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WHO OWNS THE CUSTOMER? DISENTANGLINGCUSTOMER LOYALTY IN INDIRECT DISTRIBUTION

CHANNELS

ANDREAS EGGERTUniversity of Paderborn

JRG HENSELERRadboud University Nijmegen

SABINE HOLLMANNUniversity of Paderborn

Increasing levels of vertical competition make the possession of endcustomers loyalty an issue of major concern for brand manufacturers anddistributors alike. While we can rely on a solid body of knowledge on thedrivers and outcomes of customer loyalty, empirical insights into theinterplay between different forms of customer loyalty in channels of distri-bution remain limited, ambiguous and void of theoretical explanations.Based on a nationwide survey of customers in a detail-intensive industryin the Netherlands and drawing on information integration theory andbalance theory, this research identifies a positive and unidirectional spill-over effect from customers brand loyalty to distributor loyalty. Hence,distributors can free ride on a brand manufacturers investments incustomer loyalty. From the brand manufacturers perspective, the loyaltyspillover can have positive or negative consequences, depending on thelevel of vertical competition among channel members. While the spilloverincreases end customers loyalty toward the channel, it decreases thebrand manufacturers odds of keeping end customers when it comes tothe contest between a brand manufacturer and its distributor.

Keywords: distributor loyalty; brand loyalty; loyalty spillover; vertical competition;business-to-business marketing; survey methods; structural equation modeling

INTRODUCTIONIn the past decade, the level of vertical competition

in marketing channels generally has risen (Webster2000; Ailawadi 2001; Draganska and Klapper 2007).

Vertical competition depicts the competition between

channel members at different levels in the channel

(Rosenbloom 2004, p. 82). While brand manufactur-

ers and distributors must collaborate to deliver supe-

rior value to their customers (Anderson, Narus and

Narayandas 2009; Ganesan, George, Jap, Palmatier

and Weitz 2009), they also compete for their own

shares of the value pie (Jap and Ganesan 2000; Dong,

Shankar and Dresner 2007) and, ultimately, for end

customers loyalty (Grewal, Levy and Lehmann 2004).

For example, distributors increasingly use delisting as

a competitive weapon to improve their own margins,

at the expense of suppliers (Sloot and Verhoef 2008),

and a growing number of distributors have introducedtheir own private-label brands to achieve a tighter

connection to their customers (ACNielsen 2005; Chan

Choi and Coughlan 2006). Brand manufacturers in

turn respond with direct sales that enable them to

bypass their former channel partners (Yadav and

Varadarajan 2005). Obviously, the ownership of end

customers loyalty becomes even more important

when channel partners engage in more intense vertical

competition.

The marketing literature provides a sound and grow-

ing body of knowledge on customer loyalty, its

nature, drivers and consequences. In empirical studies,

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customer loyalty is typically conceptualized and mea-

sured with regard to a single object, such as loyalty

toward the selling firm (Innis and La Londe 1994;

Wallenburg 2009) or a branded product (Chaudhuri

and Holbrook 2001). As supply chain management

deals with the coordination of multiple business

functions and entities (Giunipero, Hooker, Joseph-Matthews, Yoon and Brudvig 2008), researchers have

begun to look beyond the management of dyadic rela-

tionships and focus on the supply chain network

(Galaskiewicz 2011). In indirect distribution channels,

customers may develop distinct attitudes toward

brands versus distributors (Bloemer and Lemmink

1992). A triadic perspective impels managers to ques-

tion which form of customer loyalty to manage (Choi,

Dooley and Rungtusanatham 2001). To date, how-

ever, the potential interplay between brand and dis-

tributor loyalty is still a blind spot in supply chain

management research. Against this background, thegoal of this study is to understand whether and how

brand and distributor loyalty impact each other in

indirect distribution channels.

From a managerial standpoint, the ownership of

end customers loyalty becomes particularly important

when manufacturers and distributors engage in verti-

cal competition. Strong brand loyalty enables brand

manufacturers to prevent or retaliate against distribu-

tors competitive inroads. Distributor loyalty, in turn,

reduces brand manufacturers power base and

provides greater degrees of freedom to downstream

channel partners. Therefore, brand manufacturers and

distributors alike require a more fine-grained under-

standing of who benefits from investments in

customer loyalty to define a companys strategic

approach in the ongoing struggle for end customers

minds and hearts.

From a research perspective, we must avoid con-

founding the different forms of customer loyalty, and

a clear definition of the scope of these theoretical con-

structs is fundamental for further progress in the disci-

pline (Scheer 2008). In addition to disentangling

customers brand loyalty from their distributor loyalty,

we recognize the importance of their interaction to

channel partners. When they invest substantialresources into building and strengthening bonds with

end customers, brand manufacturers need to know

who will profit from their efforts, and distributors

wonder whether and to what extent they can harvest

their relationship-building investments. It is not suffi-

cient to disentangle different objects of customers loy-

alty; we also need a sound understanding of their

patterns of interaction. Yet, few studies (Bloemer and

Lemmink 1992; Corstjens and Lal 2000; Huber and

Herrmann 2001; Verhoef, Langerak and Donkers

2007; Hansen and Singh 2008) pursue the interplay

of various objects of customer loyalty. Among them,

theoretical foundations remain surprisingly underde-

veloped and empirical evidence is presented for all

conceivable patterns of interaction. The relationship

between customers brand loyalty and distributor

loyalty is assumed to be either (1) not existent,

(2) simple, or (3) of reciprocal nature. To date, we are

missing a sound understanding of the mechanismsthat link brand and distributor loyalty.

To overcome ambiguity with regard to the interplay


loyalty, we focus on two key contributions to supply

chain management literature. First, we develop and

test a parsimonious theoretical framework to explain

the spillover effects of customer loyalty in indirect

distribution channels. Second, we apply a structural

equation modeling approach based on instrumental

variables to identify the dominant interaction patterns


loyalty on an empirical basis.We structure the remainder of this manuscript as

follows: First, we present a literature review that high-

lights the conceptual weaknesses and empirical ambi-

guity of existing studies on customers brand and

distributor loyalty. Second, we introduce information

integration theory and balance theory as the founda-

tion on which we base our hypotheses development.

Third, we document our empirical study and its

results. Fourth, we discuss our results and their mana-

gerial implications and offer some suggestions for

further research.

LITERATURE REVIEWCustomer loyalty research has a long history in the

marketing and supply chain literature. Customer

loyalty can be defined from different perspectives

focusing on its attitudinal and behavioral facets. For

our research, we adopt the definition suggested by

Sirdeshmukh, Singh and Sabol (2002). They charac-

terize customer loyalty as an intention to perform a

diverse set of behaviors that signal a motivation to

maintain a relationship (Sirdeshmukh et al. 2002, p.

