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http://jam.sagepub.com/content/34/4/613The online version of this article can be found at:
DOI: 10.1177/0092070306286934
2006 34: 613Journal of the Academy of Marketing ScienceJyh-Shen Chiou and Cornelia Droge
Satisfaction-Loyalty FrameworkService Quality, Trust, Specific Asset Investment, and Expertise: Direct and Indirect Effects in a
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This study proposes an integrated framework explaining
loyalty responses in high-involvement, high-service luxury
product markets. The model is rooted in the traditional
(attribute satisfaction)-(overall satisfaction)-(loyalty)
chain but explicitly incorporates facility versus interac-
tive service quality, trust, specific asset investment (SAI),
and product-market expertise. The authors focus on dis-
entangling the direct versus indirect effects of model con-
structs on attitudinal versus behavioral loyalty responses.
The results support the traditional chain but also show
loyalty can be increased by building a trustworthy image
and creating exchange-specific assets. The authors found
that overall satisfaction is the precursor both to loyalty
and to building SAI. Finally, consumers have different costs
in reducing adverse selection problems with information,
and thus the negative effect of product-market expertise
on behavioral loyalty needs to be controlled if the direct
versus indirect effects of model constructs on loyalty are
to be disentangled.
Keywords: loyalty; specific asset investment; transaction
cost analysis; satisfaction; service quality
Satisfaction is a major driver of customer retention
and loyalty, and therefore achieving high consumer satis-
faction is a key goal of practitioners (Fornell, Johnson,
Anderson, Cha, and Bryant 1996; Oliver 1997). Since the
cost of obtaining a new consumer is very high and the
profitability of a loyal consumer grows with the relation-
ships duration, understanding loyalty cultivation or reten-
tion is key to long-term profitability (Bolton, Kannan, and
Bramlett 2000; Bolton, Lemon, and Verhoef 2004;
Reichheld 1996, 2001). Models of satisfaction-loyalty
chains have been proposed but often have trouble incor-
porating the many satisfied consumers who eventually
defect (Jones and Sasser 1995; Reichheld 1996). Reasons
for defections include consumer characteristics (Frank
1967; Mittal and Kamakura 2001), industry particulars
(Anderson and Sullivan 1993; Fornell 1992; Jones and
Sasser 1995), switching experience (Ganesh, Arnold,
and Reynolds 2000), and switching cost (Burnham, Frels,
and Mahajan 2003; Hauser, Simester, and Wernerfelt
1994; Jones, Mothersbaugh, and Beatty 2000; Lee and
Cunningham 2001; Lee, Lee, and Feick 2001). On the
other hand, temporary dissatisfaction may not affect
loyalty (Day 1969; Jones and Sasser 1995), for example,
by members of loyalty programs (Bolton et al. 2000).
In the high-involvement, premium, or luxury product
markets of interest to us in this research, each consumer
transaction is of very high value, and thus understanding
combinations of satisfied-defection and dissatisfied-loyaltyis crucial.
Research on loyalty in exchange relationships has
occurred in both business-to-consumer (B2C) and business-
to-business (B2B) domains. In the former, such research
often goes under the rubrics ofsatisfactionand/or customer
lifetime value research, while in B2B and/or services
marketing, it is often known as relationship marketing or
service quality research. For high-involvement, luxury
Service Quality, Trust, SpecificAsset Investment, and Expertise:Direct and Indirect Effects in
a Satisfaction-Loyalty Framework
Jyh-Shen ChiouNational Chengchi University, Taiwan
Cornelia DrogeMichigan State University
Journal of the Academy of Marketing Science.Volume 34, No. 4, pages 613-627.DOI: 10.1177/0092070306286934Copyright 2006 by Academy of Marketing Science.
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consumer product markets where close relationships
with consumers are paramount, B2B models of loyalty
seem to capture some constructs germane to B2C rela-
tionships (constructs such as trust and specific asset
investment). We thus propose a general framework that
(1) incorporates aspects of agency theory and transac-
tion cost analysis from the B2B domain into the tradi-
tional chain of consumer (attribute satisfaction)-(overallsatisfaction)-(loyalty) and (2) explicitly disentangles
direct versus indirect effects of model constructs on atti-
tudinal versus behavioral loyalty. Thus, the overarching
goal of this study is to construct an integrated framework
explaining the direct versus indirect antecedents of con-
sumer loyalty in high-involvement premium or luxury
product markets.
Specifically, our first objective is to incorporatefacil-
ity versus interactive service quality, along with attribute
satisfaction, as antecedents to both trustand overall sat-
isfaction. Service quality constructs have been examined
primarily in the B2B and consumer services literatures,
but many products actually have a high service compo-
nent. In particular, luxury product markets often have
high service content. The second objective is to investi-
gate the effects oftruston overall satisfactionand on atti-
tudinal versus behavioral loyalty (building on the work of
Chaudhuri and Holbrook 2001). Trust is important in
relationship marketing, such as in B2B contexts. While
we concur with Atuahene-Gima and Li (2002) that the
inherent value of the trust construct is in danger of being
oversold, the direct versus indirect effects of trust must
be addressed in our product-market context. Third, we
explore the relationships among satisfaction, asset speci-
ficity, and attitudinal versus behavioral loyalty. Assetspecificity, or specific asset investment (SAI), is one way
to create loyalty without maximum satisfaction (although
we argue that satisfaction makes SAI more likely). The
construct originates in the B2B transaction cost literature.
Finally, our fourth objective is to specify the role ofproduct-
market expertise: consumers with different product-market
expertise will have different costs in reducing adverse
selection problems and therefore have different propensi-
ties to stay with current brands independent of whether
they are satisfied.
The article is organized as follows. In the next sec-
tions, we provide an overview of the model, definitions
of the key constructs, and a detailed development of thehypotheses. The specific product-market context is pre-
mium cosmetics, a high-involvement, credence product
chosen because the exchange relationships with con-
sumers are very trust relevant and involve SAI. The next
section describes methodology, including measurement
and the development of a scale to measure consumer
asset specificity. The results of the hypothesis testing are
then presented, followed by the discussion of the results.
MODEL FRAMEWORK: PRODUCT-MARKETCONTEXT AND OVERVIEW
The Product-Market Context:High-Involvement, High-Service Content
We focus on high-involvement product markets encom-
passing products having a significant service component.Examples abound in home remodeling product markets,
where products are purchased (such as custom kitchen
cabinets, or bathroom remodeling) but most consumers
require design and installation services. Another example
is the premium cosmetics product market, which is the
focus of our study and thus deserves some elaboration.
Premium cosmetic products are high-involvement,
credence products that require a lot of personal service
(Bolan 2005; Ellison and Fowler 2004; Prasso 2005).
These luxury cosmetics are typically sold by highly trained
beauty consultants at dedicated (rented) counters in high-
end department stores. The consultants are usually the
employees of the cosmetics company, not the departmentstore. Their job has educational, experiential, and rela-
tional aspects and is similar in many respects to the
job of B2B salespersons. Many strong interactive rela-
tionships develop between consumers and these beauty
consultants.
The price differentials between these lines and the typi-
cal drug store or supermarket cosmetic lines are substantial.
