Description
The purpose of this paper is to test and compare different conceptual approaches for
perceived value in a service context
International Journal of Culture, Tourism and Hospitality Research
Extensions on the conceptualization of customer perceived value: insights from the airline industry
Thomas Mayr Andreas H. Zins
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Thomas Mayr Andreas H. Zins, (2012),"Extensions on the conceptualization of customer perceived value: insights from the airline industry",
International J ournal of Culture, Tourism and Hospitality Research, Vol. 6 Iss 4 pp. 356 - 376
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Extensions on the conceptualization of
customer perceived value: insights from
the airline industry
Thomas Mayr and Andreas H. Zins
Abstract
Purpose – The purpose of this paper is to test and compare different conceptual approaches for
perceived value in a service context.
Design/methodology/approach – Perceived value is an outcome construct that results from various
bene?ts received and sacri?ces devoted to achieve a particular exchange of a service. The paper
compares three different modeling approaches (Type 1, Type 2, and Type 4) for perceived value using
data from an in-?ight survey. The questionnaire covered topics such as perceived service quality and
overall satisfaction, price perception, customer value, and customer retention.
Findings – The theoretical discussion repeatedly emphasizes that only the formative modeling of
perceived value ?ts the arguments put forward in the existing literature. This study replicates and
extends a study by Lin et al. in the airline service context. The paper reports details about the impact of
the proposed seven ‘‘get’’ and ‘‘give’’ components, together with an analysis of the consequences
perceived value has on satisfaction, loyalty, and word-of-mouth.
Research limitations/implications – The ?ndings suggest extensions and improvements concerning
measurement and conceptual issues.
Practical implications – Perceived value shows a substantial effect on behavioral consequences.
Service operations must observe the perception of atmospherics emerging from the main service
encounters next to considering functional aspects.
Originality/value – Misconceptualizations of multi-item constructs are well known. However, critical
discussions and empirical tests are still scarce in the tourism ?eld. This paper tests and compares
different conceptual approaches for perceived value in a service context.
Keywords Customer value, Customer satisfaction, Loyalty, Airline industry,
Formative and re?ective indicator models, Consumer behaviour, Modelling
Paper type Research paper
Introduction
In the battle between low-cost carriers and traditional network airlines for new and loyal
customers, assessing the most important drivers for customer satisfaction and customer
retention is crucial. Should management focus on service quality issues alone, concentrate
predominantly on price arguments, or consider both? Do more considerations arise in the
case of marketing airline services effectively? Compared to the vast literature on customer
satisfaction and service quality produced in the last two decades, little research has
emerged that seeks to better understand how consumers value products and services.
Many studies report that the value construct is a signi?cant one in consumer
decision-making models (Bolton and Drew, 1991; Dodds and Monroe, 1991; Zeithaml,
1988) and deem value a multifaceted and complex construct (Petrick and Backman, 2002;
Sheth et al., 1991; Sweeney and Soutar, 2001). Some researchers propose a superiority of
perceived value over satisfaction (e.g. Sweeney et al., 1999). The conceptualization of
perceived value, as well as the antecedents and consequences of perceived value, often
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VOL. 6 NO. 4 2012, pp. 356-376, Q Emerald Group Publishing Limited, ISSN 1750-6182 DOI 10.1108/17506181211265086
Thomas Mayr is based at
the Institute for Tourism and
Leisure Studies, University
of Economics and
Business, Vienna, Austria.
Andreas H. Zins is a
Professor in the Department
of Tourism and Hospitality
Management, MODUL
University, Vienna, Austria.
Received March 2011
Revised July 2011
Accepted September 2011
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differ between studies (Cronin et al., 1997; Patterson and Spreng, 1997; Ruiz et al., 2008;
Sweeney et al., 1999; Varki and Colgate, 2001).
More recently, a growing interest in services research has gone alongside the evolution of
marketing as an applied and a ?eld of research (Vargo and Lusch, 2004; 2008). The service
literature itself reported research into customer value from various perspectives (e.g. Lin
et al., 2005; Oh, 1999; Petrick, 2002; Petrick and Backman, 2002; Ruiz et al., 2008; Sanchez
et al., 2006). Lin et al. (2005) contains a comparison of the different types of
operationalization of ‘‘perceived value’’ in the web service context. They compare three
model speci?cations (unidimensional, re?ective, and formative) using e-tail service value
data, and show that perceived value as a second-order multidimensional formative
construct is convincing in the measurement model. This formative approach outperforms
alternative perceived value model conceptualizations. All previous studies consider value
components as re?ective; whereas the work by Lin et al. (2005) is the ?rst to conceptualize
customer value as a second-order formative construct with ?rst-order components in the
web services context. Similarly, Ruiz et al. (2008) have recently conceptualized service value
as a higher-order, formative construct with bene?t and sacri?ce components. The current
study follows that perspective and examines customers’ perceptions of bene?ts weighed
against sacri?ces in the airline service context.
This research follows the approach of Lin et al. (2005), because that was the ?rst work to
perceive value in service context as being multi-dimensionally conceptualized as a
formative higher-order construct. However, we extend that work in four ways. First, we favor a
broader concept of the give-or-sacri?ce dimension, compared to the simpli?ed
conceptualization of monetary sacri?ce in the aforementioned study. This prompted us to
include additional price perceptions and an effort component that considers time and
cognitive strain. Second, we extend the get-or-bene?t dimension using functional (service
components of the airline industry) and emotional (atmospheric components) aspects.
Third, we bring the structure of the dependent variables (satisfaction, loyalty, word-of-mouth)
closer to theoretical considerations. Fourth, we explicitly and completely estimate the
measurement model for the exogenous variables, taking in three different
conceptualizations, following the model typology proposed by Jarvis et al. (2003). The
model’s three types are:
1. formative ?rst-order and formative second-order (Type 1) – the multidimensional
construct is a composite of its dimensions, and the dimensions are themselves constructs
and can be regarded as speci?c components of the second-order construct (Edwards,
2001);
2. re?ective ?rst-order and formative second-order-order constructs (Type 3) – the
second-order construct with ?rst-order formative dimensions, the ?rst-order dimensions
being measured by several re?ective manifest items; and
3. re?ective ?rst-order and re?ective second-order construct (Type 4) – the
multidimensional constructs are measured by several re?ective dimensions and the
dimensions are themselves measured by several re?ective manifest items.
This paper achieves a better understanding of the multidimensional construct perceived
value in a service context, achieved through a critical review and extension of its
conceptualization. The paper is structured accordingly: ?rst, we describe different
approaches and recent developments in conceptualizing and measuring perceived value,
followed by a discussion of how customer value should be modeled as consisting of
re?ective or formative indicators. Then, we apply the survey data to the different models, and
compare results of the various conceptualizations. Finally, we critically discuss results and
suggest future research ideas.
Perceived value – different conceptualizations
Even though several de?nitions of perceived value exist (Dodds and Monroe, 1991; Sheth
et al., 1991; Zeithaml, 1988), a well-established and most universally accepted one is ‘‘the
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consumer’s overall assessment of the utility of a product based on perceptions of what is
received and what is given’’ (Zeithaml, 1988, p. 14). In other words, perceived value involves
a trade-off between what the customer gets (e.g. quality, bene?ts, utilities) and what they
give up (e.g. prices, sacri?ces, time) to buy and consume a product. However, compared to
the vast literature in the tangible goods context, relatively few studies have focused on the
consideration of service components when de?ning and operationalizing customer
perceived value (Ruiz et al., 2008). This omission is surprising, because the traditional
product-oriented marketing approach has been questioned recently, and some have
suggested a shift from product-based marketing to a more service-based marketing
approach (Vargo and Lusch, 2004, 2008).
Two basically different perspectives of perceived value are identi?able. First, the
unidimensional conceptualization of perceived value is based on the ‘‘give versus get’’ or
‘‘bene?ts versus sacri?ces’’ trade-off concept. While these drivers are treated as
independent, the perceived value of exogenous variables is recommended to be
regarded as a separate construct on a more abstract level (Zeithaml, 1988). Following
this approach, perceived value is not de?ned by, but depends on, bene?ts received
(e.g. economic, social, and relationship) and sacri?ces made (monetary, such as price; or
non-monetary, such as time, effort, and risk). In this unidimensional conceptualization,
perceived value is seen as a global measure of overall customer value perceptions (e.g. a
single measure of utility, value for money, meeting quality and price requirements). Although
a unidimensional conceptualization lacks validity (Woodruff and Gardial, 1996; Woodruff,
1997) and cannot re?ect the complex structure of the construct (Sweeney and Soutar, 2001),
several mainstream studies use the unidimensional approach (Cronin et al., 2000; Varki and
Colgate, 2001), as does the tourism stream (Duman and Mattila, 2005; Murphy et al., 2000).
Second, as suggested by Sweeney and Soutar (2001), a wider conceptualization of perceived
value is desirable in order to capture its complex and multidimensional nature. Recent
research has offered several approaches to measuring the multidimensionality of perceived
value. Based on the conceptual frameworks developed by Sheth et al. (1991), Sweeney and
Soutar (2001) identify four distinct value dimensions (i.e. emotional value, social value,
quality/performance, and price/value for money) using a 19-item measure. Emotional value,
social value, and quality/performance are bene?t components, whereas price/value is a
sacri?ce component. They claim that their multidimensional measure (so-called PERVAL)
explains consumer choice better than does a single ‘‘value for money’’ item. Further, they
found the scale to be reliable and valid in both pre-purchase and post-purchase situations.
While the above-mentioned authors tested the PERVAL scale in a retail context, Petrick (2002)
more recently developed a multidimensional scale for measuring the perceived value of a
service. The so-called SERV-PERVAL scale consists of ?ve dimensions (i.e. behavioral price,
monetary price, emotional response, quality, and reputation) and several studies tested the
scale among cruise passengers (Petrick, 2003, 2004) and golf tourists (Petrick and Backman,
2002). Despite its reliability and validity for the measurement of perceived value, skepticism
persists concerning the level of abstraction this approach evidences (Sanchez et al., 2006).
Some commentators have identi?ed a need to broaden the scale, because the SERV-PERVAL
scale captures only the post-purchase evaluation of a service and does not include the
measurement of the perceived overall value of a purchase. One attempt to develop such a
scale is the so-called GLOVAL scale, which considers both the consumption as well as the
purchase experience (Sanchez et al., 2006). In line with these authors’ ?ndings, the
experiential view is of great signi?cance when evaluating perceived value of leisure and
tourism products. Holbrook (1999) proposes this experiential perspective, which comprises
the symbolic, hedonic, and aesthetic aspects of the consumption, and suggests that value is a
result of a speci?c consumption experience. In particular, they pay attention to the emotional
component of consumer behavior and the importance of the hedonic component in the
experiences of buying and consuming in leisure, creative, and aesthetic activities (Havlena
and Holbrook, 1986; Holbrook, 1994; Holbrook and Hirschmann, 1982). However, the most
commonly usedframework remains Zeithaml’s (1988) trade-off model (Ruiz et al., 2008). In this
study, customer perceived value in the airline service context consist of various bene?ts and
sacri?ces dimensions, and the methodology follows the trade-off model.
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Although these multidimensional models help us to better understand perceived value, they
fail to form an overall perceived value construct that maps the customer ‘‘give versus get’’
trade-off experience implied in the construct, which is inconsistent with the conceptual
de?nition of perceived value (Lin et al., 2005). Another point of criticism refers to the
component and construct level in structural models. As Turel et al. (2007) point out, if
perceived value is de?ned as an overall assessment, the value perceptions on the component
level should form an overall value. This de?nition should, in turn, affect the outcome. Thus,
consumers are expected to make mental trade-offs, which formthe overall value. According to
Lin et al. (2005) and Turel et al. (2007), perceived value may be regarded as a second-order
formative construct with ?rst-order components due to its satisfying statistical criteria (e.g. no
covariance exists between the components, because a decrease in one get-component does
not imply a change in another get-component). Additionally, certain problems arise from
treating give- and get-components as a set of perceived values and modeling them as
predictors in a structural model without considering the role of overall perceived value (Lin
et al., 2005). Research hypotheses may be set up, and analyses carried out at the component
level, but conclusions may be drawn at the perceived value level. In terms of consequences of
perceived value, the effects of perceived value on other constructs (such as satisfaction and
loyalty) are unconvincing unless analyses are carried out at the level of perceived value
instead of the component level (Lin et al., 2005).
