Relationship Between Customer Value & Pricing in Washing Machine Market

Pricing strategy & practice

The relationship between customer value and
pricing strategies: an empirical test
Anna Codini
University of Brescia, Brescia, Italy, and

Nicola Saccani and Alessandro Sicco
Department of Industrial and Mechanical Engineering, Supply Chain and Service Management Research Centre,
University of Brescia, Brescia, Italy
Abstract
Purpose – The paper seeks to fill a research gap that concerns empirical studies on value-based pricing in durable consumer goods. It aims to analyse
the relationship between value for the customer and market prices in the washing machines market.
Design/methodology/approach – The customer value of a sample of 129 washing machine models is assessed through the conjoint analysis
technique. It is then compared through a regression analysis to the market prices of the products.
Findings – The regression analysis reveals that the alignment between price and value for the customer is limited (only one of the two subsamples
presents a positive dependence among the variables).
Research limitations/implications – The study lacks explanatory power about the reasons for the misalignment between price and customer value
in the investigated sector. The results, moreover, refer to a specific product category and a specific national market, although their representativeness as
a mature durable in a mature market suggests a broader relevance of the implications. The size of the samples of the empirical research is also limited.
Practical implications – The paper provides an example and guidelines to practitioners on how to implement a customer value assessment. It
provides practitioners a deeper understanding of the consequences of misaligned pricing, and of the potential of understanding the actual value
sources for the customers.
Originality/value – The study empirically assesses the relationship between value for the customer and market prices of a category of mature durable
goods. The results support the claim that value-based pricing, although believed to be superior to other pricing policies, is still not established as a
prominent practice. Moreover, the findings contribute to the discussion on the value of environment-related attributes and their lifecycle monetary
impact on the customers. It also identifies another possible obstacle to the adoption of value-based pricing, i.e. the structure of the market, to be added
to the ones reviewed in the literature.
Keywords Pricing, Value for the customer, Conjoint analysis, Durable consumer goods, Washing machines, Regression analysis, Pricing,
Electrical goods, Italy
Paper type Research paper

1. Introduction

the main being the actual value assessment of products for the
customer (Ingenbleek, 2007; Hinterhuber, 2008).
This paper aims to contribute in filling a gap concerning
empirical studies on value-based pricing in durable consumer
goods. An analysis is carried out on the relationship between
value for the customer and market prices in the washing
machines market.
The study is based on the application of the conjoint
analysis methodology to assess the importance of different
washing machine attributes. The results allow to assign a

Recent studies (Hinterhuber, 2008) report of successful
adoption of value based-pricing strategies in diverse
businesses such as pharmaceutical, information technology,
wireless internet service provision, airlines, automotive and
biotech. However, although the benefits of value-based
pricing have been widely acknowledged (Monroe, 2003), its
application seems to be limited yet, due to practical obstacles,
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1061-0421.htm

This paper has been inspired by the activity of the ASAP Service
Management Forum, an Italian-based community where scholars and
practitioners from five Italian universities, and more than 50 leading
manufacturing companies and service providers collaborate in developing
research and dissemination in the product-services management field. For
more information see www.asapsmf.org/

Journal of Product & Brand Management
21/7 (2012) 538– 546
q Emerald Group Publishing Limited [ISSN 1061-0421]
[DOI 10.1108/10610421211276321]

538

The relationship between customer value and pricing strategies


Anna Codini, Nicola Saccani and Alessandro Sicco

Volume 21 · Number 7 · 2012 · 538 –546

value for the customer to a sample of 129 washing machines
sold on the Italian market. The value for the customer is then
compared to the actual market prices: the empirical study
shows a limited alignment between prices and value for the
customer, revealing that overpricing and underpricing of
products are common phenomena.
The paper is structured as follows. Section 2 provides a
background on the customer-value based approach to pricing
and on the washing machines industry. The research
methodology is described in section 3, as well as the
research sample. Section 4 illustrates the results of the
empirical study. The conclusions emerging from the study
and future research directions are discussed in section 5.