For example, a lipstick can be found for $3 or less at drug
stores or supermarkets but costs $15 or more at these coun-
ters. Clearly, the beauty consultants are not selling tubes of
colored wax but rather the ever-changing ideal of beauty
and the hope of achieving that look. The price differen-
tials for a variety of antiaging skin products is even greater,with some of these high-end products costing $450+
per month to use (see, e.g., Ellison and Fowler 2004).
Medicinal-type outcomes are often claimed or implied,
such as impacts on the chemistry and structure of the skin.
Often, specific products must be used in a specific sequence
at specific times of the day (such as prescription drugs);
educating consumers about this idiosyncratic product
knowledge is the job of the beauty consultants. Outcomes
are sometimes demonstrated to consumers using computer-
ized photographs, but many products effects on the skin
are long-term, and thus trust is important. The chief com-
petitors of these top-of-the-line antiaging skin products are
the spa experience and/or the plastic surgeon. This luxury
market is international, for example,Shiseidos Cle de Peau
line sells for about $500 for 30 grams in an upscale mall in
Shanghai, China (Prasso 2005).
Overview of the Model
Figure 1 presents a model of loyalty responses for high-
involvement, high-service-content product markets such as
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the premium cosmetics described above.Loyalty, as defined
by Oliver (1997), is a deeply held commitment to rebuy
or repatronize a preferred product/service consistently in
the future, thereby causing repetitive same-brand or same
brand-set purchasing, despite situational influences and
marketing efforts having the potential to cause switching
behavior (p. 392). This definition actually encompasses
two different aspectsbehavioral and attitudinal (see
Chaudhuri and Holbrook 2001; Dick and Basu 1994;
Ganesh et al. 2000; Pritchard, Havitz, and Howard 1999).
Behavioral loyalty (Loybeh) represents repeat brand pur-
chase by consumers. Attitudinal loyalty(Loyat) includes a
degree of dispositional commitment toward the brand by
consumers. We model both. Note that attitudinal loyalty is
not the same as brand attitude because the former repre-
sents an attitude toward being loyal to the brand (a conative
construct), while the latter is an attitude toward an object.In the model, perceived service quality (facility and
interactive) and attribute satisfaction are modeled as direct
antecedents to trust and overall satisfaction; trust, overall
satisfaction, asset specificity, and product-market exper-
tise are modeled as direct and/or indirect antecedents to
loyalty responses (attitudinal vs. behavioral). The models
network of constructs is rooted in the cognitive-affective-
conative loyalty framework of Oliver (1997, 1999; see also,
e.g., Chaudhuri and Holbrook 2001; Tailor and Baker
1994). The causal ordering reflects Olivers (1999) pro-
posal that the analysis needed to detect true brand loy-
alty requires researchers to assess consumer beliefs,
affect, and intention within the traditional consumer atti-tude structure (p. 35). The traditional attitude structures
order of cognitive-affective-conative responses is suitable
for high-involvement decision making, such as that char-
acteristic of premium cosmetics. Thus, perceived service
quality and attribute satisfaction precede trust and overall
satisfaction, which in turn precede loyalty responses.
Trust, asset specificity and product-market expertise
were incorporated into the model to reflect cumulative
effects over time on loyalty in high-involvement, high-
service product markets. Trust and asset specificity (con-
sumers investments in a supplier, e.g., such as represented
by loyalty points) were modeled as endogenous variables
because (1) providers past performance may affect con-
sumers perceptions of trust, and (2) overall satisfaction
will affect consumers willingness to engage in closer
relationships and invest in specific assets. In the premiumcosmetics product market, trust and SAIs are relevant
constructs, as described in the sections below. Product-
market expertise (tapping overall knowledge of the prod-
uct class) is modeled as an exogenous control variable
because it is affected more by market information and
individual factors than by model antecedents.
MODEL FRAMEWORK: HYPOTHESES
Hypotheses 1-2: Attribute Satisfactionand Perceived Service Quality as
Antecedents to Overall Satisfaction
Past research modeled two kinds of satisfaction
(Bitner and Hubbert 1994; Jones and Suh 2000). The first
one is attribute satisfaction (Satat), referring to a con-
sumers cognitive satisfaction with individual product or
service attributes. For example, Westbrook (1981) pro-
posed that satisfaction with a retail establishment is an
accumulation of separate satisfaction evaluations of
salespersons, store environments, products, and other
factors (see also Spreng, MacKenzie, and Olshavsky
1996). The second is overall satisfaction (Sat) or cumu-
lative satisfaction over time from an aggregation of trans-
action experiences (rather than a onetime transaction; seeHomburg, Koschate, and Hoyer 2005; Parasuraman,
Zeithaml, and Berry 1994). Overall satisfaction is
defined as pleasurable fulfillment and is an affective
response (Oliver 1999:34). Since Satat is defined as a cog-
nitive construct, while overall satisfaction is an affective
construct, it is hypothesized that Satat will affect overall
satisfaction for the high-involvement product markets
considered in this research (Garbarino and Johnson 1999;
Oliver 1997). Therefore (see Figure 1),
Hypothesis 1: Attribute satisfaction is positively associ-ated with overall satisfaction.
Perceived service quality evaluations are cognitive
responses at the attribute level. Consumers perceive at
least two types: (1) facility service quality (SQfac), pro-
vided by the physical environment (such as modern
equipment) and representing the tangible aspects of ser-
vice, and (2) interactive service quality (SQint) provided
by employees (such as promptness and courtesy). The
latter has been called the interactive factor, an essential
Chiou, Droge / CONSUMER SATISFACTION 615
FIGURE 1Research Model Framework
H4a
H3
H1
H2a
H4bH2b
H5
H6
H7
H8
H9H10
H11
H12
PMexpSatat
SQfac
SQint
Trust
Sat Loyat
SAI
Loybeh
NOTE: Satat = attribute satisfaction; SQfac = facility service quality;SQint = interactive service quality; Sat = overall satisfaction; Trust =perceived trust; PMexp = product-market expertise; Loyat = attitudinalloyalty; SAI = specific asset investment; Loy
beh= behavioral loyalty.
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component of perceived service quality according to the
services marketing literature (e.g., Bitner 1990; Brady
and Cronin 2001; White and Schneider 2000). In the
premium cosmetics product market, the service quality
delivered by the beauty consultant lies at the core of
firms marketing strategies.
We follow Oliver (1999) and other researchers who
maintain that perceived service quality is cognitive andthus followed (not preceded) by satisfaction. For example,
using the framework of appraisal emotional response
proposed by Lazarus (1991), Bagozzis (1992) perceived
service quality was an appraisal construct; appraisal nor-
mally precedes emotional responses such as satisfaction
(see also Carver and Scheier 1990; Oliver 1997, 1999).
Several other empirical studies also confirm the perceived
service quality satisfaction ordering, which corresponds
to the traditional attitude structure sequence (Cronin and
Taylor 1992; Patterson 2000; Woodside, Frey, and Daly
1989). Gotlieb, Grewal, and Brown (1994) directly tested
models of differing causal directions and found service
quality affects satisfaction, which in turn affects intention
(see also Taylor and Baker 1994). Therefore (see Figure 1),
Hypothesis 2: Service quality of the (a) facility and ofthe (b) interaction are positively associated withoverall satisfaction.