With regard to the relationships between unidimensional, multidimensional methods, and the
conceptual de?nition of the perceived value construct, we may identify three paradoxes (Lin
et al., 2005). First, ‘‘if treating give-get components as antecedents of perceived value in the
nomological network is justi?ed, then the value dimensions identi?ed in the multidimensional
conceptualization studies do not refer to value; consequently, these dimensions should be
distinct from value instead of being components of value’’ (Lin et al., 2005, p. 322). Second,
the relationships between give- and get-components and value are conceptually
tautological, because the structural models that analyze this relationship are per se
inadequate if the current multidimensional conceptualization of perceived value is applied.
Third, when the unidimensional and multidimensional approaches are justi?ed, the de?nition
of perceived value should be re?ned and clari?ed. Hence, Lin et al. (2005) call for a
consideration of perceived value, and the relationships between give- and get-components,
as well as the speci?cation of overall value. In the case of a trade-off model, researchers
should employ a formative conceptualization of service value; and a higher-order model
permits the distinction between measurement error on the indicator level and measurement
error at the construct level (Ruiz et al., 2008).
Results of the comparison of their three models show differences in the relationships
between give- and get- components and perceived value, and between perceived value
and post-purchase behavioral intentions (Lin et al., 2005). Regarding the relationships
between give- and get-components in the re?ective model, monetary sacri?ce, website
design, ful?llment/reliability, and security/privacy are critical components constituting value.
The results of the formative (unidimensional) model show that order ful?llment/reliability
(transaction security/privacy) are not signi?cant. Looking at the consequences, value
perceptions lead to satisfaction in all of the models. Only in the formative and re?ective
models value perceptions lead to re-patronage and positive word-of-mouth (WOM), and
perceived value actually has stronger direct effects on both re-patronage and positive WOM
intentions than does satisfaction. However, in the unidimensional model, satisfaction has
stronger impacts on post-purchase behavioral intentions than does perceived value.
Therefore, perceived value has indirect effects on post-purchase behavioral intentions
mediated by satisfaction.
Formative compared to re?ective models
In recent years, the discussion around the assessment and comparison of re?ective and
formative models has gained momentum (e.g. Coltman et al., 2008; Diamantopoulos and
Winklhofer, 2001; Diamantopoulos et al., 2008; Jarvis et al., 2003; Hair et al., 2011; Henseler
et al., 2009; Petter et al., 2007). Psychographic (e.g. customer satisfaction, perceived value)
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or psychological constructs (e.g. brand attitude and purchase intentions) are more often
de?ned as causing its indicators, and are therefore considered re?ective. In other words,
‘‘the paths relating the indicators to the factor emanate from the latent variable to the
indicator’’ (Brown, 2006, p. 351). On the other hand, managerial constructs (e.g. market
dynamism, job performance, competitive and market intelligence) and socioeconomic
variables (e.g. social status) are more frequently formed by its indicators and are therefore
formative (Jarvis et al., 2003; Mazanec, 2007). Most researchers in social sciences view
indicators as re?ective and neglect the possible formative appropriateness (Bollen and
Lennox, 1991). Similarly, Jarvis et al. (2003) report that many studies use re?ective measures
– even though they should use formative indicator measurement models. Jarvis et al. (2003)
also provide four sets of questions to determine whether a construct is formative or re?ective.
Consequently, a construct should be modeled as having formative indicators if the following
conditions are given:
1. the direction of causality is from items to construct and variation in item measures causes
variation in the construct;
2. items de?ne the construct – they need not share a common theme, but must not be
interchangeable;
3. items can have any pattern of inter-correlation, but should have the same directional
relationship, and a change in one of the indicators must not be associated with changes
in the other indicators; and
4. indicators may not have similar signi?cance of relationships with the
antecedents/consequences as the construct.
Therefore, in formative indicator models, the indicators cause the latent variable, and the
direction of causality leads from the observed measures to the latent variable respectively
(Bollen and Lennox, 1991; Bollen, 1989; Edwards and Bagozzi, 2000). In contrast, a change
in the latent variable of a model with re?ective indicators will result in a change in all
constituent indicators (Bollen and Lennox, 1991; Bollen, 1989; Diamantopoulos, 2006, 2008;
Diamantopoulos and Winklhofer, 2001). In a re?ective conceptualization, we expect all
components of customer perceptions to co-vary with one another (Jarvis et al., 2003). Taking
the example of airline services, this means that an airline may reduce a customer’s perceived
sacri?ce by offering easier booking and check-in possibilities, which enable the customer to
save time or effort – but the bene?ts of the airline for the customer remain unchanged. Thus,
a re?ective model may mis-specify the customer perceived value construct, which can lead
to incorrect measurement of the structural relationships between constructs and undermine
the validity of the statistical conclusions (MacKenzie et al., 2005). These criteria do not
account for constructs speci?ed at a more abstract level, which may include multiple
formative and/or re?ective ?rst-order dimensions. Such second-order models capture the
idea of abstract multidimensional construct de?nition (e.g. job satisfaction), which is quite
common in marketing (Jarvis et al., 2003). Similarly, Podsakoff et al. (2006) suggest
choosing higher-order constructs when modeling complex constructs. In service-based
marketing, research treated customer perceived value as a multidimensional construct
(e.g. Petrick, 2004; Petrick and Backman, 2002) and a higher-order, formative measure that
outperforms alternative operationalizations (Lin et al., 2005; Ruiz et al., 2008; Turel et al.,
2007). Considering the trade-off approach (Zeithaml, 1988), and in line with the
service-centered view of marketing that is customer-centric and market driven (Vargo and
Lusch, 2004), the present research proposes that customer perceived value may be better
conceptualized as a higher-order formative construct that includes bene?ts and sacri?ces
as ?rst-order dimensions. Following the model speci?cations proposed by Jarvis et al.
(2003), we specify four types of multidimensional second-order factor models:
1. formative ?rst-order, formative second-order (Type 1);
2. re?ective ?rst-order and formative second-order (Type 2);
3. formative ?rst-order and re?ective second-order (Type 3); and
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4. re?ective ?rst-order and re?ective second-order (Type 4). This study compares Type 1, 2
and 4 models. Type 3 has no practical relevance and has not been used relevantly in
research (Diamantopoulos et al., 2008).
Components, relationships and their conceptualization
Although the ‘‘get’’ component of perceived value is quite well understood, and much
research has been devoted to this issue, the ‘‘give’’ component still lacks in sophistication.
So far, in most studies the ‘‘give’’ element has been limited to the monetary sacri?ce a
customer has to raise. In order to satisfy airline passengers, the comprehensive
understanding of price perception is crucial. Price satisfaction is conceived to represent a
multidimensional construct (Matzler et al., 2006a), based on the following dimensions
(Matzler et al., 2006b):
B price transparency;
B price-quality ratio;
B relative price;
B price con?dence;
B price reliability; and
B price fairness.
The more information consumers have on prices of the offer (e.g. on differentiated prices),
the higher their con?dence in the superiority of the offer (Matzler et al., 2007). Companies
should consider these dimensions when monitoring customer satisfaction, and we can
assume this also applies to the airline industry.
Zeithaml (1988) shows that perceived quality leads to perceived value, which in turn leads to
purchase intention. Perceived value may lead directly to the formation of overall satisfaction
(Churchill and Surprenant, 1982); however, a varying, mediating role of customer
satisfaction may also exist between perceived value and behavioral intentions (Oh, 1999;
Petrick, 2004; Patterson and Spreng, 1997). The assumption that satis?ed customers are
loyal has been widely accepted. However, satis?ed customers may not be a suf?cient
marketing goal to create loyal customers (Cronin and Taylor, 1992; Fornell, 1992).
A second-order conceptualization of perceived value differs from the ?rst conceptualization in
two ways. First, in the second-order model, perceived value compounds the speci?c relevance
of the different dimensions of the construct. Second, the second-order conceptualization
shows the relative weight of each of the seven dimensions in representing perceived value.
Following Jarvis et al. (2003) and Lin et al. (2005), we compare three model types:
1. Type 1 (formative ?rst-order; formative second-order);
2. Type 2 (re?ective ?rst-order and formative second-order); and
3. Type 4 (re?ective ?rst-order and re?ective second-order) models.
The ?rst type of model (Type 1) conceptualizes customer perceived value as a composite of
its dimensions, whereas the dimensions are themselves constructs and conceived as
speci?c components of the second-order construct (Edwards, 2001). Another type of model
is the second (Type 2) model, where the second-order construct has ?rst-order formative
dimensions, and the ?rst-order dimensions themselves are measured by re?ective
indicators. Yet another type of model is Type 4, which represents ?rst-order dimensions
with several re?ective indicators, and these ?rst-order dimensions are themselves re?ective
indicators of an underlying second-order construct.
Methodology
This validation study considers a real-life service situation for which customers may deliver
their re?ections on the immediate consumption experience of a service. A European
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network-type airline acted as the partner company in this project. Both the representative’s
of the product strategy department and the researchers’ interests focused on substantive
?ndings about the perceptions of different service areas, the service value in total, and the
attitudinal and behavioral consequences thereof. Hence, we developed the design and the
measurement instrument together, according to the prevailing standards and guidelines for
the continuous on-board satisfaction survey.
The self-completion questionnaires were prepared in German, English, and French
(developed in close collaboration with airline representatives, translated, translated back,
and corrected) and distributed to a small number of randomly selected passengers on a
total of 53 ?ights. To keep extraneous error as small as possible, we selected only short-haul
?ights operated by Airbus A320 vehicles. In total, 519 respondents returned completed
questionnaires, which covered topics such as perceived service quality and overall
satisfaction, price perception, customer value, and customer retention. Flight attendants
asked respondents whether they were willing to complete the on-board questionnaire. With
regard to non-response, bias (de?ned as when a potential respondent refuses to answer any
question or the questionnaire in total – unit non-response) amounted to 19 percent. Thus,
total contacts amounted to 622 passengers.
Among the respondents, 45 percent bought a business class ticket, and slightly more than
half traveled for business purposes (52 percent). Forty-seven percent of the passengers
were members of the frequent ?yer programof the particular airline. The data showthat more
passengers were male (65 percent) than female (35 percent), and the major age cohort was
31-50 years, accounting for 54 percent. Ten percent of the respondents were older than 60
years. On average, respondents ?ew20 times a year, with an average share of 11 ?ights with
the participating airline.
Respondents from 38 different countries ?lled in the questionnaires. The majority were
travelers from Germany and Austria (52 percent), followed by Great Britain (14 percent) and
France (11 percent). The measures employed to validate the theoretical model represent
give- and get-components, as well as endogenous constructs usually modeled as
dependents from the service outcome. Multi-item scales covered the majority of constructs.
In order to maintain the comparability with previous on-board questionnaires, respondents
evaluated all items on a six-point Likert-type scale. We used existing scales where possible,
but also tried to develop customized scales where appropriate, considering but
notwithstanding the recent general discussion on measures by Rossiter (2002), and the
necessity of a multi-item multidimensional treatment of customer perceived value.
Table I lists the latent constructs and their respective measures. All evaluative items are
subject to a six-point agreement scale, ranging from 1 ¼ agree completely to 6 ¼ disagree
completely We constructed only the overall satisfaction measure along a continuum, ranging
from 1 ¼ very satis?ed to 6 ¼ very dissatis?ed. A scale ranging from 1 (100 percent/highly
likely) to 6 (0 percent/highly unlikely/certainly not) (loyalty, satisfaction, word-of-mouth,
reputation) measured the behavioral consequences items.
We deconstructed the concept of perceived value according to our previous discussion into
give- and get-components. Four get-components are distinguished. Initially, we included 14
different service quality attributes that the airline company regularly uses to monitor their
performance. By dropping two items (‘‘the booking procedure was uncomplicated’’ and
‘‘the ?ight was punctual’’), we arrived at a three-dimensional con?guration a of service
domains:
1. pre-?ight services (including check-in, hand-baggage check at the gate, boarding
procedure);
2. on-board services (including friendliness of attendants, in-?ight catering, entertainment
program, newspapers and journals); and
3. cabin tangibles (including seating comfort, aircraft interior, cleanliness of interior, and
technical services).
PAGE 362
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Apart fromthese usual service aspects, we added an additional dimension of ‘‘atmosphere’’
to the measurement instrument, which is represented by four items addressing the ease and
relaxing feeling during the ?ight, the time distortion feeling, and the sense of security and
safety.
In order to extend most of the previous work in this area, we conceived the give-component
to go beyond the monetary contribution the passenger must make. Consequently, we
proposed a dimension that covers the cognitive effort (for the booking process) and the time
effort (for gathering and analyzing the appropriate information about schedules and
airfares). We split the monetary sacri?ce into two aspects:
1. a price assurance dimension (covering stability of prices, price transparency, and easy
overview of fares); and
2. a dimension related to the quality of unreasonable prices (covering ‘‘cost a lot of money’’,
unfair structure of fares, and impression of expensive ?ight).
