Explaining Market Heterogeneity in Terms of Value Perceptions

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Explaining Market Heterogeneityin Terms of Value PerceptionsDavid Martín Ruiz , Carmen Barroso Castro & EnriqueMartín ArmarioPublished online: 15 Nov 2007.

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Explaining Market Heterogeneity in Termsof Value Perceptions

DAVID MARTIN RUIZ, CARMEN BARROSO CASTROand ENRIQUE MARTIN ARMARIO

In the past decade, companies and academics have become aware of

the great benefits of creating value for customers. However, little

empirical research has yet been conducted in the area of services

with respect to how customers may differ in their perceptions of

value and what variables can explain such differences. This article

provides an insight into how three relationship-based contingencies

are likely to explain market heterogeneity in customers’ perceptions

of value. In particular, we explore how special treatment of the custo-

mer by the service provider, the level of customer involvement with the

service, and the customer’s accumulated experience with a particular

company may act as predictors of market heterogeneity in the custo-

mer’s perceptions of value within a service setting. Results offer evi-

dence for the important role of relationships and experience in a

service context mainly characterised by standard encounters, and

provide interesting managerial insights to tailor strategies that effec-

tively respond to market heterogeneity.

INTRODUCTION

In recent years, companies have become aware of the strategic relevance of

maintaining a solid base of loyal customers for survival, growth, and financial

performance [Reichheld, 1996]. Scholars and successful practitioners in the

area of services have highlighted the delivery of customer value as a key strategy

for achieving customer loyalty and reducing defection rates. Despite this empha-

sis, little empirical research has addressed whether customers have different per-

ceptions of value (market heterogeneity), why this occurs, and what are the

consequences in terms of customer loyalty and company performance.

David Martın Ruiz, Assistant Professor; Carmen Barroso Castro and Enrique Martın Armario, SeniorProfessors, Dpto. de Administracion de Empresas y Marketing, University of Seville, Spain. Emails:[email protected]; [email protected]; [email protected]

The Service Industries Journal, Vol.27, No.8, December 2007, pp.1087–1110ISSN 0264-2069 print/1743-9507 onlineDOI: 10.1080/02642060701673760 # 2007 Taylor & Francis

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Many elements might play a part in the perception of value during service

delivery. The relevance of those elements is likely to vary across different service cat-

egories as well as across service consumers [Danaher, 1998]. Recently, there has been

widespread recognition that customers differ in their responses to changes in attribute

performance [Anderson and Mittal, 2000]. Distinct customer profiles might lead to

different assessment of the service provider. Unfortunately, little work has been

done in building up models that consider customer heterogeneity in service manage-

ment [Zeithaml, 2000]. Customer value analyses have not yet explicitly accounted for

buyer heterogeneity and effectively assume that buyers are all affected in the same

manner by the antecedent factors [DeSarbo et al., 2001]. Thus, the main research

objective of the present article is evaluating which customer relationship-based

characteristics are most likely to explain different patterns of value perceptions.

Therefore, we need first to identify how many latent clusters exist in a particular

market, if any. Such clusters will capture customers’ different perceptions of value

in the context of a service delivery. Then, those customer relationship-based

characteristics will be treated as clusters-belonging predictors in a service value

model composed by customer benefits and sacrifices weighted against a global

measure of perceived value.

The article is organised as follows. First, a brief review of the literature on the

concept of customer value is presented in order to identify the main components of

customer value. Then, following Zeithaml’s [2000] recommendations, the customer

is analysed from a non-aggregate perspective, exploring differences in value percep-

tion that are likely to appear due to relationship-based customer variables. Specifi-

cally, the article explores how many patterns of customer value perceptions exist in

the context of mobile communication services, and how these are shaped by three

relationship-based contingencies – (i) the delivery of relationship benefits by the

service provider; (ii) the customer’s level of involvement with the service; and (iii)

the accumulated experience with the service provider. Then the results of the research

are presented. Finally, the article concludes by discussing the implications of the find-

ings, and by providing several suggestions for further academic inquiry.

THE CONCEPT OF CUSTOMER VALUE

The relevance of delivering customer value has been widely documented. Table 1

synthesises the two major streams of research in the area of customer value. The lit-

erature is replete with various approaches for defining and measuring customer value;

however, no single conceptualisation and operationalisation of the construct is widely

accepted. Only recently has an effort been undertaken to clarify the concept and

develop theory [Woodall, 2003].

Early research on customer value is based in the pricing literature, where

perceived quality and sacrifice are the main components in determining the perceived

value of a product. The widely held view is that ‘buyers’ perceptions of value rep-

resent a tradeoff between the quality or benefits they perceive in the product relative

to the sacrifice they perceive by paying the price’ [Monroe, 1990: 46]. Zeithaml’s

1088 THE SERVICE INDUSTRIES JOURNAL

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TABLE 1

EMPIRICAL APPROACHES USED TO STUDY CUSTOMER VALUE

Author(s)/ContextNumber ofscale items Main antecedents of customer value

Unidimensional measuresDodds et al. [1991] Calculators,

stereos4 price, brand, establishment, quality

Bolton and Drew [1991] Phoneservices

1 service quality, sacrifice, customer type

Kerin et al. [1992] Supermarkets 1 purchase experience, price, qualityHartline and Jones [1996] Hotel 1 service quality, employee performance cuesCronin et al. [1997] Spectator sports,

participation sports,entertainment, health care, longdistance telephone services, fastfood

1 service quality, sacrifice

Sirohi et al. [1998] Supermarkets 1 product quality, service, relative price,competitor value

Grewal et al. [1998] Bicycles 9 quality, transaction valueGould-Williams [1999] Hotel 1 service quality, employee performance cuesSweeney et al. [1999] Electronic

appliances3 functional and technical service quality,

product quality, relative price, perceivedrisks

Blackwell et al. [1999] Pharmacy 4 sacrifices, benefits, personal preferences, andservice situation

McDougall and Levesque [2000]Dentist, car repair, restaurant,hair dresser

1 nonea

Naylor and Frank [2000] Electronicappliances

2 vendor responsiveness

Teas and Agarwal [2000]Calculators, watches

5 perceived quality, perceived sacrifice

Cronin et al. [2000] Spectator sports,participation sports,entertainment, health care, longdistance telephone services, fastfood