Hypotheses 3-4: Attribute Satisfaction,Perceived Service Quality asAntecedents to Trust
Trust is the belief that another party can be relied on
with confidence to perform role responsibilities in a fidu-
ciary manner (Doney and Cannon 1997; Morgan and Hunt1994). The domain of trust in our context is the brand
experience in its entirety (encompassing both product
and service aspects offered by the brands provider) but
not focusing on specific attributes or specific retail stores.
As in Singh and Sirdeshmukh (2000), we define trust
as a cognitive rather than affective construct. Several
researchers have proposed different dimensions of trust.
For example, Ganesan and Hess (1997) included credibility
and benevolence dimensions, while Smiths (1997) trust
construct encompasses perceptions of honesty/integrity,
reliability/dependability, responsibility, and positive
motives/intentions.
Trust is important in many high-involvement, pre-mium product markets because consumers are exposed to
costs associated with adverse selection and moral hazard,
both agency costs. Agency cost arises when the desires
or goals of the principal and the agent conflict and it is
difficult or expensive for the principal (e.g., consumer)
to verify what the agent (i.e., the provider) is actually
doing (Eisenhardt 1989). The problem of adverse selec-
tion occurs when consumers are unable to discrimi-
nate between different quality providers and thus choose
incorrectly (Akerlof 1970; Wilson 1980). Moral hazard
originates in the lack of effort on the part of the agent
(Eisenhardt 1989), and an opportunistic agent may
decide to reap greater payoffs by delivering less than
promised (Singh and Sirdeshmukh 2000). For example,
the premium cosmetics examined in our research are
characterized by secret ingredients (hence, adverse selec-
tion risks), ambiguous performance (hence, moral hazardrisks), and possibly significant social risk. Thus, transac-
tions within this product market fit the definition of trust-
relevant exchange in Sitkin and Roth (1993; see also
Singh and Sirdeshmukh 2000).
Avoiding adverse selection involves costs. Consumers
may suffer from information asymmetry in favor of
providers and thus spend time and effort searching for
more information (e.g., friends, public information) and
evaluating competitive claims. However, even if a con-
sumer can resolve the adverse selection problem ex ante,
he or she is still exposed to the problem of moral hazard
ex post (Kirmani and Rao 2000). Therefore, consumers
want to perceive that the provider is trustworthy, to
believe that the provider will act according to what was
agreed upon (Chaudhuri and Holbrook 2001; Doney and
Cannon 1997).
Consumers can evaluate several explicit and implicit
cues concerning the provider to gradually build up trust
(Doney and Cannon 1997). Among these cues, product
attribute satisfaction and perceived service quality rep-
resent evaluations of direct experiences (Singh and
Sirdeshmukh 2000). If favorably perceived, adverse selec-
tion and moral hazard concerns will be reduced, and con-
sumers will have more confidence in the provider; this in
turn will increase their trust in the provider. Thus,
Hypothesis 3: Attribute satisfaction is positively associ-ated with trust.
Hypothesis 4: Service quality of the (a) facility and ofthe (b) interaction are positively associated with trust.
Hypotheses 5-7:Trust, Satisfaction,and Loyalty Responses
In many product categories, consumers may not know
the exact outcome before buying the product and experi-
encing the associated service, and since many products
contain credence elements of quality (such as the cosmetics
we examine), some consumers may not have the abilityto discern performance even after experiencing it. For
example, Trawick and Swan (1981) claimed that ambiguous
performance tends to be misinterpreted in the direction of
a priori expectations, while Kirmani and Rao (2000) con-
cluded that moral hazard issues remain unresolved after
purchase when violations of quality claims cannot be
unambiguously recognized. For this kind of product or ser-
vice, strong consumer confidence is paramount. Thus, the
management over time of consumers trust is especially
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important in the marketing of services (Berry and
Parasuraman 1991) and credence products such as high-
end cosmetics.
Singh and Sirdeshmukh (2000) distinguished trust
before initiation of an exchange (pretrust) from trust after
an exchange (posttrust). On the basis of social exchange
theory, they proposed that consumers pretrust will have
direct influence on their postpurchase satisfaction.Therefore, one could argue that cumulative trust percep-
tions will affect cumulative satisfaction over time. In any
case, if a consumer does not trust the provider based on
past experience, he or she will probably be dissatisfied
with that provider.
Gwinner, Gremler, and Bitner (1998; see also
Chaudhuri and Holbrook 2001) provided another ratio-
nale for the relationship from trust to satisfaction. They
found that consumers in long-term relationships with
service firms experience three primary types of benefits:
confidence, social, and special-treatment benefits. Among
the three benefits, the confidence benefit (which is very
similar to trust in the current study) was the most impor-
tant to consumers across several categories of services.
Confidence benefits include a sense of reduced anxiety,
faith in the provider, reduced perceptions of anxiety and
risk, and knowing what to expect. When consumers feel
these benefits related to trust, their overall satisfaction is
enhanced over the long term. Thus,
Hypothesis 5: Trust is positively associated with overallsatisfaction.
Following Morgan and Hunt (1994) and Chaudhuri
and Holbrook (2001), we also propose that commitmentin the form of consumer attitudinal loyalty is a result of
trust. Trust and commitment are two of the most important
constructs in the relationship marketing paradigm (Morgan
and Hunt 1994; Spekman 1988), and trust seems implicit
to true consumer attitudinal loyalty (Oliver 1999:42).
Since trust involves confidence in the exchange partners
reliability and integrity, it is a necessary ingredient for a
long-term orientation because it shifts the focus to future
conditions and continuity (Doney and Cannon 1997;
Ganesan 1994). However, we propose no directrelation-
ship from trust to behavioral loyalty, thus limiting the role
of trust as suggested by Atuahene-Gima and Li (2002).
Rather, we propose in Hypothesis 11 below that trustindirectly affects behavioral loyalty through attitudinal
loyalty. Thus,
Hypothesis 6: Trust is positively associated with attitu-dinal loyalty.
Satisfied consumers are more likely to repeat purchase,
to resist competitive offers, and to generate positive word
of mouth (Anderson and Sullivan 1993; Bolton 1998;
Bolton and Lemon 1999; Cronin and Taylor 1992; Hennig-
Thurau, Gwinner, and Gremler 2002; Zeithaml, Berry, and
Parasuraman 1996). Research in the American Customer
Satisfaction Index provides additional empirical support
for loyalty responses as the major consequence of con-
sumer satisfaction (Fornell et al. 1996). In addition, since
overall satisfaction is affective attitude and attitudinal
loyalty is a conative construct, the latter is normallyhypothesized to mediate the relationship between affective
attitude and behavior in the marketing and psychology
literature (Ajzen and Fishbein 1980; Bansal, Taylor, and
James 2005). Therefore, we model a direct effect of over-
all satisfaction to attitudinal loyalty but not to behavioral
loyalty; rather, the effect on behavioral loyalty is modeled
as indirect (through attitudinal loyalty in Hypothesis 11) to
account for the fact that loyal consumers do purchase
competitive products. Thus,
Hypothesis 7: Overall satisfaction is positively associ-ated with attitudinal loyalty.
