The impact of customerrelationship characteristics oncustomer switching behavior

Differences between switchers and stayers

Juan Pablo Maicas Lopez, Yolanda Polo Redondo andFco. Javier Sese Olivan

University of Zaragoza, Zaragoza, Spain

Abstract

Purpose – The purpose of this research is to show how relationship marketing has recognized theimportance of building long-term relationships in increasing firms’ profitability and guaranteeingtheir future viability. Owing to the damaging effects that customer switching behavior could have onthe development of successful relationships, understanding customers’ switching decisions canrepresent a key step in the process of establishing, developing and maintaining successful relationalexchanges. The current study aims to contribute in this direction by introducing the heterogeneity ofcustomers in their relationship characteristics (depth, length and breadth) into the analysis ofcustomers’ propensity to switch service providers.

Design/methodology/approach – The data set used to carry out this study has been obtained froma panel survey (Home Online) of technology users in the United Kingdom, and the proposedhypotheses are tested on the fixed-line telephone sector using logistic regression. This techniqueassociates the relationship characteristics with the probability that a switch in suppliers will takeplace.

Findings – The results obtained show that the length, depth and breadth of relationships help todetermine customers’ propensity to switch fixed-telephone suppliers. Customers who maintain along-lasting relationship with the firm (length), use the service more (depth), and invest incomplementary services (breadth) will be less predisposed to switch.

Practical implications – With regard to recommendations for practitioners, the paper highlightsthe need for firms to renew both acquisition and retention strategies in order to take individualcustomer information into account. This should help them to identify and retain the most valuablecustomers and to optimally allocate marketing resources (from switching-prone tonon-switching-prone customers).

Originality/value – The main contribution of the paper is to consider relationship characteristics inthe analysis of customer switching behavior. This research shows that the heterogeneity observed inthe depth, length and breadth of customer-firm relationships explains the differences in the propensityto switch service providers.

Keywords Customer relations, Servicing, Suppliers, Relationship marketing

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0960-4529.htm

This paper has benefited from financial aid from the Spanish Ministry of Science andTechnological and FEDER (SEJ2005/05968 and SEJ2005/01856) and the county council inAragon, by recognizing the authors of this work as members of the research group Generes (S09)as well as multidisciplinary groups (PM062).

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Managing Service QualityVol. 16 No. 6, 2006pp. 556-574q Emerald Group Publishing Limited0960-4529DOI 10.1108/09604520610711909

IntroductionOver the past decades, marketing literature has recognized the existence of a shift inthe nature of customer-firm relationships from a transactional to a relational approach(Ganesan, 1994). This change has generated an increasing interest in the study of thefactors and mechanisms determining the establishment, development and maintenanceof successful relational exchanges (Morgan and Hunt, 1994). As a consequence,practices that have been common up to now, which focused on attracting newcustomers and gaining a large market share, have given way to policies aimed atconsolidating the firm’s customer base and retaining the existing customers (Dick andBasu, 1994; Dwyer, 1997)

Nevertheless, this retention strategy should be implemented very carefully because,on the one hand, retaining a customer is costly[1] (Reinartz et al., 2005), and on the otherhand, not all customers generate the same value for the firm[2] (Woo and Fock, 2004).For this reason, it is not efficient to retain all the users from the customer baseindiscriminately (Dowling and Uncles, 1997), since retention per se is not a sufficientcondition to obtain greater profitability (Reinartz and Kumar, 2000). Rather, firms needto consider how much each individual customer is worth to them in order to optimallysegment the market and allocate their marketing resources (Reinartz and Kumar, 2003).

In this respect, the literature has shown that the longer customers stay in arelationship with the company, the more value they generate (Reichheld, 1996b), so theperiod of time a relationship is maintained is one of the fundamental factorsdetermining the value that the customer provides to the firm (Berger and Nasr, 1998;Rosset et al., 2002). Customer switching behavior is consequently a serious threat to theachievement of long-term relationships (Ganesh et al., 2000). Hence firms need to studycarefully the processes determining customers’ switching decisions if they are tomanage their customer bases successfully (Bansal et al., 2005).

Marketing research has dedicated much effort to studying long-term relationshipsand the factors determining them (Ganesan, 1994; Dick and Basu, 1994), but rather lessto the factors motivating customers to switch suppliers (Keaveney, 1995). At the sametime, the literature on switching behavior has mainly focused on the antecedents ofsuch processes (Roos, 1999), rather than on characterizing customer differences interms of their greater or lesser propensity to switch service providers (Keaveney andParthasarathy, 2001).