We considered satisfaction as comprising a mixture of emotional and cognitive reactions to
the service delivery (Oliver, 1997). Hence, we measured this by a traditional ‘‘overall
satisfaction’’ statement, an item addressing the emotional category of ‘‘happiness with the
airline’’, and a more cognitively based evaluation of the overall service received. We split the
loyalty domain into two facets or dimensions:
1. the re-booking probability; and
2. the word-of-mouth intention.
The customer retention aspect is covered by four items re?ecting resistance to change (two
indicators), a re-booking probability for the same route (one indicator), and the ?rst-choice
preference in general (one indicator). We added reputation of the airline to the model only for
identi?cation reasons (for the formative model variant), but reputation is not considered to
contribute to the explanatory value of the model. Word-of-mouth is mapped by a single
indicator (‘‘the likelihood of recommending the airline’’).
Results of the measurement models
We chose the partial least square (PLS) model because of its capacity to calculate both
re?ective and formative models (Fornell and Bookstein, 1982). Following the guidelines to
consider prior to PLS modeling (Marcoulides and Saunders, 2006), after data screening and
examination of the distributional characteristics we examined the psychometric properties of
all variables in the model (Table I), and the relationships between the variables and
constructs in the model. Missing data or item non-response are a part of almost any
research, and various alternative approaches have been identi?ed to deal with this
phenomenon (Little and Rubin, 1987). The present research assessed a mean value
imputation for handling missing data. We performed PLS model estimation using SmartPLS
(Ringle et al., 2005). Within the PLS approach, the evaluation of re?ective constructs include
content and indicator validity, construct, and convergent reliability. The indicator validity
speci?es what part of an indicator’s variance is explained by the latent variable. Loadings
larger than 0.7 are acceptable within PLS models, and loadings smaller than 0.4 call for
removal (Hulland, 1999). All indicators exceed the critical value of 0.4; however, 12 percent
of all indicators show values lower than 0.7, and we did not need to remove any indicator
(see Table I). The seven value drivers (four get- and three give-components) have composite
reliability (CR) ratios in the range between 0.77 and 0.91. All dimensions are above the
recommended threshold level of 0.7 (Hair et al., 1998).
Convergent validity represents the shared variance by the construct and its corresponding
items, and is measured by the AVE average variance extracted (Fornell and Larcker, 1981).
In addition to convergent validity, we examined the discriminant validity by comparing the
average variance extracted with the squared correlations between any two latent constructs
(Fornell and Larcker, 1981; Anderson and Gerbing, 1992). The AVE passes in all but one
case (on-board services) the threshold recommended of 50 percent. However, all constructs
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ful?lled the Fornell-Larcker criterion. On the indicator level, we could not observe any high
cross-loadings.
In behavioral research, results of self-reported data may be biased through possible
common method predisposition resulting from multiple sources, such as consistency motif,
social desirability, acquiescence, and positive and negative affectivity (Podsakoff et al.,
2003). These authors propose statistical procedures to address concerns regarding
common method biases. In particular, we also applied their proposed technique ‘‘controlling
for the effects of an unmeasured latent method factor’’ to PLS recently (Liang et al., 2007).
Using PLS to assess common method variance, a latent method factor with all measures as
indicators should be added to the structural model. Following the recommendations and
testing procedure by Liang et al. (2007), this latent method factor is linked to all indicators,
whereas each indicator must be converted to a single-indicator construct.
The results are reported for the re?ective model, because the two models do not show
substantial statistical difference regarding the signi?cance, and the signs remained
unchanged. The average substantively explained variance of the indicators is 0.006, and
the average method-based variance is 0.648. Comparing substantive variance to method
variance, the ratio is 1:109. Additionally, most of the method factor loadings are insigni?cant,
and the indicators’ method variances are considerably lower than their substantive variances.
We conclude that common method bias is not an issue and that the method is appropriate.
As Diamantopoulos (2006) points out, reliability becomes an irrelevant criterion for
assessing measurement quality for formative measurement models, because it is the validity
that should be secured and focused on. Rossiter (2002) recommends as a ?rst step to use
theoretical and expert opinion to assess the validity of formative indicators. Second,
statistical analyses could help in assessing the validity of formative indicators. An indicator
should not enter into a formative index if the indicator has no signi?cant impact on the index
or if an indicator exhibits high multicollinearity (Henseler et al., 2009). However, formative
indicators should not be dropped simply because of statistical analysis, because they may
substantially change the context of the formative index (Jarvis et al., 2003; Diamantopoulos,
2008). In formative models, correlations amongst causal indicators are expected to be not
particularly high (Diamantopoulos and Siguaw, 2006; Rossiter, 2002).
In order to assess statistical signi?cance, we used bootstrap analysis with 1,000
sub-samples. This method contrasts with re?ective measures, where factor loadings
should be assessed, and weights should be assessed in the formative case. These weights
represent information about the extent to which each indicator contributes to the respective
construct (Mathwick et al., 2001). All indicators showsigni?cant values (see Table I; p-values
,0.05). Unfortunately, no common understanding exists regarding how to proceed with
multicollinearity in formative models. The variance in?ation factor (VIF) helps in determining
the degree of multicollinearity, and values of VIF greater than 10 indicate a critical level of
multicollinearity (Henseler et al., 2009). In the current study, VIF was below the critical level
for all indicators. We conducted an additional assessment of discriminant validity by
comparing the average variance extracted fromeach construct with its communal variances
shared with other constructs. Table II shows the inter-construct correlations (off the diagonal)
and the square roots of the average variance extracted (on the diagonal). Because the
square root of the average variance extracted for all constructs was higher than their shared
variances, discriminant validity can be con?rmed.
Results of the structural models
Following the test design propagated by Lin et al. (2005), and focusing on the three model
types proposed by Jarvis et al. (2003), we tested three different structural models, taking the
same data set of the airline passenger sample. After having estimated the measurement
models for the respective latent variables, we identi?ed the structural relationships for three
different model types where a second-order latent variable captured perceived value (see
Figures 1-3). This higher-order construct follows the multidimensional approach of customer
perceived value and accounts for the trade-off (Ruiz et al., 2008).
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The ?rst variant (Type 1) treats perceived value as a second-order multidimensional formative
construct, which turned out to be the most appropriate model, because this conceptualization
better ?ts the theoretical framework proposed for customer perceived value, and has also
been modeled in recent studies (Lin et al., 2005; Ruiz et al. 2008). We assumed that the
indicators in our study caused the seven give- and get-components. The conception as a
Table II Correlation matrix
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]
Atmosphere [1] 0.84
Cabin tangibles [2] 0.47 0.79
0.51
a
Pre?ight services [3] 0.35 0.42 0.72
0.36
a
0.41
a
On-board services [4] 0.52 0.45 0.29 0.68
0.53
a
0.49
a
0.31
a
Price assurance [5] 0.30 0.32 0.18 0.26 0.84
0.30
a
0.30
a
0.16
a
0.26
a
Unreasonable price [6] 20.15 20.09 20.09 20.04 20.15 0.74
20.14
a
20.08
a
20.07
a
20.04
a
20.14
a
Effort [7] 20.16 20.11 20.16 20.18 20.27 0.17 0.85
20.20
a
20.11
a
20.17
a
20.17
a
20.28
a
0.16
a
Satisfaction [8] 0.60 0.44 0.39 0.59 0.27 20.12 20.18 0.81
0.62
a
0.44
a
0.40
a
0.58
a
0.28
a
20.11
a
20.19
a
Loyalty [9] 0.59 0.40 0.30 0.39 0.30 20.16 20.14 0.60 0.81
0.60
a
0.40
a
0.30
a
0.39
a
0.30
a
20.15
a
20.16
a
0.60
a
Word-of-mouth [10] 0.58 0.34 0.26 0.45 0.29 20.17 20.13 0.59 0.73 N/A
0.58
a
0.34
a
0.26
a
0.45
a
0.29
a
20.16
a
20.15
a
0.59
a
0.73
a
Reputation [11] 0.53 0.43 0.27 0.38 0.31 20.09 20.11 0.43 0.51 0.42 0.82
0.54
a
0.44
a
0.27
a
0.39
a
0.31
a
20.09
a
20.15
a
0.44
a
0.51
a
0.42
a
Notes:
a
Re?ective model; diagonal elements are square roots of the average variance extracted (AVE)
Figure 1 PLS path model Type 1: formative ?rst-order and formative second-order
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second-order multidimensional re?ective construct (with formative measurement
speci?cations of the indicators – Type 2; with re?ective measurement speci?cations of the
indicators – Type 4) is quite similar in structure, but completely contrary to the formative
approach, and spreads its in?uences across seven different value components.
According to the typology by Jarvis et al. (2003) the results are presented as follows: Type 1,
Type 2, and Type 4.
Figure 2 PLS path model Type 2: re?ective ?rst-order and formative second-order
Figure 3 PLS path model Type 4: re?ective ?rst-order and re?ective second-order
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We assessed the structural model (inner model) in PLS by evaluating R
2
of the endogenous
latent variables, the estimates for path coef?cients, and Cohen’s (1988) effect size f
2
. Chin
(1998) proposes R2 values of 0.67, 0.33, and 0.19 in the PLS path models as substantial,
moderate, and weak, respectively.
Regarding the Type 1 model, R
2
of the latent variables, reputation (0.21) and satisfaction
(0.25) show weak values; while loyalty (0.38), perceived value (0.40), and word-of-mouth
(0.56) show moderate values. For the inspection of the inner model’s path coef?cients
(which can be interpreted as the standardized parameter estimates), Table III and
Figures 1-3 provide the necessary information. In order to determine the statistical
signi?cance of the path coef?cients, we used the bootstrapping technique (1,000
samples). The table columns reveal more details by listing the respective t-values for
each parameter. The formative model results in signi?cant regression paths to perceived
value from two out of four get-components (atmosphere and on-board services), and two
out of three give-components (price assurance and unreasonable price). Figures 1–3
depict insigni?cant paths as dotted lines. As assumed theoretically, the relationship
between unreasonable price (‘‘this ?ight cost a lot of money’’, ‘‘I found the tariff structure
unfair’’, ‘‘I have the impression that the ?ight was expensive’’) and the perceived value is
negative. The paths from perceived value to satisfaction, loyalty and word-of-mouth, as
well as from satisfaction to loyalty and word-of-mouth, and from loyalty to word-of-mouth,
show signi?cant values. The effect size of f
2
can be calculated for each effect in the path
model, as the increase in R
2
relative to the proportion of variance of the endogenous
latent variable that remains unexplained (Henseler et al., 2009). Cohen (1988) proposes
f
2
values of 0.02, 0.25 and 0.35 as small, medium, and large effects, respectively. Since
all of the observed values are in the range between 0.02 and 0.25 we cannot speak about
large effects.
In the re?ective ?rst-order, formative second-order model (Type 2) moderate R
2
values for
word-of-mouth (0.58), perceived value (0.39), loyalty (0.38), and satisfaction (0.25) were
quite similar to the ?rst model type. However, reputation (0.11) fell under the ‘‘weak’’ level. All
path coef?cients of the seven value components, except for cognitive and time effort, turned
out be signi?cant. The strongest links turn out to atmosphere and price assurance followed
by on-board services, cabin tangibles and pre-?ight services. The negatively framed give
component ‘‘unreasonable price’’ shows the correct negative sign. The paths from
perceived value to satisfaction, loyalty and word-of-mouth as well as from satisfaction to
loyalty and word-of-mouth and from loyalty to word-of-mouth showed signi?cant values. We
Table III Parameter estimates for three model types
Type 1 Type 2 Type 4
Formative ?rst-order,
formative second-order
model
Re?ective ?rst-order,
formative second-order
model
Re?ective ?rst-order,
re?ective second-order
model
Path coef?cients t-value Path coef?cients t-value Path coef?cients t-value
Atmosphere !Perceived value
a
0.24 3.37 0.57 14.36 0.46 10.33
Cabin tangibles !Perceived value
a
20.01 NS 0.29 6.06 0.29 5.91
Pre-?ight services !Perceived value
a
0.08 NS 0.27 6.01 0.27 5.84
On-board services !Perceived value
a
0.12 2.10 0.36 9.07 0.37 8.61
Price assurance !Perceived value
a
0.18 2.70 0.39 9.34 0.39 9.74
Unreasonable price !Perceived value
a
20.32 4.79 20.18 3.67 20.47 7.88
Cognitive and time effort !Perceived value
a
20.01 NS 20.39 NS 20.18 3.62
Perceived value !Satisfaction 0.50 7.59 0.50 3.95 0.50 11.45
Perceived value !Loyalty 0.15 2.78 0.16 3.42 0.16 3.35
Perceived value !Word–of –mouth 0.43 7.27 0.09 3.28 0.09 2.33
Satisfaction !Loyalty 0.52 7.64 0.52 2.63 0.52 11.15
Satisfaction !Word–of –mouth 0.36 4.87 0.21 8.30 0.21 4.21
Loyalty !Word–of –mouth 0.67 14.51 0.57 8.30 0.57 13.52
Perceived value !Reputation 0.15 2.4 0.08 2.44 0.33 7.18
Note:
a
Arrow points in the opposite direction for the re?ective model
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could not detect any large effects. Additionally, the prediction relevance assessing
Stone-Geisser’s Q
2
(Stone, 1974; Geisser, 1974) using blindfolding procedure can only be
applied to endogenous latent variables that have a re?ective measurement model
operationalization. The Stone-Geisser criterion assesses the prediction of the endogenous
latent variable’s indicators of a model. As suggested by Fornell and Cha (1994), all values
should be greater than 0, which we also con?rmed.