2 service quality, sacrifice

Sirdeshmukh et al. [2002] Retailclothing, airline travel

4 trust in frontline employee behaviours, trustin management policies and practices

Yang and Peterson [2004] On-linebanking

5 nonea

Multidimensional measures Components of customer value (# items)de Ruyter et al. [1997] Hotel 15 emotional value (5), practical value (5),

logical value (5)Rust et al. [2000] Airlines 14 value for money (3), brand equity (5),

retention equity (6)Sweeney and Soutar [2001] Durables 19 emotional value (5), social value (4), price

(4), performance/quality (6])Mathwick et al. [2001] Internet and

catalogue shopping19 aesthetics (6), playfulness (5), service

excellence (2), customer ROI (6)Lam et al. [2004] Courier services

(business-to-business)10 service quality (5), price competitiveness (5)

Wang et al. [2004] Security firms 18 functional value (4), social value (3),emotional value (5), perceived sacrifice (6)

EXPLAINING MARKET HETEROGENEITY 1089

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[1988] initial customer value model has been empirically assessed in a variety of

different product categories and with numerous attribute cues [e.g. Dodds et al.,

1991; Grewal et al., 1998; Kerin et al., 1992; Naylor and Frank, 2000; Sweeney

and Soutar, 2001; Sweeney et al., 1999; Teas and Agarwal, 2000; Yang and Peterson,

2004]. These studies conceptualise customer value as a unidimensional construct.

On the other hand, a multidimensional approach aims to capture the whole

richness of customer value, identifying the major components of this construct.

Thus, value has been described as depending on a combination of monetary and

non-monetary sacrifice, quality, performance, and disconfirmation experiences that

represent a ‘richer, more comprehensive measure of customers’ overall evaluation

of a service than service quality’ [Bolton and Drew, 1991: 383]. However, there

have been different proposals regarding the components of customer value [Holbrook,

1994; Lam et al., 2004; Ruyter et al., 1997; Sheth et al., 1991; Woodall, 2003].

Following this approach, we identify and discuss four major drivers for creating cus-

tomer value in a service context: service quality, brand equity, confidence benefits,

and sacrifice.

Dimensions of Service Value

It is widely accepted that the delivery of a high-value offering must stand on quality

[Berry, 1995]. There can be two types of quality involved in a service delivery

context. If the company service delivery is built upon a core physical good – for

example, a cellular (mobile) phone in wireless communication services – product

quality will be a component of perceived value for the customer [Rust and Oliver,

1994]. Although it has been argued that product quality is a component that compe-

titors can easily imitate [Parasuraman and Grewal, 2000], product quality does have

the advantage of being ‘visible’ and easily evaluated, in contrast to credence com-

ponents, such as service quality or relational benefits. Brucks et al. [2000] identified

ease of use, functionality, duration, auxiliary services, performance, and prestige as

abstract dimensions that induce product quality judgements for consumer durables.

In addition, independently of where an offering stands on the continuum of products

and services, perceived service quality should be an essential pillar of customer

service value [Gronroos, 1995]. Service quality is difficult for competitors to

imitate [Parasuraman and Grewal, 2000], and it therefore represents a basis for differ-

entiation [Berry, 1995] and competitive advantage [Reichheld and Sasser, 1990]. A

general service quality assessment is the result of service encounters, which have

been referred to as ‘moments of the truth’ [Gronroos, 1997]. From the customer’s per-

spective, they represent times at which the value of the service is apparent, because

customers have the ability at such times to test whether the service company is

able to keep its promises. From the service provider’s perspective, each service

encounter represents an opportunity to show the potential value of its services to

its customers [Bitner, 1995].

A second dimension of service value is brand equity. Berry and Parasuraman

[1991] stated that building brand equity represents a great source of value creation

for the customer. Similarly, Rust et al. [2000] identified brand equity as one key

pillar in the customer equity framework. There is a significant opportunity to create


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value in services by enhancing personal bonding with customers through a company’s

brands [Berry, 2000]. According to this author, to strengthen bonding between custo-

mers and brands it is necessary to differentiate the brand beyond mere economics.

Brand must create feelings of proximity, affection, and trust. Cultivating brand

equity in services is especially important, given the intangible nature of the ‘invisible

purchase’ that services represent for the customer [Berry, 2000]. As a consequence,

brand equity plays the role of a signalling investment for the customer in a wide

number of service settings [Singh and Sidershmusk, 2000]. Therefore, brand equity

is likely to be one dimension of perceived customer value in services, and a path to

value creation for the customer.

Another value dimension that should be considered is the relational benefits deliv-

ered by the service provider to some customers. A number of authors have discussed

how these benefits affect customer assessments of the service provider [Bolton et al.,

2000; Gremler and Gwinner, 2000; Price and Arnould, 1999; Reynolds and Beatty,

1999]. Gronroos [1997] reported that a relationship has a value of its own, acting as a

softener in case of discrete service failures, since the relational customer judges the

relationship with the provider as a whole. Building on the early work of Barnes

[1994], Bendapudi and Berry [1997], and Berry [1995], Gwinner et al. [1998] devel-

oped, and empirically supported, a typology of three relational benefits – (i) psychologi-

cal benefits or confidence, (ii) social benefits or friendship, and (iii) special treatment or

functional benefits. Psychological or confidence benefits refer to feelings of trust and

anxiety reduction. As customers engage in relational behaviour and accumulate

service encounter experiences, their level of uncertainty decreases since knowledge

of the service provider rises. Social benefits refer to the friendship, recognition, and fra-

ternisation that might arise between the customer and the service provider – which

pertain to the emotional part of the relationship and are characterised by personal rec-

ognition of customers by employees, the customer’s own familiarity with employees,

and the creation of friendships between customers and employees, because service

encounters are mostly social encounters [Czepiel, 1990]. Finally, special treatment

refers to functional benefits such as better service conditions, better buying decisions,

advice, preferences, and economic advantages [Beatty et al., 1996]. In Gwinner

et al.’s (1998) typology of relational benefits, confidence benefits were found to be

the most important such benefit received by customers across a wide range of industries.