In order to extend the literature on customer switching behavior the main objectiveof this research is to analyze the differences among customers in their propensity toswitch service providers. For this purpose, we study the impact of the differences incustomer relationship characteristics (length, depth and breadth) on the probability toswitch suppliers. Based on evidence from marketing and social literature, the length,depth and breadth of customer-firm relationships are expected to strongly influencecustomers’ switching decisions, helping us to explain the differences betweenswitchers and stayers[3].

Most papers studying relationship characteristics have analyzed their impact ondifferent relational constructs like satisfaction (Bolton, 1998), number of servicespurchased and customer referrals (Verhoef et al., 2002), profitable lifetime duration(Reinartz and Kumar, 2003) or trust and perceived quality (Grayson and Ambler, 1999).However, less attention has been paid to the study of the impact of customerrelationship characteristics on customer switching behavior.

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To carry out this research, our paper uses a panel survey (Home Online) whichgathers individual level information about the use of information and communicationstechnologies (ICT) in the United Kingdom. The availability of information at theindividual level provides us with sufficient detail to study the customers’ behavior andanalyze their switching decisions in the fixed-line telephone industry.

The remainder of this work is structured as follows: in the following section webriefly review the literature on switching behavior, and then we formulate and justifyour hypotheses. The next section is dedicated to describing the sample, the variablesused in the empirical analysis and the methodology employed. We then analyze andbriefly discuss the results. Finally, we present our main conclusions, implications formanagement, the main limitations of the work and future lines of research.

Switching behaviorSwitching and profitabilityRelationship marketing has taken on a key role in marketing theory and practice inrecent years and has postulated the importance of relationships for improving firms’profitability and future viability (Berry, 1983; Morgan and Hunt, 1994; Gronroos, 1995).Much of this interest can be traced to the benefits that the literature attributes tomaintaining long-term relationships. Recent research has shown that continuingcustomers have: a lower price sensitivity (Keaveney, 1995), greater usage of the firm’sservices (Bolton and Lemon, 1999), greater receptivity to the firm’s new products(Hawkins et al., 2004), increased predisposition to engage in positive word-of-mouthand a greater resistance to competitors’ persuasion attempts (Dick and Basu, 1994).

Apart from increasing the firm’s revenues, continuing customers contribute tocutting its costs, since they are less expensive to serve (Ganesh et al., 2000), the positiveword-of-mouth they spread attracts new customers and reduces the costs needed toattract them (Keaveney and Parthasarathy, 2001), and the uncertainty in the exchangesdiminishes due to the experience accumulated throughout the relationship (Heide andWeiss, 1995).

Therefore, losing a customer is a serious setback for the firm in terms of its presentand future earnings. In addition to losing the benefits discussed above, the firm needsto invest resources in attracting new customers to replace the ones it has lost(advertising, promotion, initial discounts). Peters (1987) shows that it can cost fivetimes more to acquire a new customer than to retain an old one. Consequently,retaining the current customer base is much more attractive than searching for newcustomers.

Furthermore, the consequences that losing a customer can have for the firm(negative word-of-mouth, damage on brand image, reputation) are expected to have aharmful impact on the firm’s future prospects. These effects could reduce the firm’schances of acquiring new customers at later stages, or raise the costs of doing so (tocounteract these negative opinions).

As a consequence of this, and as relationship marketing postulates, relationshipshave become central to marketing theory and practice and to firms’ corporate strategy.Thus, understanding the processes determining customers’ switching decisionsrepresents a decisive step in successfully establishing, developing and maintainingbuyer/seller relationships.

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Switching behavior. Literature reviewThe central role of relationships in the marketing literature and the harmful effects ofcustomer switching behavior have led academics and practitioners to pay increasingattention to this topic. Recent research has tried to establish the bases for improvingthe understanding of customers’ switching behavior and can be articulated in threemain areas:

(1) the processes underlying customer switching decisions;

(2) the specific factors that motivate switching; and

(3) the heterogeneous nature of the firms’ customer base and its relevance in orderto analyze the differences between switchers and stayers.

Regarding the first one (1), the work of Roos (1999) distinguishes three determinants ofswitching decisions using the Switching Path Analysis Technique (SPAT): pushingdeterminants (the reason to switch to another supplier), pulling determinants (factorsthat motivate the customers to come back to the original supplier) and swayers (theydo not cause switching by themselves; they can only mitigate or strengthen theswitching decision). In the same line, Bansal et al. (2005), drawing from migrationliterature, establish a model of customers’ switching behavior that identifies theprocess by which customers decide to switch service providers. Similar to Roos (1999),they distinguish push, pull and mooring variables that are central to the switchingprocess. Roos et al. (2004) extend this line of research by analyzing the differences insuch processes between five different service industries, showing the differentialimpact of the determinants identified in function of the industry configuration.