Note that Hypotheses 5-7 as a setstate that (1) trust
has both a direct effect on attitudinal loyalty (Hypothesis
6) and an indirect effect through overall satisfaction
(Hypotheses 5 and 7), and (2) neither trust nor overall
satisfaction have a direct effect on behavioral loyalty
(rather, their effects are indirect, through attitudinal loyalty,
for example).
Hypotheses 8-11: Overall Satisfaction,Asset Specificity, and Loyalty
Asset specificity refers to investments in assets that arededicated to a particular supplier and whose redeploy-
ment entails considerable switching costs (Williamson
1985). These idiosyncratic SAIs to support a particular
exchange relationship may take different forms: they
may be physical assets, monetary assets, knowledge, per-
sonal relationships, skills, and so on (Williamson 1991).
For example, the exchanges between a premium cosmet-
ics company and consumers involve SAI. Consumers
have to spend time getting acquainted with several dif-
ferent product types, functions, combinations, and suit-
ability for occasion and skin texture; this leads to
knowledge asset specificity. In addition, because of the
way in which premium cosmetics brands are sold, con-sumers often engage in social relationships with favorite
beauty consultants and possibly with other consumers
(an invisible social SAI).
Asset specificity is a very important concept in trans-
action cost analysis because it can cause dependence on
the supplier and hence discourage switching (Ganesan
1994; Joshi and Stump 1999). Asset specificity can be
viewed as a type of switching cost (Burnham, Frels, and
Mahajan 2003; Dick and Basu 1994; Hauser, Simester,
Chiou, Droge / CONSUMER SATISFACTION 617
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and Wernerfelt 1994; Jones, Mothersbaugh, and Beatty
2000; Lee and Cunningham 2001; Lee, Lee, and Feick
2001). Firms can encourage idiosyncratic SAI on the part
of the consumer: loyalty rewards programs such as those
based on service or product usage levels, cobranded
credit cards, or frequent flyer mileage programs are
examples. Consumers can lose unredeemed reward
points or other benefits if they switch to other suppliers,and thus these SAIs encourage consumer retention.
A consumers investment of specific assets in a provider
gives the provider some control over the consumer (Jap
and Ganesan 2000). The most prominent B2B solution
offered by transaction cost analysis to safeguard specific
asset investments is vertical integration (Williamson 1985).
However, unlike firms, it is very difficult for a consumer
to vertically integrate the functions provided by the
provider (DiMaggio and Louch 1998). Therefore, rational
consumers will try to avoid dependency in unsatisfactory
relationships (that perhaps they dont want to last) by
reducing the buildup of SAI. On the other hand, a consumer
will increase SAI with a satisfactory provider. Therefore,
Hypothesis 8: Overall satisfaction is positively associ-ated with SAIs (asset specificity).
Asset specificity creates dependency because consid-
erable switching costs are involved to replace the
provider (Heide and John 1988; Joshi and Stump 1999).
A consumer may not be fully satisfied and indeed feel lit-
tle attitudinal loyalty but still wont want to switch sup-
pliers because of SAI. Therefore, asset specificity should
be a directantecedent ofbehavioral loyalty (Klemperer
1987; Wernerfelt 1985). A more difficult question iswhether attitudinal loyalty will also be affected by SAI
(and thus the effects of SAI on behavioral loyalty would
be indirect as well as direct).
We propose that attitudinal loyalty is affected by SAI
since most SAIs are built up because of the consumers
willingness to engage in a long-term relationship. A long-
term orientation may be prerequisite to securing the rents
from SAI (Williamson 1985). In addition, consumers may
gradually perceive that SAI increases exchange efficiency
(Gwinner et al. 1998; Stauss, Chojnacki, Decker, and
Hoffmann 2001). Gwinner et al. (1998) found confidence,
social, and special-treatment benefits from long-term rela-
tional exchanges: the latter two are created through SAI bythe consumer and by the supplier. Examples include sales-
clerks communicating more efficiently with consumers
because of human specific assets, consumers reducing
buying task complexity through knowledge SAI, and loy-
alty rewards programs creating SAI through nontransfer-
able points or bonuses (Bolton et al. 2000). Therefore,
Hypothesis 9: Specific asset investments are positivelyassociated with attitudinal loyalty.
Hypothesis 10: SAIs are positively associated withbehavioral loyalty.
Finally, attitudinal loyalty affects behavioral loyalty.
This assertion is well rooted in most attitude and satis-
faction research (Ajzen and Fishbein 1980; Dick and
Basu 1994; Oliver 1997).
Hypothesis 11: Attitudinal loyalty is positively associatedwith behavioral loyalty.
Hypothesis 12: Product-Market Expertiseand Behavioral Loyalty
Product-market expertise comprises overall knowl-
edge levels of brands, product types, usage methods, pur-
chase information, and so on in the product market and
represents the ability to perform product- and market-
related tasks successfully. Expertise is different concep-
tually and theoretically from familiarity (Alba and
Hutchinson 1987; Park, Mothersbaugh, and Feick 1994):
familiarity is a function of product-related experiences,
which may or may not result in expertise.
Consumers search for more product-market informa-
tion in part to reduce adverse selection problems. However,
information search costs deter a consumer from switch-
ing providers (Sharma and Patterson 2000; Thibault
and Kelley 1959). Consumers generally have a disutility
for cognitive effort, and less effort is expended when
using the same product or brand (Alba and Hutchinson
1987). Also, a consumer may have an existing informa-
tion schema, and acquiring additional information may
cause dissonance and disruption between the existingschema and the new one (Festinger 1957). In summary,
the goal of reducing information search cost encourages
the inertia of staying with the current provider, and
consumers exhibit behavioral but not necessarily attitu-
dinal loyalty.
We argue furthermore that both information search
costs and information resolution costs will be higher for
those consumers with less product-market expertise, and
thus product-market expertise and behavioral loyalty will
be inversely related. Research on the structuring of con-
cepts (Alba and Hutchinson 1987) supports this asser-
tion. Experts as compared to novices have higher abilities
to (1) categorize below the basic category, thus formingfiner discriminations with greater reliability and permit-
ting consideration of a more homogeneous set of alterna-
tives when need is specific (Rosch, Mervis, Gray,
Johnson, and Boyes-Braem 1976), and (2) categorize
above the basic category, thus making more abstract
comparisons with greater reliability and permitting con-
sideration of a more heterogeneous set of alternatives
when need is general (Adelson 1984; Schoenfeld and
Herrmann 1982). Therefore, expert consumers have lower
618 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE FALL 2006
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costs of resolving information asymmetry than novices,
even if both groups are exposed to the same amount of
information. The findings of Capraro, Broniarczyk, and
Srivastava (2003) also lend support to the following: in
studying the factors that influence defection, they found
that the level of objective and subjective knowledge
about alternatives has a positive and direct effect on the
likelihood of defection. Thus, it is important to controlfor the impact of expertise:
Hypothesis 12: Product-market expertise is negativelyassociated with behavioral loyalty.