In short, trust is considered to be a key factor for developing successful relationships

between service providers and consumers [Vazquez et al., 2005]. We expect that

only some part of the company’s customer base will receive this kind of benefit,

especially friendship and special treatment. Therefore, the present study considers

that only confidence benefits can be generalised as part of customer service value.

Finally, customers have to face a number of sacrifices in order to obtain the

service. Sacrifices involve both monetary and non-monetary costs for the customer.

Paying the price of the service is the obvious monetary sacrifice, and this is therefore

clearly a component of service value [Voss et al., 1998] – although relatively easy to

imitate. Eventually for some customers non-monetary sacrifices might be even more

important than monetary sacrifices in customer choice. As time has become a scarce

resource in today’s society, this topic has become increasingly relevant. Some


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exploratory works have shown that service convenience is a multidimensional con-

struct involving various elements – such as the time, effort, and energy expended

to obtain the service. Time spent in making the decision and time spent waiting are

also relevant [Berry et al., 2002]. Service convenience reduces non-monetary costs

and affects consumers’ overall evaluation of the service, including satisfaction with

the service and perceived service quality and fairness.

According to consumers’ willingness to pay for convenience or to sacrifice conven-

ience for a lower price, the customer may be more oriented to monetary or

non-monetary sacrifices. Recent research defines convenience orientation as the value

consumers place on goods and services with inherent time- or effort-saving character-

istics [Brown, 1990]. Researchers agree that convenience orientation has a major impact

on consumers’ buying decisions, and cost-oriented and convenience-oriented consu-

mers have been found to be significantly different. This evidence suggests that per-

ceived sacrifices should be measured by formative indicators [Mackenzie et al., 2005].

In view of the above discussion, the present study adopts a multidimensional

concept of customers’ perceived value. This framework allows an identification of

the general dimensions driving customer perceptions of service value: quality, brand

equity, confidence benefits, and perceived sacrifice. In addition, a multidimensional

construct can be more appropriate to explore customers’ heterogeneity in a service

setting. Next, the study explores market heterogeneity literature and discusses how

customer relationship variables can explain the different patterns of value perceptions.

MARKET SEGMENTATION

There is an extensive conceptual and empirical literature on market segmentation,

which is a broad topic for research given its managerial relevance. The segmentation

approach is primarily derived from existing customer heterogeneity in the market.

However, typical consumer market segmentation approaches focus on either the

individual’s geo-demographic, socioeconomic, psychographic characteristics, or

product-based characteristics, mostly in relation to customers’ purchase behaviours

or satisfaction measures [Senguder, 2003]. There is much empirical evidence in

this regard. For instance, Mittal and Kamakura [2001] have reported significant

differences in repurchase behaviour related to overall satisfaction ratings according

to customers’ varying demographic characteristics – such as sex, education, age,

and marital status. However, when one of the chief characteristics of the market is

its heterogeneity, there is a need to include other variables in order to segment it

more adequately. This would permit a greater depth of knowledge of the variables

influencing consumer behaviours [Gonzalez and Bello, 2002]. Yet very few studies

have explored relationship-based variables for segmentation in relation to value per-

ceptions, where this research endeavours to make its major contribution.

Customer Relationship-based Heterogeneity

There is some evidence in the literature suggesting how customer relationship-based

heterogeneity determines the relationship between the service provider and the


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customer [Bolton, 1998; Danaher, 1998; Garbarino and Johnson, 1999]. Danaher

[1998] explored a variety of segmentation methods to ascertain customer groups

whose overall satisfaction responded differently to changes in attribute performance,

identifying distinct segments in the airline industry and among the users of inter-

national telephone assistance. Similarly, Garbarino and Johnson [1999] tested the

relative importance of theatre visitor attributes, finding meaningful variation

between regular theatre-goers and occasional ticket-buyers. Bolton [1998] found

that customers who have many months of experience with an organisation weigh

prior cumulative satisfaction more heavily (and new information less heavily) for pur-

poses of future behaviour. Perhaps the only research that has specifically addressed

the issue of customer heterogeneity and value perceptions is that conducted by

DeSarbo et al. [2001]. In their study, they proposed a finite-mixture methodology

to estimate the unknown number of market segments, as well as the perceived

value drivers at the market level. Therefore, although these researchers began by

assuming customer heterogeneity, they did not set an a priori known number of cus-

tomer segments. Rather, they stated that analysis on the basis of a priori customer seg-

mentation provides no assurance that an optimal segmentation is in place with respect

to a customer value model of interest. Following this methodology, they found signifi-

cant differences in perceptions of value for money among customers of an electric

utility company.

Development of Hypotheses

As DeSarbo et al. [2001: 847] stated, ‘a major omission in previous customer value

models has been their authors’ failure to incorporate heterogeneity in the integrations

of the underlying dimensions of value’. Therefore, our first objective or working

premise needs to determine the number of latent segments existing in the market.

Such segments – a priori unknown – will capture how customers behave differently

with respect to the way they form their value perceptions. Then, our three hypotheses

speculate about what relationship-based variables may determine individuals belong-

ing to a particular latent cluster. We discuss them next.

Relational marketing aims to create customer loyalty through relationship value

creation [Gronroos, 1997]. Therefore, it might be expected that those customers

who are receiving some kind of special benefits from the service provider would

be inclined to value such benefits, particularly those that are more difficult to

imitate by competitors. In particular, relational benefits have been stated as a major

reason for the customer to maintain a relationship with a service provider. This is

why relational customers might appreciate the benefits – which are more likely

to be specific to the relationship – and discount the sacrifices – which are likely to

be at least common to all providers. Sometimes, relational customers are even

willing to pay higher prices and make more effort in order to get benefits they will

not receive from another service provider. In contrast, customers emphasising price

have been evidenced as the most difficult to keep [Reichheld, 1996]. Some differences

for low and high relational customers have already been demonstrated at the

component-evaluation level [Garbarino and Johnson, 1999]. Recently, four types or


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service relationships have been identified depending on the degree of emotional

attachment between the customer and the service provider [Coulter and Ligas, 2004].

The above discussion leads to the following hypothesis being presented:

H1: Customer’s level of relational benefits delivered by the service provider will

influence the market segment that the customer belongs to in terms of value

perceptions.