However, in the switching behavior literature, most attention has been paid to (2)the specific factors that motivate switching. Keaveney (1995), in one of the mostinfluential pioneering works, identifies eight factors behind customers’ switchingdecisions in service industries, including core service failures, pricing, employeeresponses to service failures, attraction by competitors, or inconvenience. Gerrard andCunningham (2004) carry out a similar analysis for bank services, incorporating theweight that customers give to each incident that provokes switching. They obtainsimilar factors to Keaveney’s pioneering work.

Together with the variables mentioned above, the literature has also identified thefollowing as determinants of customers’ decisions to switch suppliers: dissatisfaction(Swinyard and Whitlark, 1994), perceived quality (Rust and Zahoric, 1993), awarenessof alternatives (Capraro et al., 2003), location (Jones et al., 2003) and switching costs(Klemperer, 1995; Burnham et al., 2003).

All these works help to identify the processes and factors that motivate switchingdecisions, but provide no information about the differences between switchers andstayers. For this reason, (iii) a recent stream of research deals with the heterogeneousnature of the customer base in more detail, finding significant differences in switchingbehavior between customers. In this respect, the work of Ganesh et al. (2000) in thefinancial services sector shows that switchers differ from stayers in aspects such assatisfaction, involvement and loyalty. Keaveney and Parthasarathy (2001), examiningonline services, associate these differences with aspects relating to attitude, behaviorand socio-demographic characteristics.

The scarce literature within this stream of research and the difficulties forextrapolating the results to other markets and other countries are good indicators of

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the need to improve our understanding in this line. The current work aims to extendempirical research in the switching behavior literature introducing the heterogeneitythat exists in the characteristics defining the customer-provider relationship: thelength, depth and breadth.

Formulation of hypothesesTo our knowledge, little research to date has focused on examining the impact of thecharacteristics that define the supplier-customer relationship on consumers’ switchingbehavior. But the conditions under which this relationship develops are likely to playan important role in the maintenance of long-term relationships (Ganesan, 1994),decisively influencing the duration of the relationship and consequently customers’switching decisions (Reinartz and Kumar, 2003). Some of the aspects characterizing therelationship are service usage, the time since purchasing the service and the acquisitionof complementary services. These factors capture the basic elements defining arelationship: the depth, length and breadth, respectively (Verhoef et al., 2001; Boltonet al., 2004).

Customers’ service usage is a good indicator of the depth of the relationship betweensupplier and customer (Bolton et al., 2004) and shows, to a great extent, the customers’intentions of continuing their association in the future (Reinartz and Kumar, 2003).Increased use of the service provides customers with a greater knowledge of thecompany and of the processes required to use its services satisfactorily (Alba andHutchinson, 1987). In addition, it favors the development of specific skills that are noteasily transferable to other suppliers (Keaveney and Parthasarathy, 2001).

At the same time, increased service usage raises the number of exchanges betweenthe parties and the customer’s identification with the firm. The greater experienceacquired through these interactions and the development of strong bonds with thesupplier help to reduce the uncertainty of the exchanges and increase customers’perceived cost of switching suppliers (Bendapudi and Berry, 1997). Similarly,customers who use the service more, develop a strong, generally positive attitudetowards it (Keaveney and Parthasarathy, 2001), which reduces their sensitivity tofailures in its delivery (Oliver, 1997). Consequently our first hypothesis is formulated asfollows.

H1. Fixed-telephone usage negatively influences the probability of switchingsuppliers.

The time since the customer purchased the service (time since service purchase) isanother aspect that is likely to influence the strength of the relationship and thecustomer’s switching decisions (Bolton et al., 2004). The literature postulates that theearly stages of the relationship are critical for its future continuity or dissolution(Bolton, 1998; Bowman, 2004). The probability of switching in these initial stages ismuch greater than in the later ones (Ongena and Smith, 2001; Israel, 2005).

However, as the relationship develops, confidence in one’s impressions about thesupplier increases (Verhoef et al., 2002), trust develops between the parties (Gwinneret al., 1998), and the cost of switching suppliers rises (Shapiro and Varian, 1999).Moreover, customers weigh prior cumulative satisfaction heavily when they aredeciding whether to maintain the relationship (Bolton, 1998). These aspects favor thecontinuity of the relationship, so that we can expect a greater propensity to switch

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suppliers among more recent customers than among those with long relationships withthe company. Consequently, our second hypothesis is as follows:

H2. The recent acquisition of the fixed-telephone line positively influences theprobability of switching suppliers.

