 2012). Second,
companies that measure customer satisfaction do not always act on the results obtained
(Dutka, 1994; Ograjenšek and Gal, 2012; Fecikova, 2004). Finally, as organizations
become more experienced with CSM, problems become increasingly apparent. For
example, Jones and Sasser (1995) observed that satisfaction data do not always correlate
highly with organizational performance. This was supported by customers who
responded that they were satisfied with the organization but purchased goods and
services elsewhere. Finding a strong relationship between satisfaction scores and
performance does not ensure economic success. In the long run, the level of satisfaction
may decline; customers’ attitudes and desires change, and new competition may emerge.
Quality may no longer provide a clear competitive advantage. Butz and Goodstein
(1996) found that an increasing number of managers reported that product innovation
and quality no longer provided a competitive advantage. Because of this situation, the
search for a competitive advantage has shifted from internal processes and structure to
markets and customers. As a result, an increasing number of organizations are
reorienting their strategy for superior value delivery (Band, 1991; Gale, 1994; Ryu et al.,
2012). These authors usually cite one or more of the following four types of evidence to
support their position:
(1) widely publicized success stories (e.g. AT&T, Federal Express, Xerox, Eastman
Chemical Company);
(2) analysis of profit impact of marketing strategy data that shows a strong
relationship among quality, market share and profitability (Gale, 1994);
(3) studies that show a positive relationship between market orientation and
organizational performance (Jaworski and Kohli, 1993; Griffin and Edwards,
2012; Narver and Slater, 1990; Qu, 2014); and
(4) analyses of costs demonstrating that customer retention is substantially less
expensive than customer acquisition (Birch, 1990; Hennig-Thurau, 2004).
Because of the overwhelming number of authors encouraging organizations to provide
customer value, the question becomes, how to do it? This article will provide valid
methods that an organization can use to measure the value it provides to customers.
What exactly is customer satisfaction?
Customer satisfaction is a psychological concept that involves the feeling of well-being
and pleasure that results from obtaining what one hopes for and expects from an
appealing product and/or service (World Tourism Organization, 1985). Social
psychologists, marketing researchers and students of consumer behavior have studied
customer satisfaction and dissatisfaction extensively. The increasing importance of
quality in the service and manufacturing industries has also created a proliferation of
research, with tens of thousands of academic and trade articles published on this topic
within the past three decades (Peterson and Wilson, 1992). Several conferences have
been devoted to the subject, and extensive literature reviews have been published
(Barsky, 1992; Day, 1977; Oh and Parks, 1997; Ross et al., 1987; Ariffin et al., 2012; Kilic
and Bekar, 2012; Cronin et al., 2000; Djekic et al., 2014; Lin and Mattila, 2010; Deng et al.,
2013; Szymanski and Henard, 2001).
The result of all this research has been the development of nine distinct theories of
customer satisfaction. The majority of these theories are based on cognitive psychology;
some have received moderate attention, while other theories have been introduced
without any empirical research. The nine theories include expectancy disconfirmation,
assimilation or cognitive dissonance, contrast, assimilation-contrast, equity, attribution,
comparison level, generalized negativity and value precept (Oh and Parks, 1997).
Numerous researchers have attempted to apply customer satisfaction theories
developed by consumer behaviorists to lodging (Barsky, 1992; Ekinci and Riley, 1998;
Bilgihan et al., 2014), restaurant (Dube et al., 1994; Oh and Jeong, 1996; Lee et al., 2012;
Weiss et al., 2005), airline (Elkhani et al., 2014), foodservice (Almanza et al., 1994; Lee
et al., 2012) and tourism (Hudson and Shephard, 1998; Pizam and Milman, 1993; Zehrer
et al., 2014) to investigate the applicability of customer satisfaction to the hospitality and
tourism industries.
Although various approaches explain customer satisfaction and dissatisfaction, the
most widely used is the one proposed by Oliver (1980), who proposed the expectancy
disconfirmation theory. According to this theory, which has been tested and confirmed
in several studies (Oliver and DeSarbo, 1988; Piercy, and Ellinger, 2015; Tong, and
Walther, 2015; Lankton, and McKnight, 2012; Hsu, 2003), customers purchase goods and
services with pre-purchase expectations about anticipated performance. Once the
product or service has been purchased and used, outcomes are compared against
expectations. When the outcome matches expectations, confirmation occurs.
Disconfirmation occurs when there are differences between the expectations and the
outcomes. Negative disconfirmation occurs when product or service performance is less
than expected. Positive disconfirmation occurs when product or service performance is
better than expected. Satisfaction is caused by confirmation or positive disconfirmation
of consumer expectations, and dissatisfaction is caused by negative disconfirmation of
consumer expectations.
Customer satisfaction can also be defined as satisfaction based on an outcome or a
process. Vavra’s (1997, p. 4) outcome definition of customer satisfaction characterizes
satisfaction as the end state resulting from the experience of consumption. This end
state may be a cognitive state of reward, an emotional response to an experience or a
comparison of rewards and costs to the anticipated consequences. Vavra also defined
customer satisfaction based on a process, emphasizing the perceptual, evaluative and
psychological processes that contribute to customer satisfaction (Vavra, 1997, p. 4). In
this definition, satisfaction is assessed during the service delivery process.
Several researchers believe the satisfaction process is subjective in expectations but
objective in perceptions of product attributes, or outcome. Thus, Klaus (1985, p. 21)
defined satisfaction “[…] as the customer’s subjective evaluation of a consumption
experience, based on some relationship between the customer’s perceptions and
objective attributes of the product”. Others point out that what is perceived (outcome)
and what is expected are subjective and, therefore, psychological phenomena – not
reality (Maister, 1985; Tsitskari et al., 2009). The importance of the subjective nature of
the process cannot be overstated. As expectations and perceptions are psychological
phenomena, they are susceptible to external influences and manipulation. As an
Customer
satisfaction
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illustration of how expectations can be explicitly manipulated, Sasser et al. (1979, p. 89)
noted:
Some restaurants follow the practice of promising guests a waiting time in excess of the
expected time. If people are willing to agree to wait this length of time, they are quite pleased
to be seated earlier, thus starting the meal with a more positive feeling (Maister, 1985, p. 114).
6
An example of creating low customer expectations is a restaurant in Orlando, Florida,
which calls itself Warm Beer and Lousy Food. Once a customer has experienced a
reasonable meal at this restaurant, he or she is pleasantly surprised and leaves satisfied.
Manipulating perceptions of outcome is also a common practice in some hotels where
front-office clerks mention nonchalantly that a particular suite in the hotel is the favorite
of a famous personality. The intention is to influence the customer’s perception and
suggest that the hotel must be good, as an “expert” frequents it.
Satisfaction is not a universal phenomenon, and not everyone gets the same
satisfaction out of the same hospitality experience. Customers have different needs,
objectives and experiences that influence expectations. To a student on a limited budget,
a lunch composed of fast-food items in the crowded and noisy school cafeteria may be a
highly satisfying experience, while the same experience may be perceived as completely
unsatisfying by an affluent executive discussing a business transaction. The same
customer may also have different needs and expectations for different meal occasions or
at different times of the day (Davis and Stone, 1985, p. 31). The student in our previous
example will not be highly satisfied if his college friends take him out for a “birthday
meal” celebration at the school cafeteria. Therefore, it is important to gain a clear idea of
customer needs and objectives that correspond to different kinds of satisfaction. This
requires segmenting the market, because no service or product can offer everyone the
same degree of satisfaction (World Tourism Organization, 1985).
To summarize, an individual’s satisfaction with outcomes of a hospitality experience
is due to a comparison of these outcomes with expectations. Expectations are a mutable
internal standard based on many factors, including needs, objectives, personal or
vicarious experiences with the same establishment restaurant, with similar
establishments and the availability of alternatives (i.e. are there any other
establishments in town?). This view is supported by Mazursky (1989, p. 338), who
suggested:
Experiences beyond those with the focal brands may lead to different normative standards
employed by consumers in evaluating performance. Possible norms, according to this view,
include perceived best brand, the most recently used brand, a brand used by a reference
person, products competing for the same needs, and the like.
Changes in satisfaction with a meal experience may result from changes in the
perception of the actual quality of outcomes received, or from changes in the
expectations against which these outcomes are compared. Changes in expectations can
result from a change in needs (i.e. hungry vs full; tired vs rested), change in objectives
(i.e. business trip vs leisure trip), new personal or vicarious experiences (i.e. recently had
a superb hospitality experience at another hotel) and any other influences that make
salient a particular quality of outcomes (i.e. it is a very hot day and the restaurant is not
air-conditioned) (McCallum and Harrison, 1985; Gan and Lu, 2012).
Research in customer satisfaction and service quality has led to increasing research
efforts to look at new ways to evaluate these concepts. Historically, scholars assumed
that a linear relationship exists between satisfaction or dissatisfaction and
disconfirmation or performance evaluations. Researchers such as Oliva et al. (1992)
proposed a catastrophe model that theorized the nature of the relationship of satisfaction
with transaction costs and brand loyalty. This theory hypothesizes that satisfaction and
dissatisfaction occur at different points; specifically, these behaviors are associated with
transaction costs and brand loyalty, and are not monotonic. Various authors (Cadotte
et al., 1987; Kisang et al., 2012; Cronin and Taylor, 1992; Oliver and Swan, 1989; Kim and
Li, 2009; Matzler et al., 2006; Mavlikaeva et al., 2014; Seto-Pamies, 2012) used structural
modeling and found underlying causal dynamics among the customer service model
constructs.
The components of satisfaction
Unlike material products or pure services, most hospitality experiences are an amalgam
of products and services. Therefore, satisfaction with a hospitality experience such as a
hotel stay or a restaurant meal is the sum total of satisfaction with individual elements
or attributes of all products and services that make up the experience. Marketing experts
do not agree about classifying the elements of service encounters. Reuland et al. (1985,
p. 142) observed that hospitality services consist of a harmonious mixture of three
elements:
(1) the material product in a narrow sense, which in the case of a restaurant is the
food and beverages;
(2) the behavior and attitude of the employees who are responsible for hosting the
guest, serving the meal and beverages and come in direct contact with the
guests; and
(3) the environment, such as the building, the layout, the furnishing, the lighting in
the restaurant, etc.
Czepiel et al. (1985, p. 13), however, suggested that satisfaction with a service is a
function of satisfaction with two independent elements: the functional element (the food
and beverage in a restaurant) and the performance-delivery element (the service). To
prove the independence of the two elements, the authors claimed that restaurant clients
can respond separately to elements that differ one from the other: “‘the service was great,
the food poor’ or conversely”.
Davis and Stone (1985, p. 29) divided the service encounter into two elements: direct
and indirect services. For example, direct services may be the actual check-in/check-out
process in hotels, while indirect services include parking facilities, concierge, public
telephones for guests’ use, etc.
Lovelock (1985, p. 272) sorted service attributes into two groups: core and secondary.
Airline service provides a good example. Customers first make inquiries and
reservations, then check their baggage, get seat assignments, check-in at the gate,
receive on-board service in flight and retrieve their baggage at the destination airport.
