Journal of the Academy of Marketing Science-2006-Chiou-613-27

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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

614 JOURNAL OF THE ACADEMY OF MARKETING SCIENCE FALL 2006



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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.



5/16

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,




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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




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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




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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


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,


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.



11/16

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.


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.



12/16

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




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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.


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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APPENDIX (continued)

Construct Item Scale Measurea

SQint6 1-5 Salesclerks at XYZ sales counters can answer your questions

SQint7 1-5 Salesclerks at XYZ sales counters understand your specific needs

SQint8 1-5 XYZ brand shows a sincere interest in solving problems

Attribute Satat 1 1-5 Please rate your satisfaction level on product quality

Satisfaction ( = .80) Satat 2 1-5 Please rate your satisfaction level on product effectiveness

Satat 3 1-5 Please rate your satisfaction level on product variety

Satat 4 1-5 Please rate your satisfaction level on product packages

Satat 5 1-5 Please rate your satisfaction level on product odor

Satat 6 1-5 Please rate your satisfaction level on overall quality

Product Market PMexp1 1-5 Compared to average people, I know cosmetics well

Expertise ( = .92) PMexp2 1-5 Compared to average people, I thoroughly understand how to purchase cosmetic products

PMexp3 1-5 I have broad exposure to cosmetic product-related information

PMexp4 1-5 I know cosmetic products thoroughly

PMexp5 1-5 I know all kinds of new information regarding cosmetic products

Perceived Trust Trust1 1-5 XYZ brand is very honest

( = .89) Trust2 1-5 XYZ brand is very reliable

Trust3 1-5 XYZ brand is responsible

Trust4 1-5 XYZ brand understands consumers

Trust5 1-5 XYZ brand is always professional

Trust6 1-5 XYZ brand acts with good intentionsOverall Satisfaction Sat1 1-5 I am happy about my decision to choose XYZ brand

( = .94) Sat2 1-5 I believe I did the right thing when I used XYZ brand

Sat3 1-5 Overall, I am satisfied with the decision to use XYZ brand

Specific Asset SAI1 1-5 If I switch to other cosmetics brands, I have to spend a lot of time explaining my

Investments (SAIs) cosmetics usage behavior to the salesclerks

( = .90) SAI2 1-5 Cosmetic products from other brands may not fit my skin well because I believe that

my skin is used to XYZ brand

SAI3 1-5 If I switch to other cosmetics brands, I have to spend a lot of t ime understanding

how to use their products

SAI4 1-5 If I switch to other cosmetics brands, I have to spend a lot of t ime understanding

their product lines

SAI5 1-5 If I switch to other cosmetics brands, I will lose social relat ionships with XYZ

salesclerks and have to start new ones with new brands

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

Loyat3 1-5 I consider myself to be a loyal patron of XYZ brand

Loyat4 1-5 To me, XYZ brand is the same as other cosmetics brands (reversed)

Behavioral Loybeh1 (b) If you had $100 to buy cosmetics at this moment, how much would you spend

Loyalty ( = .84) on XYZ cosmetic products? (Scale was calculated by monetary proportion devoted

to XYZ cosmetics brand.)

Loybeh2 (b) Times purchasing XYZ cosmetics in the past 12 months (divided by times purchasing

any cosmetic products)

Loybeh3 (b) $ amount devoted to XYZ cosmetics in the past 12 months (divided by $ amount

devoted to any cosmetic products)

a. XYZ brand substitutes for the real name.b. These proportions were calculated by the researchers from behaviors reported in response to open-ended questions.



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