decisions, while limited emphasis was given to customers’
demands and needs. Carricano et al. (2010), based on 28
interviews with pricing managers in large companies in
France found that, even if “value” orientation in pricing is
highly diffused, it is still difficult to have it practically
implemented at the company level.
Value-based pricing requires the evaluation of the value that
customers attach to a product or a service through formal
market research. A company-wide marketing orientation may
facilitate such a pricing process: however, some of the cited
studies point out a very limited role of the marketing function
in pricing decisions.
Other studies concern the perceived price and price fairness
in particular (Campbell, 1999, 2007; Haws and Bearden,
2006; Diller, 2008). For instance, in their study in the DVD
market, Cockrill and Goode (2010) examined the perceived
price fairness, actual pricing and price decay in a short-life
cycle market. The comparison among the prices of six UK
retailers for a range of movies released over eight months and
the perceived perception of fair price of 500 UK adults
revealed a considerable gap between actual prices and
perceived fair prices of DVDs, especially for older items.
Some studies about price fairness, instead, test the
acceptability of price changes, analyzing the effects of price
changes on consumers’ perception of a fair price. Such studies
conclude that price increases in line with cost increases are
perceived as fair, while price increases not justified by costs
are perceived as unfair (Dickson and Kalapuraka, 1994;
Huang et al., 2005; Bolton and Alba, 2006; Choi and Mattila,
2009).
However, despite the attention devoted by the literature to
value-based pricing policies, few empirical studies provide
guidelines on how to adopt this approach, and empirical
comparisons between the market prices and the actual value
for the customer.
In order to contribute to fill this gap, this paper reports a
field research in the washing machine sector, aimed to assess
the alignment between value for the customer and market
prices: this is done on a large database of products actually
sold on the market. The paper also provides an example of
customer value measurement, through the adoption of the
conjoint analysis technique.

2. Customer value-based approach to pricing
2.1 Customer value-based pricing
The customer value-based approach sets the price of an
offering based on the value assigned by the customer, rather
than based on costs or on competition (Busacca et al., 2004).
So, value-based pricing is defined as the extent to which a
firm, in the process of price determination, uses information
on the perceived relative advantages that it offers and on how
customers will trade off these advantages against price
(Ingenbleek, 2007).
According to several studies (Cannon and Morgan, 1990;
Monroe, 2003; Ingenbleek et al., 2003, Docters et al., 2004),
customer value-based pricing is preferable to other pricing
strategies. The increasing endorsement of customer valuebased strategies among academics and practitioners is based
on the general recognition that sustained profitability lies in
understanding the sources of value for the customers, by
designing products, services and solutions that meet
customers’ needs, by setting prices as a function of value
and implementing consistent pricing policies (Hinterhuber,
2008). Measuring or developing an understanding of
customer value is important to firms. First, because it
informs on customers’ willingness to pay: firms that engage in
value-based pricing will not charge lower prices than
necessary. Second, firms that adopt value-based pricing are
able to match perceived benefits (by customers) with
products’ price, so they can increase purchase intentions
(Grewal et al., 1998). Therefore, understanding and including
in the price definition the value perceived by the customer
may lead to both higher sales and higher profit margins. As
suggested by Piercy et al. (2010), designing a value-based
pricing strategy is pivotal in developing new business models.
Moreover, Stamer and Diller (2006) suggest that price
management should be concerned with price segment
structures in order to increase the effectiveness and the
efficiency of consumer targeting. Finally, Ingenbleek et al.
(2010), using a structural equations model, show that valueinformed pricing has a strong effect on new product
performance.
Despite the benefits of customer-based approaches to
pricing, however, these methods still play a relatively minor
role in business strategies. For instance, Avlonitis and
Indounas (2006) analyzed the pricing methods in six
different services sectors in Greece: costs and competitors’
prices were found the two main elements that trigger pricing

2.2 The washing machine industry: pricing and value
for the customer
Major domestic appliances (washing machines, refrigerators,
dishwashers, etc.) are an integral part of households’ everyday
life, and represent one among the most relevant durable
consumer goods industries. They constitute a long-term and
relatively important investment for families due to its
relatively high cost and low purchase frequency. The
industry relies on a responsive supply chain (Fisher, 1997),
pursuing at the same time the minimization of manufacturing
and logistics costs and the maximization of logistic service
(Perona et al., 2001), However, manufacturers’ strategies can
be considered product-oriented (Saccani et al., 2006): they
are very active in innovating products and in promoting
responsible usage of environmental resources. Production of
washing machines of classes lower than A (the most efficient
one) has dramatically reduced since the end of the 1990s, and
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Volume 21 · Number 7 · 2012 · 538 –546