The Type 4 (re?ective ?rst-order, re?ective second-order) outer model results obviously show
exactly the same values as the Type 2 model. However, as seen in Table II, path coef?cients
differ between Type 2 and Type 4, due to the varying conceptualizations of the second-order
construct perceived value. In both models all path coef?cients show signi?cant values and
similar values for the relationship to the endogenous variables.
Comparing the path coef?cients for the seven give- and get-components to the higher-order
construct perceived value, the assumption of a re?ective measurement could lead to a false
conclusion. In the correct formative ?rst-order, formative second-order model the paths from
cabin tangibles, pre-?ight services and cognitive and time effort to perceived value are not
signi?cant.
In all three model variants the three strongest relationships to an endogenous construct
appear fromloyalty to word-of-mouth, followed by satisfaction to loyalty and perceived value
to satisfaction. In the Type 1 results we ?nd that the path from perceived value to
word-of-mouth and satisfaction to word-of-mouth are still well above the recommended level
of 0.2 (Chin, 1998). In contrast to the Type 1 and Type 2 models, the relationship between
perceived value and reputation in the fully re?ective model (Type 4) was among the
strongest. We can see the same tendency for the link between perceived value and
word-of-mouth in the Type 1 model.
Discussion and implications
From the theoretical discussion and considerations above, we regard perceived value as an
outcome construct that results from various bene?ts received and sacri?ces devoted to
achieve a particular exchange of a service. From this perspective, the modeling of
perceived value as a second-order multidimensional re?ective construct would not be
justi?ed at all. A typical assumption when treating components as being re?ective is the high
inter-correlation. However, we ?nd no reason why the get-component of customer perceived
value should be correlated to the give-component. Of course, the question can be raised
whether a higher-order construct is necessary at all to grasp the domain of perceived value.
This is, however, a question of model parsimony, because allowing multiple paths to explain
the association between perceived value (components) and other cognitive and behavioral
dependent constructs is different from the perspective of restricting the number of paths to
only a single or few ones emitting from a higher-order construct called perceived value. For
the current study, we determined a priori the relevance of modeling perceived value as a
separate (higher-order) construct. Against the theoretical considerations already outlined,
yet following the approach of the study by Lin et al. (2005) we added the re?ective model
variant to the research design for comparative reasons.
Starting from the results published by Lin et al. (2005), we must raise some critical issues.
First, dropping satisfaction from the proposed explanatory structural model does not ?t the
wisdom of the available consumer behavior literature (Cronin et al., 2000; Oh, 1999).
Second, Lin et al. (2005) simply dropped some indicators if discriminant validity between
perceived value and satisfaction could not be established. We, by contrast, suggest to
extend the scope of meaning of the concept of perceived value. Third, negative paths
between satisfaction on the one side and re-patronage and word-of-mouth on the other are
hardly explainable in the formative and re?ective models (Lin et al., 2005, p. 332). Fourth, we
dismissed the non-signi?cant components of perceived value in the formative model variant,
on the justi?cation that they no longer constituted value (Lin et al., 2005, p. 330). This is
contrary to the recommended practice of assessing formative indicators, because removing
non-signi?cant indicators only makes sense if this does not harm the theoretical meaning of
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the construct (Bollen and Lennox, 1991; Diamantopoulos and Winklhofer, 2001; Petter et al.,
2007). As Diamantopoulos (2006) clari?es, that multicollinearity may occur if the error term
for the formative factor is small. Consequently, the insigni?cant paths from effort and the two
get components cabin tangibles and pre-?ight services do not imply that they do not
contribute to the meaning of perceived value in the formative perspective. Overall, in the
false re?ective and the correct formative modeling variant all proposed value components
(give and get) play a signi?cant role. Interestingly, the contribution of the more emotional and
situative element of atmosphere has the highest impact of the get-components on the overall
value perception.
The atmospheric element in the service encounter is not a new issue (e.g. Bitner, 1990; Bitner
et al., 1990). However, atmospheric components have been an underestimated factor in many
service quality instruments so far. Incorporating the cognitive and time effort perceived by the
customer into the overall value concept seems plausible. The estimated models show at least
that the give component should not be limited to the monetary contribution or perception. In
the results of Type 4 model we ?nd the highest path coef?cients with unreasonable price.
However, identi?cation and measurement need further improvement. So far, we may conclude
that in the airline passenger context, price transparency, stability, and clarity issues are more
important than price levels or other non-monetary sacri?ces.
Turning now to the implications that perceived value may have on various other behavioral
constructs (e.g. loyalty, reputation, word-of-mouth) we here address the other three
questions related to the study of Lin et al. (2005). In any of the three modeling attempts
satisfaction appears to be a distinct concept that, in turn, is strongly driven by perceived
value. If perceived value is narrowly de?ned as a value-for-money evaluation, all dependent
constructs in the proposed model show much weaker links and levels of explanation.
Unfortunately, a comparison of the study results of Lin et al. (2005) is dif?cult, because the
parameter estimates outlined show confusing results as to whether they are unstandardized
or standardized parameters. Furthermore, the approach of Lin et al. (2005) did not fully
follow the typology of Jarvis et al. (2003) and they did not estimate the full models.
Satisfaction achieved reasonable reliability and validity scores with the current airline data.
And yet, all tested nomological relationships turn out to be plausible. Mid-level links are
evident between satisfaction and behavioral loyalty and word-of-mouth.
Finally, in contrast to the e-tail study, we found the link between loyalty and word-of-mouth to
be relevant. The model parameters are signi?cant and the highest in all three model variants,
ranging from 0.57 (Type 2 and 4) to 0.67 (Type 1). Hence, in view of the current study results
the recommendation of dropping satisfaction from an explanatory model that focuses
strongly on perceived value (Lin et al., 2005) cannot be supported, and might be based on
an artifact due to identi?cation problems with the data.
Conclusion and limitations
This study in the airline context investigated different conceptual approaches for perceived
value further to challenge or corroborate the ?ndings reported by Lin et al. (2005) for a web
shop context. Most previous research considers perceived value as a unidimensional
construct representing the value-for-money evaluation of a tangible product or service
consumption. From this perspective, researchers still recognized the construct of perceived
value as a trade-off concept between bene?ts and sacri?ces. A more recent stream of
research developed a multidimensional framework for perceived value splitting predominantly
the get-component into various functional and hedonic bene?ts. However, some of these
frameworks either completely ignore the trade-off idea behind this overall evaluation
(e.g. Mathwick et al., 2001; Sweeney and Soutar, 2001) or do not consider a higher-order
perspective, which would facilitate the comparative impact analysis of the underlying value
drivers (e.g. Petrick and Backman, 2002; Sweeney and Soutar, 2001). More recently,
accounting for the shift from product-oriented marketing to a more service oriented marketing
research the focus on service aspects has been considered as evident (Vargo and Lusch,
2004, 2008). Several studies following this service-oriented marketing research claim have
suggested a formative second-order multidimensional construct to be the most appropriate
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conceptualization for perceived value (Lin et al., 2005; Ruiz et al., 2008). Following these
arguments, we tested three alternative models (i.e. Type 1 formative ?rst-order, formative
second-order; Type 2 re?ective ?rst-order, formative second-order-order, and Type 4 re?ective
?rst-order, re?ective second-order) and compared them to one another.
Previous tourism-related studies modeled perceived value as a multidimensional construct:
in the hospitality area (Al-Sabbahy et al., 2004; Kashyap and Bojanic, 2000; Jayanti and
Ghosh, 1996), in the travel area (Petrick and Backman, 2002; Petrick, 2004), and for travel
agencies (Sanchez et al., 2006). Whereas service quality and satisfaction studies have been
conducted in very different sectors and for quite a diversity of products and services, the
application of an extended concept of perceived value in the passenger airline sector has so
far been missing. For this reason, this study embedded a part replication of the conceptual
approach followed by Lin et al. (2005) in this highly competitive service sector, and
contrasted and three types of varying formative and re?ective higher-order models as
suggested by Jarvis et al. (2003). Overall, the comparison of the three different conceptual
models results in substantially different ?ndings: either on the exogenous part, on the
endogenous part, or both. Hence, a mis-speci?cation of a model for perceived value would
generate ?awed conclusions and managerial recommendations. However, repeating the
claim made by Lin et al. (2005, p. 334), specifying an appropriate model ‘‘with reason’’ is
important. Following the principles for determining re?ective and formative constructs,
conceptualizing perceived value as a formative latent construct is evident and highly
plausible. By comparing the three alternative structural models for perceived value, the data
from a sample of European airline passengers better ?tted the proposed conceptualization
as a ?rst-order and second-order formative construct (Type 1).
In view of the preferred model for perceived value (Type 1), the responses from airline
passengers prompt us to assume that both give- and get-components in?uence perceived
value. Give-components such as price (unreasonable price and price assurance) play the
dominant role for the overall judgment. However, on-board services, together with the entire
atmosphere are also important. Perceived value explains a substantial proportion of the
observed variance in some major dependent constructs such as satisfaction, loyalty, and
word-of-mouth intention. Recognizing that perceived value has a strong effect on
satisfaction and word-of-mouth is important. However, satisfaction has a substantial effect
on loyalty. Managerially, this means that service operations should consider not only
functional aspects for their routine service quality monitoring, but should also observe the
perception of atmospherics emerging from the main service encounters.
Extensions and improvements to this replication study are suggested, especially regarding
measurement and conceptual issues. Some of the latent constructs may be strengthened in
terms of composite reliability by improving the wording of their respective manifest variables
and adding some more relevant facets. Another enhancement could be tested by employing
different scale types. Danaher and Haddrell (1995) report comparatively better results when
using con?rmation/discon?rmation scales for the explanation of overall service satisfaction.
Because price perceptions revealed neither a strong direct nor indirect effect on the
perceived value construct, whether related price factors or the subjective price awareness
play a signi?cant role is questionable. For explaining the outcome (e.g. behavioral or
attitudinal loyalty) in such a structural model to differentiate among truly and spuriously loyal
passengers would be interesting (Zins, 2001). We expect that a much higher degree of
explanation would emerge for those customers who are really free and independent to
choose between different service providers. Furthermore, future research should consider
possible moderators, which may lead to confronting results (e.g. income, travel class
booked). In particular, business travelers may perceive service encounters in the airline
differently from the passengers traveling for leisure purposes do, due to their differing
aspiration level (Ringle et al., 2011).
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pp. 59-87.
About the authors
Thomas Mayr acquired his doctoral degree from the Wirtschaftsuniversita¨ t Wien (WU),
where he was a Research and Teaching Assistant at the Institute for Tourism and Leisure
Studies from 2004 to 2008. During that time he lectured in Marketing and Tourism. His main
research focus is on tourism behavior, customer satisfaction research and tourism
marketing. Since 2008, Dr Mayr has been working in the Tourism Department of the Austrian
Federal Ministry for Economy, Family and Youth (Division for International Affairs).