20). Consequently, we define distributor loyalty as a

customers intention to perform a diverse set ofbehaviors that signal a motivation to maintain a rela-

tionship with a distributor, whereas brand loyalty rep-

resents a customers intention to perform a diverse set

of behaviors that signal a motivation to maintain a

relationship with the brand.

Early studies dealt predominantly with brand loyalty

(Copeland 1923; Jacoby and Chestnut 1978; Dick

and Basu 1994; Oliver 1999), but since the develop-

ment of a relationship-oriented perspective, customer

loyalty toward the selling firm has become a key focus

of interest (e.g., Innis and La Londe 1994; Wallenburg

2009). Although loyalty research thus is generally

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well-established, there are still some blind spots. Most

studies are limited to a single form of loyalty, directed

toward a single object, so they offer little insight

into the interplay among different forms of customer

loyalty. Palmatier, Scheer and Steenkamp (2007b)

caution, for example, against intermingling loyalty

toward the selling firm with salesperson-owned loy-alty. In Table 1, we summarize existing research that

investigates customers brand and distributor loyalty.

As Table 1 reveals, we can find empirical evidence

for any possible relationship between brand and dis-

tributor loyalty. Some studies find them empirically

unrelated (Frank, Massy and Lodahl 1969; Mittal,

Kumar and Tsiros 1999; Homburg and Giering 2001),

whereas others identify an association between brand

and distributor loyalty but do not reveal the direction

of this link (Cunningham 1961; Tranberg and Hansen

1985). A third group of studies assumes that one form

of customer loyalty drives the other, although again,the direction of the link is by no means clear. Some

studies imply that brand loyalty leads to distributor

loyalty (Corstjens and Lal 2000; Huber and Herrmann

2001; Sudhir and Talukdar 2004; Hansen and Singh

2008), whereas others suggest a reverse causal rela-

tionship (Carman 1970; Bloemer and Lemmink 1992;

Ewing 2000; Ailawadi, Neslin and Gedenk 2001;

Bonfrer and Chintagunta 2004; Verhoef et al. 2007).

We even find research support for a reciprocal rela-

tionship between both forms of customer loyalty

(Ailawadi, Pauwels and Steenkamp 2008).

In addition to this empirical ambiguity, existing stud-

ies suffer from two conceptual weaknesses. First, their

theoretical foundations remain underdeveloped. All

the studies in Table 1 rely on conventional wisdom or

ad hoc rationales to justify the assumed relationship

between brand and distributor loyalty. Second, as Ja-

coby and Chestnut (1978, p. 82) state, any form oftrue loyalty requires the opportunity of being dis-

loyal, but few studies documented in Table 1 recog-

nize such an environment. For example, empirical

research in the European automotive industry features

car dealers that remained locked in to one manufac-

turers brand (Bloemer and Lemmink 1992; Homburg

and Giering 2001; Huber and Herrmann 2001; Verhoef

et al. 2007). In studies of private labels, the store

brands are only listed by that store (Cunningham

1961; Carman 1970; Tranberg and Hansen 1985; Cors-

tjens and Lal 2000; Bonfrer and Chintagunta 2004;

Sudhir and Talukdar 2004; Ailawadi et al. 2008; Han-sen and Singh 2008). In these empirical settings, the

identified relationship between brand loyalty and dis-

tributor loyalty may mirror an idiosyncratic market

structure rather than general spillover effects.

In summary, current insights into the interplay

between customers brand and distributor loyalty

remain limited. Against the background of these gaps,

we undertake an empirical study in an industrial con-

text that offers customers a variety of branddistribu-

tor combinations providing an unconstrained

opportunity of being disloyal.

TABLE 1

Empirical Evidence of the Interplay of Customer Loyalties

Discovered Relationship Study Context

Distributor loyalty and brandloyalty are unrelated

Frank et al. (1969) RetailingHomburg and Giering (2001) AutomobileMittal et al. (1999) Automobile

Distributor loyalty and brandloyalty covary

Cunningham (1961) Retailing/private labelsTranberg and Hansen (1985) Retailing/private labels

Brand loyalty leads todistributor loyalty

Corstjens and Lal (2000) Retailing/private labelsHansen and Singh (2008) Retailing/private labelsHuber and Herrmann (2001) AutomobileSudhir and Talukdar (2004) Retailing/private labels

Distributor loyalty leads tobrand loyalty

Ailawadi et al. (2001) Retailing/private labelsBloemer and Lemmink (1992) AutomobileBonfrer and Chintagunta (2004) Retailing/private labelsCarman (1970) Retailing/private labelsEwing (2000) AutomobileVerhoef et al. (2007) Automobile

Distributor loyalty leads tobrand loyalty and vice versa

Ailawadi et al. (2008) Retailing/private labels

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THEORETICAL FRAMEWORK ANDHYPOTHESES

Our unit of analysis is a professional customer who

currently purchases goods through an indirect distribu-

tion channel that contains a brand manufacturer and a

distributor. We analyze the customerdistributor

brand manufacturer triad from the customers point of

view. Customers consider two different objects, the dis-

tributor and the brand manufacturer, so we distinguish

distributor loyalty from brand loyalty. For this study,

we analyze the interdependence of these forms of cus-

tomer loyalty and investigate their impact on channel

switching. Building on previous research (Chen, John

and Narasimhan 2008), we conceptualize channel

switching as customers intention to switch to a differ-

ent distribution channel offered by the incumbent

brand manufacturer if the current distribution channel

is no longer available. We develop our conceptual

model in three steps: First, we formulate hypothesesabout the interdependence of distributor loyalty and

brand loyalty. Second, we theorize the joint impact of

the two forms of customer loyalty on channel switch-

ing. Third, we complement our model by including

control and instrumental variables.

To shed light on the potential interplay between

brand and distributor loyalty, we draw on informa-

tion integration theory (Anderson 1971, 1981). Infor-

mation integration theory has been frequently applied

in operations management, marketing channels and

supply chain research (e.g., Gaeth, Levin, Chakraborty

and Levin 1991; Pullman, Verma and Goodale 2001;Roggeveen, Bharadwaj and Hoyer 2007). It provides a

theoretical lens that explains how customers form

judgments when multiple information cues are avail-

able. According to information integration theory, cus-

tomers process multiple chunks of information in a

two-step process. First, all relevant cues are weighted

according to their relevance to the individual. Second,

the weighted information cues are summated to deter-

mine the individuals overall response to the evaluated

entity (Carlson and White 2008).

What are the relevant information cues when assess-

ing distributor loyalty? Louviere and Gaeth (1987,

p. 28) identify four high-level decision concepts thatlargely explain supplier choice decisions: product

assortment, price, quality and convenience. A recent

meta-analysis (Pan and Zinkhan 2006) suggests that

product assortment has the greatest impact on store

choice, and empirical research further demonstrates

that store loyalty depends on the properties of the

products and brands sold (Sirohi, McLaughlin and

Wittink 1998; Baker, Parasuraman, Grewal and Voss

2002; Amine and Gadenat 2003).