METHOD
Sample
Because we cannot identify this premium cosmetics
company, we will call it XYZ. XYZ is the companys
family brand that is attached to numerous individual
products (like Kelloggs __). A preliminary qualitative
study was conducted. Four consumers of XYZ were
recruited for in-depth interviews. Reasons for loyal or
disloyal behaviors were elicited, and the effects of all
constructs on loyalty were then probed. The results of
this preliminary study were used to explore the proposed
model qualitatively, to create a pool of items for the asset
specificity scale, and to modify the service quality scale.
For data collection quality, a professional market
research firm was hired to collect data by telephone.
Interviews took about 10 minutes. The member database
of the XYZ cosmetics company served as the sampling
frame. About 30,000 members had signed up to receivethe latest product/service/event information and coupons
for new products (but only those who spend more than
$300 per year get full benefits). Stratified random sampling
by age-group was used, ensuring that nonrespondents
were replaced by respondents of the same age-group. The
target 300 completed questionnaires represent a response
rate of35 percent of 857 contacts, which is high because
members are motivated and interviewers were highly
trained. All respondents were female and aged 25 to
54 years (M= 35.6, SD = 6.54), 77 percent had jobs, and
94 percent had at least high school. Respondentsprofiles
were not significantly different from nonrespondents on
these demographic variables, and thus we concluded thatnonresponse bias is not a problem.
One important issue is whether the fact that the respon-
dents were members could have an impact on model
results. If a respondent has very little or no experience with
the focal company, it is very difficult to assess model con-
structs, especially trust and asset specificity. Thus, experi-
ence itself is not a problem, while inexperience may cause
serious biases. The question is whether these members
generated adequate variance in loyalty. We examined the
variance of the amount actually purchased in the past
year. Variance was very high, and about half of the respon-
dents can be classified as inactive (i.e., spent something,
but not the $300 necessary for full benefits of member-
ship). Therefore, not all respondents are very loyal, and this
sample appears to have adequate variance.
Measurement
Where possible, established scales were used. Construct
names, Cronbachs alphas, and specific scale wordings
are shown in the measurement appendix.
Service quality (facility and interactive). These measures
were drawn from the shortened SERVQUAL used by Teas
(1993). The item opening hours was dropped because all
premium cosmetics are sold in high-end department stores
with the same hours of operation. In addition, the term
employee was substituted by salesclerk at the sales
counter to more accurately reflect the encounter point with
consumers. Finally,only perceptions of service quality were
used; perceptions are adequate for explaining the variance
in dependent constructs, as opposed to objectively diagnos-
ing actual shortfalls (see Zeithaml et al. 1996).
Attribute satisfaction. Attribute satisfaction was oper-
ationalized by asking Please rate your satisfaction with the
following product attributes of XYZ brand. The 5-point
Likert-type scales were anchored by very dissatisfied/
very satisfied. The six attributes were selected on the
basis of a pretest exploring important attributes of pre-
mium cosmetics.
Overall satisfaction. Overall satisfaction was a three-item
construct taken from Oliver Likert-type (1980). The 5-point
Likert-type scales were anchored by strongly disagree/
strongly agree.
Perceived trust. Perceived trust was measured by six
items revised from Smith (1997). The items included
honesty, reliability, responsibility, and motives/intentions
and were rated on 5-point Likert-type scales ranging
from strongly disagree to strongly agree.
SAI. Since most scales tapping transaction-specific
assets were developed for B2B situations, SAI measuresfor consumers were developed according to Churchills
(1979) recommendations. A pool of items was created by
consulting industry experts and cosmetics consumers.
For example, the pool included items focusing on con-
sumer product knowledge (e.g., regarding product lines
or usage methods), items focusing on salesclerks (e.g.,
consumers knowledge of, and social relationship with,
salesclerks; salesclerks professional knowledge), items
focusing on product-consumer fit (e.g., the fit of the brand
Chiou, Droge / CONSUMER SATISFACTION 619
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to the consumers skin). All items referred to assets that
could be lost if brand usage were terminated (Burnham
et al. 2003).
Probing for asset specificity usually involves asking
whether consumers have invested time, energy, or money
specifically to accommodate suppliers (Jap and Ganesan
2000; Joshi and Stump 1999). However, preliminary
qualitative work showed that respondents had difficultiesin answering these questions. Unlike B2B consumers,
most premium cosmetics consumers do not devote much
thought to cost or value. To help consumers identify
visible and invisible assets, we tried to use phrases
emphasizing what the loss of the asset would mean. This
probing method produced better reactions from con-
sumers. Therefore, most of the SAI items were rephrased
accordingly; four of the final six specifically state, If
I switch to other cosmetic brands . . . so that the respon-
dent is aided in identifying the value. The preliminary
scales were then pretested with 35 users. Internal consis-
tency and item-to-total correlation analyses showed that
one item (concerning salesclerks) did not fit (< .4), and
therefore it was dropped. Exploratory factor analysis sup-
ported unidimensionality of the final six retained items.
Product-market expertise. The five measures of product-
market expertise were adapted from Park et al.s (1994)
self-assessed knowledge scales. However, the items were
broader, including knowledge of purchase methods and
new information. The 5-point Likert-type scales ranged
from strongly disagree to strongly agree.
Loyalty. Attitudinal loyalty was measured by using
scales developed from Selin, Howard, Udd, and Cable(1988) and Muncy (1983) (see also Pritchard et al. 1999).
The 5-point Likert-type scales ranged from strongly dis-
agree to strongly agree. The three behavioral loyalty scales
were modified from Pritchard et al.s (1999). They tapped
future monetary proportion intention, purchase frequency
proportion in the past 12 months, and monetary proportion
in the past 12 months devoted to XYZ (see appendix).
Measurement Model Testing and Results
The two-step procedure proposed by Anderson and
Gerbing (1988) was used. First, confirmatory factor analysis
(CFA) evaluated construct validity, and then hypotheseswere tested. All models used the covariance matrix as
input to LISREL 8.5.
The CFA results for overall fit were 2(824) = 1,233.14,
p = .00; Comparative Fit Index (CFI) = .98, Nonnormed
Fit Index (NNFI) = .98, Incremental Fit Index (IFI) = .98,
root mean square error of approximation (RMSEA) = .041,
standardized root mean square residual (RMR) = .046.
These indices were acceptable (Bollen 1989; Hoyle and
Panter 1995; Hu and Bentler 1995). Convergent validity
was assessed by examining the indicator loadings: all were
significant (see Tables 1 and 2, which present the measure-
ment results from the full structural model). In addition,
reliabilities were adequate (see appendix). Thus, conver-
gent validity was supported. However, of the 43 measure-
ment estimates, 5 were below 0.65. We did not engage in
model trimming by dropping these measures (the main
results do not change much in any case).
A common test of discriminant validity is determining
whether the confidence interval around two constructs
correlation includes 1 (Smith and Barclay 1997). None
of the 36 included 1. A more conservative test involves
comparing models that either free or constrain to 1 the
phi value and testing for a significant decrease in fit: in all
36 cases, the overall fit significantly decreased. Therefore,
discriminant validity was adequate.