Each activity is an operations task that is secondary to the core product of physically
transporting passengers and their bags between two airports. However, these
secondary tasks have more potential to generate customer dissatisfaction if they are
performed poorly. In a restaurant situation, Lovelock’s core attribute is composed of the
food and beverage, while his secondary attribute is everything else, including service,
environment, etc.
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Lewis (1987), too, classified service encounter attributes into two groups: essential
and subsidiary. The essential attributes are identical to Czepiel’s functional, Davis and
Stone’s direct, Reuland and colleagues’ product and Lovelock’s core, that is the food and
beverage in the meal experience. However, Lewis’ subsidiary attributes are more
comprehensive than Davis and Stone’s indirect, Czepiel’s performance delivery or
Lovelock’s secondary, and include such factors as accessibility, convenience of location,
availability and timing and flexibility, as well as interactions with those who provide the
service and with other customers. This is equivalent to a combination of the behavior
and environment elements in the Reuland et al. (1985) model.
Other researchers support the idea that service encounter attributes are
situation-specific and thus cannot be classified into universal elements. For example,
Fiebelkorn (1985) conducted a study at Citibank and found that overall satisfaction with
Citibank as one of a customer’s banks (or his or her only bank) was based on satisfaction
with the last encounter with the bank in five main areas: teller encounter, platform
encounter, automatic teller machine encounter, phone encounter and problem encounter.
He concluded that the common thread running through all five service-encounter types
is that customers want “prompt service by people who know what to do and how to do
it, and who care about them as valued customers” (Fiebelkorn, 1985, p. 185).
McMullan and O’Neill (2010) suggested a different approach to customer satisfaction
by analyzing the impact of emotions and dissonance on tourist satisfaction and future
behavioral intentions. The authors created the following six scales that examine
different attributes of customer satisfaction with a tourist destination: cognitive
dissonance (CD), which included items measuring conflicting emotions about
the destination; cognitive product satisfaction (CPS), which measures satisfaction with
specific products at the destination such as shopping, restaurants, beaches, etc.;
cognitive service satisfaction (CSS), which is concerned with measuring satisfaction
with services such as hours of operation, staff willingness to help and accessibility to
local sites; emotional satisfaction (ES), which reflects more people’s confidence in their
destination choice as well as items that create positive or negative feelings such as
people’s friendliness and hospitality at the destination, a good variety of attractions or
the cleanliness of the destination; future behavioral intentions (FBI); and overall visitor
satisfaction (OVS). McMullan and O’Neill claimed that their scales present a more
accurate approach to measuring customer satisfaction and demonstrate a higher
predictive power.
Dimensions of satisfaction
The following models assess the quality of services.
SERVQUAL
In service organizations, the quality of a service is assessed during the actual delivery of
the service – usually an encounter between the customer and a service contact person.
Parasuraman et al. (1985, 1988, 1991) identified the following five generic dimensions of
service quality (SERVQUAL) that must be present in the service delivery for customer
satisfaction:
(1) Reliability: The ability to perform the promised services dependably and
accurately.
(2) Responsiveness: The willingness to help customers and provide prompt service.
(3) Assurance: The knowledge and courtesy of employees as well as their ability to
convey trust and confidence.
(4) Empathy: The provision of caring, individualized attention to customers.
(5) Tangibles: The appearance of physical facilities, equipment, personnel and
communication materials.
Customer
satisfaction
The model conceptualizes service quality as a gap between customers’ expectations (E)
and the perception of the service providers’ performance (P). According to Parasuraman
et al. (1985), service quality should be measured by subtracting customers’ perception
scores from customer expectation scores (Q ⫽ P – E). The larger the positive score, the
larger the positive amount of service quality or vice versa. The gap that may exist
between the customers’ expected and perceived service is not only a measure of the
quality of the service but also a determinant of customer satisfaction or dissatisfaction.
Measuring the gap between expected and perceived service is a routine method of
utilizing customer feedback. Zeithaml et al. (1988) suggested a model that details the
gaps between customer expectations and the actual service delivered (Figure 1). Vavra
(1997, 2002) identified a sixth gap, namely, the difference between customers’ desired
service and their expected service.
Since being introduced in 1988, SERVQUAL has been used in hundreds of studies,
including numerous studies in the hospitality and tourism industries (Fick and Ritchie,
1991; Lee and Hing, 1995; Luk et al., 1993; Basfirinci, and Mitra, 2015; Altuntas et al.,
2012; Seto-Pamies, 2012; Hansen, 2014). SERVQUAL was also used by Knutson et al.
(1991) to create a lodging-specific instrument called LODGSERV, a 26-item index
designed to measure consumer expectations for service quality in the hotel experience.
LODGSERV, however, is not as popular among hospitality and tourism researchers as
9
Figure 1.
Hospitality service
quality gap
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SERVQUAL and was used in a limited number of studies (Ekinci et al., 1998; Patton
et al., 1994).
However, SERVQUAL has also been criticized (Babakus and Boller, 1992; Brown
et al., 1993; Carman, 1990; Finn and Lamb, 1991; Smith, 1995; Yoon and Ekinci, 2003;
Torres, 2014). The main criticisms of the model involve the application of expectations
and the gap scoring. First, the conceptualization of expectation as a comparison
standard in the model is difficult to measure. Second, if the variables are difficult to
measure, then, by implication, the gap score becomes that much less secure as a
measurement. Third, some methodological issues arise. Finally, scholars have doubted
the universal quality of the dimensions (Ekinci et al., 1998, p. 355).
ECOSERVE
ECOSERVE is an extension of SERVQUAL with eco-tangible dimensions. ECOSERVE
was introduced by Khan (2002) as a measure of the service quality of an ecotourist
destination. ECOSERVE is a 30-item scale presented in the following six generic
dimensions of service quality that should be present in environment-friendly
destinations:
(1) Tangibles: Material and appearance of the personnel reflects local influence.
(2) Eco-tangibles: Physical facilities and equipment are safe and appropriate to the
environment.
(3) Reliability: Ability to perform the promised service dependably and accurately.
(4) Responsiveness: Willingness to help customers and provide prompt service.
(5) Assurance: Knowledge and courtesy of the employees and their ability to convey
trust and confidence, and provide necessary information.
(6) Empathy: Caring, individualized attention the restaurant provides its customers.
The eco-tangible dimension has 11 items that highlight the necessity of using
environmental and sociocultural issues to measure tangible dimensions. According to
Khan (2002), ecotourists possess a hierarchy in service quality expectations, and
eco-tangibles are at the top of this hierarchy. This implies that ecotourists emphasize
environment-friendly facilities and equipment. This study was replicated by Khan and
Kang Duck (2005) in southwest Korea, and the results were consistent with the previous
study, although environment-friendly facilities were less important to Korean
ecotourists.
RENTQUAL
Another SERVQUAL expansion, which was adapted to measure the service quality of
the rental car business, is RENTQUAL. Private transportation services play a
significant role in tourism where there is a constant requirement of moving from one
place to another. According to the US Travel Association (2009), this is especially true in
the USA, where travel by private automobiles is the primary means of transportation
used by leisure travelers (76 per cent).
Ekiz et al. (2009) developed the 18-item RENTQUAL. In this model, the following six
dimensions measure customer satisfaction with rental car services:
(1) Comfort: Comfort of the car.
(2) Delivery: Procedures of vehicle delivery such as pick-up and return location and
accessory use.
(3) Handling over: Car condition and information.
(4) Security: Mechanical and external condition of the car.
(5) Ergonomics: Describing mechanical comforts of the car.
(6) Accessibility: Employee and company availability and service.
The results demonstrated that security was the most important dimension in overall
customer satisfaction, followed by delivery procedures.
SERICSAT
SERICSAT was created by George et al. (2007) to measure customer satisfaction with
service recovery. SERICSAT used the SERVQUAL paradigm and justice theory to
create a scale measuring the relationship between justice and service recovery
satisfaction in tourism. The study identified three dimensions of the SERICSAT scale,
which were the following:
(1) Recovery Satisfaction with Procedural Justice (RSPJ): A dimension that
addresses the investigation of the complaint and the process used in the
recovery.
(2) Recovery Service with Interactional Justice (RSIJ): A dimension that addresses
communication between customer and staff.
(3) Recovery Satisfaction with Distributive Justice (RSDJ): A dimension that
addresses consequences of the complaint and compensation.
The researchers found that although a satisfactory remedy to a complaint results in
continual patronage and loyal customers, the downside of a satisfactory conclusion is
increased future complaint behavior.
Other models
Cronin and Taylor (1992) created SERVPERF to measure the performance of the service.
The scale is similar to SERVQUAL with the same performance items. Tkaczynski and
Stokes (2010) adapted SERFPERF for festivals and named it FESTPERF. Saravanan
and Kannan (2012) used SERVPERF for rural retailers. Similarly, Stevens et al. (1995)
adapted SERVQUAL for measuring service quality in restaurants and named it
DINESERV. Lages and Fernandes (2005) created SERPVAL, a service personal values
scale that has three dimensions that are positively related to satisfaction. HOLSAT
(Tribe and Snaith, 1998) was used to evaluate holiday satisfaction. The scale was
validated by other researchers (Truong and Foster, 2006, 2005; Truong, 2005).
Overall satisfaction versus satisfaction with individual attributes
In the previous section, we discussed that satisfaction with a hospitality experience is
the sum total of satisfaction with the individual elements or attributes of all products
and services that make up the experience. Though superficially this statement makes
sense, in reality the matter is more complicated. The question is whether when
customers experience the attributes of the hospitality experience, they form a set of
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12
independent impressions of each attribute and compare them with expectations of the
same attributes. Is the overall level of satisfaction determined by the arithmetic sum
total of these impressions? The answer depends on one’s belief about the process of
consumer choice. More specifically, the answer is related to whether one believes that
consumer choice behavior can be explained by compensatory or non-compensatory
models.
Non-weighted compensatory models presume that customers make trade-offs of one
attribute for another to make a decision; that is, a weakness in one attribute is
compensated by strength in another. In the hotel stay example, if the guestroom is small
and uncomfortable, but the service is good, the overall satisfaction with the hotel
experience might still be high; a small and uncomfortable room is traded-off with good
service, because they were of equal importance to the customer. Weighted
compensatory models (sometimes referred to as expectancy-value models) also assume
that people have a measurement of belief about the existence of an attribute, but that
each attribute has an importance weight relative to other attributes. Using this model in
our previous example, because the guestroom quality was rated higher in its relative
importance than service was, the overall satisfaction with the hotel experience will be
dissatisfaction.
Non-compensatory models (no trade-offs of attributes) can take one of two forms:
conjunctive or disjunctive. In conjunctive models, consumers establish a minimum
acceptable level for each important product attribute and make a choice (or become
satisfied) only if each attribute equals or exceeds the minimum level. In the restaurant
example, each of the three attributes of quality of food and beverage, quality of the
service and the ambiance of the restaurant must surpass a threshold before overall
satisfaction occurs. If ambience did not pass this threshold, no matter how good the food
and the service was, the result is overall dissatisfaction.