Against this background of empirical evidence, we

assume that customers attitudes (such as brand

loyalty) toward the products listed by a distributor

belong to the relevant information cues to form dis-

tributor loyalty judgments. As a relevant information

cue, brand loyalty will play a role in the subsequent

information integration process. More precisely,

higher levels of brand loyalty will contribute to dis-

tributor loyalty and we thus propose a positive spill-over effect from brand loyalty to distributor loyalty:

H1: Brand loyalty has a positive effect on distributorloyalty.

Based on information integration theory, one can

also build a case for the reversed spillover effect. If

customers perceive the distributor and its sales efforts

as a relevant part of the brand manufacturers total

market offering, they will weight and integrate distrib-

utor information cues when forming their brand

loyalty judgments. In particular, higher levels of dis-

tributor loyalty will lead to a more positive responseto the brand manufacturer. This direction of causality

has been proposed in several publications (Bloemer

and Lemmink 1992; Ailawadi et al. 2001; Verhoef

et al. 2007). To account for both directions of the

potential spillover effect, we model a reciprocal rela-

tionship between brand and distributor loyalty and

hypothesize that:

H2: Distributor loyalty has a positive effect on brandloyalty.

As long as a brand forms part of a distributors assort-

ment, customers can exhibit distributor loyalty and

brand loyalty simultaneously. Disintermediation inter-rupts this coexistence of the two forms of customer

loyalty. More specifically, if a brand manufacturer

implements a direct sales approach and abandons the

indirect distribution channel, customers may no longer

exhibit brand loyalty and distributor loyalty simulta-

neously. Instead, they must accept the direct sales

approach and stay loyal to the brand or switch to a

different brand and stay loyal to the distributor. For

some customers, disintermediation thus implies chan-

nel switching. Therefore, a channel switching scenario

constitutes an acid test for customer loyalty.

Relationship marketing theory explicates that thedecision to stay with a supplier is strongly influenced

by a customers loyalty toward the supplier (Dick and

Basu 1994; Morgan and Hunt 1994). If a brand can-

not be bought through the current channel, brand

loyalty makes it more likely that a customer will per-

form channel switching. Vice versa, distributor loyalty

causes a customer to refrain from customer channel

switching. Empirical research in retailing has shown

that distributor loyalty prevents customers from

switching a distributor and that brand loyalty leads to

a decrease in brand switching (Emmelhainz, Stock

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and Emmelhainz 1991; Campo, Gijsbrechts and Nisol

2000).

More recent research in relationship marketing

suggests that buyers look beyond the dyads they are

involved in when they decide upon channels (Wuyts,

Stremersch, Van den Bulte and Franses 2004), which

corroborates the need for a triadic perspective. Havingexplanatory power in triads, balance theory offers a

motivational theory of attitude change (Heider 1958)

and change in interpersonal relationships (Burnstein

1967). Balance theory implies people experience a

consistency motive that functions similar to a drive

toward psychological balance. Thus, people strive to

achieve balance in their attitudes and interpersonal

relationships. According to Eagly and Chaiken (1998,

p. 281), this theory offers the most systematic and

enduring approach to understanding attitudes rela-

tions. Balance theory has particularly demonstrated

its ability to explain important phenomena of supplychain management and marketing (Choi and Hartley

1996; Phillips, Liu and Costello 1998; Russell and

Stern 2006). A system of relationships is balanced if

the affect valences of all the relationships would result

in a positive product if they were multiplied together.

A triad is thus balanced if it contains an even number

of negative relationships.

In the context of indirect distribution channels, bal-

ance theory explains customers simultaneous attitudes

toward a brand and a distributor by depicting the

customer as being engaged in a triad of cognitive

elements that connect through positive or negative

relationships. The meaning of any link between two

parties A and B can be described manifestly, such as

A visits B, A buys B or A lists B. On the left-

hand side of Figure 1 (at time t0), the system of rela-

tionships includes a customer in a triad with a distrib-

utor and a brand. If the distributor carries the brand

in its assortment, the customer assumes a positive

relationship between the distributor and brand. In thebeginning, the customer exhibits loyalty to both the

distributor and the brand, and the system of relation-

ships is in a state of balance (Choi and Wu 2009)

(another possible state of balance would result if a

customer had neither brand loyalty nor distributor

loyalty).

In case of disintermediation, the relationship

between the brand and the distributor becomes nega-

tive (t1 in Figure 1), creating an imbalanced system

of relationships from the customers point of view.

According to balance theory, this customer wants to

restore a balanced state by changing one positiverelationship into a negative one (see the two differ-

ent options at t2 in Figure 1). Finally, the triad con-

sists of one positive and two negative relationships,

which enables the customer to regain psychological

balance.

Drawing on relationship marketing theory and

balance theory, we assume that customers channel

switching intention depends on the respective levels

of brand and distributor loyalty and propose the

following hypotheses:

H3: Brand loyalty has a positive effect on channel

switching.

BrandDistributor

+

Customer

+ +

t0

t2

BrandDistributor

-

Customer

+ +

BrandDistributor-

Customer

- +

BrandDistributor-

Customer

+ -

t1

dairtdecnalabdairtdecnalab imbalanced triad

FIGURE 1Balance Theory Applied to Evaluations of Indirect Distribution Channels

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H4: Distributor loyalty has a negative effect on chan-nel switching.

Next, we propose that H3 and H4 should not be

regarded in isolation, but as part of a customers thought

process. This means that customers do not exhibit chan-

nel switching by assessing the level of either their brandloyalty or their distributor loyalty in isolation. Instead,

they react according to the outcome of a comparison

between the two forms of loyalty. To develop this argu-

ment, we rely on another element of balance theory,

namely the principle of minimum effort.

Rebalancing relationship systems occurs according

to the principle of minimum effort (Rosenberg and

Abelson 1960). Individuals rebalance relationships

while keeping their systems unchanged as far as possi-

ble, which for instance implies altering the fewest

possible relationships (Burnstein 1967). Applied to

our context, the principle of minimum effort entails

that customers rebalance their cognitive system suchthat their levels of brand loyalty and distributor loy-

alty change as little as possible. If a customer needs to

adjust a loyal relationship to disloyalty, the mental

effort becomes larger the higher the current level of

loyalty. Customers can thus reduce their mental effort

if they diminish their loyalty toward the entity that

they have been less loyal to in comparison with the

other entity. If customers weight their brand loyalty

against their distributor loyalty, it is indispensable not

only to examine the effects of brand loyalty and distrib-

utor loyalty together but also to acknowledge that

customers draw a comparison between the two formsof loyalty. Dick and Basu (1994) emphasize that a

customers relative appraisal of objects is likely to

provide a stronger indication of repeat patronage than

their attitude toward a brand determined in isolation.