Customer involvement is likely to affect customers’ perceptions and, conse-

quently, loyalty [Ostrom and Iacobucci, 1995]. According to literature, there are

two types of involvement – ego involvement and purchase/situational involvement

[Ganesh et al., 2000]. Ego or personal involvement refers to the particular interest

of an individual in a specific product or service – for example, hairdressing;

whereas purchase involvement relates to the level of concern in the purchase

process that is triggered by the need to consider a particular purchase – for

example, a wedding haircut. Purchase involvement can best be understood as the

cost, effort, or investment in a purchase. It is the outcome of a person’s interaction

with a product and the purchase situation [Beatty et al., 1988].

If a customer is more involved, he or she usually places higher value on the

service received [Zeithaml and Bitner, 2000]. In addition, involved customers have

some degree of expertise in the market. Because they spend more time seeking infor-

mation, available alternatives, and so on, they are in a better situation to judge the

service provider [Kotler, 1994]. Involved customers perceive higher risks in relation

to service delivery, and they are therefore more likely to tolerate greater sacrifices in

order to find a provider capable of satisfying their high expectations and needs

[Gassenheimer et al., 1998]. As a result of the above discussion, the following

hypothesis is presented:

H2: Customer’s level of involvement with the service will influence the market

segment that the customer belongs to in terms of value perceptions.

Because customer perceptions are shaped over time, customer value is a dynamic

construct. Parasuraman [1997] proposed a framework for research in which custo-

mers were split into non-customers, first-time customers, mid-term customers, and

long-term customers. Similarly, Mittal and Katrichis [2000] argued that newly

acquired and loyal customers should be treated as distinct segments. For services,

models of the duration of individual customer–firm relationships have shown that

the effect of satisfaction changes over time. Thus, customers who have a long experi-

ence with a service provider seem to have an ‘accumulated degree of satisfaction’,

which leads to a security margin in case of service failures [Bolton, 1998]. Oliver

[1997] contended that customer value relies on the time the judgement is made,

and pointed out that sacrifices are more important before the purchase and during

the early stages of a relationship with the provider, whereas benefits become more

relevant as customer usage of the service is prolonged. Experience and credibility

of services may be responsible for this behaviour, since inexperienced customers

have no previous guidelines to assess the service they are going to receive, and there-

fore they usually rely on external attributes such as price before the purchase is made.


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There is already some evidence of experience heterogeneity affecting value. For

instance, Naylor and Frank [2000] detected differences in value perceptions between

a group of first-time customers and a group of returning visitors to a health resort.

Bolton et al. [2004] also proposed that service usage is a significant source of

customer value that should be taken into consideration when driving marketing

expenditures across customer segments.

According to these arguments, the following hypothesis is therefore presented:

H3: Customer’s accumulated level of experience with the service provider will

influence the market segment that the customer belongs to in terms of value

perceptions.

METHOD

Industry Selection, Data Collection, and Sampling

The empirical research is conducted within the Spanish wireless telephone

industry, selecting the final customer as the unit of analysis. This industry

ensures some degree of market heterogeneity, given existent customer behaviour

differences, mainly due to the relative newness of the service [Sharma and Ojha,

2004].

Data has been collected through personal interviews at the central railway station

and airport in a major city in Spain during January and February 2001. Interviews

lasted an average of 25 minutes. The study obtained 995 fully completed question-

naires. Of the respondents, 47 per cent are male, and almost 70 per cent are aged

between 18 and 30 years, reflecting the popularity of the use of mobile phones

among young adults. Out of the different types of contracts available for this

service, company contracts represented 10 per cent of the sample, and since these

customers do not personally face the monetary sacrifices, they were removed from

the sample, leaving 877 valid responses.

Measures

The measurement scales for the independent variables of the study are the follow-

ing: (i) social benefits are estimated with the five-item scale developed by Gwinner

et al. [1998] – these indicators capture the degree of familiarity and friendship of

the customer with the service provider; (ii) level of involvement of the respondent

with the service was measured with three items taken from the research of Stell and

Donoho [1996]. These indicators captured customers’ interest in the service, the

perceived relevance of the service for their daily life, and the time spent of gather-

ing information about the service; [iii] customers’ accumulated experience with

their current provider was estimated by asking about the duration of their service

relationship and their daily level of usage. Less than 20 per cent of customers

had switched providers, so most of the experience information referred to their

current provider. The correlation analysis revealed no multicollinearity among the

three variables.


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Table 2 displays scale properties of the constructs present in the value model.

Scales were chosen on the basis of their empirical validation and their length,

since the scope of the study recommended not using long multi-item scales for

the questionnaire. The scale to measure product quality – the cellular device pro-

vided by the company – was taken from Brucks et al. [2000]. Among the differ-

ent approaches to measure service quality [Chiu and Lin, 2004], we adopted the

tool proposed by Cronin et al. [1997], which was developed from Parasuraman

et al.’s [1985] initial conceptual work on the topic. Ten items converging into

a single factor were found to be a very reliable shorter tool than other approaches

like Servqual [Parasuraman et al., 1988]. Brand image was assessed with three

items from Rust et al.’s [2000] customer equity framework. Confidence benefits

items were chosen from the relational benefits scale developed by Gwinner

et al. [1998]. Next, the sacrifice index is made up of three items capturing both

monetary and non-monetary aspects, taken from Cronin et al. [2000]. Finally, a

global measure of value for money was needed to act as related outcome for

the value component measure in our model. Six items were developed from pre-

vious empirical literature and pre-tested accordingly, showing valid and reliable

estimation properties.

Overview of Analytical Procedures

The data analysis begins with a confirmatory factor analysis, whereby the measure-

ment model is estimated noting five latent components of service value – product

quality, service quality, brand image, confidence benefits, and perceived sacrifice.

Scale reliability and validity assessment are included within the measurement

model estimation in Table 2. Next, we conduct a latent segmentation analysis with

the different value components. The objective is to identify different profiles of cus-

tomer behaviours depending on their perceptions. Finally, we perform a regression

analysis in order to determine to what extent customer’s involvement, customer’s

accumulated experience with the service provider, and the delivery of relational

benefits contribute to explain the customer’s assignation to a specific latent cluster.