Andreas H. Zins is an Associate Professor at the Institute for Tourism and Leisure Studies of
the Wirtschaftsuniversita¨ t Wien (WU) and Full Professor of Tourism Management at MODUL
University, Vienna. Dr Zins lectures in entrepreneurship, marketing, tourism marketing, and
modeling of consumer and travel behavior. His research interests are tourism behavior,
marketing research, social impacts, computer-assisted and web-based interviewing, theme
parks and related leisure attractions. Andreas H. Zins is the corresponding author and can
be contacted at: [email protected]
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doc_673345841.pdf
The purpose of this paper is to test and compare different conceptual approaches for
perceived value in a service context
International Journal of Culture, Tourism and Hospitality Research
Extensions on the conceptualization of customer perceived value: insights from the airline industry
Thomas Mayr Andreas H. Zins
Article information:
To cite this document:
Thomas Mayr Andreas H. Zins, (2012),"Extensions on the conceptualization of customer perceived value: insights from the airline industry",
International J ournal of Culture, Tourism and Hospitality Research, Vol. 6 Iss 4 pp. 356 - 376
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Extensions on the conceptualization of
customer perceived value: insights from
the airline industry
Thomas Mayr and Andreas H. Zins
Abstract
Purpose – The purpose of this paper is to test and compare different conceptual approaches for
perceived value in a service context.
Design/methodology/approach – Perceived value is an outcome construct that results from various
bene?ts received and sacri?ces devoted to achieve a particular exchange of a service. The paper
compares three different modeling approaches (Type 1, Type 2, and Type 4) for perceived value using
data from an in-?ight survey. The questionnaire covered topics such as perceived service quality and
overall satisfaction, price perception, customer value, and customer retention.
Findings – The theoretical discussion repeatedly emphasizes that only the formative modeling of
perceived value ?ts the arguments put forward in the existing literature. This study replicates and
extends a study by Lin et al. in the airline service context. The paper reports details about the impact of
the proposed seven ‘‘get’’ and ‘‘give’’ components, together with an analysis of the consequences
perceived value has on satisfaction, loyalty, and word-of-mouth.
Research limitations/implications – The ?ndings suggest extensions and improvements concerning
measurement and conceptual issues.
Practical implications – Perceived value shows a substantial effect on behavioral consequences.
Service operations must observe the perception of atmospherics emerging from the main service
encounters next to considering functional aspects.
Originality/value – Misconceptualizations of multi-item constructs are well known. However, critical
discussions and empirical tests are still scarce in the tourism ?eld. This paper tests and compares
different conceptual approaches for perceived value in a service context.
Keywords Customer value, Customer satisfaction, Loyalty, Airline industry,
Formative and re?ective indicator models, Consumer behaviour, Modelling
Paper type Research paper
Introduction
In the battle between low-cost carriers and traditional network airlines for new and loyal
customers, assessing the most important drivers for customer satisfaction and customer
retention is crucial. Should management focus on service quality issues alone, concentrate
predominantly on price arguments, or consider both? Do more considerations arise in the
case of marketing airline services effectively? Compared to the vast literature on customer
satisfaction and service quality produced in the last two decades, little research has
emerged that seeks to better understand how consumers value products and services.
Many studies report that the value construct is a signi?cant one in consumer
decision-making models (Bolton and Drew, 1991; Dodds and Monroe, 1991; Zeithaml,
1988) and deem value a multifaceted and complex construct (Petrick and Backman, 2002;
Sheth et al., 1991; Sweeney and Soutar, 2001). Some researchers propose a superiority of
perceived value over satisfaction (e.g. Sweeney et al., 1999). The conceptualization of
perceived value, as well as the antecedents and consequences of perceived value, often
PAGE 356
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VOL. 6 NO. 4 2012, pp. 356-376, Q Emerald Group Publishing Limited, ISSN 1750-6182 DOI 10.1108/17506181211265086
Thomas Mayr is based at
the Institute for Tourism and
Leisure Studies, University
of Economics and
Business, Vienna, Austria.
Andreas H. Zins is a
Professor in the Department
of Tourism and Hospitality
Management, MODUL
University, Vienna, Austria.
Received March 2011
Revised July 2011
Accepted September 2011
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differ between studies (Cronin et al., 1997; Patterson and Spreng, 1997; Ruiz et al., 2008;
Sweeney et al., 1999; Varki and Colgate, 2001).
More recently, a growing interest in services research has gone alongside the evolution of
marketing as an applied and a ?eld of research (Vargo and Lusch, 2004; 2008). The service
literature itself reported research into customer value from various perspectives (e.g. Lin
et al., 2005; Oh, 1999; Petrick, 2002; Petrick and Backman, 2002; Ruiz et al., 2008; Sanchez
et al., 2006). Lin et al. (2005) contains a comparison of the different types of
operationalization of ‘‘perceived value’’ in the web service context. They compare three
model speci?cations (unidimensional, re?ective, and formative) using e-tail service value
data, and show that perceived value as a second-order multidimensional formative
construct is convincing in the measurement model. This formative approach outperforms
alternative perceived value model conceptualizations. All previous studies consider value
components as re?ective; whereas the work by Lin et al. (2005) is the ?rst to conceptualize
customer value as a second-order formative construct with ?rst-order components in the
web services context. Similarly, Ruiz et al. (2008) have recently conceptualized service value
as a higher-order, formative construct with bene?t and sacri?ce components. The current
study follows that perspective and examines customers’ perceptions of bene?ts weighed
against sacri?ces in the airline service context.
This research follows the approach of Lin et al. (2005), because that was the ?rst work to
perceive value in service context as being multi-dimensionally conceptualized as a
formative higher-order construct. However, we extend that work in four ways. First, we favor a
broader concept of the give-or-sacri?ce dimension, compared to the simpli?ed
conceptualization of monetary sacri?ce in the aforementioned study. This prompted us to
include additional price perceptions and an effort component that considers time and
cognitive strain. Second, we extend the get-or-bene?t dimension using functional (service
components of the airline industry) and emotional (atmospheric components) aspects.
Third, we bring the structure of the dependent variables (satisfaction, loyalty, word-of-mouth)
closer to theoretical considerations. Fourth, we explicitly and completely estimate the
measurement model for the exogenous variables, taking in three different
conceptualizations, following the model typology proposed by Jarvis et al. (2003). The
model’s three types are:
1. formative ?rst-order and formative second-order (Type 1) – the multidimensional
construct is a composite of its dimensions, and the dimensions are themselves constructs
and can be regarded as speci?c components of the second-order construct (Edwards,
2001);
2. re?ective ?rst-order and formative second-order-order constructs (Type 3) – the
second-order construct with ?rst-order formative dimensions, the ?rst-order dimensions
being measured by several re?ective manifest items; and
3. re?ective ?rst-order and re?ective second-order construct (Type 4) – the
multidimensional constructs are measured by several re?ective dimensions and the
dimensions are themselves measured by several re?ective manifest items.
This paper achieves a better understanding of the multidimensional construct perceived
value in a service context, achieved through a critical review and extension of its
conceptualization. The paper is structured accordingly: ?rst, we describe different
approaches and recent developments in conceptualizing and measuring perceived value,
followed by a discussion of how customer value should be modeled as consisting of
re?ective or formative indicators. Then, we apply the survey data to the different models, and
compare results of the various conceptualizations. Finally, we critically discuss results and
suggest future research ideas.
Perceived value – different conceptualizations
Even though several de?nitions of perceived value exist (Dodds and Monroe, 1991; Sheth
et al., 1991; Zeithaml, 1988), a well-established and most universally accepted one is ‘‘the
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consumer’s overall assessment of the utility of a product based on perceptions of what is
received and what is given’’ (Zeithaml, 1988, p. 14). In other words, perceived value involves
a trade-off between what the customer gets (e.g. quality, bene?ts, utilities) and what they
give up (e.g. prices, sacri?ces, time) to buy and consume a product. However, compared to
the vast literature in the tangible goods context, relatively few studies have focused on the
consideration of service components when de?ning and operationalizing customer
perceived value (Ruiz et al., 2008). This omission is surprising, because the traditional
product-oriented marketing approach has been questioned recently, and some have
suggested a shift from product-based marketing to a more service-based marketing
approach (Vargo and Lusch, 2004, 2008).
Two basically different perspectives of perceived value are identi?able. First, the
unidimensional conceptualization of perceived value is based on the ‘‘give versus get’’ or
‘‘bene?ts versus sacri?ces’’ trade-off concept. While these drivers are treated as
independent, the perceived value of exogenous variables is recommended to be
regarded as a separate construct on a more abstract level (Zeithaml, 1988). Following
this approach, perceived value is not de?ned by, but depends on, bene?ts received
(e.g. economic, social, and relationship) and sacri?ces made (monetary, such as price; or
non-monetary, such as time, effort, and risk). In this unidimensional conceptualization,
perceived value is seen as a global measure of overall customer value perceptions (e.g. a
single measure of utility, value for money, meeting quality and price requirements). Although
a unidimensional conceptualization lacks validity (Woodruff and Gardial, 1996; Woodruff,
1997) and cannot re?ect the complex structure of the construct (Sweeney and Soutar, 2001),
several mainstream studies use the unidimensional approach (Cronin et al., 2000; Varki and
Colgate, 2001), as does the tourism stream (Duman and Mattila, 2005; Murphy et al., 2000).
Second, as suggested by Sweeney and Soutar (2001), a wider conceptualization of perceived
value is desirable in order to capture its complex and multidimensional nature. Recent
research has offered several approaches to measuring the multidimensionality of perceived
value. Based on the conceptual frameworks developed by Sheth et al. (1991), Sweeney and
Soutar (2001) identify four distinct value dimensions (i.e. emotional value, social value,
quality/performance, and price/value for money) using a 19-item measure. Emotional value,
social value, and quality/performance are bene?t components, whereas price/value is a
sacri?ce component. They claim that their multidimensional measure (so-called PERVAL)
explains consumer choice better than does a single ‘‘value for money’’ item. Further, they
found the scale to be reliable and valid in both pre-purchase and post-purchase situations.
While the above-mentioned authors tested the PERVAL scale in a retail context, Petrick (2002)
more recently developed a multidimensional scale for measuring the perceived value of a
service. The so-called SERV-PERVAL scale consists of ?ve dimensions (i.e. behavioral price,
monetary price, emotional response, quality, and reputation) and several studies tested the
scale among cruise passengers (Petrick, 2003, 2004) and golf tourists (Petrick and Backman,
2002). Despite its reliability and validity for the measurement of perceived value, skepticism
persists concerning the level of abstraction this approach evidences (Sanchez et al., 2006).
Some commentators have identi?ed a need to broaden the scale, because the SERV-PERVAL
scale captures only the post-purchase evaluation of a service and does not include the
measurement of the perceived overall value of a purchase. One attempt to develop such a
scale is the so-called GLOVAL scale, which considers both the consumption as well as the
purchase experience (Sanchez et al., 2006). In line with these authors’ ?ndings, the
experiential view is of great signi?cance when evaluating perceived value of leisure and
tourism products. Holbrook (1999) proposes this experiential perspective, which comprises
the symbolic, hedonic, and aesthetic aspects of the consumption, and suggests that value is a
result of a speci?c consumption experience. In particular, they pay attention to the emotional
component of consumer behavior and the importance of the hedonic component in the
experiences of buying and consuming in leisure, creative, and aesthetic activities (Havlena
and Holbrook, 1986; Holbrook, 1994; Holbrook and Hirschmann, 1982). However, the most
commonly usedframework remains Zeithaml’s (1988) trade-off model (Ruiz et al., 2008). In this
study, customer perceived value in the airline service context consist of various bene?ts and
sacri?ces dimensions, and the methodology follows the trade-off model.
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Although these multidimensional models help us to better understand perceived value, they
fail to form an overall perceived value construct that maps the customer ‘‘give versus get’’
trade-off experience implied in the construct, which is inconsistent with the conceptual
de?nition of perceived value (Lin et al., 2005). Another point of criticism refers to the
component and construct level in structural models. As Turel et al. (2007) point out, if
perceived value is de?ned as an overall assessment, the value perceptions on the component
level should form an overall value. This de?nition should, in turn, affect the outcome. Thus,
consumers are expected to make mental trade-offs, which formthe overall value. According to
Lin et al. (2005) and Turel et al. (2007), perceived value may be regarded as a second-order
formative construct with ?rst-order components due to its satisfying statistical criteria (e.g. no
covariance exists between the components, because a decrease in one get-component does
not imply a change in another get-component). Additionally, certain problems arise from
treating give- and get-components as a set of perceived values and modeling them as
predictors in a structural model without considering the role of overall perceived value (Lin
et al., 2005). Research hypotheses may be set up, and analyses carried out at the component
level, but conclusions may be drawn at the perceived value level. In terms of consequences of
perceived value, the effects of perceived value on other constructs (such as satisfaction and
loyalty) are unconvincing unless analyses are carried out at the level of perceived value
instead of the component level (Lin et al., 2005).