In many contexts, customers can purchase complementary services[4] to improve thebenefits of the core service and satisfy their needs more precisely. Investing incomplementary services is a good indicator of the breadth of the relationship (Boltonet al., 2004), as well as of its strength and importance (Kamakura et al., 2003). Makingthis type of investment increases the number of interactions between the parties of therelationship and raises the cost of switching (Shapiro and Varian, 1999; Chen andForman, 2004).

At the same time, purchasing these services from the same company increasescustomers’ awareness about the supplier and the quality of the services it supplies, sothat customers can form more realistic and accurate expectations about the futureperformance of the services supplied by the company. In this way, accurate predictiveexpectations mean the absence of disconfirmation and, hence, an increase ofsatisfaction and repurchase intentions (Anderson and Sullivan, 1993).

Customers, by purchasing complementary services, can also signal to the firm theirdesire to continue the relationship and achieve common objectives and goals, whichbenefit both parties and avoid opportunistic behaviors. In this respect, the literaturehas shown that the number of services bought from a firm has a positive effect onrelationship duration (Reinartz and Kumar, 2003), which translates into a reduction inthe probability of switching. Consequently:

H3. Investing in complementary services has a negative effect on the probabilityof switching suppliers.

Description of sample, variable measurement and methodologyDataThe data used for testing the above hypotheses come from a panel survey carried outin the United Kingdom during the period 1998-2001 (Home Online) Brynin (2002). Thissurvey, conducted by the Institute for Social and Economic Research at the Universityof Essex and financed by British Telecommunications, collects individual informationand examines information and communications technology (ITC) access and behavior.In order to carry out this study, we constructed a database for the fixed-line telephonesector using the information provided by this panel survey. The final sample used inthis analysis consists of 272 individuals.

Fixed telephony is, without doubt, an interesting industry. First, it constitutes one ofthe most important sectors not only in the communications field, but also in theeconomy as a whole. Second, it has reached a mature state with penetration rates closeto 80 percent in almost all developed countries[5]. Hence, the analysis of customerswitching behavior is managerially very relevant for fixed telephony firms, as they arecompeting for existing customers (instead of newcomers). Third, the nature of the dataallows us to study actual customer switching behavior instead of intended switchingbehavior. Finally, the recent liberalization of the sector has encouraged the entry ofnew competitors that have increased the range of possible providers. This allowscustomers to choose from a greater set of alternatives[6].

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Description of variablesThe dependent variable of the study distinguishes between two categories ofcustomers: those who have switched telephone companies (switchers) and those whohave not (stayers). Table I shows the variables used in this work, along with a shortdescription of them and of their different categories, as well as their expected effect onthe probability of switching suppliers. In order to control for alternative factors thatcould influence switching behavior, we introduce some socio-demographic and attitudecharacteristics[7].

Tables II and III show the descriptive statistics of the individuals in the sample. Aswe can see from the tables, the rate of change is very low (8.5 percent)[8], and thecustomers use the telephone service on average for one hour per week. Variability inservice usage is high, which reflects the existing heterogeneity among the consumers.Most of the individuals have not acquired either answering machine services (75percent) or a more powerful line (94.5 percent), nor did they purchase their line recently(91.2 percent).

MethodologyIn order to analyze the customers’ switching behavior in the fixed-line telephoneservices, we employed the logistic regression technique (Menard, 1995), which allows

Variable Description CategoriesEffect onswitching

Dependentvariable

Switch 1 Switch0 No switch

Relationshipcharacteristics

Depth H1. Usage of fixed-linetelephone service

Fixed-telephone usagein hours per week

Numeric 2

Length H2. Recent purchase ofservice

Recent purchase offixed-line telephoneservices

1 Yes0 No

þ

Breadth H3a. Complementaryservices: ISDN line

Purchase of ISDN line 1 Yes0 No

2

H3b. Complementaryservices: separateanswer-phone

Purchase ofanswer-phone service

1 Yes0 No

2

Note: The ISDN (Integrated Services Digital Network) line increases the speed of connections (up tofive times) and allows simultaneous transmission of voice and data since it provides point-to-pointdigital connectivity

Table I.Description of studyvariables

Quantitative variablesMean SD Minimum Maximum

Service usage (hours per week) 0.924 2.085 0.081 19.25Net monthly pay (£) 1,055.1 886.7 17.33 11,000Age 41.5 12.2 16 75Number of telephones in household 2.15 0.87 1 4

Table II.Descriptive statistics

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us to explore the influence of a set of explanatory variables (quantitative andqualitative) on the probability that a switch in suppliers will occur. Specifically, in thelogistic regression analysis, the dependent variable takes just two values: one if thecustomer switches, zero otherwise. The model takes the following form:

LnPrðSwitchÞ

1 2 PrðSwitchÞ

� �¼ gþ bxþ lzþ 1

where PR(Switch) is the probability that the switch occurs, 1 2 PR(Switch) theprobability that it does not, x is a vector containing relationship characteristics, z is avector of customer socio-demographic and attitude variables included for controlpurposes, b and l are the vectors containing the coefficients to be estimated by themodel using the maximum likelihood method.