That is, whether a customer chooses to perform a

channel switch depends on his or her relative levels of

distributor loyalty and brand loyalty and the joint

effects of the two forms. Based on these arguments, we

expect that customers perform channel switching if their

brand loyalty exceeds their distributor loyalty, whereas

customers refrain from channel switching if their

distributor loyalty exceeds their brand loyalty. Hence:

H5: Channel switching is more likely to occur ifbrand loyalty exceeds distributor loyalty.

Finally, we include control and instrumental vari-

ables in our model. The extant marketing literature

suggests that the most important antecedent of cus-

tomer loyalty is customer satisfaction (Fornell 1992).

Not only does including customer satisfaction into

our conceptual model decrease the omitted variable

bias but it also helps control for potential recency

effects (Sirdeshmukh et al. 2002). We therefore

control for the direct effects of customers satisfaction

with the distributor and with the brand in terms of

their respective forms of loyalty. To allow for the

estimation of reciprocal paths between both forms of

loyalty, we added two instrumental variables (customers

overall number of distributors and customers overall

number of brands in the category) to our conceptual

model (Kenny 1979). Figure 2 shows our conceptual

model, which captures the interdependence of brand

loyalty and distributor loyalty and links both forms of

customer loyalty to channel switching.

EMPIRICAL STUDY

Study DesignTo test our set of hypotheses, we conducted a

nationwide survey in the medical instruments industry

in the Netherlands. More specifically, we surveyed

dental practices, selected dental burrs as our focal

product, and gathered information on customers rela-

tionships with their main distributor and main dental

burr manufacturer. This specific industrycountry

combination is an appropriate setting for research on

different forms of customer loyalty as the market

structure does not restrict customers in their choices,

in that all the brands are available at alternative dis-

tributors, and every distributor offers different brands.

The industry is characterized as detail intensive (Spi-

teri and Dion 2004), which indicates that distributors

explain and sell their products at the customers site;

thereby making geographical proximity an irrelevant

factor from a customer loyalty perspective. The focal

product, a dental burr, was substitutable and custom-

ers were not in a lock-in situation created by some

form of asset specificity. In our sample, customers

named 21 different primary suppliers and 15 different

primary brands, which strongly implies they have the

Customer

Brand

Relationship

Customer

Distributor

Relationship

Channel

Switching

Number of

Brands

Number of

Distributors

Brand

Loyalty

Distributor

Loyalty

Customer

Brand

Satisfaction

Customer

Distributor

Satisfaction

H3+

H4

H1+

H2+

FIGURE 2Conceptual Model

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means to be disloyal (Jacoby and Chestnut 1978).

Therefore, we are researching a situation of active

loyalty in which customers remain loyal to their

distributors or brands because they want to (Bloemer

and Kasper 1995).

The survey instrument consists of four parts. In the

first part, participants named their primary supplierwhen they purchased medical instruments, then

assessed their levels of distributor loyalty and satisfac-

tion. In the second part, these respondents named

their primary brand in a given product category and

indicated at which distributor they purchase this

brand. Next, they reported their levels of brand loyalty

and satisfaction. In the third part, we asked for infor-

mation about their channel switching intention.

Finally, the participants described themselves and

their companies.

We adapted existing scales to measure our constructs.

All measures employed a 7-point Likert-type scaleranging from strongly agree to strongly disagree

unless otherwise noted. We used three-item scales to

capture distributor loyalty and brand loyalty, borrow-

ing indicators from Chaudhuri and Holbrook (2001)

as well as Palmatier et al. (2007b). The indicators for

distributor satisfaction and brand satisfaction were

previously used by Cannon and Perreault (1999) and

Fornell, Johnson, Anderson, Jaesung and Bryant

(1996). Finally, we measured channel switching with a

seven-point semantic differential scale that consisted of

three questions presenting scenarios in which the

customer had to decide between staying loyal to the

distributor or to the brand (Verbeke, Farris and Thurik

1998; Sloot and Verhoef 2008). The scenario questions

had the anchors I would rather buy our primary burr

brand from a different supplier and I would rather

buy a different burr brand from our current supplier.

Together, these questions form a reflective multi-item

measure for our channel switching construct. We list all

item formulations in Appendix A.

We pretested the survey instrument with 10 poten-

tial respondents using a think-aloud technique, which

demonstrated that the respondents could distinguish

between the two loyalty objects while completing the

questionnaire. It also led to some minor changes inwording. As we adapted existing scales to our research

setting, we also conducted a quantitative pilot test

with 48 respondents. Reliability and exploratory factor

analysis demonstrated good psychometric properties

of the adapted scales. All items loaded on a single

factor, which explained more than 50 percent of the

variance, and Cronbachs alpha was consistently

above the 0.7 threshold (Nunnally 1978).

We sent the final survey packages to 1,982 dental

practices, which were randomly selected from a

database provided by a commercial list broker. The

survey packages contained a cover letter, addressed to

the purchasing decision maker of the company,

requesting that he or she fill out the survey and offer-

ing a summary report as an incentive. We identified

the purchasing decision maker for dental burrs by

means of individual telephone calls prior to sending

out the survey package in order to increase the quality

and quantity of responses. Three weeks later, we senta follow-up letter and a copy of the initial survey. This

procedure resulted in 500 responses (overall response

rate: 25.2 percent). To ensure a comparable perspec-

tive on the distribution channel, we selected 339

questionnaires for our empirical analysis that were

answered by companies that purchase their main

dental burr brand from their main distributor.

On average, the responding firms had been buying

from their primary supplier for 15.8 years

(SD = 9.1 years) and purchasing their primary brand

for 17.2 years (SD = 9.0 years). As these figures indi-

cate, our focal industry relies heavily on long-termcustomer relationships. In total, 89 percent of the

dental practices were run by one dentist, and 11 per-

cent by multiple dentists employing up to 30 assis-

tants. A total of 15.7 percent served up to 2,000, 61.1

percent up to 4,000, 15.9 percent up to 6,000 and 7.3

percent more than 6,000 patients. Among the respon-

dents, 79.6 percent were men, 16.2 percent were

women, and 4.1 percent did not specify a gender. In

total, 92.1 percent of the respondents had been work-

ing at their current practice for more than 5 years.

Comparisons between early (first third) and late

(last third) respondents on all constructs used in the

model show no statistically significant mean differ-

ences (p > 0.05), which indicates that nonresponse

bias is not a concern for our data (Armstrong and

Overton 1977).