With regard to the relationships between unidimensional, multidimensional methods, and the
conceptual de?nition of the perceived value construct, we may identify three paradoxes (Lin
et al., 2005). First, ‘‘if treating give-get components as antecedents of perceived value in the
nomological network is justi?ed, then the value dimensions identi?ed in the multidimensional
conceptualization studies do not refer to value; consequently, these dimensions should be
distinct from value instead of being components of value’’ (Lin et al., 2005, p. 322). Second,
the relationships between give- and get-components and value are conceptually
tautological, because the structural models that analyze this relationship are per se
inadequate if the current multidimensional conceptualization of perceived value is applied.
Third, when the unidimensional and multidimensional approaches are justi?ed, the de?nition
of perceived value should be re?ned and clari?ed. Hence, Lin et al. (2005) call for a
consideration of perceived value, and the relationships between give- and get-components,
as well as the speci?cation of overall value. In the case of a trade-off model, researchers
should employ a formative conceptualization of service value; and a higher-order model
permits the distinction between measurement error on the indicator level and measurement
error at the construct level (Ruiz et al., 2008).
Results of the comparison of their three models show differences in the relationships
between give- and get- components and perceived value, and between perceived value
and post-purchase behavioral intentions (Lin et al., 2005). Regarding the relationships
between give- and get-components in the re?ective model, monetary sacri?ce, website
design, ful?llment/reliability, and security/privacy are critical components constituting value.
The results of the formative (unidimensional) model show that order ful?llment/reliability
(transaction security/privacy) are not signi?cant. Looking at the consequences, value
perceptions lead to satisfaction in all of the models. Only in the formative and re?ective
models value perceptions lead to re-patronage and positive word-of-mouth (WOM), and
perceived value actually has stronger direct effects on both re-patronage and positive WOM
intentions than does satisfaction. However, in the unidimensional model, satisfaction has
stronger impacts on post-purchase behavioral intentions than does perceived value.
Therefore, perceived value has indirect effects on post-purchase behavioral intentions
mediated by satisfaction.
Formative compared to re?ective models
In recent years, the discussion around the assessment and comparison of re?ective and
formative models has gained momentum (e.g. Coltman et al., 2008; Diamantopoulos and
Winklhofer, 2001; Diamantopoulos et al., 2008; Jarvis et al., 2003; Hair et al., 2011; Henseler
et al., 2009; Petter et al., 2007). Psychographic (e.g. customer satisfaction, perceived value)
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or psychological constructs (e.g. brand attitude and purchase intentions) are more often
de?ned as causing its indicators, and are therefore considered re?ective. In other words,
‘‘the paths relating the indicators to the factor emanate from the latent variable to the
indicator’’ (Brown, 2006, p. 351). On the other hand, managerial constructs (e.g. market
dynamism, job performance, competitive and market intelligence) and socioeconomic
variables (e.g. social status) are more frequently formed by its indicators and are therefore
formative (Jarvis et al., 2003; Mazanec, 2007). Most researchers in social sciences view
indicators as re?ective and neglect the possible formative appropriateness (Bollen and
Lennox, 1991). Similarly, Jarvis et al. (2003) report that many studies use re?ective measures
– even though they should use formative indicator measurement models. Jarvis et al. (2003)
also provide four sets of questions to determine whether a construct is formative or re?ective.
Consequently, a construct should be modeled as having formative indicators if the following
conditions are given:
1. the direction of causality is from items to construct and variation in item measures causes
variation in the construct;
2. items de?ne the construct – they need not share a common theme, but must not be
interchangeable;
3. items can have any pattern of inter-correlation, but should have the same directional
relationship, and a change in one of the indicators must not be associated with changes
in the other indicators; and
4. indicators may not have similar signi?cance of relationships with the
antecedents/consequences as the construct.
Therefore, in formative indicator models, the indicators cause the latent variable, and the
direction of causality leads from the observed measures to the latent variable respectively
(Bollen and Lennox, 1991; Bollen, 1989; Edwards and Bagozzi, 2000). In contrast, a change
in the latent variable of a model with re?ective indicators will result in a change in all
constituent indicators (Bollen and Lennox, 1991; Bollen, 1989; Diamantopoulos, 2006, 2008;
Diamantopoulos and Winklhofer, 2001). In a re?ective conceptualization, we expect all
components of customer perceptions to co-vary with one another (Jarvis et al., 2003). Taking
the example of airline services, this means that an airline may reduce a customer’s perceived
sacri?ce by offering easier booking and check-in possibilities, which enable the customer to
save time or effort – but the bene?ts of the airline for the customer remain unchanged. Thus,
a re?ective model may mis-specify the customer perceived value construct, which can lead
to incorrect measurement of the structural relationships between constructs and undermine
the validity of the statistical conclusions (MacKenzie et al., 2005). These criteria do not
account for constructs speci?ed at a more abstract level, which may include multiple
formative and/or re?ective ?rst-order dimensions. Such second-order models capture the
idea of abstract multidimensional construct de?nition (e.g. job satisfaction), which is quite
common in marketing (Jarvis et al., 2003). Similarly, Podsakoff et al. (2006) suggest
choosing higher-order constructs when modeling complex constructs. In service-based
marketing, research treated customer perceived value as a multidimensional construct
(e.g. Petrick, 2004; Petrick and Backman, 2002) and a higher-order, formative measure that
outperforms alternative operationalizations (Lin et al., 2005; Ruiz et al., 2008; Turel et al.,
2007). Considering the trade-off approach (Zeithaml, 1988), and in line with the
service-centered view of marketing that is customer-centric and market driven (Vargo and
Lusch, 2004), the present research proposes that customer perceived value may be better
conceptualized as a higher-order formative construct that includes bene?ts and sacri?ces
as ?rst-order dimensions. Following the model speci?cations proposed by Jarvis et al.
(2003), we specify four types of multidimensional second-order factor models:
1. formative ?rst-order, formative second-order (Type 1);
2. re?ective ?rst-order and formative second-order (Type 2);
3. formative ?rst-order and re?ective second-order (Type 3); and
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4. re?ective ?rst-order and re?ective second-order (Type 4). This study compares Type 1, 2
and 4 models. Type 3 has no practical relevance and has not been used relevantly in
research (Diamantopoulos et al., 2008).
Components, relationships and their conceptualization
Although the ‘‘get’’ component of perceived value is quite well understood, and much
research has been devoted to this issue, the ‘‘give’’ component still lacks in sophistication.
So far, in most studies the ‘‘give’’ element has been limited to the monetary sacri?ce a
customer has to raise. In order to satisfy airline passengers, the comprehensive
understanding of price perception is crucial. Price satisfaction is conceived to represent a
multidimensional construct (Matzler et al., 2006a), based on the following dimensions
(Matzler et al., 2006b):
B price transparency;
B price-quality ratio;
B relative price;
B price con?dence;
B price reliability; and
B price fairness.
The more information consumers have on prices of the offer (e.g. on differentiated prices),
the higher their con?dence in the superiority of the offer (Matzler et al., 2007). Companies
should consider these dimensions when monitoring customer satisfaction, and we can
assume this also applies to the airline industry.
Zeithaml (1988) shows that perceived quality leads to perceived value, which in turn leads to
purchase intention. Perceived value may lead directly to the formation of overall satisfaction
(Churchill and Surprenant, 1982); however, a varying, mediating role of customer
satisfaction may also exist between perceived value and behavioral intentions (Oh, 1999;
Petrick, 2004; Patterson and Spreng, 1997). The assumption that satis?ed customers are
loyal has been widely accepted. However, satis?ed customers may not be a suf?cient
marketing goal to create loyal customers (Cronin and Taylor, 1992; Fornell, 1992).
A second-order conceptualization of perceived value differs from the ?rst conceptualization in
two ways. First, in the second-order model, perceived value compounds the speci?c relevance
of the different dimensions of the construct. Second, the second-order conceptualization
shows the relative weight of each of the seven dimensions in representing perceived value.
Following Jarvis et al. (2003) and Lin et al. (2005), we compare three model types:
1. Type 1 (formative ?rst-order; formative second-order);
2. Type 2 (re?ective ?rst-order and formative second-order); and
3. Type 4 (re?ective ?rst-order and re?ective second-order) models.
The ?rst type of model (Type 1) conceptualizes customer perceived value as a composite of
its dimensions, whereas the dimensions are themselves constructs and conceived as
speci?c components of the second-order construct (Edwards, 2001). Another type of model
is the second (Type 2) model, where the second-order construct has ?rst-order formative
dimensions, and the ?rst-order dimensions themselves are measured by re?ective
indicators. Yet another type of model is Type 4, which represents ?rst-order dimensions
with several re?ective indicators, and these ?rst-order dimensions are themselves re?ective
indicators of an underlying second-order construct.
Methodology
This validation study considers a real-life service situation for which customers may deliver
their re?ections on the immediate consumption experience of a service. A European
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network-type airline acted as the partner company in this project. Both the representative’s
of the product strategy department and the researchers’ interests focused on substantive
?ndings about the perceptions of different service areas, the service value in total, and the
attitudinal and behavioral consequences thereof. Hence, we developed the design and the
measurement instrument together, according to the prevailing standards and guidelines for
the continuous on-board satisfaction survey.
The self-completion questionnaires were prepared in German, English, and French
(developed in close collaboration with airline representatives, translated, translated back,
and corrected) and distributed to a small number of randomly selected passengers on a
total of 53 ?ights. To keep extraneous error as small as possible, we selected only short-haul
?ights operated by Airbus A320 vehicles. In total, 519 respondents returned completed
questionnaires, which covered topics such as perceived service quality and overall
satisfaction, price perception, customer value, and customer retention. Flight attendants
asked respondents whether they were willing to complete the on-board questionnaire. With
regard to non-response, bias (de?ned as when a potential respondent refuses to answer any
question or the questionnaire in total – unit non-response) amounted to 19 percent. Thus,
total contacts amounted to 622 passengers.
Among the respondents, 45 percent bought a business class ticket, and slightly more than
half traveled for business purposes (52 percent). Forty-seven percent of the passengers
were members of the frequent ?yer programof the particular airline. The data showthat more
passengers were male (65 percent) than female (35 percent), and the major age cohort was
31-50 years, accounting for 54 percent. Ten percent of the respondents were older than 60
years. On average, respondents ?ew20 times a year, with an average share of 11 ?ights with
the participating airline.
Respondents from 38 different countries ?lled in the questionnaires. The majority were
travelers from Germany and Austria (52 percent), followed by Great Britain (14 percent) and
France (11 percent). The measures employed to validate the theoretical model represent
give- and get-components, as well as endogenous constructs usually modeled as
dependents from the service outcome. Multi-item scales covered the majority of constructs.
In order to maintain the comparability with previous on-board questionnaires, respondents
evaluated all items on a six-point Likert-type scale. We used existing scales where possible,
but also tried to develop customized scales where appropriate, considering but
notwithstanding the recent general discussion on measures by Rossiter (2002), and the
necessity of a multi-item multidimensional treatment of customer perceived value.
Table I lists the latent constructs and their respective measures. All evaluative items are
subject to a six-point agreement scale, ranging from 1 ¼ agree completely to 6 ¼ disagree
completely We constructed only the overall satisfaction measure along a continuum, ranging
from 1 ¼ very satis?ed to 6 ¼ very dissatis?ed. A scale ranging from 1 (100 percent/highly
likely) to 6 (0 percent/highly unlikely/certainly not) (loyalty, satisfaction, word-of-mouth,
reputation) measured the behavioral consequences items.
We deconstructed the concept of perceived value according to our previous discussion into
give- and get-components. Four get-components are distinguished. Initially, we included 14
different service quality attributes that the airline company regularly uses to monitor their
performance. By dropping two items (‘‘the booking procedure was uncomplicated’’ and
‘‘the ?ight was punctual’’), we arrived at a three-dimensional con?guration a of service
domains:
1. pre-?ight services (including check-in, hand-baggage check at the gate, boarding
procedure);
2. on-board services (including friendliness of attendants, in-?ight catering, entertainment
program, newspapers and journals); and
3. cabin tangibles (including seating comfort, aircraft interior, cleanliness of interior, and
technical services).
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Apart fromthese usual service aspects, we added an additional dimension of ‘‘atmosphere’’
to the measurement instrument, which is represented by four items addressing the ease and
relaxing feeling during the ?ight, the time distortion feeling, and the sense of security and
safety.
In order to extend most of the previous work in this area, we conceived the give-component
to go beyond the monetary contribution the passenger must make. Consequently, we
proposed a dimension that covers the cognitive effort (for the booking process) and the time
effort (for gathering and analyzing the appropriate information about schedules and
airfares). We split the monetary sacri?ce into two aspects:
1. a price assurance dimension (covering stability of prices, price transparency, and easy
overview of fares); and
2. a dimension related to the quality of unreasonable prices (covering ‘‘cost a lot of money’’,
unfair structure of fares, and impression of expensive ?ight).