RESULTS
Overall Structural Model:Testsof the Hypotheses
The results for the full structural model were 2(840) =
1,251.99, NNFI = .98, CFI = .98, IFI = .98, RMSEA =
.041; RMR = .028 (standardized = .048). The model con-
verged in 16 iterations, and the t-rule for identification
holds (Bollen 1989). Overall fit was good. The squared
multiple correlations for the structural equations were as
follows: trust, .58; satisfaction, .71; SAI, .23; attitudinal
620 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE FALL 2006
TABLE 1Factor Loadings for Exogenous Constructs
Unstandardized Completely
Measurement Solution ( t-values; Standardized
Model all at p < .05) Solution
Facility service SQfac1 1 .62
quality SQfac
2 1.33 (6.37)x .72
Interactive service SQint1 1 .73
quality SQint2 1.02 (12.36) .73
SQint3 1.08 (13.74) .80
SQint
4 1.05 (14.01) .82
SQint5 1.02 (12.98) .76
SQint6 1.03 (13.14) .77
SQint7 1.01 (12.24) .72
SQint8 0.99 (11.60) .68
Attribute Satat1 1 .74
satisfaction Satat2 1.18 (13.26) .79
Satat3 0.75 (9.32)x .56
Satat4 0.57 (6.82)x .42
Satat5 0.73 (7.91)x .48
Satat6 0.94 (13.20) .79
Product-market PMexp1 1 .84
expertise PMexp2 0.98 (16.72) .82PMexp3 1.14 (18.08) .87
PMexp4 0.99 (16.91) .83
PMexp5 1.09 (17.31) .84
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loyalty, .72; and behavioral loyalty, .44. Thus, a substantial
proportion of variance in each of these constructs is
explained.
We tested each hypothesis by examining path signifi-
cance (Table 3). Part A of Table 4 shows the standardized
indirect effects, while Part B of Table 4 shows the stan-
dardized total effects. The results in Table 3 show thatattribute satisfaction significantly influenced perceived
trust and overall satisfaction, supporting Hypotheses 1
and 3. In addition, interactive service quality (but not
facility service quality) significantly affected perceived
trust and overall satisfaction, thus partially supporting
Hypotheses 2 and 4. Facility service quality plays no role
in the model, as the total effects in Table 4 clearly show
(first column in Table 4, Part B).
Trust was found to positively affect overall satisfaction
(Hypothesis 5) as well as attitudinal loyalty (Hypothesis 6).
Hypotheses 7 and 8 were also supported since overall
satisfaction was found to influence attitudinal loyalty
and SAI. Consistent with Hypotheses 9 and 10, SAIaffected both attitudinal and behavioral loyalty, and atti-
tudinal loyalty affected behavioral loyalty, supporting
Hypothesis 11. Finally, product-market expertise was
negatively related to behavioral loyalty (.17, p < .05),
supporting Hypothesis 12.
Other than the column for facility service quality, the
t-values for total effects in Part B of Table 4 are all signif-
icant at .05 or better, indicating that constructs antecedent
in a chain of effects have a significant downstream impact.
For example, attribute satisfaction has (1) a direct effect
on overall satisfaction (.61, as per Hypothesis 1, Table 3);
(2) an indirect effect on overall satisfaction through trust
(.12, as per Table 4, Part A), making a total effect of .73
(Table 4, Part B); and (3) total effects, all of which are
indirect, on SAI (.35), attitudinal loyalty (.54), and
behavioral loyalty (.29).
Competing Models: Examining
Other Direct or Indirect Paths
Since our goal is to untangle direct versus indirect
effects within a complex chain of constructs, it is impor-
tant to verify that other paths are not significant. One pos-
sibility is that the immediate antecedents to the loyalty
constructs (i.e., product-market expertise, trust, and over-
all satisfaction) may directly affect both attitudinal and
behavioral loyalty. The issue is important because (1) we
model attitudinal loyalty as an important mediatorof the
impacts of trust and satisfaction on behavioral loyalty,
and (2) we model product-market expertises impact at
the behavioral (not attitudinal) loyalty level. If either
proves false, the chain of direct and indirect effects wouldbe significantly different, having major theoretical and
practical implications. To test the alternative model, the
direct links between (expertise)-(attitudinal loyalty),
(overall satisfaction)-(behavioral loyalty), and (trust)-
(behavioral loyalty) were added. The difference in 2 was
not significant, 2(837) = 1,246.94, 2 = 5.05, df = 3,
p > .05). Therefore, product-market expertise affects only
behavioral loyalty, and trusts and overall satisfactions
effects on behavioral loyalty are indirect. In addition,
Chiou, Droge / CONSUMER SATISFACTION 621
TABLE 2Factor Loadings for Endogenous Constructs
Unstandardized Completely
Measurement Solution ( t-values; Standardized
Model all at p < .05) Solution
Facility service Trust1 1 .74
quality Trust2 1.09 (14.02) .82
Trust3 1.08 (13.15) .77
Trust4 1.13 (12.90) .75
Trust5 1.08 (12.30) .72
Trust6 1.18 (12.78) .75
Overall Sat1 1 .90
satisfaction Sat2 1.00 (25.02) .92
Sat3 1.02 (26.28) .94
Specific asset SAI1 1 .66
investments SAI2 1.39 (12.52) .83
SAI3 1.41 (13.18) .88
SAI4 1.36 (12.74) .85
SAI5 1.12 (10.94) .71
SAI6 1.04 (11.13) .72
Attitudinal Loyat1 1 .84
loyalty Loyat2 1.16 (18.29) .89
Loyat3 0.97 (13.92) .72Loy
at4 0.35 (4.83)x .29
Behavioral Loybeh1 1 .74
loyalty Loybeh2 1.27 (13.94) .90
Loybeh3 0.91 (12.71) .77
TABLE 3Tests of the Hypotheses
Completely
Path Path Coefficientsa Standardized
Hypothesis 1: SatatSat .82 (7.93,p < .05) .61
Hypothesis 2a: SQfacSat .13 (1.29, ns)xxix .09
Hypothesis 2b: SQint
Sat .20 (3.39,p < .05) .19
Hypothesis 3: SatatTrust .56 (7.65,p < .05) .56
Hypothesis 4a: SQfacTrust .13 (1.43, ns)xxix .11
Hypothesis 4b: SQintTrust .19 (3.57,p < .05) .23
Hypothesis 5: TrustSat .28 (2.94,p < .05) .21
Hypothesis 6: TrustLoyat .29 (3.06,p < .05) .19
Hypothesis 7: SatLoyat .39 (5.23,p < .05) .35
Hypothesis 8: SatSAI .46 (7.42,p < .05) .48
Hypothesis 9: SAILoyat .56 (8.09,p < .05) .48
Hypothesis 10: SAILoybeh .16 (5.15,p < .05) .47
Hypothesis 11: LoyatLoybeh .07 (2.83,p < .05) .24
Hypothesis 12: PMexpLoybeh .05 (3.12,p < .05) .17
NOTE: Satat = attribute satisfaction; SQfac = facility service quality;SQint = interactive service quality; Sat = overall satisfaction; Trust =perceived trust; PMexp = product-market expertise; Loyat = attitudinalloyalty; SAI = specific asset investment; Loy
beh= behavioral loyalty.
a. t-value in parentheses.