Consequently, this technique allows us to estimate the probability that theindividual presents one of the two possible values (switch or no switch) in function ofthe different values adopted by the explanatory variables. As a result, we will be ableto study the role played by customer relationship characteristics in distinguishingbetween switching-prone and non-switching-prone customers.

Qualitative variablesn %

SwitchNo switch 249 91.5Switch 23 8.5

Answer phoneYes 68 25No 204 75

ISDN lineYes 15 5.5No 257 94.5

Recent acquisition of core serviceYes 24 8.8No 248 91.2

Marital statusMarried 183 67.3Single 89 32.7

Employment statusWorking 252 92.6Not working 20 7.4

SexMale 126 46.3Female 146 53.7

Family structureAlone 39 14.3Couple without children 63 23.2Couple with children 170 62.5

Table III.Descriptive statistics

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Analysis of resultsTo analyze the results we estimated two different models: the first (Model 1) includesonly the relationship characteristics to explain customers’ switching behavior, and thesecond (Model 2) incorporates the socio-demographic and attitude variables in order tocontrol for the impact of customer heterogeneity.

Before analyzing the results of the estimation, Table IV reports the results ofvarious tests measuring the goodness of fit of the different models to the data.Specifically, the classification table shows the distribution that the model makes, fromthe individual calculations of the probabilities of the event occurring (probability ofswitching)[9], of each of the cases between the two alternatives: switch and no switch.Comparing the values predicted by the model with the observed ones, the totalaccuracy of the two models is around 90 percent, while the partial values varysignificantly from one model to the next, with Model 2 presenting the best allocationbetween the two categories (90.4 percent accuracy for those that do not change and 56.5percent for those that do). The chi-square test, which tests the null hypothesis that allthe model’s parameters are zero, is rejected at the 1 percent level in Model 2 and at the 5percent level in Model 1. The Hosmer-Lemeshow test (Hosmer and Lemeshow, 1989),which measures the goodness of fit of the model[10], is not rejected in either of themodels. Therefore, the models present a good statistical fit.

Table V reports estimation results for the logistic regression models. It provides thefollowing information for each model: the first column presents the coefficients of themodel whose sign indicates how the explanatory variables affect the probability ofswitching suppliers. A positive sign in this coefficient indicates that the variable has apositive effect on the probability of switching providers – i.e. an increase in this factorincreases the risk that the event of interest (the switch) will occur. Conversely, anegative sign reduces the risk that this change will occur. The second column offers thestandard deviation and the third, the odds ratio, which is a statistic that evaluates therisk that an event will occur if a particular factor is present[11].

Analyzing the estimation results of Model 1, we can infer that the coefficientaccompanying the variable service usage has a negative sign (b ¼ 20:241, consistentwith the proposed hypothesis H1), but is not statistically significant (r . 0.10). Thecoefficient that measures the length of the relationship has a positive sign (b ¼ 1:632;r . 0.01), showing that customers who have recently purchased a fixed-line telephoneservice have a significantly greater propensity to switch than customers who have not(as the value of the odds-ratio – greater than one – indicates). The variablecomplementary services, in contrast, presents diverging results. The coefficients forthe variables answer phone service and ISDN line both have a negative sign(b ¼ 21:380; b ¼ 20:565, respectively), but the second is not statistically significant(r , 0.10).

Analyzing the results of Model 2, which incorporates customer heterogeneity in thedemographic and attitude characteristics, we can see that the coefficient accompanyingthe variable service usage is negative and significant (b ¼ 20:650; r , 0.10),indicating that increasing its value (holding the other factors constant) has a negativeeffect on the likelihood that customers will switch their fixed-line telephone provider(H1 is not rejected). The variable recent purchase of the fixed-line telephone service hasa positive effect on switching probability (b ¼ 1:809; r , 0.01), so that customers whohave recently purchased their service have a significantly greater propensity to switch

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Cla

ssifi

cati

onta

ble

a

Pre

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Sta

tist

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Ob

serv

edS

wit

chN

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rect

(%)

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419

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56.5

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Table IV.Goodness-of-fit statistics

Customerrelationship

characteristics

565

Mod

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559

5.11

41.