We considered satisfaction as comprising a mixture of emotional and cognitive reactions to
the service delivery (Oliver, 1997). Hence, we measured this by a traditional ‘‘overall
satisfaction’’ statement, an item addressing the emotional category of ‘‘happiness with the
airline’’, and a more cognitively based evaluation of the overall service received. We split the
loyalty domain into two facets or dimensions:
1. the re-booking probability; and
2. the word-of-mouth intention.
The customer retention aspect is covered by four items re?ecting resistance to change (two
indicators), a re-booking probability for the same route (one indicator), and the ?rst-choice
preference in general (one indicator). We added reputation of the airline to the model only for
identi?cation reasons (for the formative model variant), but reputation is not considered to
contribute to the explanatory value of the model. Word-of-mouth is mapped by a single
indicator (‘‘the likelihood of recommending the airline’’).
Results of the measurement models
We chose the partial least square (PLS) model because of its capacity to calculate both
re?ective and formative models (Fornell and Bookstein, 1982). Following the guidelines to
consider prior to PLS modeling (Marcoulides and Saunders, 2006), after data screening and
examination of the distributional characteristics we examined the psychometric properties of
all variables in the model (Table I), and the relationships between the variables and
constructs in the model. Missing data or item non-response are a part of almost any
research, and various alternative approaches have been identi?ed to deal with this
phenomenon (Little and Rubin, 1987). The present research assessed a mean value
imputation for handling missing data. We performed PLS model estimation using SmartPLS
(Ringle et al., 2005). Within the PLS approach, the evaluation of re?ective constructs include
content and indicator validity, construct, and convergent reliability. The indicator validity
speci?es what part of an indicator’s variance is explained by the latent variable. Loadings
larger than 0.7 are acceptable within PLS models, and loadings smaller than 0.4 call for
removal (Hulland, 1999). All indicators exceed the critical value of 0.4; however, 12 percent
of all indicators show values lower than 0.7, and we did not need to remove any indicator
(see Table I). The seven value drivers (four get- and three give-components) have composite
reliability (CR) ratios in the range between 0.77 and 0.91. All dimensions are above the
recommended threshold level of 0.7 (Hair et al., 1998).
Convergent validity represents the shared variance by the construct and its corresponding
items, and is measured by the AVE average variance extracted (Fornell and Larcker, 1981).
In addition to convergent validity, we examined the discriminant validity by comparing the
average variance extracted with the squared correlations between any two latent constructs
(Fornell and Larcker, 1981; Anderson and Gerbing, 1992). The AVE passes in all but one
case (on-board services) the threshold recommended of 50 percent. However, all constructs
VOL. 6 NO. 4 2012
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ful?lled the Fornell-Larcker criterion. On the indicator level, we could not observe any high
cross-loadings.
In behavioral research, results of self-reported data may be biased through possible
common method predisposition resulting from multiple sources, such as consistency motif,
social desirability, acquiescence, and positive and negative affectivity (Podsakoff et al.,
2003). These authors propose statistical procedures to address concerns regarding
common method biases. In particular, we also applied their proposed technique ‘‘controlling
for the effects of an unmeasured latent method factor’’ to PLS recently (Liang et al., 2007).
Using PLS to assess common method variance, a latent method factor with all measures as
indicators should be added to the structural model. Following the recommendations and
testing procedure by Liang et al. (2007), this latent method factor is linked to all indicators,
whereas each indicator must be converted to a single-indicator construct.
The results are reported for the re?ective model, because the two models do not show
substantial statistical difference regarding the signi?cance, and the signs remained
unchanged. The average substantively explained variance of the indicators is 0.006, and
the average method-based variance is 0.648. Comparing substantive variance to method
variance, the ratio is 1:109. Additionally, most of the method factor loadings are insigni?cant,
and the indicators’ method variances are considerably lower than their substantive variances.
We conclude that common method bias is not an issue and that the method is appropriate.
As Diamantopoulos (2006) points out, reliability becomes an irrelevant criterion for
assessing measurement quality for formative measurement models, because it is the validity
that should be secured and focused on. Rossiter (2002) recommends as a ?rst step to use
theoretical and expert opinion to assess the validity of formative indicators. Second,
statistical analyses could help in assessing the validity of formative indicators. An indicator
should not enter into a formative index if the indicator has no signi?cant impact on the index
or if an indicator exhibits high multicollinearity (Henseler et al., 2009). However, formative
indicators should not be dropped simply because of statistical analysis, because they may
substantially change the context of the formative index (Jarvis et al., 2003; Diamantopoulos,
2008). In formative models, correlations amongst causal indicators are expected to be not
particularly high (Diamantopoulos and Siguaw, 2006; Rossiter, 2002).
In order to assess statistical signi?cance, we used bootstrap analysis with 1,000
sub-samples. This method contrasts with re?ective measures, where factor loadings
should be assessed, and weights should be assessed in the formative case. These weights
represent information about the extent to which each indicator contributes to the respective
construct (Mathwick et al., 2001). All indicators showsigni?cant values (see Table I; p-values
,0.05). Unfortunately, no common understanding exists regarding how to proceed with
multicollinearity in formative models. The variance in?ation factor (VIF) helps in determining
the degree of multicollinearity, and values of VIF greater than 10 indicate a critical level of
multicollinearity (Henseler et al., 2009). In the current study, VIF was below the critical level
for all indicators. We conducted an additional assessment of discriminant validity by
comparing the average variance extracted fromeach construct with its communal variances
shared with other constructs. Table II shows the inter-construct correlations (off the diagonal)
and the square roots of the average variance extracted (on the diagonal). Because the
square root of the average variance extracted for all constructs was higher than their shared
variances, discriminant validity can be con?rmed.
Results of the structural models
Following the test design propagated by Lin et al. (2005), and focusing on the three model
types proposed by Jarvis et al. (2003), we tested three different structural models, taking the
same data set of the airline passenger sample. After having estimated the measurement
models for the respective latent variables, we identi?ed the structural relationships for three
different model types where a second-order latent variable captured perceived value (see
Figures 1-3). This higher-order construct follows the multidimensional approach of customer
perceived value and accounts for the trade-off (Ruiz et al., 2008).
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The ?rst variant (Type 1) treats perceived value as a second-order multidimensional formative
construct, which turned out to be the most appropriate model, because this conceptualization
better ?ts the theoretical framework proposed for customer perceived value, and has also
been modeled in recent studies (Lin et al., 2005; Ruiz et al. 2008). We assumed that the
indicators in our study caused the seven give- and get-components. The conception as a
Table II Correlation matrix
[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11]
Atmosphere [1] 0.84
Cabin tangibles [2] 0.47 0.79
0.51
a
Pre?ight services [3] 0.35 0.42 0.72
0.36
a
0.41
a
On-board services [4] 0.52 0.45 0.29 0.68
0.53
a
0.49
a
0.31
a
Price assurance [5] 0.30 0.32 0.18 0.26 0.84
0.30
a
0.30
a
0.16
a
0.26
a
Unreasonable price [6] 20.15 20.09 20.09 20.04 20.15 0.74
20.14
a
20.08
a
20.07
a
20.04
a
20.14
a
Effort [7] 20.16 20.11 20.16 20.18 20.27 0.17 0.85
20.20
a
20.11
a
20.17
a
20.17
a
20.28
a
0.16
a
Satisfaction [8] 0.60 0.44 0.39 0.59 0.27 20.12 20.18 0.81
0.62
a
0.44
a
0.40
a
0.58
a
0.28
a
20.11
a
20.19
a
Loyalty [9] 0.59 0.40 0.30 0.39 0.30 20.16 20.14 0.60 0.81
0.60
a
0.40
a
0.30
a
0.39
a
0.30
a
20.15
a
20.16
a
0.60
a
Word-of-mouth [10] 0.58 0.34 0.26 0.45 0.29 20.17 20.13 0.59 0.73 N/A
0.58
a
0.34
a
0.26
a
0.45
a
0.29
a
20.16
a
20.15
a
0.59
a
0.73
a
Reputation [11] 0.53 0.43 0.27 0.38 0.31 20.09 20.11 0.43 0.51 0.42 0.82
0.54
a
0.44
a
0.27
a
0.39
a
0.31
a
20.09
a
20.15
a
0.44
a
0.51
a
0.42
a
Notes:
a
Re?ective model; diagonal elements are square roots of the average variance extracted (AVE)
Figure 1 PLS path model Type 1: formative ?rst-order and formative second-order
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second-order multidimensional re?ective construct (with formative measurement
speci?cations of the indicators – Type 2; with re?ective measurement speci?cations of the
indicators – Type 4) is quite similar in structure, but completely contrary to the formative
approach, and spreads its in?uences across seven different value components.
According to the typology by Jarvis et al. (2003) the results are presented as follows: Type 1,
Type 2, and Type 4.
Figure 2 PLS path model Type 2: re?ective ?rst-order and formative second-order
Figure 3 PLS path model Type 4: re?ective ?rst-order and re?ective second-order
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We assessed the structural model (inner model) in PLS by evaluating R
2
of the endogenous
latent variables, the estimates for path coef?cients, and Cohen’s (1988) effect size f
2
. Chin
(1998) proposes R2 values of 0.67, 0.33, and 0.19 in the PLS path models as substantial,
moderate, and weak, respectively.
Regarding the Type 1 model, R
2
of the latent variables, reputation (0.21) and satisfaction
(0.25) show weak values; while loyalty (0.38), perceived value (0.40), and word-of-mouth
(0.56) show moderate values. For the inspection of the inner model’s path coef?cients
(which can be interpreted as the standardized parameter estimates), Table III and
Figures 1-3 provide the necessary information. In order to determine the statistical
signi?cance of the path coef?cients, we used the bootstrapping technique (1,000
samples). The table columns reveal more details by listing the respective t-values for
each parameter. The formative model results in signi?cant regression paths to perceived
value from two out of four get-components (atmosphere and on-board services), and two
out of three give-components (price assurance and unreasonable price). Figures 1–3
depict insigni?cant paths as dotted lines. As assumed theoretically, the relationship
between unreasonable price (‘‘this ?ight cost a lot of money’’, ‘‘I found the tariff structure
unfair’’, ‘‘I have the impression that the ?ight was expensive’’) and the perceived value is
negative. The paths from perceived value to satisfaction, loyalty and word-of-mouth, as
well as from satisfaction to loyalty and word-of-mouth, and from loyalty to word-of-mouth,
show signi?cant values. The effect size of f
2
can be calculated for each effect in the path
model, as the increase in R
2
relative to the proportion of variance of the endogenous
latent variable that remains unexplained (Henseler et al., 2009). Cohen (1988) proposes
f
2
values of 0.02, 0.25 and 0.35 as small, medium, and large effects, respectively. Since
all of the observed values are in the range between 0.02 and 0.25 we cannot speak about
large effects.
In the re?ective ?rst-order, formative second-order model (Type 2) moderate R
2
values for
word-of-mouth (0.58), perceived value (0.39), loyalty (0.38), and satisfaction (0.25) were
quite similar to the ?rst model type. However, reputation (0.11) fell under the ‘‘weak’’ level. All
path coef?cients of the seven value components, except for cognitive and time effort, turned
out be signi?cant. The strongest links turn out to atmosphere and price assurance followed
by on-board services, cabin tangibles and pre-?ight services. The negatively framed give
component ‘‘unreasonable price’’ shows the correct negative sign. The paths from
perceived value to satisfaction, loyalty and word-of-mouth as well as from satisfaction to
loyalty and word-of-mouth and from loyalty to word-of-mouth showed signi?cant values. We
Table III Parameter estimates for three model types
Type 1 Type 2 Type 4
Formative ?rst-order,
formative second-order
model
Re?ective ?rst-order,
formative second-order
model
Re?ective ?rst-order,
re?ective second-order
model
Path coef?cients t-value Path coef?cients t-value Path coef?cients t-value
Atmosphere !Perceived value
a
0.24 3.37 0.57 14.36 0.46 10.33
Cabin tangibles !Perceived value
a
20.01 NS 0.29 6.06 0.29 5.91
Pre-?ight services !Perceived value
a
0.08 NS 0.27 6.01 0.27 5.84
On-board services !Perceived value
a
0.12 2.10 0.36 9.07 0.37 8.61
Price assurance !Perceived value
a
0.18 2.70 0.39 9.34 0.39 9.74
Unreasonable price !Perceived value
a
20.32 4.79 20.18 3.67 20.47 7.88
Cognitive and time effort !Perceived value
a
20.01 NS 20.39 NS 20.18 3.62
Perceived value !Satisfaction 0.50 7.59 0.50 3.95 0.50 11.45
Perceived value !Loyalty 0.15 2.78 0.16 3.42 0.16 3.35
Perceived value !Word–of –mouth 0.43 7.27 0.09 3.28 0.09 2.33
Satisfaction !Loyalty 0.52 7.64 0.52 2.63 0.52 11.15
Satisfaction !Word–of –mouth 0.36 4.87 0.21 8.30 0.21 4.21
Loyalty !Word–of –mouth 0.67 14.51 0.57 8.30 0.57 13.52
Perceived value !Reputation 0.15 2.4 0.08 2.44 0.33 7.18
Note:
a
Arrow points in the opposite direction for the re?ective model
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could not detect any large effects. Additionally, the prediction relevance assessing
Stone-Geisser’s Q
2
(Stone, 1974; Geisser, 1974) using blindfolding procedure can only be
applied to endogenous latent variables that have a re?ective measurement model
operationalization. The Stone-Geisser criterion assesses the prediction of the endogenous
latent variable’s indicators of a model. As suggested by Fornell and Cha (1994), all values
should be greater than 0, which we also con?rmed.