Disjunctive models are similar to conjunctive models, with one exception. Rather
than establishing a minimum level for all important attributes, in conjunctive models,
consumers establish such levels only for one or a few attributes, for example the food in
the restaurant example (Lewis and Chambers, 1989, p. 157).
Data from studies recently conducted in tourism and hospitality enterprises (Cadotte
and Turgeon, 1988; Mazursky, 1989) support the disjunctive models. In a 1978 study
conducted among 432 foodservice firms representing 22,000 foodservice units, Cadotte
and Turgeon asked company executives to list the type and frequency of their guests’
complaints and compliments. As indicated in Table I, the data from the surveys showed
that:
[…] some restaurant attributes are more likely to earn guest complaints than compliments.
Availability of parking, hours of operation, traffic congestion, noise level, and spaciousness of
the establishment all appear in the top-ten complaint list […] In contrast, guests express
appreciation for high performance in some areas, but rarely complain when performance is
so-so.
The survey results suggested that guests react favorably to a clean neat restaurant, neat
employees, ample portions, and responsiveness to complaints. The quality and quantity of
service, food quality, helpfulness of the employees, and the prices of drinks, meals, and other
services appear on the list of most frequent complaints and the list of the most frequent
compliments.” (Cadotte and Turgeon, 1988, p. 47).
Attribute
Availability of parking
Traffic congestion in establishment
Noise level
Spaciousness of establishment
Neatness of establishment
Size of portions
Employee appearance
Responsiveness to complaints
Quality of service
Food quality
Helpful attitude of employees
Quantity of service
Prices of drinks, meals and service
Management knowledge of service
Availability of food on menu
Beverage quality
Variety of service
Uniformity of establishment appearance
Quality of advertising
Convenience of location
Quietness of surroundings
Accuracy of bill
Litter outside restaurant
Reservations system
Complaint rank
Compliment rank
1
2
5
8
11
15
17
20
3
7
6
10
4
23
16
24
21
26
25
15
18
19
22
13
19
26
24
18
5
5
7
9
1
2
3
8
10
11
12
13
14
15
16
17
21
22
23
25
Category
Customer
satisfaction
Dissatisfier
Satisfier
13
Critical
Neutral
Source: Cadotte and Turgeon (1998, p. 46)
Following these findings, Cadotte and Turgeon divided the attributes into the following
four categories:
[…] satisfiers, dissatisfiers, critical, and neutral. Satisfiers were attributes in which unusual
performance apparently elicited compliments and satisfaction, but an average performance or
even the absence of the feature did not cause dissatisfaction or complaints. Large food
portions, smartly dressed employees, and clean and neat restaurants are all examples of a
restaurant satisfier. Normal food portions, regularly dressed employees, and not as neat
restaurants do not cause dissatisfaction. In contrast, large food portions and well-groomed and
smartly dressed employees please restaurant guests. Satisfiers represent an opportunity to
shine, to move ahead of the pack, and to stand out from the crowd (Cadotte and Turgeon, 1988,
p. 51).
Dissatisfiers were more likely to earn a complaint for low performance or the absence of
a desired feature than anything else. However, an operation that exceeds the threshold
performance standard does not receive compliments on the attribute. Parking and
excessive noise are good examples of dissatisfiers; they must be provided and
maintained at a minimum or sufficient level. However, efforts to achieve a higher
performance level will not be appreciated by customers or cause them satisfaction.
Dissatisfiers in particular require management control to prevent poor performance.
Minimum standards should be established, and the focus should be on maintaining
Table I.
Comparative
rankings of food
service attribute
compliments and
complaints
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14
these standards: “Be as good as your competition, but do not waste resources trying to
be better” (Cadotte and Turgeon, 1988, p. 51):
Critical attributes elicit complaints (dissatisfaction) and compliments (satisfaction), depending
on the situation. Quality of service, food quality, and helpful attitude of employees ranked high
in eliciting complaints and compliments. Critical factors deserve special attention, because of
their potential for hurting and helping a business. Similar to dissatisfiers, minimum standards
must be set to avoid negative responses to your service. For the critical attributes, the objective
is to raise performance beyond the norm (Cadotte and Turgeon, 1988, p. 51).
Neutral attributes received neither many compliments nor many complaints, which
probably indicated that these attributes were either not salient to guests or easily
brought up to guests’ standards.
Cadotte and Turgeon observed that the classification of these factors is not permanent
but constantly changes. Some dissatisfier-type attributes were probably critical at one time.
Higher industry standards, though, may have improved performance to the extent that most
restaurants meet guest requirements for these factors. For example, in warm climates, the
availability of reliable air-conditioning in hotels or restaurants was a critical factor; today,
with the advent of modern refrigeration technology, all hotels and restaurants in such
climates have it. Having more will not satisfy anyone, but when the air-conditioning breaks
down, suddenly everyone becomes dissatisfied.
If Cadotte and Turgeon’s findings are confirmed by other studies, we might revise the
prevailing theory about the nature of customer satisfaction and dissatisfaction and
reject the notion that satisfaction and dissatisfaction are two extremes on one
continuum. Instead, we might accept a modification of a theory advanced many years
ago about job satisfaction. In this theory, Herzberg et al. (1959) proposed that job
satisfaction and dissatisfaction are two extremes on two continua. On one continuum
(the motivation continuum), we have satisfaction versus no-satisfaction, while on the
other (the hygiene continuum), we have dissatisfaction versus no-dissatisfaction. In
Herzberg’s opinion, the variables, the presence or absence of which cause satisfaction or
no-satisfaction, are not the same that cause dissatisfaction or no-dissatisfaction.
Although Herzberg confirmed his theory by using a particular research method (the
critical incident), few other researchers managed to duplicate his results by using
alternative methods. In most cases, although some variables operate solely on one
continuum (i.e. working conditions were found to be a hygiene factor, or dissatisfier),
others (i.e. salary) appeared in the hygiene (dissatisfier) continuum as well as the
motivator (satisfier) continuum. Applying the same rationale to Cadotte and Turgeon’s
findings, one might conclude that if supported by other studies, customer satisfaction or
dissatisfaction could also be explained as a process that operates in three continua: the
first for satisfaction, the second for dissatisfaction and the third (critical) for common
factors that can cause satisfaction as well as dissatisfaction.
However, until then, we must still operate under the assumption that satisfaction and
dissatisfaction are two extremes that operate on one continuum. Therefore, we propose
that customers’ overall satisfaction with a hospitality service encounter is the sum total
of the difference between their perceived outcome and expectations related to a group of
weighted attributes, some of which carry minimum thresholds, plus an additional
mysterious factor that Gronroos (1984) called image and Lewis called overall feeling
(Lewis, 1987, pp. 84-85). The following equation (modified from Lewis and Chambers,
1989, p. 157) gives a mathematical depiction of overall customer satisfaction:
Aijk ⫽
兺
n
wikBijk
Customer
satisfaction
with
Bijk ⬎ I
Where,
Aijk
wik
Bijk
n
i
15
⫽ consumer k’s overall satisfaction score for hospitality enterprise j;
⫽ the importance weight assigned by consumer k to attribute i;
⫽ consumer k’s rating of the amount of the attribute I offered by enterprise j;
⫽ the number of product/service attributes; and
⫽ a minimum level (threshold).
As to the question of identifying the individual attributes in the hospitality experience,
and determining their relative importance weights as well as their minimum threshold
levels, the answer must be determined by each enterprise for each customer segment.
However, based on previous research findings, these attributes can be identified, and
their relative importance determined among consumers of certain products or services.
By using importance–performance analysis, hospitality and tourism researchers such
as Evans and Chon (1989), Green (1993), Chacko and Dimanche (1994), Martin (1995),
Duke and Persia (1996), Opperman (1996), Mount (1997), Go and Zhang (1997), Oh and
Parks (1998), Hudson and Shephard (1998), Feng et al. (2014), Chen, (2013), Kong (2014),
Sheng et al. (2014), Griffin and Edwards (2012) and a few others identified not only the
gaps between the customer’s and the service provider’s perception of quality but also
determined the gap between importance and performance.
Following Reuland et al. (1985), we suggest that a hospitality experience consists of
the following three groups of elements: the material product, the environment and the
employees’ behavior and attitude. For example, in the restaurant case, the material
product is the food and beverage, the environment is the physical attributes of the
restaurant and the behavior and attitude are the restaurant’s staff conduct before and
during the meal (Table II).
Measuring customer satisfaction
Key measurement issues
CSM serves two roles, providing information and enabling communication with
customers. Perhaps the primary reason for measuring customer satisfaction is to collect
information, either regarding what customers say that should be done differently or to
assess how well an organization is currently meeting its customer needs (Vavra, 1997,
2002). A secondary, but no less important, function of CSM in hospitality enterprises is
that by surveying customers, an organization demonstrates its interest in
communicating with them – finding out their needs, pleasures, displeasures and overall
well-being. Although measuring the satisfaction of every customer is impossible, those
whose opinions are solicited and others who observe this process are given a sense of
importance and recognition.
The reasons for measuring customer satisfaction may vary from organization to
organization. Naumann (1995), however, believed that the following five objectives are
the most common:
IJCHM
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Material product
Environment
Behavior and attitude
16
Quality of F&B
Portion size
Variety of menu choices
Food and beverage
consistency
Range of tastes, textures, aromas, colors
Correct F&B temperatures
Appearance of F&B
Price of meal/drinks/service
Availability of menu items
Cleanliness of restaurant
Location and accessibility
Size and shape of room
Furniture and fittings
Color scheme
Lighting
Temperature and ventilation
Acoustics (noise level)
Spaciousness of restaurant
Neatness of restaurant
Employees’ appearance
Availability of parking
Hours of operation
Friendliness
Competence
Courtesy
Efficiency and speed
Helpfulness
Professionalism
Responsiveness to special
requests
Responsiveness to complaints
Table II.
Product/service
attributes in a
restaurant meal
experience
Source: Pizam and Ellis (1999)
(1) Get close to the customer: Understand what attributes are the most important to
customers, find which attributes affect the customer’s decision-making, the
relative importance of the attributes and get a performance evaluation of how
well the firm is delivering each attribute.
(2) Measure continual improvement: The attributes significant to the customer are
linked directly to value-added processes in the firm and are put into a form
consistent with the internal measurements used to evaluate the process.
(3) Achieve customer-driven improvement: Not all customers are an equally
valuable source of innovation. This requires a comprehensive database that
tracks not only sales but also sources of innovations.
(4) Measure competitive strengths and weaknesses: Determine customer perceptions
of competitive choices. This is achieved by surveying possible and future
customers as well as current and past customers.
(5) Link CSM data to internal systems (Naumann, 1995, pp. 22-27).
Attributes to be measured
To measure customer satisfaction, one must anticipate which dimensions or attributes of the
product or service customers use in their overall quality assessment. In previous sections, we
discussed generic service dimensions, such as the ones proposed by Fitzsimmons and
Fitzsimmons (1998), Czepiel et al. (1985), Lovelock (1985) and Fiebelkorn (1985), and
hospitality-specific dimensions, such as the ones proposed by Reuland et al. (1985), Davis
and Stone (1985), Lewis (1987) and Cadotte and Turgeon (1988).