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**

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703

6.10

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read

th):

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one

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ice

21.

380

*0.

767

0.25

22

2.84

9*

*1.

228

0.05

8H

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read

th):

ISD

Nli

ne

20.

565

1.11

80.

570

21.

997

1.64

10.

136

Custom

erdem

ographics

Inco

me

20.

001

0.00

10.

999

Ag

e2

0.05

4*

0.02

90.

947

Employmentsituation

Not

wor

kin

g0.

318

1.09

51.

375

Wor

kin

g(R

ef.)

––

Sex

Mal

e2

0.12

70.

638

0.88

0F

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e(R

ef)

––

Maritalstatus

Mar

ried

2.71

2*

**

1.04

915

.055

Not

mar

ried

(Ref

.)–

–N

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lep

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old

0.43

70.

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1.54

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structure

Alo

ne

2.18

6*

1.15

38.

899

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ple

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0.37

60.

749

1.45

7C

oup

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ith

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dre

n(R

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erattitude

Enjoyspeaking

Ag

ree

0.25

40.

775

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22.9

298.

487.

721

0.00

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ree

(Ref

.)–

–Keepin

touch

Ag

ree

20.

418

0.91

40.

659

Nei

ther

agre

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ree

5.61

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1.76

127

3.31

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ree

(Ref

.)–

–In

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2.21

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00.

110

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777

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550

0.06

2

Notes:

* r,

0.10

;*

* r,

0.05

;*

** r

,0.

01

Table V.Estimation results

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566

suppliers than those maintaining a long-term relationship with the company. H2 is,consequently, not rejected.

Customers investing in complementary services – purchasing an ISDN line oranswering machine service – are less likely to switch fixed-telephone suppliers thancustomers not making such investments (b , 0), as the hypothesis postulates,although the effect of the first of these variables is not statistically significant. H3,therefore, is partially rejected.

Including socio-demographic and attitude characteristics in the model, apart fromserving for control purposes, allows us to analyze the influence of certain factors suchas income, occupation, sex, age, or attitude towards fixed-line telephone services on therisk of switching suppliers. Although we did not formulate any hypotheses about theassociation between these variables and the likelihood of switching, our analysisshows that age, marital status, family structure and attitude towards the telephoneservices significantly affect the risk that switching will occur. For instance,individuals’ age has a negative effect on their propensity to switch fixed-telephonecompanies (b ¼ 20:054, r , 0.10), meaning that older users are less likely to switchservice providers than younger ones.

Discussion and conclusionsThe marketing literature highlights that customers differ in their propensity to switchservice providers (Ganesh et al., 2000) and that buyer-seller relationships differ in theirlength, depth and breadth[12]. Our research tries to contribute to a greaterunderstanding of customer switching behavior by showing that these differences incustomer relationship characteristics explain the propensity to switch serviceproviders.

The model that considers only the relationship characteristics does not ratify H1,since the variable service usage is not statistically significant. But the inclusion ofsocio-demographic and attitude variables changes this result, showing that serviceusage contributes to deepening the relationship between the parties (Bolton et al., 2004).This result leads us to conclude that we need to control for customer heterogeneity indemographics and attitudes if we are to explain customers’ switching behavioraccurately. The logic underlying this argument is that customer heterogeneity in thefixed-line telephone market comes not only from the differences in relationshipcharacteristics, but also from other sources. Therefore, an accurate analysis of theeffect of the relationship characteristics on switching decisions needs to consider theheterogeneity in customers’ socio-demographic and attitude variables.

In the model which controls for customer attitude and demographics we find thatthe characteristics defining the relationship (time since purchasing the service –length, usage of telephone service – depth, investment in complementary services –breadth) significantly influence customers’ switching decisions (although thehypothesis regarding investment in complementary services has been partiallyrejected). In particular, service usage reduces the probability of switching andstrengthens the relationship between the parties, encouraging them to seek a long-termorientation.

Likewise, the time since purchasing the service has a positive influence on the futurecontinuity of relationships, therefore reducing the risk of switching. In our paper wehave measured this construct as a dummy variable that distinguishes between two

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groups of customers: those who have acquired the service recently and those who havenot. This has allowed us to test the hypothesis that in the first stages of a relationshipthe probability of the association being terminated is higher. Hence, the estimationresults show that recent customers are more likely to switch fixed-telephone suppliersthan older ones. An explanation for this result is that, as the relationship between theparties develops over time, trust, experience and switching costs all increase, positivelyinfluencing its future continuity (Verhoef et al., 2002; Bell et al., 2005).