Reliability and Validity of MeasurementWe assessed the multi-item scales in terms of their

reliability and validity. Cronbachs alpha values are

well above the threshold of 0.7 (Nunnally 1978),

indicating a satisfactory level of internal consistency

reliability. Since the wording of the scale items

distributor loyalty, brand loyalty, customer distributor

satisfaction and customer brand satisfaction

is rela-tively similar, Cronbachs alpha might be inflated.

This would be the case if the interitem correlations

were not purely caused by substantial covariation but

also by a commonly applied method, such as word-

ing. To exclude this possibility, in Appendix B we pro-

vide a sensitivity analysis illustrating how the model

estimates would have changed if the reliability had in

fact been smaller than reported above.

Using confirmatory factor analysis (CFA), we evalu-

ated additional psychometric properties of our

constructs. The scale properties are reported in Appen-

dix C. We estimated two confirmatory factor models:

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In addition to the standard model, we estimated a

model with an orthogonal general factor capturing

(and thereby controlling) potential common method

variance (Podsakoff, MacKenzie, Lee and Podsakoff

2003). As Table 2 shows, both CFA models yield an

acceptable and relatively similar model fit. However,

the majority of fit measures are in favor of the modelcontaining the general factor (except for the parsi-

mony-adjusted goodness-of-fit). Moreover, the fit of

the CFA Model 2 is significantly better than the fit of

the standard CFA Model 1. Thus, our indicators share a

significant amount of common variance, which poten-

tially (but not necessarily) signifies common method

variance. However, the common variance accounts for

only 13.8 percent of the total variance in the data,

which is clearly below the average of comparable stud-

ies (Lance, Dawson, Birkelbach and Hoffman 2010).

For both CFA models, the standardized factor loadings

and the average variance extracted (AVE) indicate highlevels of indicator reliability and convergent validity.

For one out of the 38 error correlations, a modification

index slightly larger than 10 was observed. Since un-

constraining this error variance would alter the error

correlation between a brand satisfaction and a brand

loyalty indicator by only 0.077, and since both indi-

cators formed part of well-established scales, we did

not alter the measurement model. Apart from the men-

tioned error correlation, the modification indices did

not suggest dropping any particular variable (Schreiber,

Nora, Stage, Barlow and King 2006). To examine discri-

minant validity, we used Fornell and Larckers (1981)

criterion; the smallest square root of an AVE exceeds

the absolute correlation of every pair of our variables.

The construct correlations and descriptive statistics are

reported in Table 3, which is based on the results of

both CFA models. In addition, we ran a series of nested

CFA model comparisons in which we constrained the

covariance between each pair of constructs to one

(Anderson and Gerbing 1988; Bagozzi and Yi 1988).

As the chi-square difference was significant for all the

pairs of constructs (Dv2 > 5.8 atDdf= 1), we had fur-ther support for discriminant validity among the con-

structs. This support for discriminant validity is

particularly important in our study context, because it

empirically confirms that customers distinguish

between brand and distributor loyalty. Overall, we can

conclude that our measurement models exhibit accept-

able levels of reliability and validity.

RESULTS AND DISCUSSION

Results

We employed covariance-based structural equationmodeling (AMOS 19) and used maximum likelihood

as the estimation method for testing our hypotheses.

First, we use the implied variancecovariance matrix

resulting from the first CFA model. To model the reci-

procal path between customers brand and distributor

loyalty, we adopted an approach suggested by

Sirdeshmukh et al. (2002) and refined it for use in a

structural equation modeling setting (Kenny, Kashy

and Bolger 1998). Two measures were taken to ensure

correct model estimation. First, to address the danger

of nonunique parameters when modeling reciprocal

paths in cross-sectional research, we used instrumental

variables (Kenny 1979). Second, we allowed the

disturbances of the reciprocally related constructs to

covary (Wong and Law 1999).

TABLE 2

Results of the Confirmatory Factor Analyses

Statistic CFA Model 1 CFA Model 2No General Factor General Factor Affecting All Indicators Equally

v2 216.079 206.408

df 100.000 99.000p-value 0.000 0.000GFI 0.931 0.934AGFI 0.894 0.898PGFI 0.608 0.604NFI 0.943 0.946NNFI 0.957 0.960IFI 0.969 0.971CFI 0.968 0.971AIC 322.079 314.408CAIC 577.857 575.012

Comparison: Dv2 = 9.671, Ddf= 1, p = 0.002

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TABLE3

DescriptiveStatisticsand

ConstructCorrelations

Construct

Mean

SD

ConstructCorrelation

s(t-Values)

1

2

3

4

5

6

7

1

Channel

switching

4.2

0

1.8

4

0.7

7

0.3

6

(7.0

8)

0.1

4

(2.6

0)

0.2

7

(5.1

5)

0.0

2

(0.3

7)

0.1

6

(2.9

8)

0.1

6

(2.9

8)

0.7

9

2

Distributor

loyalty

5.8

3

1.0

1

0.2

0(3.7

5)

0.7

3

0.1

3

(2.4

1)

0.6

5

(15.7

0)

0.1

2

(2.2

2)

0.4

0

(8.0

1)

0.3

0

(5.7

7)

0.8

2

3

Brandloyalty

5.2

4

1.1

8

0.2

3(4.3

4)

0.3

0(5.7

7)

0.6

8

0.0

4

(0.7

3)

0.5

9

(13.4

1)

0.3

0

(5.7

7)

0.2

6

(4.9

4)

0.7

5

4

Customer

distributor

satisfaction

5.8

1

0.9

1

0.0

9(1.6

6)

0.7

3(19.

71)

0.2

0

(3.7

5)

0.7

7

0.0

1

(0.1

8)

0.3

5

(6.8

6)

0.2

1

(3.9

4)

0.9

0

5

Customer

brand

satisfaction

5.8

4

0.8

6

0.1

1

(2.0

3)

0.3

2(6.2

0)

0.6

8(17.0

3)

0.2

8

(5.3

5)

0.8

0

0.2

8

(5.3

5)

0.1

6

(2.9

8)

0.9

3

6

Numberof

distributors

3.3

6

1.6

3

0.2

1(3.9

4)

0.2

4(4.5

4)

0.1

5

(2.7

9)

0.1

5

(2.7

9)

0.1

0

(1.8

5)

0.9

6

0.1

7

(3.1

7)

1.0

0

7

Numberof

brands

3.1

3

1.3

1

0.2

2(4.1

4)

0.1

3(2.4

1)

0.1

0

(1.8

5)

0.0

2

(0.3

7)

0.0

1

(0.1

8)

0.2

3(4.3

4)

0.9

4

1.0

0

Numbersbelow

thediagonalre

presentconstructcorrelationswhennogeneralfactorismodeled

;italicnumbersintheuppertrianglerepresent

constructcorrelationswhen

a

generalfactorisincluded

in

the

model.Bold

numberson

thed

iagonalshow

the

square

rooto

fthe

average

varianceextracted.