The Type 4 (re?ective ?rst-order, re?ective second-order) outer model results obviously show
exactly the same values as the Type 2 model. However, as seen in Table II, path coef?cients
differ between Type 2 and Type 4, due to the varying conceptualizations of the second-order
construct perceived value. In both models all path coef?cients show signi?cant values and
similar values for the relationship to the endogenous variables.
Comparing the path coef?cients for the seven give- and get-components to the higher-order
construct perceived value, the assumption of a re?ective measurement could lead to a false
conclusion. In the correct formative ?rst-order, formative second-order model the paths from
cabin tangibles, pre-?ight services and cognitive and time effort to perceived value are not
signi?cant.
In all three model variants the three strongest relationships to an endogenous construct
appear fromloyalty to word-of-mouth, followed by satisfaction to loyalty and perceived value
to satisfaction. In the Type 1 results we ?nd that the path from perceived value to
word-of-mouth and satisfaction to word-of-mouth are still well above the recommended level
of 0.2 (Chin, 1998). In contrast to the Type 1 and Type 2 models, the relationship between
perceived value and reputation in the fully re?ective model (Type 4) was among the
strongest. We can see the same tendency for the link between perceived value and
word-of-mouth in the Type 1 model.
Discussion and implications
From the theoretical discussion and considerations above, we regard perceived value as an
outcome construct that results from various bene?ts received and sacri?ces devoted to
achieve a particular exchange of a service. From this perspective, the modeling of
perceived value as a second-order multidimensional re?ective construct would not be
justi?ed at all. A typical assumption when treating components as being re?ective is the high
inter-correlation. However, we ?nd no reason why the get-component of customer perceived
value should be correlated to the give-component. Of course, the question can be raised
whether a higher-order construct is necessary at all to grasp the domain of perceived value.
This is, however, a question of model parsimony, because allowing multiple paths to explain
the association between perceived value (components) and other cognitive and behavioral
dependent constructs is different from the perspective of restricting the number of paths to
only a single or few ones emitting from a higher-order construct called perceived value. For
the current study, we determined a priori the relevance of modeling perceived value as a
separate (higher-order) construct. Against the theoretical considerations already outlined,
yet following the approach of the study by Lin et al. (2005) we added the re?ective model
variant to the research design for comparative reasons.
Starting from the results published by Lin et al. (2005), we must raise some critical issues.
First, dropping satisfaction from the proposed explanatory structural model does not ?t the
wisdom of the available consumer behavior literature (Cronin et al., 2000; Oh, 1999).
Second, Lin et al. (2005) simply dropped some indicators if discriminant validity between
perceived value and satisfaction could not be established. We, by contrast, suggest to
extend the scope of meaning of the concept of perceived value. Third, negative paths
between satisfaction on the one side and re-patronage and word-of-mouth on the other are
hardly explainable in the formative and re?ective models (Lin et al., 2005, p. 332). Fourth, we
dismissed the non-signi?cant components of perceived value in the formative model variant,
on the justi?cation that they no longer constituted value (Lin et al., 2005, p. 330). This is
contrary to the recommended practice of assessing formative indicators, because removing
non-signi?cant indicators only makes sense if this does not harm the theoretical meaning of
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the construct (Bollen and Lennox, 1991; Diamantopoulos and Winklhofer, 2001; Petter et al.,
2007). As Diamantopoulos (2006) clari?es, that multicollinearity may occur if the error term
for the formative factor is small. Consequently, the insigni?cant paths from effort and the two
get components cabin tangibles and pre-?ight services do not imply that they do not
contribute to the meaning of perceived value in the formative perspective. Overall, in the
false re?ective and the correct formative modeling variant all proposed value components
(give and get) play a signi?cant role. Interestingly, the contribution of the more emotional and
situative element of atmosphere has the highest impact of the get-components on the overall
value perception.
The atmospheric element in the service encounter is not a new issue (e.g. Bitner, 1990; Bitner
et al., 1990). However, atmospheric components have been an underestimated factor in many
service quality instruments so far. Incorporating the cognitive and time effort perceived by the
customer into the overall value concept seems plausible. The estimated models show at least
that the give component should not be limited to the monetary contribution or perception. In
the results of Type 4 model we ?nd the highest path coef?cients with unreasonable price.
However, identi?cation and measurement need further improvement. So far, we may conclude
that in the airline passenger context, price transparency, stability, and clarity issues are more
important than price levels or other non-monetary sacri?ces.
Turning now to the implications that perceived value may have on various other behavioral
constructs (e.g. loyalty, reputation, word-of-mouth) we here address the other three
questions related to the study of Lin et al. (2005). In any of the three modeling attempts
satisfaction appears to be a distinct concept that, in turn, is strongly driven by perceived
value. If perceived value is narrowly de?ned as a value-for-money evaluation, all dependent
constructs in the proposed model show much weaker links and levels of explanation.
Unfortunately, a comparison of the study results of Lin et al. (2005) is dif?cult, because the
parameter estimates outlined show confusing results as to whether they are unstandardized
or standardized parameters. Furthermore, the approach of Lin et al. (2005) did not fully
follow the typology of Jarvis et al. (2003) and they did not estimate the full models.
Satisfaction achieved reasonable reliability and validity scores with the current airline data.
And yet, all tested nomological relationships turn out to be plausible. Mid-level links are
evident between satisfaction and behavioral loyalty and word-of-mouth.
Finally, in contrast to the e-tail study, we found the link between loyalty and word-of-mouth to
be relevant. The model parameters are signi?cant and the highest in all three model variants,
ranging from 0.57 (Type 2 and 4) to 0.67 (Type 1). Hence, in view of the current study results
the recommendation of dropping satisfaction from an explanatory model that focuses
strongly on perceived value (Lin et al., 2005) cannot be supported, and might be based on
an artifact due to identi?cation problems with the data.
Conclusion and limitations
This study in the airline context investigated different conceptual approaches for perceived
value further to challenge or corroborate the ?ndings reported by Lin et al. (2005) for a web
shop context. Most previous research considers perceived value as a unidimensional
construct representing the value-for-money evaluation of a tangible product or service
consumption. From this perspective, researchers still recognized the construct of perceived
value as a trade-off concept between bene?ts and sacri?ces. A more recent stream of
research developed a multidimensional framework for perceived value splitting predominantly
the get-component into various functional and hedonic bene?ts. However, some of these
frameworks either completely ignore the trade-off idea behind this overall evaluation
(e.g. Mathwick et al., 2001; Sweeney and Soutar, 2001) or do not consider a higher-order
perspective, which would facilitate the comparative impact analysis of the underlying value
drivers (e.g. Petrick and Backman, 2002; Sweeney and Soutar, 2001). More recently,
accounting for the shift from product-oriented marketing to a more service oriented marketing
research the focus on service aspects has been considered as evident (Vargo and Lusch,
2004, 2008). Several studies following this service-oriented marketing research claim have
suggested a formative second-order multidimensional construct to be the most appropriate
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conceptualization for perceived value (Lin et al., 2005; Ruiz et al., 2008). Following these
arguments, we tested three alternative models (i.e. Type 1 formative ?rst-order, formative
second-order; Type 2 re?ective ?rst-order, formative second-order-order, and Type 4 re?ective
?rst-order, re?ective second-order) and compared them to one another.
Previous tourism-related studies modeled perceived value as a multidimensional construct:
in the hospitality area (Al-Sabbahy et al., 2004; Kashyap and Bojanic, 2000; Jayanti and
Ghosh, 1996), in the travel area (Petrick and Backman, 2002; Petrick, 2004), and for travel
agencies (Sanchez et al., 2006). Whereas service quality and satisfaction studies have been
conducted in very different sectors and for quite a diversity of products and services, the
application of an extended concept of perceived value in the passenger airline sector has so
far been missing. For this reason, this study embedded a part replication of the conceptual
approach followed by Lin et al. (2005) in this highly competitive service sector, and
contrasted and three types of varying formative and re?ective higher-order models as
suggested by Jarvis et al. (2003). Overall, the comparison of the three different conceptual
models results in substantially different ?ndings: either on the exogenous part, on the
endogenous part, or both. Hence, a mis-speci?cation of a model for perceived value would
generate ?awed conclusions and managerial recommendations. However, repeating the
claim made by Lin et al. (2005, p. 334), specifying an appropriate model ‘‘with reason’’ is
important. Following the principles for determining re?ective and formative constructs,
conceptualizing perceived value as a formative latent construct is evident and highly
plausible. By comparing the three alternative structural models for perceived value, the data
from a sample of European airline passengers better ?tted the proposed conceptualization
as a ?rst-order and second-order formative construct (Type 1).
In view of the preferred model for perceived value (Type 1), the responses from airline
passengers prompt us to assume that both give- and get-components in?uence perceived
value. Give-components such as price (unreasonable price and price assurance) play the
dominant role for the overall judgment. However, on-board services, together with the entire
atmosphere are also important. Perceived value explains a substantial proportion of the
observed variance in some major dependent constructs such as satisfaction, loyalty, and
word-of-mouth intention. Recognizing that perceived value has a strong effect on
satisfaction and word-of-mouth is important. However, satisfaction has a substantial effect
on loyalty. Managerially, this means that service operations should consider not only
functional aspects for their routine service quality monitoring, but should also observe the
perception of atmospherics emerging from the main service encounters.
Extensions and improvements to this replication study are suggested, especially regarding
measurement and conceptual issues. Some of the latent constructs may be strengthened in
terms of composite reliability by improving the wording of their respective manifest variables
and adding some more relevant facets. Another enhancement could be tested by employing
different scale types. Danaher and Haddrell (1995) report comparatively better results when
using con?rmation/discon?rmation scales for the explanation of overall service satisfaction.
Because price perceptions revealed neither a strong direct nor indirect effect on the
perceived value construct, whether related price factors or the subjective price awareness
play a signi?cant role is questionable. For explaining the outcome (e.g. behavioral or
attitudinal loyalty) in such a structural model to differentiate among truly and spuriously loyal
passengers would be interesting (Zins, 2001). We expect that a much higher degree of
explanation would emerge for those customers who are really free and independent to
choose between different service providers. Furthermore, future research should consider
possible moderators, which may lead to confronting results (e.g. income, travel class
booked). In particular, business travelers may perceive service encounters in the airline
differently from the passengers traveling for leisure purposes do, due to their differing
aspiration level (Ringle et al., 2011).
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About the authors
Thomas Mayr acquired his doctoral degree from the Wirtschaftsuniversita¨ t Wien (WU),
where he was a Research and Teaching Assistant at the Institute for Tourism and Leisure
Studies from 2004 to 2008. During that time he lectured in Marketing and Tourism. His main
research focus is on tourism behavior, customer satisfaction research and tourism
marketing. Since 2008, Dr Mayr has been working in the Tourism Department of the Austrian
Federal Ministry for Economy, Family and Youth (Division for International Affairs).
Andreas H. Zins is an Associate Professor at the Institute for Tourism and Leisure Studies of
the Wirtschaftsuniversita¨ t Wien (WU) and Full Professor of Tourism Management at MODUL
University, Vienna. Dr Zins lectures in entrepreneurship, marketing, tourism marketing, and
modeling of consumer and travel behavior. His research interests are tourism behavior,
marketing research, social impacts, computer-assisted and web-based interviewing, theme
parks and related leisure attractions. Andreas H. Zins is the corresponding author and can
be contacted at: [email protected]
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