Three software packages were used to apply this technique: SPSS 12.0, EQS 6.1,

and Latent Gold 3.0.

TABLE 2

BAYESIAN INFORMATION CRITERIA (BIC)

Model Likelihood # Of parameters BIC

1 segment 27301,0656 10 14671,1582 segments 26567,1348 21 13279,2273 segments 26301,9047 32 12824,6974 segments 26211,1023 43 12719,0225 segments 26144,5122 54 12661,7726 segments 26114,2801 65 12677,238


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ANALYSIS OF RESULTS

Measurement Model and Structural Model Results

An assessment of the measures of the constructs is performed with a confirmatory

factor analysis running EQS software. Model statistics obtained for second order con-

struct scales are reported in the appendix. Almost every indicator is found to be posi-

tive and significant, except for two items regarding product quality (duration and

prestige). Forthcoming technological advances in the industry might be partially

responsible for the low consistency of this item (‘it will take a while until I have to

change my cell phone’). Although providing prestige self-reported information is

always controversial, some customers owning the best brand new phones strongly

agreed with the prestige item (‘my cell phone reflects my prestige and social

status’), whereas others with the same models completely disagreed. Such a pattern

of responses caused a distribution that leads to low internal consistency for such

items.

As displayed in Table 2, service value dimensions worked quite well in general

terms (GFI ¼ 0.93, RMSEA ¼ 0.061). Correlations among the constructs are also

presented, along with the square root of their average extracted variances. According

to these results, discriminant validity is confirmed given that the square of the corre-

lation among constructs is lower than their respective average extracted variances.

To evaluate the complete structural model, the remaining items have been trans-

formed into a single measure capturing the latent variable scores for each first order

dimension – product quality, service quality, brand image, confidence benefits, and

sacrifice. Results reflect an appropriate adjustment of the model to the data

(GFI ¼ 0.95, RMSEA ¼ 0.076). For all aggregated respondents, confidence benefits

and service quality – followed by product quality and brand image – are the custo-

mers’ strongest determinants of value perception within the Spanish wireless tele-

phone industry. In this context, sacrifices seem to have contributed less to

customers’ perceptions of value. However, models that ignore heterogeneity can

yield a distorted picture and can therefore generate misleading inferences [Ansari

et al., 2000]. Because individuals are likely to be heterogeneous in their perceptions

and evaluations of unobserved constructs, the effects that relationship variables have

on value perceptions are estimated.

Latent Segmentation

Next we proceed to conduct a latent segmentation analysis using the five customer

value dimensions. Latent cluster analysis [Desarbo et al., 1992; Wedel and

Kamakura, 2000], also known as finite mixture regression, is a multivariate model

whose purpose is to find subgroups of cases from a certain number of variables,

such that underlying segments from the general population can be identified. This

methodology assumes that all of the dataset cannot be explained with a single distri-

bution of probabilities; rather, it requires a mixture of them. Thus, each cluster is

formed by the cases that belong to a specific distribution. This method assumes that

individual preferences constitute a population that is a mixture of several segments

of unknown size. It follows that it is impossible to know, a priori, which individual


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belongs a particular segment [Picon, 2004]. It is necessary to separate the samples by

identifying the number of segments and estimating the parameters that define each of

them. This is considered an optimum segmentation because the number of market seg-

ments is not set by the researcher. The technique provides the optimum number of clus-

ters that should divide the market. The estimation method begins with a hierarchical

cluster and continues with the iterative algorithm EM (expectation-maximisation),

until the combination of models and number of clusters is found that enables the col-

lection of more information. Latent cluster segmentation requires the identification of

the number of segments by a statistical criterion [Gonzalez and Santos, 2003]. One of

the more commonly used indicators is the Bayesian Information Criteria (BIC), which

is displayed in Table 2.

Table 2 suggests that there are five latent segments of customer in this market

according to customer value dimensions. Table 3 shows those clusters in terms of

their value perceptions, whereas Table 4 shows the size of every cluster and its profile.

According to the present results, the market can be divided into five latent seg-

ments (depending on customer value dimensions). The first cluster is the largest,

grouping those customers that perceive an average level for all the customer value

dimensions (37.35 per cent of the sample). The second cluster is formed by those cus-

tomers showing the lowest perceptions in the five value dimensions (27.51 per cent of

the sample). This cluster is relatively similar to the fifth cluster (4.26 per cent of the

sample) given their low perceptions of product quality, service quality and sacrifices,

but confidence and image figures are even lower than cluster number two. On the

other hand, the third cluster (21.66 per cent of the sample) collects the customers

with the highest value perceptions of their service provider, like cluster four

(8.82 per cent of the sample), except for the higher values of confidence benefits

and image that these customers perceive, making it slightly different from cluster

three. In conclusion, the latent segmentation analysis reveals the existence of five

clusters whose value perceptions are different from each other. Brand image and con-

fidence benefits account for the major proportion of these differences, while product

quality and sacrifices are less dispersed among customers. These results confirm the

existence of market heterogeneity in terms of customer value perceptions.

Once the latent segments are identified, our interest is to explore if some

relationship-based variables are significant predictors in the individual-cluster assig-

nation process, in order to anticipate their value perceptions. In particular, we explore

TABLE 3

VALUE PERCEIVED LATENT CLUSTERS

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Wald p-value R2

Product quality 20.0826 20.5081 0.5268 1.0170 20.9530 535.0602 1.7e-114 0.3041Service quality 0.1311 20.6588 0.7195 1.1889 21.3807 1078.858 2.9e-232 0.6498Brand image 0.2248 20.8518 1.1851 1.7376 22.2956 1611.578 9.1e-348 0.6578Confidence

benefits0.2641 20.8121 1.0950 2.0874 22.6344 1748.193 2.1e-377 0.6861

Sacrifices 0.0944 20.6197 0.5619 1.1364 21.1730 345.882 1.4e-73 0.3035Intercept 0.9019 0.5854 0.3463 20.5526 21.2809 151.423 1.0e-31


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how the delivery of special benefits for the customer by the service provider, the level

of customer involvement with the service, and the customer’s accumulated experi-

ence with a particular company may act as predictors of market heterogeneity in cus-

tomers’ perceptions of value within a service setting.