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since the antecedents of attitudinal versus behavioral
loyalty are different, our contention that these are two
different constructs is further supported.
Other possible challenges to the hypothesized model
are that facility and/or interactive service quality mayaffect loyalty directly or that attribute satisfaction may
affect loyalty directly (i.e., that these effects are not
mediated by other constructs such as overall satisfaction).
To test this assertion, the linksfrom service quality (both
facility and interactive) and attribute satisfaction to atti-
tudinal and behavioral loyalty were freed (six new paths).
The results show that the difference in chi-square was not
significant, 2(834) = 1,246.26, 2 = 5.73, df= 6,p > .05).
Freeing the links from service quality and from attribute
satisfaction in separate models leads to the same conclu-
sion. Thus, service quality and attribute satisfaction affect
attitudinal and behavioral loyalty indirectly through overall
satisfaction and trust.
DISCUSSION
This study proposed an integrated framework explain-
ing loyalty responses in high-involvement, high-service
premium product markets. The model is rooted in the
traditional (attribute satisfaction)-(overall satisfaction)-
(loyalty) chain but explicitly differentiated attitudinal
from behavioral loyalty and incorporated (1) facility
versus interactive service quality to account for the high-
service component in these product markets; (2) trust
and SAI to reflect high-end cosmetics consumers
demand for credence and involvement; and (3) product-market expertise, which was modeled as an exogenous
control variable inversely affecting only behavioral
loyalty. The results supported the core traditional chain
but also supported the roles of service quality, trust, and
SAI in increasing consumers loyalty. We focused on
disentangling the direct versus indirect effects of model
constructs, and these results are discussed in the follow-
ing sections.
The Effects of Service Qualityand Attribute Satisfaction
The results show that attribute satisfaction and inter-active service quality (but not facility service quality)
generate overall satisfaction and trust. Of these four rela-
tionships, only the (attribute satisfaction)-(overall satis-
faction) link forms part of the traditional B2C chain,
while the other links have some counterparts in the B2B
or services literatures. The impact of service quality was
predictable in our high-involvement, high-service pre-
mium product market, but this is not necessarily true in
product markets that have lower service content.
622 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE FALL 2006
TABLE 4Analysis of Indirect and Total Effects
A. Completely Standardized Indirect Effects (t-values in parentheses)
SQfac SQ
int Sat
at Trust Sat SAI
Trust NA NA NA NA NA NA
(Hypothesis 4a) (Hypothesis 4b) (Hypothesis 3)
Sat 0.02 0.05 0.12 NA NA NA(ns) (2.31) (2.97) (Hypothesis 5)
SAI 0.03 0.12 0.35 0.10 NA NA
(ns) (3.68) (6.30) (2.75) (Hypothesis 8)
Loyat 0.01 0.19 0.54 0.12 0.23 NA
(ns) (4.48) (8.98) (2.79) (6.43) (Hypothesis 9)
Loybeh 0.02 0.10 0.29 0.12 0.37 0.11
(ns) (3.80) (6.18) (3.34) (7.18) (2.71)
B. Completely Standardized Total Effects ( t-values in parentheses)
SQfac SQ
int Sat
at Trust Sat SAI Loy
at
Trust 0.11 0.23 0.56 NA NA NA NA
(ns) (3.57) (7.65)
Sat 0.06 0.24 0.73 0.21 NA NA NA
(ns) (4.17) (10.64) (2.94)
SAI 0.03 0.12 0.35 0.10 0.48 NA NA(ns) (3.68) (6.30) (2.75) (7.42)
Loyat 0.01 0.19 0.54 0.32 0.59 0.48 NA
(ns) (4.48) (8.98) (4.52) (8.32) (8.09)
Loybeh 0.02 0.10 0.29 0.12 0.37 0.58 0.24
(ns) (3.80) (6.18) (3.34) (7.18) (7.71) (2.83)
NOTE: SQfac = facility service quality; SQint = interactive service quality; Satat = attribute satisfaction; Trust = perceived trust; Sat = overall satisfac-tion; SAI = specific asset investment; Loyat = attitudinal loyalty; Loybeh = behavioral loyalty.
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Supporting Ganesh et al.s (2000) assertion, interactive
service quality (it is termed people factorin their study)
has a stronger impact on overall satisfaction than facility
service quality dimensions. The encounters with service
personnel appear to be key to a consumers overall satis-
faction and, as our results show, to overall trust. Facility
service quality, however, had no impact in our model.
One possible reason is that almost all sales counters ofpremium cosmetics brands are modern and appealing,
and hence facilities do not differentiate competitors. In
other contexts with higher variance in facilities (such as
restaurants), facility service quality may indeed be a
driver for trust and satisfaction (or at least a driver for
reducing dissatisfaction). This, as well as the impact of
other types of service quality, remains for future research.
Neither attribute satisfaction nor service quality had
any direct effects on attitudinal or behavioral loyalty;
rather, these effects were indirectthrough trust, overall
satisfaction, and asset specificity. However, these results
do not suggest that marketers can ignore the details of
attribute satisfaction and interactive service quality by
focusing only on overall satisfaction or other intermedi-
ate links in the chain. The antecedents matter; for exam-
ple, these antecedents effects on perceived trust show
marketers how to build trustworthy images to reduce
moral hazard issues in the exchange relationship.
The Effects of Trust, Satisfaction,and Asset Specificity (SAI)
We found that trust affects overall satisfaction. As
in Singh and Sirdeshmukh (2000), we defined trust as
a cognitive construct and hence argued that it precedesoverall satisfaction, which we defined as affective
(following Oliver 1999). Our arguments were rooted in
the transaction costs associated with adverse selection
and moral hazard, concepts originating in the B2B litera-
ture and applicable to the high-involvement, high-service
product markets considered in this research. Nonetheless,
in low-involvement contexts where an affective-cognitive
causal ordering may dominate, trust may be the conse-
quence of overall satisfaction. Causal ordering contin-
gent on high- versus low-involvement context is an area
for future research on trust in consumer markets.
Our results show that both trust and overall satisfaction
affect attitudinal loyalty. The effect of overall satisfactionon attitudinal loyalty is not new, of course. However, our
research demonstrates the pervasive effects of trust,
effects that are both direct on attitudinal loyalty and indi-
rect through satisfaction, resulting in a total standardized
effect of .32 on attitudinal loyalty. Marketers of high-
involvement, high-service premium products should not
neglect building trust, whose domain was defined in this
research as the total experience with the brand/company.
We did not define trust on an attribute basis, such as in
trust that the antiwrinkle skin cream will reduce under-eye
wrinkles. Although standard in the trust literature, our
approach may be a limitation in understanding the exact
nature of trust because only some attributes may be cre-
dence attributes. A more disaggregated conceptualization
and measurement of trust may be theoretically and man-
agerially valuable.
Next, neither trust nor overall satisfaction directlyaffects behavioral loyalty; rather, these effects are signif-
icant but indirectthrough attitudinal loyalty (which pre-
cedes behavioral loyalty in our high-involvement context,
exactly as the traditional chains ordering suggests).