The attributes management might want to measure may be unimportant or irrelevant to
customers’ needs. Therefore, by surveying customers about the importance of each attribute,
managers can obtain valuable and incontestable information about which attribute to
include in the measurement. The objective is to balance management’s key information
needs with customers’ needs and issues (Vavra, 1997, p. 112).
Once the dimensions or attributes that should be measured are chosen, there is a high
likelihood that the total number of items in the questionnaire may be too high. Therefore,
the list of items must be reduced (Vavra, 1997, p. 114). The most satisfactory and least
biased way to reduce the number of items on a questionnaire is through factor analysis.
Factor analysis is a statistical technique that identifies correlations among a list of
issues or items. This can be used to identify common issues or problems among different
groups of customers and grouping them to be dealt with together.
In addition to measuring satisfaction with various product service attributes, the
customer satisfaction questionnaire should include items related to the customer profile.
These can consist of basic demographics (i.e. sex, education, income, profession,
geographical origin, etc.), psychographics (lifestyle) and other miscellaneous variables
related to the number of individuals in the party, frequency of visiting a hospitality
enterprise, frequency of visiting the current establishment, etc.
Frequency and method of measurement
Questionnaires that contain some of these measurements should be distributed either on
a continual or periodic basis to all customers at the end of their experience. Under no
circumstances should the questionnaires be left on the table before the meal is completed
or in the hotel guestroom before check-out. If the questionnaire is left in advance, it will
be completed only by those who had an exceptional experience or a very poor one. At the
end of the experience, the staff should distribute the questionnaire and ask the customer
to complete it. To increase the completion rate, customers should be given some form of
incentive such as a discount on the next restaurant meal, or hotel stay.
If the establishment cannot afford to distribute questionnaires to every customer,
then a method for random sampling should be devised. For example, every third
customer at dinner and every fifth customer at lunch should be approached and asked to
complete a questionnaire. In a restaurant, the customers to be approached should be
seated throughout the restaurant so that all waitstaff are represented in the sample.
Questionnaires should be distributed throughout the seven days of the week, so that
weekdays and weekends are included. All questionnaires should be coded in advance
for date, and in the case of restaurants, for meal (lunch or dinner) and table number, to
enable analysis by day, menu items and waitstaff. Alternatively, customers can be given
an URL and asked to complete an online survey at their leisure or given a tablet such as
an Apple iPad or Android and asked to complete an online survey on the spot.
Data analysis
The data should be analyzed weekly, and the results compared with previous weeks.
The mean and standard deviation should be computed for the global or overall
satisfaction variables as well as for each product or service attribute. To understand
better the satisfaction or dissatisfaction of each market segment, separate analyses
should be conducted for each identifiable market segment (leisure travelers, business
travelers, conventioneers, etc.).
To determine the relative importance (weights) of each product or service attribute,
an establishment can either conduct a periodic study to determine how customers rate
the attributes or run a multiple regression with the global satisfaction variable as the
dependent variable and each product or service attribute as the independent variables.
The beta weights for each independent variable in the regression will be equivalent to
the importance rates of the attributes. As the relative importance of the product or
Customer
satisfaction
17
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18
service attributes changes from time to time, these weights should be computed at least
once a year and if possible more than that.
Types of measurement
Most CSMs use ordinal and discrete rating scales such as Likert-type scales, which
typically contain an odd number of options, usually 5 to 7. One end is labeled the “most
positive” end, while the other one is labeled the “most negative”, with “neutral” in the
middle of the scale. The labels “most positive” and “most negative” could be replaced
with “agree” or “disagree”.
For example:
How satisfied were you with the quality of food served to you?
1
Highly Dissatisfied
2
3
4
Neither Sat. nor Dissat.
5
Highly Satisfied
or alternatively:
The quality of food served to me was excellent
1
Strongly Disagree
2
3
4
Neither Agree nor Disagree
5
Strongly Agree
However, Likert-type scales might introduce an acquiescence bias, where respondents
have a higher tendency to give a positive response. One way to negate this problem is to
introduce items in negations, where agreement implies disagreement with the construct.
Acquiescence bias might be reduced by reversing items; however, that might introduce
other errors, as people may respond differently to solely deleteriously worded items.
Friborg et al. (2006) found by using inferential, reliability statistics and structural
equations that the semantic format fits measurement modes, model fit and
uni-dimensionality better than a Likert-type scale. The semantic differential (SD) scale
was originally created by Osgood (1964). The SD measures people’s reactions to
stimulus words and concepts in terms of ratings on bipolar scales defined with
contrasting adjectives at each end.
An example of an SD scale for the “quality of food in a restaurant” would be:
The quality of food served to me was:
1
Bad
2
3
4
5
Neither Good nor Bad
6
7
Good
Similarly, Ding and Ng (2008) adjusted SD scales by using the theory of personal
construct and a full grid technique, which includes in-depth interviews. This approach
in developing SD scales includes interviewees’ input and removes researcher bias.
Another method for measuring customer satisfaction, needs and requirements was
developed by Kano et al. (1984) and is referred to as the Kano model. The Kano model is
widely used for understanding customers’ opinions, concerns and/or needs and their
impact on customer satisfaction. According to this model, customer needs can be
grouped into three categories: basic needs, expected needs and excitement needs. Basic
and excitement needs are unspoken, but expected needs are expressed. Basic needs and
expected needs are expected, and excitement needs are not. However, meeting
customers’ unexpected needs is important (Wang and Ji, 2010), although some unspoken
needs are difficult to explain or predict. Kano’s model is valuable in explaining human
needs (Kvist and Klefsjö, 2006). More specifically, Kano and his colleagues hypothesized
that customer requirements could be classified through customer questionnaires where
one question consists of two parts:
Customer
satisfaction
19
Q1. How do you feel if a certain attribute is present in the product or service
(functional)?
Q2. How do you feel if a certain attribute is not present in the product or service
(dysfunctional)?
Responses range from I like it that way (1), It must be that way (2) and I am neutral (3)
to I can live with it that way (4) and I dislike it that way (5). Once the responses are
collected, they are classified into one of the six categories: A ⫽ Attractive; M ⫽ Must Be;
O ⫽ One-dimensional; I ⫽ Indifferent; R ⫽ Reversal; and Q ⫽ Questionable. Then the
results are tabulated according to Kano’s table and ranked. As part of the analysis, mode
statistics can be applied (Table III).
Some researchers proposed a quantitative analysis of Kano’s model to address
customer needs by calculating customer satisfaction and customer dissatisfaction
points and plotting relationship curves (Berger et al., 1993). The Kano model was
successfully used in hospitality research (Chang and Chen, 2011; Shahin and Zairi, 2009;
Gregory and Parsa, 2013), and a combination of Kano’s model and SERVQUAL was
used by Tan and Pawitra (2001) as a tool for evaluating customer satisfaction. Basfirinci
and Mitra (2015) integrated SERVQUAL and Kano modeling. Kim et al. (2009)
integrated DINESERV and Kano modeling.
CRs
1. Like
2. Must-be
Dysfunctional
3. Neutral
4. Live with
5. Dislike
Functional
Q
R
R
R
R
A
I
I
I
R
A
I
I
I
R
A
I
I
I
R
Q
M
M
M
Q
Notes: A ⫽ Attractive; O ⫽ One-dimensional; M ⫽ Must-be; I ⫽ Indifferent; R ⫽ Reverse;
Q ⫽ Questionable 7
Sources: Berger et al. (1993); Kano et al. (1984)
Table III.
Kano’s model
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Global issues and cultural differences in customer satisfaction
When global CSMs are designed, regional and cultural aspects must be taken into
account. When Chadee and Mattsson (1995) and Scott and Shieff (1993) measured
customer satisfaction, they found significant cross-cultural differences. Services and
products important to Asians may be completely different from those sought by
Europeans. Culture has an impact on perception, problem solving and cognition and
often leads to differences in satisfaction levels for a single product between different
global customers. Global customers may have different expectations, different ways of
evaluating performance and differing uses of response scale formats and may be
influenced differently by the number of response positions in any question scale (Vavra,
1997, p. 430).
The majority of customer satisfaction research has been conducted in industrialized
economies, and very little research has been conducted in Africa, the Middle East, South
America, Latin America and large portions of Asia. The question therefore is what can be
done to avoid confounds and problems of global cross-cultural customer satisfaction
research? The satisfaction survey design, process and data must be as comparable across
countries as possible. If the results for a country are used only within that country,
equivalence is not an issue; if the results are to be used to gain a global picture, compatibility
is essential. Differing languages, levels of literacy, interpretations of constructs and cultural
behavior must all be taken into account when a foreign customer satisfaction survey is
created. Similar survey designs may or may not be usable in different socio-cultural
environments or even within a single work environment, as language and culture may not be
homogeneous with a customer’s corporation or workplace.
To deal with differences in socio-cultural behaviors, Vavra suggests looking into the
“emic-etic dilemma”. An emic approach is based on recognition of the differences between
cultures and acknowledgment of the importance of each culture’s idiosyncrasies. This
requires creating a different survey, with different questions, a different method of
measurement and administration for each different culture. An etic approach is based on the
belief that certain industry standards, requirements, values and behaviors are continual and
transcend. This would allow for a “universal” type of evaluation to be developed and used
cross-culturally. Each method has benefits but also inherent problems.
In the emic approach, it is much more difficult to compare results from different
cultures, and each cultural evaluation could be misinterpreted by evaluators from other
cultures. Should the etic approach be used, caution must be used to avoid applying it in
an unchecked manner or abuse the measures – imposing them on cultures without the
adaptation or sensitivity to use the evaluative materials correctly (Vavra, 1997,
pp. 431-432).
Whether the etic or emic approach is used to evaluate global customers’ satisfaction,
some sort of equivalence must be established within and between surveys. Vavra
identified the following two major categories of equivalence that should be considered
when creating or administering international customer satisfaction surveys:
establishment and measurement.
Establishing equivalence
• Construct equivalence: This is the most basic form of equivalence. This asks
whether a construct or phenomenon exists in another culture. It should never be
assumed that a construct exists in another culture without extensive qualitative
research demonstrating the construct’s cultural existence.
• Conceptual equivalence: Concepts may be culturally hidebound. For example,
indifference may not exist within another culture’s parameters.
• Functional equivalence: This addresses the question of whether products and
services are expected to deliver identical functions across various cultures.
Consumer goods may vary in sameness or function.
• Category equivalence: The way objects, stimuli and behavior are grouped varies
cross-culturally and may influence customer response. An example is making
scales with fewer responses more applicable in some cultures.
Measurement equivalence
• Scalar equivalence: Do corporate chosen scales function similarly in different
cultures? It is a good idea to test a scale’s acceptance in a culture before applying
the scale.
• Item/linguistic equivalence: The items on the questionnaire must be able to be
translated accurately so that the linguistic meaning remains similar across
cultures.