Multinomial Logistic Regression

We conduct a multinomial logistic regression setting the relationship-based variables –

customer involvement, social benefits and customer’s accumulated experience – as pre-

dictors, while the dependent variable is associated with each of the five latent clusters

identified previously. Information regarding the pseudo R-square is reported

in Table 5. On the other hand, the parameter estimation is performed using cluster

four as the comparison reference; the results are displayed in Table 6.

TABLE 4

CLUSTER SIZES AND PROFILES

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5

Size 0.3775 0.2751 0.2166 0.0882 0.0426Product quality1 0.3417 0.5323 0.0168 0.0000 0.10922 0.4702 0.3739 0.1020 0.0015 0.05233 0.4853 0.2042 0.2607 0.0211 0.02864 0.3720 0.1148 0.3899 0.1131 0.01015 0.2087 0.1478 0.3171 0.3147 0.0117Mean 4.0614 3.6360 4.6709 5.1611 3.1911Service quality1 0.1004 0.7385 0.0039 0.0000 0.15722 0.4757 0.4494 0.0393 0.0000 0.03563 0.7243 0.1426 0.1229 0.0001 0.01014 0.4514 0.0424 0.4487 0.0524 0.00515 0.1389 0.0049 0.4674 0.3848 0.0041Mean 3.8623 3.0724 4.4507 4.9201 2.3504Brand image1 0.1120 0.6665 0.0021 0.0049 0.21452 0.4849 0.4822 0.0328 0.0001 0.00003 0.6952 0.1515 0.1265 0.0268 0.00004 0.4556 0.0530 0.4252 0.0662 0.00005 0.1301 0.0160 0.5018 0.3521 0.0000Mean 3.8374 2.7608 4.7978 5.3502 1.3170Confidence benefits1 0.1370 0.6588 0.0012 0.0000 0.20302 0.4784 0.5025 0.0191 0.0000 0.00003 0.6452 0.1594 0.1954 0.0000 0.00004 0.4772 0.0468 0.4720 0.0040 0.00005 0.1986 0.0156 0.4106 0.3751 0.0000Mean 4.3248 3.2486 5.1557 6.1481 1.4263Sacrifices1 0.2060 0.5439 0.0880 0.0087 0.15352 0.4478 0.3837 0.1166 0.0351 0.01683 0.5080 0.2466 0.1883 0.0363 0.02074 0.4550 0.1364 0.3073 0.0814 0.01995 0.2749 0.0474 0.3933 0.2832 0.0011Mean 3.5219 2.8077 3.9894 4.5639 2.2544


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An exploration of the results displayed in Table 6 reveals some interesting find-

ings. First, the coefficients of customer involvement and customer’s accumulated

experience are significant in model 1. However, the odds ratio of social benefits do

not reach the threshold value required to be accepted [p , 0.05]. Given the features

of the politomic logistic regression, a coefficient represents the proportion against the

alternative that serves as a comparative reference. In this regard, a positive value of

the Bij indicates that, other conditions being equal, the chosen alternative will have a

higher weight than the comparison reference alternative [Hosmer and Lemeshow,

2000]. Results reported in Table 6 imply that the proportion between the likelihood

of belonging to the first cluster against the fourth (which is the comparative reference)

increases as the customer’s degree of involvement rises (since the odds ratio is posi-

tive 0.317). In short, as the service company is able to increase the customer’s invol-

vement, the probability of presenting an average perception of value is higher. On the

other hand, the odds ratio of the customer’s accumulated experience with the service

provider is negative, which means that as the customer’s experience increases, the

likelihood of belonging to the first cluster against the likelihood of belonging to the

fourth cluster diminishes. These results provide support for hypothesis H3. Thus,

the customer’s accumulated experience with the service provider influences the

market segment that the customer belongs to in terms of value perceptions. In

addition, as this accumulated experience increases, it is more likely that they are

TABLE 5

MODEL ADJUSTMENT INFORMATION

Model

Adjustment criteria Likelihood proportion test

AIC BIC 22 log likelihood Chi-squared DF Sig.

Null 2792,96 2792,96 2792,960Final 2438,43 2496,79 2414,43 378,527 12 .000

N ¼ 995 Pseudo R-squared Nagelkerke ¼ 0.340.

TABLE 6

PARAMETER ESTIMATION

Model B Wald Sig.

1 R. Benefits 20.065 0.849 0.357Involvement 0.317 61.405 0.000Experience 20.003 4.824 0.028





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assigned to cluster number four – where value perceptions are high – rather than to

the first cluster – where customers present average value perceptions. In short,

customers who have a long experience with a service provider seem to have an

accumulated degree of satisfaction, since service benefits become more relevant as

customer usage of the service is prolonged.

Results regarding customer involvement show that a customer’s level of involve-

ment with the service influences the market segment that the customer belongs to in

terms of value perceptions, supporting hypothesis H1. The sign of this coefficient indi-

cates that an increment on customer’s involvement will most likely classify the customer

in a cluster where the perceptions of value dimensions are average, and not high as could

be expected. A detailed analysis of the data reported in Table 7 indicates that customers

in cluster four are significantly the most involved with the service. Therefore we can con-

clude that the customer’s involvement is relevant in order to perceive average and high

levels in value dimensions, but not to assign the customer to any of the five clusters.

The analysis of the second model provides results similar to the first model.

Again, both customer involvement with the service and the customer’s accumulated

experience have significant coefficients. In this case, the social benefits coefficient is

on the grounds of an acceptable level of significance. Nevertheless, there is a clear

TABLE 7

ANOVA TEST RESULTS: CUSTOMER INVOLVEMENT AND LATENT SEGMENTS

sum of thesquares

Degrees offreedom f sig.

Inter-group 112.112 4 8.874 0.000Intra-group 1058.127 991Total 1170.239 995

Scheffe test for multiple comparisonsI. Customer’sinvolvement (mean) J Customer’s involvement

Meandifference (I-J) Sig.