Thet-values

areprintedinparantheses.

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To explore H1H4, we estimate our conceptual model

and find a chi-square fitting function with a value of

10.22 at 4 degrees of freedom (p = 0.037). The relative

fit indices (comparative fit index = 0.990; normed fit

index= 0.984, non-normed fit index= 0.948, incre-

mental fit index = 0.990) and the absolute indicators

of fit (goodness-of-fit index = 0.991, standardized rootmean residual = 0.021, root mean squared error of

approximation = 0.068) show that the proposed

model allows for an adequate representation of our

empirical data. In particular, the stability index value of

0.005 suggests a robust estimation of the reciprocal

paths (Bentler and Freeman 1983; Kaplan, Harik and

Hotchkiss 2001).

Accounting for different constellations of the links

between customers brand and distributor loyalty, we

find a dominant link, such that brand loyalty has a

positive effect on distributor loyalty. Although we

allow for a reciprocal effect, distributor loyalty has nosignificant influence on brand loyalty. Thus, we can

confirm H1 but find no support for H2.

As hypothesized, distributor and brand loyalty have

opposite effects on channel switching intention. Brand

loyalty positively affects it (path coefficient esti-

mate = 0.35), whereas distributor loyalty negatively

influences channel switching intentions (path coeffi-

cient estimate = 0.24), thereby supporting H3 and

H4.

Our model explains 19 percent of the observed

variance in channel switching. This modest amount of

variance explanation is not surprising given the wealth

of situational variables that might impact channel

switching (Dick and Basu 1994). As this research

focuses on explaining the interdependence of distribu-

tor loyalty, brand loyalty and channel switching rather

than predicting channel switching, significant path

parameters are more important than R2 values forassessing our results. Parameter estimates, correspond-

ing levels of significance and R2 values are reported in

Table 4. The rightmost column of Table 4 shows the

results if the implied variancecovariance matrix result-

ing from the second CFA model (including a general

factor) was used. Obviously, all hypothesis tests remain

unaffected. Consequently, the findings of our study

remain valid even if the construct measurement was

affected by some common method variance.

Before testing the remaining hypothesis, H5, we

transform it so that it is more accessible for conven-

tional statistical tests. The following equation is amathematical notation for H5 and expresses that

channel switching depends on how brand loyalty and

distributor loyalty are perceived:

channel switching b

brandloyalty distributor loyalty f

b brandloyalty|fflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflffl}

H3

b distributor loyalty|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}

H4

f1

In Equation (1), b represents the strength of the

effect of the difference between brand and distributor

TABLE 4

Parameter Estimates

Dependent Variable Independent Variable Standardized Estimates Result

Without GeneralFactor

With GeneralFactor

Distributor loyalty R2 = 0.57 R2 = 0.46Brand loyalty 0.20*** 0.18** H1 supportedCustomer distributor

satisfaction0.68*** 0.61***

Number of distributors 0.11**

0.14**

Brand loyalty R2 = 0.48 R2 = 0.37Distributor loyalty 0.03 n.s. 0.08 n.s. H2 not supportedCustomer brand

satisfaction0.67*** 0.57***

Number of brands 0.12** 0.18***

Channel switching R2 = 0.19 R2 = 0.17Brand loyalty 0.35*** 0.23*** H3 supported

H4 supportedDistributor loyalty 0.24*** 0.34***

Number of distributors 0.16** 0.06 n.s.Number of brands 0.19*** 0.10

p < 0.10; **p < 0.01; ***p < 0.001; n.s. not significant.

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loyalty when everything else remains equal. The dis-

turbance term stands for the proportion of variance

in channel switching that cannot be explained by this

difference. Essentially, Equation (1) states that the

coefficients of brand loyalty (hypothesized in H3) and

distributor loyalty (hypothesized in H4) are equal in

absolute magnitude. An alternative way of expressingH5 is thus: The effects of distributor loyalty and

brand loyalty on channel switching are equal in abso-

lute magnitude but differ with respect to their sign.

To test whether the coefficients of the effects of

brand loyalty and distributor loyalty on channel

switching differ in absolute magnitude, we conduct a

Lagrange multiplier test. The test shows that a model

that constrains the two effects so that they sum up to

zero (b3 + b4 = 0) does not differ significantly from

an unconstrained model in terms of fit (Dv2 = 2.93 at

Ddf= 1), which means that H5 cannot be rejected.

Put together, these empirical results support the the-ory that channel switching depends on the difference

between brand loyalty and distributor loyalty.

Finally, we assess the indirect effect of brand loyalty

on channel switching. The standardized coefficient of

the indirect effect is 0.046. According to Preacher

and Hayes (2008), statistical inference of indirect

effects should be based on bootstrapping (Efron and

Tibshirani 1993) in order to cope with the non-

normal distribution underlying indirect effects. We

performed bootstrapping with 1,000 bootstrap sam-

ples and obtained a bias-corrected confidence interval

of the indirect effect of [0.091; 0.019]. The indirect

effect is significantly different from zero at p = 0.001.

DiscussionIn the modern competitive environment, channel

partners often need to collaborate to build strong

bonds with end customers (Anderson et al. 2009;

Ganesan et al. 2009), even as they compete for cus-

tomers loyalty (Grewal et al. 2004). When it comes

to the contest between a brand manufacturer and its

distributor, the channel partner that primarily owns

customers loyalty may reap most of the benefits of

their common relationship-building efforts. Brand

manufacturers may establish direct sales, and distribu-tors can develop private-label brands; in these settings,

understanding the interactions between brand loyalty

and distributor loyalty has great significance for chan-

nel partners facing increasing vertical competition. To

date, however, theoretical insights into the interplay

between these forms of customer loyalty have been

surprisingly limited, and the empirical results have

remained contradictory.

Against this background, our research provides several

important insights. First, it demonstrates that custom-

ers brand and distributor loyalty can be conceptualized

and measured as two distinct constructs in an industrial

channel context. This approach contributes to growing

literature that works to disentangle central marketing

constructs, including trust (Fang, Palmatier, Scheer and

Li 2008) and loyalty (Palmatier, Scheer, Houston,

Evans and Gopalakrishna 2007a; Palmatier et al.

2007b). To deepen our understanding of relationship

marketing approaches in a channel setting, we mustembrace its complexity (Scheer 2008) and demarcate

different objects pertaining to focal variables. Con-

founding the true objects of customer loyalty by includ-

ing them within a single measurement model can lead

to erroneous implications for marketing theory and

marketing practice (Palmatier et al. 2007b). We pursue

a more fine-grained perspective on relationship vari-

ables and recommend that channel research applica-

tions continue to do so.