• Sample equivalence: There is a likelihood that there might be a more homogenous
sample across cultures because all survey participants will have chosen,
purchased and used the same product or service to participate in the customer
satisfaction survey. This homogeneity may not exist in other circumstances
(Vavra, 1997, p. 434).
When global or cross-cultural satisfaction surveys are designed, purchase behavior
should also be taken into account. Product or service categories and the appropriateness
or relevance of different products vary cross-culturally (Vavra, 1997, p. 439). In addition,
the way in which purchases are made is also relevant. For example, in the case of
customer satisfaction surveys for food shoppers, the difference between Americans’
weekly supermarket trips and Asians’ daily trips to the market plays a part in how these
surveys are constructed and utilized and to whom they are administered.
Finally, customers’ attitudes and psychographics must be taken into account when
surveys are written. Questions relating to lifestyles and individual values are subject to
a wide range of cultural interpretations. Cultural values must be considered when
customer satisfaction surveys are written, and common values recognized and a special
point made to create allowances for country- or culture-specific concepts or measures.
Knowledge of cultural attitudes and taboos helps the surveyor create a courteous,
understandable and concise questionnaire (Vavra, 1997, p. 440).
A final consideration when administering a questionnaire across multiple cultures is
the language to be used. Global customers should be surveyed in their native language;
thus, the instrument must be translated into the various languages needed. The four
main methods of translating survey instruments are direct, back, de-centered and
parallel translation.
(1) Direct translation: The use of bilingual translators to translate the original into
the required languages.
Customer
satisfaction
21
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(2) Back translation: Translation of the questionnaire by a native speaking bilingual
into the desired language followed by a translation back to the original language
by another bilingual native speaker.
(3) De-centered translation: Successive translations and re-translations, so that the
final questionnaires use terms and phasing that are equally meaningful in both
languages.
(4) Parallel translation: The translation is continually performed by a committee of
translators, all equally conversant in both languages, until the committee agrees
that the questionnaires have nearly equal meanings in both languages.
In conclusion, if properly designed, administered and analyzed, the process of
monitoring customer satisfaction can be beneficial to any hospitality enterprise and
make the difference between offering a mediocre product and an excellent, quality
product.
Online surveys
Modern technology offers many conveniences and benefits to researchers. Online
surveys are easier to distribute, faster, cost-effective and flexible (Wright, 2005).
Numerous online companies provide cost-effective services for creating and distributing
surveys, collecting data and performing basic analyses through one central system. Any
good online survey company lets researchers easily outline the online questions and
sends the online link to answer surveys online. However, some companies offer more
sophisticated options such as flexible survey look, skip logic, randomization, Web site
integration and basic data analysis.
The following low-priced online survey tools provide survey-building and viewing
reports: SurveyMonkey (www.surveymonkey.com), Zoomerang (www.zoomerang.
com) and SurveyGizmo (www.surveygizmo.com). These instruments are useful for
simple surveys on a smaller scale that do not require extensive and more sophisticated
options such as skip logic or result analysis. Free options have a limited number of
questions to be created and number of responses within a certain timeframe. Paid
options provide more flexibility. For more advanced survey-building with sophisticated
tools such as advanced survey logic, advanced question formats, survey template
library and basic data analysis, the following should be considered (Leland, 2011):
Qualtrics (2015), QuestionPro (www.questionpro.com), KeySurvey (www.keysurvey.
com), ProProfs survey maker (www.proprofs.com), SurveyMoz (www.surveymoz.com)
and Survey Face (www.surveyface.com).
Innovative online scales of customer satisfaction
Paper-based surveys are time-consuming and have design and space limitations. Online
surveys provide more engaging opportunities to measure satisfaction, perhaps
providing more user-friendly and more organized ways for people to take surveys.
Online surveys allow flexibility in the design of scales that was not possible with paper
surveys. For example, with online survey, one can use sliding scales, heat maps,
graphics, flexible amounts of space for text entries, graphic design and much more. The
following are some of the new online scales for measuring customers’ satisfaction.
Sliding scales with an A-F grade make it possible for researchers to assign a numerical
value for a grade selected by the customer (Figure 2).
Sliding scales with multiple attributes give the respondents the opportunity to view
their multiple attributes ratings and compare them (Figure 3).
A sliding scale with continuous values is a more flexible rating scale that allows
rating multiple attributes in decimal values and provides an absolute zero (Figure 4).
Visual analog scale (VAS) is a monochrome or color-enhanced graphic scale
developed in the medical field to measure a characteristic believed to range across a
continuum of values and cannot be directly measured, such as the amount of pain that a
patient feels. The VAS can be easily adapted to hospitality satisfaction measurement.
The VAS is a straight line (vertical or horizontal) of a given length whose ends are
marked by adjectives of two extremes of the experience to be measured. Persons
completing a survey make a mark closest to how they feel. Individual visual perception
is more extensive than a simple quantitative statement (Pointer, 2003) (Figures 5 and 6).
Customer
satisfaction
23
Data mining
The increasing use of technology in the hospitality and tourism industries has created
large quantities of consumer data. However, identifying information in those large
quantities of consumer data, consumer expenditure and online customer complaint or
feedback can be difficult. A relatively new method by which customer satisfaction data
can be measured and analyzed is data mining or big data analysis. “Big Data starts with
large-volume, heterogeneous, autonomous sources with distributed and decentralized
control and seeks to explore complex and evolving relationships among data” (Wu et al.,
2014, p. 99). Data mining extracts meaningful patterns and builds predictive
customer-behavior models that aid in decision-making (Magnini et al., 2003, p. 94).
Figure 2.
Sliding scale with
A-F grade
Figure 3.
Sliding scale with
multiple attributes
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Figure 4.
Sliding scale with
continuous values
7
orange
6
5
yellow
4
Excellent
Colors red
pink
Highly dissatisfied
8
Good
9
Acceptable
Bad
Figure 5.
Colored enhanced
VAS with discrete
values
Terrible
Abhorrent
10
3
2
1
lime
green
Highly satisfied
0
Source: Created by authors
Data mining has advantages over classic statistical model building. As researchers
often ignore model assumptions and limitations, traditional statistical models are often
biased. Statistical measures of customer satisfaction are often highly correlated, which
in turn can create biased results. Furthermore, traditional statistical analyses generally
assume normality and a linear relationship, which often is not the case. Data-mining
techniques do not have these limitations and are superior to traditional statistical
analysis. The ability to handle very large, complex numeric and text data is another
advantage of data mining (Magnini et al., 2003). Extensive customer feedback and
reviews online are an unmeasured and untapped wealth of information about customer
satisfaction. A method that can accurately extract information from databases is needed
(Lau et al., 2005). Text data provide reach information in an opaque manner and cannot
be analyzed with traditional statistical tools. Unfortunately, human analysis results in
Customer
satisfaction
25
Figure 6.
VAS with continuous
values
extensive work overload or analysis involves an insignificant amount of information
from a database, which leaves a wealth of information intact (Nasukawa and Nagano,
2001).
Summary and conclusions
We discussed the raison d’être of measuring customer satisfaction as a proxy for
determining service quality. We defined the concept in the context of hospitality and
tourism and analyzed the various components. We emphasized that most hospitality
experiences are an amalgam of products and services, and thus, satisfaction with a
hospitality experience is the sum total of satisfaction with the individual elements or
attributes of all the products and services that make up the experience. We then listed
the various indices proposed for analyzing service quality, such as Parasuraman et al. ’s
(1985, 1988, 1991) SERVQUAL; Khan’s (2002) ECOSERVE; Ekiz et al. ’s (2009)
RENTQUAL; George et al.’s (2007) SERICSAT; Cronin and Taylor’s (1994) SERVPERF;
Tkaczynski and Stokes’ (2010) FESTPERF; Lages and Fernandes’ (2005) SERPVAL;
Stevens et al.’s (1995) DINESERV; and Tribe and Snaith’s (1998) HOLSAT.
We then discussed the issue of overall satisfaction with a service experience versus
satisfaction with individual attributes. We asked whether when customers experience
the attributes of the hospitality experience, they form a set of independent impressions
of each and compare them with the expectations of the same attributes and then the
resultant overall level of satisfaction is determined by the arithmetic sum total of these
impressions. We suggested that the answer to this question depends on whether
consumer choice behavior could be explained by compensatory or non-compensatory
models.
A major portion of the article was devoted to the actual measurement of customer
satisfaction. This was done by first listing the roles of CSMs, such as providing
information to management and enabling communication with customers. Second, we
analyzed the different attributes that should be measured, followed by the frequency
and the specific methods of measurements that are appropriate for hospitality
establishments. Finally, the subject of data analysis was depicted, and the uses of
various statistical techniques were enumerated. We examined various scales that are
used to measure customer satisfaction, such as Likert, Osgood semantic differentia
(Osgood, 1964), Kano (Kano et al., 1984) and Berger (Berger et al., 1993).
IJCHM
28,1
26
The measurement of customer satisfaction across different countries and
nationalities was addressed in the next portion of the article. This was achieved by
discussing the “emic-etic dilemma” and its relevance to the design of customer
satisfaction surveys across the world. Issues such as cross-cultural equivalencies and
methods of translating the wording in questionnaires were examined. Finally, the
subject of measurements via online surveys was addressed, and a partial listing of basic
and advanced online survey tools, such as SurveyMonkey, Zoomerang, SurveyGizmo,
Qualtrics, QuestionPro, KeySurvey, ProProfs survey maker, SurveyMoz and Survey
Face, was provided. In addition, we described several innovative online scales for
measuring customer satisfaction, such as sliding scales and VAS.
In conclusion, this article provided an up-to-date comprehensive review of the
measurement of customer satisfaction in hospitality enterprises from theoretical and
practical perspectives. We hopefully provided readers and future researchers with a
swift and convenient way of educating themselves in this evolving field.
References
Abubakar, B. and Mavondo, F. (2014), “Tourism destinations: antecedents to customer
satisfaction and positive word-of-mouth”, Journal of Hospitality Marketing and
Management, Vol. 23 No. 8, pp. 833-864.
Almanza, B.A., Jaffe, W. and Lin, L. (1994), “Use of the service attribute matrix to measure
consumer satisfaction”, Hospitality Research Journal, Vol. 17 No. 2, pp. 63-75.
Altuntas, S., Dereli, T. and Yilmaz, M. (2012), “Multi-criteria decision making methods based
weighted SERVQUAL scales to measure perceived service quality in hospitals: a case study
from Turkey”, Total Quality Management and Business Excellence, Vol. 23 Nos 11/12,
pp. 1379-1395.
Ariffin, H.F., Bibon, M.F. and Abdullah, R.P.S.R. (2012), “Restaurant’s atmospheric elements: what
the consumer wants”, Social and Behavioral Science, Vol. 38, pp. 380-387.
Assael, H. (1987), Consumer Behavior and Marketing Action, 3rd ed., PWS-Kent, Boston, MA.