Cluster 1 Cluster 2 0.37097 0.001(4,6878) Cluster 3 20.34567 0.005

Cluster 4 20.71274 0.000Cluster 5 0.41997 0.190


Cluster 4 21.08371 0.000Cluster 5 0.04900 0.999


Cluster 4 20.36708 0.105Cluster 5 0.76564 0.001

Cluster 4 (5,4006) Cluster 1 0.71274 0.000Cluster 2 1.08371 0.000Cluster 3 0.36708 0.105Cluster 5 1.13271 0.000

Cluster 5 (4,2679) Cluster 1 20.41997 0.190Cluster 2 20.04900 0.999Cluster 3 20.76564 0.001Cluster 4 21.13271 0.000


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difference between the models relative to the sign of the odds ratio for the customer’s

involvement, which is negative. Thus, the second model indicates not only that differ-

ent levels of involvement determine different perceptions of value (as proposed by

H2), but also that a growing involvement will increase the likelihood of belonging

to cluster 4 – where value perceptions are higher – rather than belonging to

cluster 2 – where value perceptions are low.

On the other hand, customer involvement is the only significant variable influen-

cing the customer’s assignation to latent clusters in the third model; in this model the

sign is the same as in the first model. Finally, the fifth model exhibits a major differ-

ence from the other models since the delivery of relational benefits influences the

market segment that the customer belongs to in terms of value perceptions, as pro-

posed by H1. In addition, the negative sign indicates that as customers receive

more relational benefits, the probability of being assigned to cluster four – where

value perceptions are higher – increases. Therefore, it might be expected that those

customers who have an ongoing relationship with a service provider would be

inclined to value special benefits.

DISCUSSION

Customer value has been claimed to be the basis of competitive advantage in the

twenty-first century. However, how service companies can provide such value for

their customers remains under-researched. A lack of useful tools and frameworks is

partially responsible for this situation, which usually leads to massive marketing

research in an attempt to establish ‘customer value’. At a time when customisation

and customer relationship management are claimed to be the new panacea to the

establishment of a position of superiority, the present study evaluates how different

customers assess the value they have been delivered. Specifically, the present study

has explored the moderating effects of customer relationship-based heterogeneity

over their value perceptions in the context of a particular consumer service.

Market Heterogeneity

The present study addresses the call for a non-aggregate research approach when

assessing customer value. The authors find evidence that certain relationship-based

variables – the delivery of relational benefits, the level of customer involvement,

and the level of the customer’s accumulated experience with the service provider –

influence the process of value perception. In general terms, service quality, confi-

dence benefits and brand image are the strongest determinants of service value in

mobile communications. However, there are significant differences between the

five latent segments identified, since they have different perceptions of value dimen-

sions. Thus, the latent segmentation analysis concludes that customers can be classi-

fied into five clusters depending on their value perceptions. These value segments

display their major differences in brand image and confidence benefits. On the con-

trary, customers perceive sacrifice and product quality more similarly across seg-

ments. Therefore, this study demonstrates that the existence of a service

relationship, a customer’s level of involvement with the service and level of


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experience with the service provider will influence the market segment that the cus-

tomer belongs to in terms of value perceptions.

In addition, the analysis of the results of the different models specify that, as customers

are more involved with service, have accumulated more experience with their service

provider, and receive relational benefits from their service relationship, it is more likely

that their value perceptions are medium or high, and vice versa. In this regard, the role

of customer involvement has proven to be especially relevant in these relationships.

To sum up, marketing scholars have been aware that various customers are differ-

ent and should be served differently – even personally – because they have unique

needs, but scholars are only starting to apply this concept to research and models.

Marketing researchers have usually taken an aggregate approach when analysing cus-

tomer data, rather than taking into account the fact that different customers might

have different perceptions. In conclusion, it is important to understand what the

underlying basis of such heterogeneity is and the aim of this article is to contribute

to advance in this topic of research.

Managerial insights

Industry recommendations. The present findings allow some pertinent insights for

management of mobile communication services. As the data analysis has shown, per-

ceptions of customer value are very much dependent upon service quality, brand

image, and confidence benefits and less upon product quality and sacrifice. Despite

the fact that all elements of the service are not equally important, all of them contrib-

ute significantly to customers’ service value perceptions. Therefore, in order to

acquire and retain customers a certain minimum level should be reached. In other

words, customers must be satisfied with every aspect of the service, although, depend-

ing on the customer, some service aspects should be emphasised. It would be a great

waste to lose a customer because he or she is not happy with one element of service

delivery, while perceptions of the others are satisfactory.

According to our results, building a strong image of service quality is a compel-

ling strategy in building service value. However, in order to achieve retention, wire-

less companies should focus on enhancing bonds between their customers and their

brands. A relational benefits programme will be the key to retaining those customers

who are more profitable. Personalisation in the service delivery process is a significant

feature for customers with unique needs. A prior step in enhancing a personalised

service experience is to identify the attributes driving service quality, and to differen-

tiate among them appropriately. These drivers would clearly be linked to unique indi-

vidual needs – for example, an international executive would be very interested in

getting consistent service conditions worldwide for wireless communications.

On the other hand, there are customers whose main interest is price and

non-monetary sacrifice. These customers are most concerned about expense and

service convenience (for example, how to purchase credit, calling rates, and so on).

Low tariffs and special features related to convenience would be very valuable for

this segment – for example, packages offering special rates for friends and short

text messages are especially attractive to teenagers. These customers need a

company to do the job in the simplest possible fashion.


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Another relevant issue for managers relates to the latent segmentation that is

present at the market, as well as understanding that there are certain variables char-

acterising the relationship between the customer and their service provider, and

how the customer evaluates it. In fact, the present study has found that the customer’s

level of involvement with the service, their experience with the service provider, and

the delivery of relational benefits are variables that the company may act upon with

their marketing strategies in order to improve the value perception of their customers.

LIMITATIONS AND PROPOSALS FOR FURTHER RESEARCH

The authors are aware of certain limitations in the present study. However, these

limitations do point to interesting future lines of research.

New Emerging Concepts: Sacrifices and Service Convenience

An interesting stream of research concerns those sacrifices that customers face in

order to be served, especially in a typically convenient service [Berry et al., 2002].

Further research is needed in the pricing of services, and how price relates to other

drivers of customer value. Also, service convenience (customer’s effort and time to

get the service) is a demanding issue given the modern lifestyles of many customers.