Second, to the best of our knowledge, this study offers

the first theoretical framework for the spillover effects

of customer loyalty in a channel context. Former stud-ies have relied on ad hoc rationales and conventional

wisdom to hypothesize patterns of interaction among

different forms of loyalty (e.g., Corstjens and Lal 2000;

Verhoef et al. 2007; Hansen and Singh 2008), whereas

we use information integration theory and balance the-

ory as conceptual bases for our hypothesis develop-

ment. All-in-all, these theories predict that brand and

distributor loyalty are not independent from each other

and jointly drive channel switching. Our empirical

study confirms a relationship between both forms of

customer loyalty and demonstrates that brand and dis-

tributor loyalty should be considered two distinct yet

interrelated constructs in channel research.

Third, our study reveals that both forms of loyalty

have approximately the same direct impact on cus-

tomers channel switching intention. This point may

seem initially like a counterargument against disentan-

gling brand loyalty from distributor loyalty, but a

closer look at their total impact reveals more subtle

differences. In addition to its direct impact, customer

brand loyalty exhibits a negative indirect effect on

channel switching through its positive spillover on

distributor loyalty. As it strengthens the overall bond

with the final customer, this spillover effect also

increases distributor-owned loyalty and thereforeweakens the relative position of brand manufacturers

in the ongoing competition for customers loyalty.

Thus, we must account for spillover effects to grasp

the whole picture and better understand the total

impact of brand and distributor loyalty on customers

channel switching intention.

CONCLUSIONS

ImplicationsDistributors must recognize that customers develop

two distinct forms of loyalty. When they conduct

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customer loyalty tracking studies, these distributors

need to monitor not only customers loyalty to their

own company but also their loyalty to primary brands

in different product categories. As our results indicate,

brand and distributor loyalty have an evenly strong

impact on channel switching, which means that

distributors can assess customer proneness to verticalcompetition by comparing the absolute levels of

brand loyalty and distributor loyalty for each cus-

tomer or segment. Customers who exhibit higher

brand loyalty than distributor loyalty represent those

most at risk for the distributor.

In many cases, distributors pursue efficiency gains

through brand delisting (Sloot, Fok and Verhoef

2006), but such actions involve the risk of losing

brand-loyal customers. As our findings show, when

customers must decide between two mutually exclu-

sive relationships, they retain the relationship partner

to which they are more loyal.For brand manufacturers, we offer three important

implications. First, brand manufacturers should not

delegate customer relationship maintenance to distrib-

utors. Instead, they need to establish and foster their

own relational bonds with customers, because invest-

ing in brand loyalty enhancements can effectively

protect them against vertical competition.

Second, brand manufacturers need a fine-grained

understanding of who benefits from investments in

brand loyalty. That is, we find an unidirectional spill-

over effect from brand loyalty to distributor loyalty,

such that investments in brand loyalty do not only serve

the brand manufacturers best interest but also enhance

the distributors. Distributors benefit from manufactur-

ers investments to increase brand loyalty, but the

reverse is not true. For manufacturers, this important

insight should help them balance the benefits and costs

of building customer loyalty across channel partners

and (re)negotiate appropriate channel contracts.

Third, manufacturers should evaluate investments in

customer loyalty according to the character of their

distribution channel. Channel partners may have

either a cooperative or a competitive relationship

(Heide and John 1990; Ellram and Edis 1996;

Cannon and Perreault 1999; Choi et al. 2001). If thedistribution channel is mainly characterized by coop-

eration, the spillover effect makes the entire distribu-

tion channel stronger than competing distribution

channels. When it comes to contracts and profit shar-

ing, distributors should acknowledge the brand manu-

facturers investments in customer loyalty, because the

distributor will benefit from them in the long run.

However, if the distribution channel is characterized

by intrachannel competition, the spillover effect is less

desirable for the brand manufacturer, because it

decreases its total impact on customers channel

switching intentions. Distributors can free ride on the

manufacturers investments in customer loyalty, which

makes it crucial for brand manufacturers to establish

a compensation scheme for their brand loyalty efforts

before implementing them.

In summary, customers do not automatically belong

to the brand manufacturer or distributor; rather, both

channel partners can earn customers loyalty andstrengthen their position, even in conditions of

increasing vertical competition.

Limitations and Avenues for Further ResearchAs in any research project, the design of our empiri-

cal study creates certain limitations with regard to the

interpretation of the results. In the following, we

address the most relevant limitations and their conse-

quences, as well as avenues for further research.

First, we operationalized channel switching as an

intention expressed by individual purchasers facing a

what-if scenario. The high level of volitional andbehavioral control in an industrial purchasing context,

as expressed in the form of planning and deliberate

decision making, should induce a strong link between

behavioral intention and actual behavior (Ajzen

1985). However, we recognize the conceptual and

empirical difference between these constructs; inten-

tion does not always lead to behavior. Managers espe-

cially need to know whether channel switching

intention actually leads to channel switching behav-

ior. Empirical studies using behavioral data therefore

would be a valuable research addition for triangula-

tion and parameter validation purposes.

Second, we conducted our analysis in a single indus-

try with its specific characteristics. Although this

approach is typical in supply chain management and

marketing research (Hansen, Singh and Chintagunta

2006) and we carefully selected the industry to avoid

behavioral patterns evoked by particular distributor

brand relationships (e.g., between retailers and private

labels) or special legal norms (e.g., the European

block exemption regulation in automobile selling), we

cannot exclude industry-specific extraneous factors. As

in the articles from which we draw, our focus on a

single industry may impose constraints on the gener-

alizability of our findings. Our research setting is adetail-intensive industry (Spiteri and Dion 2004),

making generalizations to fundamentally different

industries a future research opportunity. Replications

in other industries and subsequent meta-analyses are

encouraged to increase external validity and provide a

more complete picture of industry contingencies.

Third, our study takes a relationship marketing per-

spective and thus departs from the idea that channel

switching depends on customers joint evaluations of

their relationships with both a brand and a distribu-

tor. We implicitly assume that customers practice

relational exchanges, although customers might also

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exchange on a transactional basis, making brand and

distributor loyalty less relevant. Consequently, our

model is not able to predict channel switching in

transactional exchanges. Channel switching in transac-

tional exchanges therefore may not be well predicted

by our model. We concentrate on the interplay

between brand and distributor loyalty and their simul-taneous effect on channel switching, but further stud-

ies could broaden the scope and devote more

attention to explaining the antecedents of channel

switching. If channel switching is the key explanan-

dum, it would be worthwhile to include other brand-

and distributor-related variables, as well as additional

extraneous factors, into a more holistic model of

channel switching.