Moreover, the literature has shown that customers’ investments in complementaryservices have a positive effect on relationship enhancement and maintenance (Shapiroand Varian, 1999). This type of investment can signal customers’ desire to continue therelationship in order to achieve mutual benefits and to avoid opportunistic behaviors(that can threaten relationship continuity). In this way, the fact that investing incomplementary services favors the creation of lasting relationships stronglyencourages companies to extend the range of services they offer to give users theopportunity to make this type of investment (cross-buying – Reinartz and Kumar,2003). The incentive is very strong since the literature has shown that expanding therange of goods or services offered (cross-selling) greatly benefits firms in terms ofhigher profitability and more effective customer retention (Kamakura et al., 2003).

The two services that we have considered as complementary to the core service(voice service) in our analysis – answer phone and ISDN line – have contradictoryeffects on the switching decision. On the one hand, and in line with the hypothesisproposed here, acquiring answering machine services reduces the probability of usersswitching their fixed-line telephone company. In contrast, investing in an ISDN linedoes not have a significant influence on the customer’s switching decisions (althoughthe sign is in line with the hypothesis proposed). A possible explanation for this resultis that customers could perceive the ISDN line as a different core service that is notrelated to the voice service[13]. From this point of view, customers’ switching decisionsabout fixed-telephone services would be independent from those relating to ISDN lines.An ISDN line cannot strictly be considered a complementary service, but should betreated differently.

As a consequence, the length, depth and breadth of customer-firm relationshipscontribute to explaining the differences in customer switching behavior. Thosecustomers who maintain a long-lasting relationship with the firm (length), use theservice more (depth), and invest in complementary services (breadth) will be lesspredisposed to switch.

Practical implicationsThe first step in successfully building long-term relationships is to identify the firm’spotential customers properly (those who provide the most value to a firm) and toattract them using the appropriate marketing techniques. In this sense, the marketingliterature has shown that the customers that provide more profitability to the firm arethose who have a lower propensity to switch service suppliers and hence, are morelong-term oriented (Reichheld, 1996a).

Thus, in order to build profitable relationships firms should not attempt to attractall customers in the market indiscriminately, but focus on those who are more valuableto the company. As our research shows, it is possible to accomplish this objective bydifferentiating between switching and non-switching-prone users on the basis of

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customer heterogeneity (in the characteristics that define their relationships, customerdemographics and attitudes).

Consequently, customer acquisition strategies should pay particular attention to thespecific characteristics of each individual customer, since these variables significantlyaffect customers’ decisions to continue the relationship in the future. Knowing theirpropensity to use the service or to make investments in complementary services, forexample, would be a decisive step to optimally identify the most profitable users and,on this basis, allocate marketing resources (directing them at the segment that is moreprone to maintain long-term relationships).

The rapid development and diffusion of ICT provides firms nowadays with a hugevolume of information about the customers. This, undoubtedly, offers them anexcellent opportunity to segment the market and achieve stable and long-lastingrelationships[14].

Once firms have established relationships with their customers, they should focustheir strategies on relationship development and enhancement. To do this, managersshould encourage customers to increase their service usage (depth), to invest incomplementary services (breadth), and to remain in the relationship (length). Our workhas shown that these aspects strengthen the relationships and diminish the probabilitythat a switch in suppliers will occur, contributing to an increase in the customerlifetime value. Practices like increasing the range of complementary services the firmoffers or implementing loyalty programs can help the company to accomplish theseobjectives.

Therefore, our work shows that firms should take into account customerheterogeneity when designing acquisition and retention strategies. This should helpmanagers to better understand customer switching decisions, and on this basis tooptimally segment the market and reallocate marketing resources from less profitablegroups (higher propensity to switch) to more profitable ones (lower propensity toswitch).

Limitations and future lines of researchSome limitations of our research should be noted and they are the starting point forfurther research. Recent investigations have shown that many other factors intervenein customers’ switching decisions apart from the ones discussed in this work. Aspectssuch as the level of satisfaction (Ganesh et al., 2000), trust between the parties (Morganand Hunt, 1994) and the perceived quality of the service (Zeithaml et al., 1996) allintervene in the process of switching suppliers, and these relational constructs areexpected to differ between customers (Ganesh et al., 2000). A joint analysis of theinfluence of these variables together with the variables analyzed in the current workcould provide a better understanding of customers’ switching behavior. This is apotentially interesting avenue for future research.