Babakus, E. and Boller, G.W. (1992), “An empirical assessment of the SERVQUAL scale”, Journal
of Business Research, Vol. 24 No. 3, pp. 253-268.
Band, W.A. (1991), Creating Value for Customers: Designing and Implementing a Total Corporate
Strategy, Wiley, New York.
Barsky, J.D. (1992), “Customer satisfaction in the hotel industry meaning and measurement”,
Journal of Hospitality and Tourism Research, Vol. 16 No. 1, pp. 51-73.
Basfirinci, C. and Mitra, A. (2015), “A cross cultural investigation of airlines service quality
through integration of Servqual and the Kano model”, Journal of Air Transport
Management, Vol. 42, pp. 239-248.
Berger, C., Blauth, R., Boger, D., Bolster, C., Burchill, G., DuMouchel, W. and Shen, D. (1993),
“Kano’s methods for understanding customer-defined quality”, Center for Quality
Management Journal, Vol. 2 No. 4, pp. 3-36.
Berkman, H.W. and Gilson, C. (1986), Consumer Behavior: Concepts and Strategies, 3rd ed., Kent,
Boston, MA.
Bilgihan, A., Okumus, F., Nusair, K. and Bujisic, M. (2014), “Online experiences:
flow theory, measuring online customer experience in e-commerce and managerial
implications for the lodging industry”, Information Technology and Tourism, Vol. 14
No. 1, pp. 49-71.
Birch, E.N. (1990), “Focus on value: in creating customer satisfaction”, Research Report No. 944,
The Conference Board, New York, NY, pp. 3-4.
Brown, T.S., Churchill, G.A. and Peter, J.P. (1993), “Research note: improving the measurement of
service quality”, Journal of Retailing, Vol. 69, pp. 127-139.
Butz, H.E. and Goodstein, L.D. (1996), “Measuring customer value: gaining the strategic
advantage”, Organizational Dynamics, Vol. 24, No. 3, pp. 63-77.
Cadotte, E.R. and Turgeon, N. (1988), “Key factors in guest satisfaction”, Cornell Hotel and
Restaurant Quarterly, Vol. 28 No. 4, pp. 45-51.
Cadotte, E.R., Woodruff, R.B. and Jenkins, R.L. (1987), “Expectations and norms in models of
consumer satisfaction”, Journal of Marketing Research, Vol. 24 No. 3, pp. 305-314.
Carman, J.M. (1990), “Consumer perceptions of service quality: an assessment of the SERVQUAL
dimensions”, Journal of Retailing, Vol. 66 No. 1, pp. 33-55.
Chacko, H.E. and Dimanche, F. (1994), “Improving services using importance-performance
analysis: the case of convention and visitor bureau”, paper presented at the Council of Hotel,
Restaurant and Institutional Education conference, Palm Spring, June.
Chadee, D. and Mattsson, J. (1995), “Customer satisfaction in tourist service encounters”, Journal
of Travel and Tourism Marketing, Vol. 4.
Chang, K. and Chen, M. (2011), “Applying the Kano model and QFD to explore customers’ brand
contacts in the hotel business: a study of a hot spring hotel”, Total Quality Management,
Vol. 22 No. 1, pp. 1-27.
Chen, X. (2013), “The Modified Importance-performance Analysis Method and its Application in
Tourist Satisfaction Research. (English)”, Tourism Tribun /Lvyou Xuekan, Vol. 28 No. 11,
pp. 59-66.
Cronin, J.J., Brady, M.K. and Hule, G.T.M. (2000), “Assessing the effects of quality, value, and
Consumer satisfaction on consumer behavioral intentions in service environments”,
Journal of Retailing, Vol. 76 No. 2, pp. 193-218.
Cronin, J.J. and Taylor, S.A. (1992), “Measuring service quality: a reexamination and extension”,
Journal of Marketing, Vol. 56 No. 3, pp. 55-68.
Cronin, J.J. Jr and Taylor, S.A. (1994), “SERVPERF versus SERVQUAL: reconciling
performance-based and perceptions-minus-expectations measurement of service quality”,
The Journal of Marketing, Vol. 58 No. 1, pp. 125-131.
Czepiel, J.A., Solomon, M. R. Suprenant, C.F. and Gutman, E.G. (Eds) ( 1985), “Service encounters:
an overview”, in The Service Encounter: Managing Employee Customer Interaction in
Service, Lexington Books, Business Lexington, MA, pp. 3-15.
Davis, B. and Stone, S. (1985), Food and Beverage Management, 2nd ed., Butterworth-Heinemann,
Oxford.
Day, R. (1977), “Consumer satisfaction, dissatisfaction, and complaining behavior”, in Day, R.
(Ed.), Symposium Proceedings, School of Business, University of Indiana, Bloomington, IN,
20-22 April.
Deng, J.W., Yeh, L.M., Sung, L.M. (2013), “A customer satisfaction index model for international
tourist hotels: integrating consumption emotions into the American customer satisfaction
index”, International Journal of Hospitality Management Vol. 35, pp. 133-140.
Ding, Z. and Ng, F. (2008), “A new way of developing semantic differential scales with
personal construct theory”, Construction Management and Economics, Vol. 26 No. 11,
pp. 1213-1226.
Customer
satisfaction
27
IJCHM
28,1
28
Djekic, I., Tomic, N., Smigic, N., Tomasevic, I., Radovanovic, R. and Rajkovic, A. (2014), “Quality
management effects in certified Serbian companies producing food of animal origin”, Total
Quality Management and Business Excellence, Vol. 25 Nos 3/4, pp. 383-396.
Dube, L., Renaghan, L.M. and Miller, J.M. (1994), “Restaurant management: measuring
satisfaction for strategic management”, Cornell Hotel and Restaurant Administration
Quarterly, Vol. 35 No. 1, pp. 39-47.
Duke, C.R. and Persia, M.A. (1996), “Performance-importance analysis of escorted tour
evaluations”, Journal of Travel and Tourism Marketing, Vol. 5 No. 3, pp. 207-222.
Dutka, A. (1994), AMA Handbook for Customer Satisfaction, NTC Business Books, Lincolnwood,
IL.
Ekinci, Y. and Riley, M. (1998), “A critique of the issues and theoretical assumptions in service
quality measurement in the lodging industry: time to move the goal-posts?” International
Journal of Hospitality Management, Vol. 7 No. 4, pp. 349-362.
Ekinci, Y., Riley, M. and Fife-Schaw, C. (1998), “Which school of thought? The dimensions of resort
hotel quality”, International Journal of Contemporary Hospitality Management, Vol. 10
Nos 2/3, pp. 63-67.
Ekiz, E.H., Bavik, A. and Arasli, H. (2009), “RENTQUAL: a new measurement scale for car rental
services”, Tourism Management, Vol. 57 No. 2, pp. 135-153.
Elkhani, N., Soltani, S. and Jamshidi, M. (2014), “Examining a hybrid model for e-satisfaction and
e-loyalty to e-ticketing on airline websites”, Journal of Air Transport Management, Vol. 37,
pp. 36-44.
Evans, M. and Chon, K.C. (1989), “Formulating and evaluating tourism policy using
importance-performance analysis”, Hospitality Research Journal, Vol. 13 No. 3, pp. 203-213.
Fecikova, I. (2004), “An index method for measurement of customer satisfaction”, The TQM
Magazine Vol. 16 No. 1, pp. 57-66.
Feng, M., Mangan, J. Wong, C., Xu, M. and Lalwani, C. (2014), “Investigating the different
approaches to importance – performance analysis”, Service Industries Journal, Vol. 34
No. 12, pp. 1021-1041.
Fick, G.R. and Ritchie, J.R.B. (1991), “Measuring Service quality in the travel and tourism
industry”, Journal of Travel Research, Vol. 30 No. 2, pp. 2-9.
Fiebelkorn, S.L. (1985), “Retail service encounter satisfaction: model and measurement”, in
Czepiel, J.A., Solomon, M.R., Suprenant, C.F. and Gutman, E.G. (Eds), The Service
Encounter: Managing Employee Customer Interaction in Service Business, Lexington
Books, Lexington, MA, pp. 18-194.
Finn, D.W. and Lamb, C.W. (1991), “An evaluation of the SERVQUAL scale in a retail setting”, in
Holman, R.H. and Solomon, M.R. (Eds), Advances in Consumer Research, Vol. 18,
Association for Consumer Research, Provo, UT.
Fitzsimmons, J.A. and Fitzsimmons, M.J. (1998), Service Management: Operations, Strategy, and
Information Technology, Irwin/McGraw-Hill, Boston, MA.
Friborg, O., Martinussen, M. and Rosenvinge, J.H. (2006), “Likert-based vs. semantic
differential-based scorings of positive psychological constructs: a psychometric
comparison of two versions of a scale measuring resilience”, Personality and Individual
Differences, Vol. 40 No. 5, pp. 873-884.
Gale, B.T. (1994), Managing Customer Value, Free Press, New York, NY.
Gan, L. and Lu, T. (2012), “Study on visitors’ expectations, use and evaluation to museum
interpretation system: an analysis based on knowledge needs(English)”, Tourism Tribune/
Lvyou Xuekan, Vol. 27 No. 9, pp. 56-64.
Ganguli, S. and Roy, S.K. (2011), “Generic technology-based service quality dimensions in
banking: impact on customer satisfaction and loyalty”, International Journal of Bank
Marketing, Vol. 29 No. 2, pp. 168-189.
George, B.P., Salgaonkar, P. and Hegde, P.G. (2007), “SERICSAT: the development of a
preliminary instrument to measure service recovery satisfaction in tourism”, International
Journal of Hospitality and Tourism Administration, Vol. 8 No. 1, pp. 21-42.
Gil, I., Berenguer, G. and Cervera, A. (2008), “The roles of service encounters, service value, and job
satisfaction in achieving customer satisfaction in business relationships”, Industrial
Marketing Management, Vol. 37 No. 8, pp. 921-939.
Go, F. and Zhang, W. (1997), “Applying importance-performance analysis to Beijing as an
international meeting destination”, Journal of Travel Research, Vol. 35 No. 4, pp. 42-49.
Green, C.G. (1993), “Using customer survey data to develop marketing strategies in college
university foodservices”, Journal of College and University Foodservice, Vol. 1 No. 1,
pp. 39-51.
Gregory, A.M. and Parsa, H.G. (2013), “Kano’s model: an integrative review of theory and
applications to the field of hospitality and tourism”, Journal of Hospitality and Marketing
and Management, Vol. 22 No. 1, pp. 25-46.
Griffin, T. and Edwards, D. (2012), “Importance – performance analysis as a diagnostic tool for
urban destination managers”, Anatolia: An International Journal of Tourism and
Hospitality Research, Vol. 23 No. 1, pp. 32-48.
Gronroos, C. (1984), “A service quality model and its marketing implications”, European Journal
of Marketing, Vol. 18 No. 4, pp. 36-44.
Ha, Y. and Hyunjoo, I. (2012), “Role of web site design quality in satisfaction and word of mouth
generation”, Journal of Service Management, Vol. 23 No. 1, pp. 79-96.