Fourth, we focus on only one product category. As

long as customers purchase in a single product category

from the distributor, our model is unambiguous, but

when they purchase across several product categoriesfrom one distributor, channel switching can become

more complex: Do customers switch the channel only

to purchase in the category of the delisted brand, or do

they move all their business away from the distributor?

If the distributors assortment is broader than the prod-

uct line of the brand manufacturer, altering its distribu-

tion programs from indirect distribution to direct

selling should encourage customers to limit their chan-

nel switching to specific product categories. However, if

the distributor delists one or more brands, customers

might be inclined to switch completely to another

distributor. Our model cannot offer definite answers to

these complex questions, so additional research is

needed to address these issues.

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Andreas Eggert (Ph.D., University of Kaiserslautern)

is Chaired Professor of Marketing at the University of

Paderborn in Paderborn, Germany. His research inter-

ests focus on the creation and appropriation of value

in business relationships. Dr. Eggerts work has

appeared in many prestigious academic and profes-

sional journals, including the Journal of Marketing, theJournal of Service Research, the Journal of Business

Research, the European Journal of Marketing, the Journal

of Marketing Theory and Practice, Industrial Marketing

Management and the Journal of Business-to-Business

Marketing.

Jrg Henseler (Ph.D., University of Kaiserslautern)

is Associate Professor of Marketing in the Institute for

Management Research at Radboud University Nijme-

gen, The Netherlands. He also has an appointment as

Visiting Professor at ISEGI at the New University of

Lisbon, Portugal. Dr. Henselers research interestsinclude service management, relationship manage-

ment and structural equation modeling. He has pub-

lished his work in many scholarly journals, including

the International Journal of Research in Marketing the

Journal of the Academy of Marketing Science, and Struc-

tural Equation Modeling: A Multidisciplinary Journal. He

also has edited two handbooks on partial least

squares path modeling.

Sabine Hollmann (Ph.D., University of Paderborn)

is the Deputy Head of the Corporate Electronic Busi-

ness Department at Phoenix Contact in Blomberg,

Germany. Dr. Hollmanns research interests include

the management of effective buyersupplier relation-

ships and customer loyalty in supply chains. Her work

has been published in the Journal of Business and

Industrial Marketing and in the Proceedings of the 2007

and 2009 American Marketing Association Educators

Conference.

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APPENDIX AScale Items

Construct Indicator Item Formulation

Channel switching CS1 Imagine your primary dental supplier experienced serious problemsdelivering your primary burr brand. How would you react? (1) Iwould rather buy our primary burr brand from a different supplier

(7) I would rather buy a different burr brand from our currentsupplier

CS2 Imagine your primary burr brand was no longer available from yourcurrent supplier any more. How would you react? (1) I would ratherbuy our primary burr brand from a different supplier(7) I wouldrather buy a different burr brand from our current supplier

CS3 Imagine your primary burr brand was only available directly fromthe manufacturer. How would you react? (1) I would rather buy ourprimary burr brand directly from the manufacturer(7) I would ratherbuy a different burr brand from our current supplier

Distributor loyalty DL1 I consider our primary dental supplier as first choice when buying

dental supplyDL2 For my next purchases, I will consider our primary dental supplier as

our first choiceDL3 I will use our primary supplier the very next time purchasing dental

supplyBrand loyalty BL1 Our primary burr brand is my first choice when buying dental burrs

BL2 I am committed to our primary burr brandBL3 For my next purchases, I will consider our primary burr brand as my

first choiceCustomer distributor

satisfactionCDS1 Overall, I am very satisfied with our primary supplierCDS2 I am very pleased with what our primary dental supplier does for usCDS3 Our primary dental supplier meets my expectations

Customer brandsatisfaction CBS1 Overall, I am very satisfied with our primary burr brandCBS2 I am very pleased with the performance of our primary burr brand

CBS3 Our primary burr brand meets my expectationsNumber of

distributorsND Currently, with about how many dental suppliers does your practice

have a purchasing relationship?Number of brands NB Which of the following burr brands has your practice bought within

the last year? [full enumeration of available brands in the market,multiple answer options possible]

APPENDIX B

Sensitivity Analysis of Estimates with Respect to a Potentially Overstated Internal ConsistencyReliabilityThe measurement items of the four constructs customer brand satisfaction, customer distributor satisfaction,

brand loyalty and distributor loyalty are relatively similar. There is a possibility that the inter-item correlation is

partly due to common wording instead of substantial contents. This would imply that the reliability is overstated

and that structural equation modeling would not sufficiently correct for measurement error.

To check for potential threats to the estimates validity, we explicitly model measurement item error correlations

that are orthogonal to the substantial construct meaning. The figure below illustrates how an increase in item error

correlations would affect the standardized path coefficients. Measurement error correlations of 0.05 or 0.10 would

not substantially alter the findings. For error correlations of 0.15 and above, two implausible results emerge: First,

customer brand satisfaction and customer distributor satisfaction would explain more than 60 percent of the vari-

ance of brand loyalty and distributor loyalty, respectively. This is more than twice the amount typically found for

this relationship (i.e., 28 percent, see Szymanski and Henard 2001). Second, the negative effect of distributor loyalty

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on brand loyalty would become significant a finding running counter to all previous findings (cf. Table 1). Error

correlations of 0.25 or larger would yield an explained variance of distributor loyalty larger than one, resulting in a

Heywood case.

APPENDIX CScale Characteristics

Construct Cronbachs a Indicator CFA Model 1 CFA Model 2

Loading AVE Loading AVE

Channel switching 0.82 CS1 0.79 0.63 0.77 0.59

CS2 0.90 0.87CS3 0.67 0.64

Distributor loyalty 0.84 DL1 0.66 0.67 0.55 0.53DL2 0.93 0.84DL3 0.85 0.77

Brand loyalty 0.77 BL1 0.72 0.56 0.61 0.46BL2 0.69 0.65BL3 0.84 0.78

Customer distributor satisfaction 0.93 CDS1 0.90 0.81 0.75 0.60CDS2 0.87 0.75CDS3 0.93 0.81

Customer brand satisfaction 0.95 CBS1 0.95 0.87 0.80 0.64

CBS2 0.95 0.83CBS3 0.90 0.77

Number of distributors ND 1.00 1.00 0.96 0.93Number of brands NB 1.00 1.00 0.94 0.89

.

CFA Model 1 has the standard parameterization; CFA Model 2 includes an orthogonal general factor impacting

all indicators equally.

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0.00 0.05 0.10 0.15 0.20standardizedp

athc

oefficient

Inter-item correlation due to overly similar wording

Distributor LoyaltyCustomer Distributor Satisfaction

Brand LoyaltyCustomer Brand Satisfaction

Channel Switching Brand Loyalty

Distributor LoyaltyBrand Loyalty

Brand LoyaltyDistributor Loyalty

Channel SwitchingDistributor Loyalty


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