energy-efficient appliances dominate sales in most western
markets.
A number of studies related to washing machines pricing
issues can be found in literature. Jung (1958) analyzed the
price variations of washing machines in Chicago among
different retailers. Foxall (1972), through an empirical
analysis on electrical appliances, argued against the costplus pricing theory, observing that firms are “rather more
marketing- and consumer-oriented” than expected. Bayus
(1992) found that customers’ loyalty to washing machines
brands is positively influenced by the replacement age. Also,
marketing campaigns have a role in influencing customer
loyalty. Martinez and Polo (1996) suggested that the speed of
acceptance of innovations for washing machines may be
considered higher than for other durable consumer products.
This finding is supported by the work of Mukherjee and
Hoyer (2001): their empirical study shows that the
introduction of novel attributes in low-complexity product
categories (such as washing machines as compared to more
technology-endowed consumer products) improve the
evaluation of new products, since customers’ understanding
and usage of new features entails low learning costs.
Stamer and Diller (2006) analyzed the relation between
price and customer segment for washing machines (together
with other durables), identifying five segments, namely:
“brand conscious buyers” (who have high quality expectations
and are reluctant to search for low price), “discount buyers”
(who aim at simplifying the choice process, targeting discount
shops), “optimizers”(who are prepared to invest time and
effort for price rewards), “high price shoppers” (with high
quality and brand preferences, and for which price has an
important signaling role) and “price seekers” (who consider
price as the prominent decision criterion).
According to Tellis and Wernerfelt (1987) high quality is
more likely to imply higher prices for high cost, long-life
durables than other consumer products, because customers
are more likely to perform search activities for these products.
The empirical analysis by Tellis (1989) in the major appliance
industry supports this view, but shows that it explains only a
short percentage (6 percent in the studied sample) of price
variation, while the impact of corporate aspects (size, strategy
and, indirectly, brand) was found to be the most relevant.
Barbiroli and Focacci (2003) analyzed the nature of the
correspondence between the commercial value (price) and the
objective quality of durables among which washing machines.
Quality was assessed through a technical performance index
function of energy consumption, water consumption,
capacity, maximum spin speed, and length of the washing
cycle. Their empirical analysis over a sample of 62 product
models showed that for a company’s range of products, there
is no exact correspondence between the variation in technical
characteristics and the variation in price although, on the
overall sample, a linear regression model was generally valid.
The value of environmental attributes, in particular energy
consumption, was addressed by recent studies. Sammer and
Wustenhagen (2006) with a survey-based conjoint analysis
explored consumers’ stated choices for washing machines in
Switzerland. They found that eco-labeling coupled with life
cycle cost information disclosure affects consumers’
purchasing decisions, and that environmental preservation

has a value per se for surveyed customers beyond its life cycle
cost effects. Mills and Scleich (2010) analyze the role of
labeling, customer information and purchase propensity for
household appliances in the German market. Washing
machines owners showed a higher level of knowledge of
appliance energy class than the other appliances investigated
(freezers, refrigerators and dishwashers), as well as the highest
level of class-A appliance owners (65 percent). Finally,
Deutsch (2010) found that life cycle cost disclosure guides
consumers toward choosing products with lower energy and
water consumption, but only to a little degree.
None of the studies reviewed above, however, analyses the
relationship between market prices and value for the customer
in the washing machine sector.