Hansen, K.V. (2014), “Development of SERVQUAL and DINESERV for measuring meal
experiences in eating establishments”, Scandinavian Journal of Hospitality and Tourism,
Vol. 14 No. 2, pp. 116-134.
Hayes, B.E. (1997), Measuring Customer Satisfaction: Survey Design, Use, and Statistical Analysis
Methods, 2nd.ed., ASQ Quality Press, Milwaukee, WI.
Helm, S., Eggert, A. and Garnefeld, I. (2010), “Modeling the impact of corporate reputation on
customer satisfaction and loyalty using partial least squares”, Handbook of partial Least
Squares, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 515-534.
Hennig-Thurau, T. (2004), “Customer orientation of service employees: its impact on customer
satisfaction, commitment, and retention”, Journal of Service Management, Vol. 15 No. 5,
pp. 460-478.
Herzberg, E., Mausner, B. and Snyderman, B. (1959), The Motivation to Work, Wiley, New York,
NY.
Hsu, C.H. (2003), “Mature motorcoach travelers’ satisfaction: a preliminary step toward
measurement development”, Journal of Hospitality and Tourism Research, Vol. 27 No. 3,
p. 291.
Hudson, S. and Shephard, G.W.H. (1998), “Measuring service quality at tourist destinations: an
application of importance-performance analysis to an Alpine Ski Resort”, Journal of Travel
and Tourism Marketing, Vol. 7 No. 3, pp. 61-77.
Jaworski, B. and Kohli, A. (1993), “Market orientation: antecedents and consequences”, Journal of
Marketing. Vol. 57 No. 7, pp. 53-70.
Jones, T.O. and Sasser, W.E. (1995), “Why satisfied customers defect”, Harvard Business Review.
Vol. 73 Nos 11/12, pp. 88-99.
Customer
satisfaction
29
IJCHM
28,1
30
Kano, N., Seraku, N., Takahashi, F. and Tsuji, S. (1984), “Attractive quality and must-be quality”,
The Journal of the Japanese Society for Quality Control, Vol. 14 No. 2, pp. 39-48.
Khan, M. (2002), “ECOSERV: ecotourists’ quality expectations”, Annals of Tourism Research,
Vol. 30 No. 1, pp. 109-124.
Khan, M.M. and Kang Duk, S. (2003), “Service quality expectations of travelers visiting Cheju
Island in Korea”, Journal of Ecotourism, Vol. 2 No. 2, pp. 114-125.
Kilic, B. and Bekar, A. (2012), “Üniversite Kampüsü İçerisinde Yiyecek İçecek Hizmeti Sunan
İşletmelerin Hizmet Kalitesinin Değerlendirilmesi ve Hizmet Kalitesinin Müşteri
Memnuniyetine Etkisi. (Turkish)”, Journal of Travel and Hospitality Management /Seyahat
Ve Otel Isletmeciligi Dergisi, Vol. 9 No. 3, pp. 38-51.
Kim, W., Ng, C. and Kim, Y. (2009), “Influence of institutional DINESERV on customer
satisfaction, return intention, and word-of-mouth”, International Journal of Hospitality
Management, Vol. 28 No. 1, pp. 10-17.
Kim, Y.G. and Li, G. (2009), “Customer satisfaction with and loyalty towards online travel
products: a transaction cost economics perspective”, Tourism Economics, Vol. 15 No. 4,
pp. 825-846.
Kisang, R. Hye-Rin, L. and Kim, W.G. (2012), “The influence of the quality of the physical
environment, food, and service on restaurant image, customer perceived value, customer
satisfaction, and behavioral intentions”, International Journal of Contemporary Hospitality
Management, Vol. 24 No. 2, pp. 200-223.
Klaus, P. (1985), “Quality epiphenomenon: the conceptual understanding of quality in face-to face
service encounters”, in Czepiel, J.A., Solomon, M.R., Suprenant, C.F. and Gutman, E.G. (Eds),
The Service Encounter: Managing Employee Customer Interaction in Service Business,
Lexington Books, Lexington, MA, pp. 17-33.
Knutson, B.J. (1988), “Ten laws of customer satisfaction”, Cornell Hotel and Restaurant Quarterly,
Vol. 29 No. 3, pp. 14-17.
Knutson, B., Stevens, P. Wullaert, C., Patton, M. and Yokoyama, R. (1991), “LODGSERV: a service
quality index for the lodging industry”, Hospitality Research Journal, Vol. 14 No. 2,
pp. 277-284.
Kong, H. (2014), “Are tour guides in China ready for ecotourism? An importance – performance
analysis of perceptions and performances”, Asia Pacific Journal of Tourism Research,
Vol. 19 No. 1, pp. 17-34.
Kozinets, R., de Valck, K., Wojnicki, A. and Wilner, S. (2010), “Networked narratives:
understanding word-of-mouth marketing in online communities’, Journal of Marketing,
Vol. 74 No. 2, pp. 71-89.
Kvist, A.-K.J. and Klefsjö, B. (2006), “Which service quality dimensions are important in inbound
tourism?: a case study in a peripheral location”, Managing Service Quality, Vol. 16 No. 5,
pp. 520-537.
Kwun, D., Ellyn, E. and Choi, Y. (2013), “Campus foodservice attributes and their effects on
customer satisfaction, image, and word-of-mouth”, Journal of Foodservice Business
Research, Vol. 16 No. 3, pp. 276-297.
Lages, L.F. and Fernandes, J.C. (2005), “The SERPVAL scale: a multi-item instrument for
measuring service personal values”, Journal of Business Research, Vol. 58 No. 11,
pp. 1562-1572.
Lankton, N. and McKnight, H. (2012), “Examining two expectation disconfirmation theory
models: assimilation and asymmetry effects”, Journal of the Association for Information
Systems, Vol. 13 No. 2, pp. 88-115.
Lau, K.N., Lee, K.H. and Ho, Y. (2005), “Text mining for the hotel industry”, Cornell Hotel and
Restaurant Administration Quarterly, Vol. 46 No. 3, pp. 344-362.
Lee, M., Kim, S. and Kim, W. (2012), “Service employees’ organizational citizenship behaviors and
customer-oriented behaviors: an accountability theory perceptive”, Journal of Foodservice
Business Research, Vol. 15 No. 4, pp. 335-361.
Lee, Y.L. and Hing, N. (1995), “Measuring quality in restaurant operations: an application of the
SERVQUAL instrument” International Journal of Hospitality Management, Vol. 14 Nos 3/4,
pp. 293-310.
Leland, E. (2011), A Few Good Online Survey Tools, Idealware, available at: www.idealware.org/
articles/fgt_online_surveys.php (accessed 15 July 2013).
Lewis, R.C. (1987), “The measurement of gaps in the quality of hotel services”, International
Journal of Hospitality Management, Vol. 6 No. 2, pp. 83-88.
Lewis, R.C. and Chambers, R.E. (1989), Marketing Leadership in Hospitality, Van Nostrand
Reinhold, New York, NY.
Lin, I.Y. and Mattila, A.S. (2010), “Restaurant servicescape, service encounter, and perceived
congruency on customers’ and satisfaction”, Journal of Hospitality Marketing and
Management. Vol. 19 No. 8, pp. 819-841.
Lovelock, C.H. (1985), “Developing and managing the customer-service function in the service
sector”, in Czepiel, J.A., Solomon, M.R., Suprenant, C.F. and Gutman, E.G. (Eds), The Service
Encounter: Managing Employee Customer Interaction in Service Business, Lexington
Books, Lexington, MA, pp. 265-280.
Luk, S.T.K., de Leon, C.T., Leong, F. and Li, E. (1993), “Value segmentation of tourists’
expectations of service quality”, Journal of Travel and Tourism Marketing, Vol. 2 No. 4,
pp. 23-38.
McCallum, J.R. and Harrison, W. (1985), “Interdependence in the service encounter”, in
Czepiel, J.A., Solomon, M.R., Suprenant, C.F. and Gutman, E.G. (Eds), The Service
Encounter: Managing Employee Customer Interaction in Service Business, Lexington
Books, Lexington, MA, pp. 35-48.
McMullan, R. and O’Neill, M. (2010), “Towards a valid and reliable measure of visitor
satisfaction”, Journal of Vacation Marketing, Vol. 16 No. 1, pp. 29-44.
Magnini, V.P., Honeycutt, E.D. and Hodge, S.K. (2003), “Data mining for hotel firms: use and
limitations”, The Cornell Hotel and Restaurant Administration Quarterly, Vol. 44 No. 2,
pp. 94-105.
Maister, D.H. (1985), “The psychology of waiting lines”, in Czepiel, J.A.,
Solomon, M.R. Suprenant, C.F. and Gutman, E.G. (Eds), The Service Encounter: Managing
Employee Customer Interaction in Service Business, Lexington Books, Lexington, MA,
pp. 113-123.
Martin, D.W. (1995), “An Importance/performance analysis of service providers’ perception of
quality service in the hotel industry” Journal of Hospitality and Leisure Marketing, Vol. 3
No. 1, pp. 5-16.
Matzler, K., Renzl, B. and Rothenberger, S. (2006), “Measuring the relative importance of service
dimensions in the formation of price satisfaction and service satisfaction: a case study in the
hotel industry”, Scandinavian Journal of Hospitality and Tourism, Vol. 6 No. 3, pp. 179-196.
Mavlikaeva, L., Bédiová, M. and Ryglová, K. (2014), “Current trends of researching and measuring
of quality, tourist satisfaction, loyalty and their interrelationships”, Czech Hospitality and
Tourism Papers, Vol. 10 No. 23, pp. 23-33.
Customer
satisfaction
31
IJCHM
28,1
32
Mazursky, D. (1989), “Past experience and future tourism decisions”, Annals of Tourism Research,
Vol. 16 No. 3, pp. 333-344.
Mount, D.J. (1997), “Introducing the relativity to traditional importance-performance analysis”,
Journal of Hospitality and Tourism Research, Vol. 21 No. 2, pp. 111-119.
Narver, J.C. and Slater, S.F. (1990), “The effect of a market orientation on business profitability”,
Journal of Marketing, Vol. 54 No. 10, pp. 20-35.
Nasukawa, T. and Nagano, T. (2001), “Text analysis and knowledge mining system”, IBM
Systems Journal, Vol. 40 No. 4, pp. 967-984.
Naumann, E. (1995), Customer Satisfaction Measurement and Management: Using the Voice of
the Customer, Thomson Executive Press, Cincinnati, OH.
Ograjenšek, I. and Gal, I. (2012), “The concept and assessment of customer satisfaction”, Statistics
in Practice, Vol. 107.
Oh, H. and Jeong, M. (1996), “Improving marketers’ predictive power of customer satisfaction on
expectation-based target market levels”, Hospitality Research Journal, Vol. 19 No. 4,
pp. 65-85.
Oh, H. and Parks, S.C. (1997), “Customer satisfaction and service quality: a critical review of the
literature and research implications for the hospitality industry”, Hospitality Research
Journal, Vol. 20 No. 3, pp. 36-64.