The authors expect that perceived sacrifices are extremely important for convenience

services in general, and are clearly becoming increasingly significant in every

context. Many customers are concerned with avoiding as many difficulties as possible

in their busy schedules. They might therefore consider only those companies who are

easy to do business with. Therefore, convenience represents a critical success factor

when serving such customers. Berry et al. [2002] addressed the increasing relevance

of this topic by stating several propositions regarding convenience dimensions, and

their relationship with individual differences such as experience and situational

involvement. These could not be assessed directly in the present study because a

measurement tool for service convenience is still needed.

Considering sacrifices as a whole, customers usually face a trade-off between

money and time expended – being the delivery of one feature at the expense of the

other (or at the expense of other benefits). But this relationship is not clear. It is poss-

ible that, at the same time, there will be another group of customers whose stronger

component of service value is sacrifices but who rate money expenses more highly

than convenience. These groups are not only likely to have a completely different

range of price acceptability charged for the service, but are also likely to infer and

perceive quality/benefits differently. These suggestions provide interesting directions

for further research.

Scope of the Study

Cellular phone encounters typically involve a low degree of personal interaction

between the service provider and the customer. In this context, social and functional

benefits appeared to be meaningless for the vast majority of customers, although they

should have a more significant impact in other – more personalised – service cat-

egories. Therefore, a multi-industry study could considerably enrich these findings


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by exploring how drivers of customer value are affected by the nature of the service

context. The same argument is relevant to the exploration of differences related to the

cultural background of customers. A multicultural comparison would be very helpful

to identify new contingencies likely to affect the value perceptions of service delivery.

Impact on Behaviour

How does market heterogeneity affect customer satisfaction, behaviour and company

performance measures? If customer value creation is supposed to be a real source of

competitive advantage, the linkages between company profitability and perceptions of

customer value must be tested empirically over the long term. In fact, value perceptions

should condition both actual customer loyalty and intended customer loyalty. This

relationship is also likely to be moderated by the same relationship-based contingencies.

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APPENDIX

Measurement model

Overall model fita

x2 / d.f. MDN RMSEA CFI

3.1 0.94 0.06 0.93

Service value

Internal consistency

Construct and scale itemsbStandardised

loadingCompositereliability

Coefficientalpha

Average varianceextracted

Product quality [Brucks et al., 2000] 0.87 0.80 0.63Compared to other models, my cell phone is

very easy to use0.65

Compared to other models, my cell phone isable to provide a good amount of functions

0.81

It will take time until I have to change my cellphone

NSc

Complementary services offered by [phonebrand] regarding to my cell phone areappropriate

0.85

The performance of my cell phone isexcellent

0.83

My cell phone reflects my prestige and socialstatus

NSc

Service quality [Cronin et al., 1997] 0.93 0.91 0.56In general, XYZ employees provide a reliable

and consistent service0.75

In general, XYZ employees are able toprovide the service on time

0.77

In general, XYZ employees are competentand knowledgeable

0.85

In general, XYZ employees are accessibleand easy to contact

0.76

In general, XYZ employees are polite andrespectful

0.72

(Appendix Continued)


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APPENDIX CONTINUED

Measurement model

Overall model fita

x2 / d.f. MDN RMSEA CFI

3.1 0.94 0.06 0.93

Service value

Internal consistency

Construct and scale itemsbStandardised

loadingCompositereliability

Coefficientalpha

Average varianceextracted

In general, XYZ employees listen to me andtalk to me so I can understand them

0.73

In general, XYZ employees are honest andtrustworthy

0.82

In general, XYZ establishments have a niceatmosphere

0.73

XYZ makes an effort to understand my needs 0.71In general, XYZ employees and

establishments are clean and well dressed0.60

Brand image [Rust et al., 2000] 0.89 0.86 0.68My attitude towards XYZ is very favourable 0.83XYZ image fits my personality 0.86I have positive feelings for XYZ 0.87

Confidence benefits[Gwinner et al., 1998]

0.93 0.93 0.83

I believe there is less risk that something willgo wrong in XYZ

0.87

I feel I can trust XYZ 0.92I have more confidence the service will be

performed correctly in XYZ0.92

Sacrifice indexd

[Cronin et al., 2000]0.83 0.66 0.63

The price charged in XYZ to get the service is 0.461The time required to get the service in XYZ is 0.173The effort I must do to receive the services

offered by XYZ is0.208

Value for moneye 0.96 0.95 0.71Compared to what I have had to give, the

ability of XYZ to satisfy my needs is0.83

Considering the time, effort and money spent,my assessment of the value received is

0.79

Compared to other providers, the value ofXYZ is

0.79

I think the service of XYZ meets myrequirements of quality at a reasonableprice

0.87

Considering what I have had to give, XYZservice is a good value

0.91

The service of XYZ represents good value formoney

0.89

Notes:a. Statistics are presented from Hair, Anderson, Tatham and Black [1999].b. Respondents expressed their answers in a 7-degree Likert-type scale, except for sacrifices [7-degree high-low scale],c. Some item’s standardized loading are non-significant [NS].d. In a composite index, the formative indicators are not expected to covariate [Jarvis et al., 2003].e. These items were partially reworded from previous research using value for money (see Table 1).


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Productquality

Servicequality

Brandimage

Confidencebenefits

Perceivedsacrificesb

Value formoney

Discriminant validity assessmentProduct

quality0.793a

Service quality 0.68 0.748a

Brand image 0.47 0.44 0.824a

Confidence benefits 0.66 0.69 0.42 0.905a

Perceived sacrificesb 0.49 0.43 0.33 .44 0.795a

Value for money 0.68 0.71 0.66 .67 .48 0.842a

a. Diagonal values represent the square root of average extracted variance valuesb. Sacrifices indicators are reverse-scored.

Structural model resultsStandardised

parameter estimate

Service value (SV) ! Value for money 0.894R2 0.800

SV ! Product quality 0.543SV ! Service quality 0.800SV ! Brand image 0.818SV ! Confidence benefits 0.788SV ! Sacrifice 0.626

x2 (df, p) ¼ (42, 0.00) 256.85CFI 0.968GFI 0.948SRMR 0.038RMSEA 0.076


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Documents

Explaining Market Heterogeneity in Terms of Value Perceptions