Stated differently, our results show that attitudinal versus
behavioral loyalties have different antecedent chains.
This means that it is not advisable to focus exclusively on
attitudinal loyalty constructs for managerial or theoreti-
cal insight.
Our investigation of SAI yielded several interesting
insights. This study developed a SAI scale for high-end
cosmetics, but the scales direct usefulness is limited to
cosmetics products. Our approach to SAI scale develop-
ment may be generalizable, however; highlighting what
the consumer would lose, it appeared to be better received
than the common B2B approach. This approach to SAI
requires investigation using other high-involvement, high-
service product markets. Overall, each product category
may involve unique SAIs, but it may be possible to develop
a general SAI scale at a higher conceptual level if core
commonalities can be found. Brand knowledge SAI could
be such a core commonality.
We found that SAI is influenced by overall satisfac-
tion and that SAI has separate direct effects on both atti-
tudinal loyalty and behavioral loyalty. Thus, the totaleffect of SAI on behavioral loyalty is direct andindirect
through attitudinal loyalty. This total standardized effect
was 0.58, as compared to satisfactions total effect of
0.37 (which was second in rank order); this result may be
of some importance to marketers in similar product
market contexts. Satisfactions total effect on behavioral
loyalty operates through SAI and attitudinal loyalty.
Marketers encourage consumer SAI to increase
switching cost and thus enhance consumers attitudinal
and behavioral loyalty; our results support such a strategy.
However, we focused only on consumerSAI. Although
consumer SAI is important in causing dependency and
discouraging switching behavior, mutual specific invest-ment and mutual dependency may further increase the
stability of the relationship. A supplier making idiosyn-
cratic investments is unlikely to engage in opportunistic
behavior, and the willingness to make such investments
provides signals that providers are sincere (Ganesan
1994; Singh and Sirdeshmukh 2000). Therefore, the
investment of idiosyncratic assets by the supplier can
induce SAI on the part of the consumer. These kinds of
mutual idiosyncratic investments have been explored in
Chiou, Droge / CONSUMER SATISFACTION 623
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B2B marketing (e.g., Jap and Ganesan 2000; Kerin,
Varadarajan, and Peterson 1992), and it is important that
they be researched in the consumer field.
This studys approach shares similarities with that of
relationship marketing studies. Research in relationship
marketing explains the benefits of engaging in long-term
buyer-seller relationships (cf. Gwinner, Gremler, and Bitner
1998), such as confidence, social, and special-treatmentbenefits. All these benefits will increase consumers loyalty
toward a provider. The confidence benefit is similar to the
trust benefit in this research, while the social- and special-
treatment benefits are encompassed in the concept of SAI.
Asset specificity emphasizes the nonredeployable invest-
ment in the relationship made by one party, but consumers
will reduce switching behavior only if they perceive that the
benefits received are specific to the supplier. For example,
if social-treatment benefits are from a particular employee,
then consumers may follow if that employee switches to
another company; if the special-treatment benefits can be
copied, then consumers may lose less by switching.
The Effects of Product-Market Expertise
This study confirmed that in high-involvement, high-
service product markets, consumers with more product-
market expertise are less behaviorally loyal (i.e., this
relationship was inverse). Loyalty research should control
for this effect. Note that expertise was defined in relation
to the product market as a whole and not in relation to the
brand specifically (i.e., this is not brand knowledge). For
some product markets that are considered inherently of
low involvement overall, high expertise may signal high-
involvement processing on the part of some consumers: inthis case, product-market expertise could be a moderator
of model relationships. Finally, product-market expertise
had no impact on attitudinal loyalty, which further
demonstrates that attitudinal versus behavioral loyalty are
different constructs with different antecedents.
Our result is consonant with the results by Mittal and
Kamakura (2001) and may actually provide an explana-
tion for their results. The finding of a negative relationship
also supports the emphasis of various consumer and
governmental organizations on informing and educating
the consumer: informed consumers are less behaviorally
loyal and thus as a group may encourage competition,
thereby improving quality and reducing prices over the
long term. However, the finding calls into question
whether it is a good idea for marketers to engage in
informing and educating, such as producing compar-ative print ads with extensive direct comparisons to com-
petitors on numerous product attributes. If the marketer is
tracking only attitudinal loyalty measures, the possible
reduction in behavioral loyalty may be under the radar.
Conclusion
Trust and consumer satisfaction are the seeds for
behavioral loyalty not only because they increase attitu-
dinal loyalty in a high-involvement, high-service product
market but also because they directly or indirectly per-
suade the consumer to invest in specific assets. Marketers
should not count on satisfaction alone to induce consumers
to invest in specific assets: they should try to devise
creative marketing programs that permit and encourage
consumers to make SAIs. Loyalty programs and properly
trained personnel are but two examples. In the future,
database assets may prove critical. Marketers can build up
a consumer database to accumulate data on past usage, pur-
chases, complaining behaviors, and returns. For example,
Amazon.com uses database information to determine
which product and purchase information should go to
which consumer; if a consumer switches, other online
sellers will not have this consumer knowledge. Similarly,
eBays credit system is a vehicle to increase asset speci-ficity: the greater the number of favorable transaction
records a seller or a buyer accumulates, the higher eBays
credit ranking (which is lost upon switching). Database
information increases the marketers ability to improve
the exchange, enables the marketer to avoid unprofitable
exchanges (e.g., limiting excessive returns), and the data-
base itself can be an idiosyncratic asset for the marketer
and consumer alike.
624 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE FALL 2006
APPENDIX
Measurement
Construct Item Scale Measurea
Facility Service SQfac
1 1-5 XYZ brands sales counter facilities are visually appealing
Quality ( = .62) SQfac2 1-5 XYZ brand has modern-looking equipment
Interactive Service SQint1 1-5 When you have a problem, XYZ brand shows a sincere interest in solving it
Quality ( = .91) SQint2 1-5 XYZ brand performs the service right the first time
SQint3 1-5 Salesclerks at XYZ sales counters give you prompt service
SQint4 1-5 Salesclerks at XYZ sales counters are never too busy to respond to your requests
SQint5 1-5 Salesclerks at XYZ sales counters are consistently courteous
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Chiou, Droge / CONSUMER SATISFACTION 625
APPENDIX (continued)
Construct Item Scale Measurea
SQint6 1-5 Salesclerks at XYZ sales counters can answer your questions
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Attribute Satat 1 1-5 Please rate your satisfaction level on product quality
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PMexp3 1-5 I have broad exposure to cosmetic product-related information
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Perceived Trust Trust1 1-5 XYZ brand is very honest
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SAI4 1-5 If I switch to other cosmetics brands, I have to spend a lot of t ime understanding
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SAI5 1-5 If I switch to other cosmetics brands, I will lose social relat ionships with XYZ
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SAI6 1-5 I dont think that other cosmetic brands are as congruent with my image as XYZ cosmetics
Attitudinal Loyat1 1-5 If I had to do it over again, I would choose XYZ brandLoyalty ( = .77) Loyat2 1-5 I try to use XYZ brand because it is the best choice for me
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