Oh, H. and Parks, S.C. (1998), “The role of attribute importance as a multiplicative weighting
variable in the study of hospitality consumer decision-making”, Journal of Hospitality and
Tourism Research, Vol. 21 No. 3, pp. 61-80.
Oliva, T.A., Oliver, R.A. and MacMillan, I.C. (1992), “A catastrophe model for developing service
satisfaction strategies”, Journal of Marketing, Vol. 56 No. 3, pp. 83-95.
Oliver, R.L. (1980), “A cognitive model of the antecedents and consequences of satisfaction
decisions”, Journal of Marketing Research, Vol. 17 No. 4, pp. 460-469.
Oliver, R.L. and DeSarbo, W.S. (1988), “Response determinants in satisfaction judgments”, Journal
of Consumers Research, Vol. 14 No. 4, pp. 495-507.
Oliver, R.L. and Swan, J.E. (1989), “Customer perceptions of interpersonal equity and satisfaction
in transactions: a field survey approach”, Journal of Marketing, Vol. 53 No. 2, pp. 21-35.
Opperman, M. (1996), “Convention destination images: an analysis of association meeting
planners’ perceptions”, Tourism Management, Vol. 17 No. 3, pp. 175-182.
Osgood, C.E. (1964), “Semantic differential technique in the comparative study of cultures”,
American Anthropologist, Vol. 66 No. 3, pp. 171-200.
Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1985), “A conceptual model of service quality
and its implications for future research”, Journal of Marketing, Vol. 49 Nos 9/11, pp. 41-50.
Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1988), “SERVQUAL: a multiple-item scale for
measuring consumer perceptions of service quality”, Journal of Retailing, Vol. 64 Nos 3/5,
pp. 12-37.
Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1991), “Refinement and Reassessment of the
SERVQUAL scale”, Journal of Retailing, Vol. 67 No. 4, pp. 420-450.
Patton, M., Stevens, P. and Knutson, B.J. (1994), “Internationalizing LODGSERV as a pilot study”,
Journal of Hospitality and Leisure Marketing, Vol. 2 No. 2, pp. 39-55.
Peter, P.J. and Olson, J.C. (1987), Consumer Behavior: Marketing Strategy Perspectives, Irwin,
Homewood IL.
Peterson, R.A. and Wilson, W.R. (1992), “Measuring customer satisfaction: fact and artifact”,
Journal of the Academy of Marketing Science, Vol. 20 No. 1, pp. 61-71.
Piercy, N. and Ellinger, A. (2015), “Demand- and supply-side cross-functional relationships: an
application of disconfirmation theory”, Journal of Strategic Marketing, Vol. 23 No. 1,
pp. 49-71.
Pizam, A. and Ellis, T. (1999), “Customer satisfaction and its measurement in hospitality
enterprises”, International Journal of Contemporary Hospitality Management, Vol. 11 No. 7,
pp. 326-339.
Pizam, A. and Milman, A. (1993), “Predicting satisfaction among first – time visitors to a
destination by using the expectancy disconfirmation theory”, International Journal of
Hospitality Management, Vol. 12 No. 2, pp. 197-209.
Pointer, J.S. (2003), “Evaluating the visual experience: visual acuity and the visual analogue scale”,
Ophthalmic and Physiological Optics, Vol. 23 No. 6, pp. 547-552.
Qualtrics software (2015), Copyright © 2015 Qualtrics, Provo, UT, available at: www.qualtrics.
com
Qu, R. (2014), “Market orientation and organizational performance linkage in Chinese hotels: the
mediating roles of corporate social responsibility and customer satisfaction”, Asia Pacific
Journal of Tourism Research, Vol. 19 No. 12, pp. 1399-1416.
Reuland, R., Coudrey, J. and Fagel, A. (1985), “Research in the field of hospitality”, International
Journal of Hospitality Management, Vol. 4 No. 4, pp. 141-146.
Ross, C.K., Frommelt, G., Hazelwood, L. and Chang, R.W. (1987), “The role of expectations in
patient satisfaction with medical care”, Journal of Health Care Marketing, Vol. 7 No. 4,
pp. 16-26.
Ryu, K., Lee, H.R. and Gon Kim, W. (2012), “The influence of the quality of the physical
environment, food, and service on restaurant image, customer perceived value, customer
satisfaction, and behavioral intentions”, International Journal of Contemporary Hospitality
Management, Vol. 24 No. 2, pp. 200-223.
Saravanan, R. and Kannan, P. (2012), “Application of SERVPERF for rural retailer: reliability and
factor analysis”, Asian Journal of Managerial Science, Vol. 1 No. 1.
Sasser, W.E., Olsen, J. and Wyckoff, D.D. (1979), Management of Service Operations, Allyn and
Bacon, New York, NY.
Scott, D. and Shieff, D. (1993), “Service quality components and group criteria in local
government”, International Journal of Service Industry Management, Vol. 4 No. 2, pp. 18-25.
Setó-Pamies, D. (2012), “Customer loyalty to service providers: examining the role of service
quality, customer satisfaction and trust”, Total Quality Management and Business
Excellence, Vol. 23 Nos 11/12, pp. 1257-1271.
Shahin, A. and Zairi, M. (2009), “Kano model: a dynamic approach for classifying and prioritizing
requirements of airline travelers with three case studies on international airlines”, Total
Quality Management, Vol. 20 No. 9, pp. 1003-1028.
Sheng, X., Simpson, P.M. and Siguaw, J.A. (2014), “US winter migrants’ park community
attributes: an importance – performance analysis”, Tourism Management, Vol. 43,
pp. 55-67.
Smith, A.M. (1995), “Measuring service quality: is SERVQUAL now redundant?”, Journal of
Marketing Management, Vol. 11 Nos 1/3, pp. 257-276.
Stevens, P., Knutson, B. and Patton, M. (1995), “Dineserv: a tool for measuring service quality in
restaurants”, The Cornell Hotel and Restaurant Administration Quarterly, Vol. 36 No. 2,
pp. 5-60.
Szymanski, D.M. and Henard, D.D. (2001), “Customer satisfaction: a meta-analysis of the empirical
evidence”, Journal of the Academy of Marketing Science Vol. 29 No. 1, pp. 16-35.
Customer
satisfaction
33
IJCHM
28,1
34
Tan, K.C. and Pawitra, T.A. (2001), “Integrating SERVQUAL and Kano’s model into QFD for
service excellence development”, Managing Service Quality, Vol. 11 No. 6, pp. 418-430.
Tkaczynski, A. and Stokes, R. (2010), “FESTPERF: a service quality measurement scale for
festivals”, Event Management, Vol. 14 No. 1, pp. 69-82.
Tong, S. and Walther, J. (2015), “The confirmation and disconfirmation of expectancies in
computer-mediated communication”, Communication Research, Vol. 42 No. 2, pp. 186-212.
Torres, E.N. (2014), “Deconstructing service quality and customer satisfaction: challenges and
directions for future research”, Journal of Hospitality Marketing and Management, Vol. 23
No. 6, pp. 652-677.
Tribe, J. and Snaith, T. (1998), “From SERVQUAL to HOLSAT: holiday satisfaction in Varadero,
Cuba”, Tourism Management. Vol. 19 No. 1, pp. 25-34.
Truong, T.H. (2005), “Assessing holiday satisfaction of Australian travelers in Vietnam: an
application of the HOLSAT model”, Asia Pacific Journal of Tourism Research, Vol. 10 No. 3,
pp. 227-246.
Truong, T.-H. and Foster, D. (2005), “Using HOLSAT to evaluate tourist satisfaction at
destinations: the case of Australian holidaymakers in Vietnam”, Tourism Management,
Vol. 27 No. 5, pp. 842-855.
Truong, T.H. and Foster, D. (2006), “Using HOLSAT to evaluate tourist satisfaction at
destinations: the case of Australian holidaymakers in Vietnam”, Tourism Management,
Vol. 27 No. 5, pp. 842-855.
Tsao, W. and Hsieh, M. (2012), “Exploring how relationship quality influences positive eWOM: the
importance of customer commitment”, Total Quality Management and Business Excellence,
Vol. 23 Nos 7/8, pp. 821-835.
Tsitskari, E., Vernadakis, N., Tzetsis, G., Aggeloussis, N. and Costa, G. (2009), “Expected and
perceived service quality at basketball stadiums in Greece”, World Leisure Journal, Vol. 51
No. 2, pp. 94-104.
US Travel Association (2009), available at: www.ustravel.org/news/press-kit/travel-facts-andstatistics (accessed 5 July 2013).
Vavra, T.G. (1997), Improving Your Measurement of Customer Satisfaction: A Guide to Creating,
Conducting, Analyzing, and Reporting Customer Satisfaction Measurement Programs,
ASQ Quality Press, Milwaukee, WI.
Vavra, T.G. (2002), Customer Satisfaction Measurement Simplified: A Step-By-Step Guide for ISO
9001: 2000 Certification, ASQ Quality Press, Milwaukee, WI.
Wang, T. and Ji, P. (2010), “Understanding customer needs through quantitative analysis of
Kano’s model”, International Journal of Quality and Reliability Management, Vol. 27 No. 2,
pp. 173-184.
Weiss, R., Feinstein, A.H. and Dalbor, M. (2005), “Customer satisfaction of theme restaurant
attributes and their influence on return intent”, Journal of Foodservice Business Research,
Vol. 7 No. 1, pp. 23-41.
World Tourism Organization (1985), Identification and Evaluation of Those Components of
Tourism Services Which Have a Bearing on Tourist Satisfaction and Which can be
Regulated, and State Measures to Ensure Adequate Quality of Tourism Services, World
Tourism Organization, Madrid.
Wright, K.B. (2005), “Researching Internet-based populations: advantages and disadvantages of
online survey research, online questionnaire authoring software packages, and web survey
services”, Journal of Computer-Mediated Communication, Vol. 10 No. 3.
Wu, X., Zhu, X., Wu, G.Q. and Ding, W. (2014), “Data mining with big data”, IEEE Transactions
on Knowledge and Data Engineering, Vol. 26 No. 1, pp. 97-107.
Xu, Y. and Goedegebuure, R. (2005), “Employee satisfaction and customer satisfaction: testing the
service-profit chain in a Chinese securities firm”, Innovative Marketing, Vol. 1 No. 2,
pp. 49-59.
Yoon, T.H. and Ekinci, Y. (2003), “An examination of the SERVQUAL dimensions using the
Guttman scaling procedure”, Journal of Hospitality and Tourism Research, Vol. 27 No. 1,
pp. 3-23.
Zehrer, A., Muskat, B. and Muskat, M. (2014), “Services research in tourism: advocating the
integration of the supplier side”, Journal of Vacation Marketing, Vol. 20 No. 4, pp. 353-363.
Zeithaml, V.A., Berry, L.L. and Parasuraman, A. (1988), “Communication and control processes in
the delivery of service quality”, Journal of Marketing, Vol. 52 No. 2, p. 36.
Corresponding author
Valeriya Shapoval can be contacted at: Valeriya.Shapoval@ucf.edu
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