Another potential extension concerns the nature of the data used in the analysis.Our work collects cross-sectional data about the variables of interest (Home Onlinesurvey) but, in order to study switching behavior, longitudinal information couldprovide greater detail about the switching processes and the causes behind them.Therefore, extending the current research to consider longitudinal information aboutcustomer behavior could improve our understanding of the factors determiningswitching decisions and the differences between switchers and stayers.

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Extending the analysis to other services could also offer interesting conclusions,since the fixed-line telephone sector is a mature market with a low rate of change. Othersectors in the early stages of the life cycle, with greater competitiveness and a higherrate of change could provide an interesting field of study to analyze the switchingprocess. In this sense, testing the model in a cross-industry research could be aninteresting avenue for future research.

Finally, marketing research has shown an increasing interest in the study of theprofitability of relationships and has generated many measures of the customerlifetime value (CLV, the net present value of all future earnings from an individualcustomer – Berger and Nasr, 1998; Rust et al., 2000; Gupta and Lehmann, 2003). Thesemeasures consist of two basic elements: the duration of the relationship (probability ofstaying with the firm) and the value the customer generates for the company in thedifferent interactions. A very interesting area for future research would therefore be toanalyze the influence of customer heterogeneity (in relationship characteristics –length, depth and breadth – and in demographics and attitudes) in explaining thedifferences in the value that the customers provide to the firm (CLV) (Reinartz andKumar, 2000; Venkatesan and Kumar, 2004). Including both measures jointly couldhelp firms to allocate their marketing resources more efficiently and to gain a betterunderstanding of the processes favoring the establishment, development andmaintenance of successful relational exchanges.

Notes

1. It involves the investment of resources throughout the lifetime of the relationship in the formof loyalty programs, special discounts or a frequent communication with the customer(Reinartz et al., 2005).

2. The revenue each customer generates varies in function of their individual characteristicslike, for example, the characteristics of the relationship (service usage, inter-purchase time,cross-buying).

3. In the terminology of Ganesh et al. (2000), the term switchers is used to refer to thosecustomers who have switched fixed-telephone suppliers and the term stayers to those whohave not.

4. In the context of the core fixed-line telephone service (voice services), data (fax) andanswering machine services are complementary.

5. In the United Kingdom the household penetration rate in 2004 was 90 percent.

6. In the former state, before the liberalization process, there is only one company, the stateoperator. Therefore, there is no opportunity to switch.

7. Customer demographics include: occupation, sex, age, family structure and marital status. Inaddition, we have included two variables measuring customers’ attitudes towards fixed-linetelephone services in order to control for customers’ heterogeneity regarding these factors.Specifically, customers are questioned about the extent to which they agree or disagree withthe following questions: “I enjoy speaking to people on the phone” and “I find the phoneessential for keeping in touch”.

8. The switching path is very slow in telecommunications, as we can see in papers like Kim(2005).

9. The cut-off point for classifying between the two groups (switch or no switch) is 0.15.

10. The Hosmer-Lemeshow test divides the sample of individuals into deciles from theprobabilities estimated by the model. From these values, an index is calculated that follows a

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chi-square distribution, and which tests the null hypothesis that there are no differencesbetween the observed values and the values predicted by the model. A p-value of 0.05 or lesswould result in the null hypothesis being rejected. In our model, the p-value is higher than0.05, demonstrating the goodness of fit of the model to the data.

11. The odds ratio is a relative measure of risk indicating how much more likely it is thatsomeone exposed to one factor will experience the event compared to someone not exposed toit.

12. Customers can have different service usage levels, different relationship durations, and theyusually differ in the type and number of complementary services purchased.

13. It is interesting to note that the ISDN line is more related to Internet services, which aremainly focused on data transmission. Therefore, customers may perceive the ISDN line as acore service instead of a complementary one.

14. The development of customer relationship management (CRM) is a clear example of thisquestion.

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About the authorsJuan Pablo Maicas Lopez is an assistant professor of Management at the University of Zaragoza,Spain. His research interests are focused on business strategy, industrial organization and digitaleconomy, particularly competition in the mobile telecommunications industry.

Yolanda Polo Redondo is professor of Marketing at the University of Zaragoza, Spain. Herresearch interests focus on marketing strategy, innovation adoption and diffusion, relationshipmarketing, customer behavior and pioneering advantages. She has published her works in anumber of journals including Strategic Management Journal, Research Policy, European Journalof Marketing, Journal of Marketing Management and Technovation.

Fco. Javier Sese Olivan is a PhD Candidate at the University of Zaragoza, Spain. His currentresearch areas are in customer switching behavior, relationship dynamics and consumerswitching costs, with a particular emphasis on the study of ICT industries. He is thecorresponding author and can be contacted at: javisese@unizar.es

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