3. Research methodology
3.1 Empirical research framework
The paper takes an empirical approach on the assessment of
value for the customer (VFC) and its relationship with pricing
policies. The empirical application was carried out in the
washing machines sector. The methodology is based on three
steps.
Step 1. Assessment of the VFC of product attributes
The measure of the value for the customer was carried out
through a conjoint analysis. The conjoint analysis is among
the most popular techniques for measuring customer value
and considered to guarantee valid and affordable results
(Green and Srinivasan, 1978, 1990). According to Green and
Srinivasan (1978), the term conjoint analysis can be broadly
referred to “any decompositional method that estimates the
structure of a consumer preferences given his/her overall
evaluations of a set of alternatives that are pre-specified in
terms of levels of different attributes”.
The objective of Step 1 is to achieve a quantitative measure
of customer value of product attributes and product profiles.
The customer value corresponds to the global utility deriving
from the sum of the single utility levels assigned to the specific
attributes of each product profile. Product profiles consist of
combinations of specific attributes, with the levels of these
attributes being systematically varied within the set of
offerings. Respondents are asked to provide their purchase
preference ranking for each of the product profiles. Statistical
analysis is then used to identify the value that respondents
place on each attribute.
The conjoint analysis method allows the researcher to
measure the relative values of attributes that have been
considered jointly by the respondents.
Step 2. Assessment of the VFC of the washing machine sample
This step consisted of applying the results of the conjoint
analysis to a sample of 129 washing machines, described in
the following section. According to the level of each attribute
for each product, a VFC is assigned to each washing machine
model.
Step 3. Assessment of the relation between prices and VFC
The relationship between price and customer value of the
sample products was investigated through a regression
analysis. The VFC assigned to each product profile was
compared to its actual sales price.
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3.2 The study sample: preliminary analysis
An initial database contained 450 washing machines sold in
Italy by a leading retailer, including 17 different brands. Data
collected were: price, energy consumption, water
consumption and spin-dryer speed. The availability of a
database with real data allowed to set realistic values for the
conjoint analysis. From the initial database we selected the
models with loading capacity ranging from 5 kg to 6 kg (the
most common on the market), produced by the four most
diffused brands. We obtained a study sample of 129 product
models. Descriptive statistics for the relevant variables in this
study (price, energy consumption, water consumption and
spin dryer speed) are shown in Table I.
Before performing the VFC study and assessing its
relationship with the product prices, a preliminary analysis
on the relationship between price and the product technical
characteristics was carried out. We considered two
subsamples according to the loading capacity since this
feature has a strong influence on the relationship between
price and the other variables, as discussed in the Appendix.
In each of the two subsamples (one with loading capacity
5 kg, the other with loading capacity of 5.5 or 6 kg), the
relation between price and three technical characteristics
(energy consumption, water consumption and spin dryer
speed) was tested through a multiple regression model. The
analysis showed a significant positive relationship between the
spin dryer speed and the price, for both subsamples, while
there was no statistically significant relationship between price
and energy and water consumption.

of the preliminary analysis mentioned in 3.1 and illustrated in
the Appendix. The energy class was also not considered, since
the market is made almost exclusively of class A washing
machines (in 2009, 96 percent of washing machines sold in
Italy were of class A): instead, the actual energy consumption
(KwH/cycle) was included. As well, the actual water
consumption (l/cycle) was considered instead of the washing
class. Moreover, the configuration (top versus front loading)
was discarded since more than 90 percent of the European
market is made by front-loading washing machines, testifying
an explicit preference for this configuration by customers.
The choice of the attributes was supported by a preliminary
research on a random sample of 25 customers, who were
asked in an open-ended question to state the main selection
criteria for a new washing machine. The most cited attributes
were: energy consumption, price, spin dryer speed and quality
in general. This preliminary research suggested also not
considering the availability of particular washing programs,
which emerged as not being a priority for customers.
Along with the identification of the attributes, another
important decision refers to the definition of the levels for
each attribute. The four brands selected are the most sold by
a leading Italian retailer, and the ones included in the washing
machines sample described in section 3.2. As for the other
attributes, the levels cover the range of values found in the
product sample. The selected attributes and their levels are
listed in Table II. Although the brand names are not disclosed
here for confidentiality reasons, they were openly shared with
participants during the empirical research.

4. Empirical findings

ii) Configuration of virtual product profiles
After the identification of the attributes and their levels, these
were combined to configure the virtual product profiles using
the software SPSS (orthogonal design technique). We
adopted the full profile method, which utilizes the complete
set of factors, thus providing a more realistic description of
stimuli (Green and Srinivasan, 1978).

4.1 Step 1: value for the customer of product attributes
In Step 1 (see the framework described in section 3.1) we
measured customer value through a conjoint analysis,
following the five steps reported below (Molteni, 1993).
i) Identification of the attributes and of the related levels
Green and Srinivasan (1990) recommend including no more
than six attributes in the definition of product profiles, and to
limit the number of levels for each attribute. Based on our
preliminary analyses on the sample and on previous studies on
washing machines discussed in section 2.2 (Barbiroli and
Focacci, 2003; Sammer and Wustenhagen, 2006; Deutsch,
2010; Mills and Scleich, 2010), we considered five attributes:
brand, energy consumption, water consumption, spin dryer
speed and price. Other possible attributes were taken into
consideration, but eventually discarded from the final set of
attributes. The loading capacity was not included as a result

Table II The relative importance of the five attributes
%
Price
Energy consumption
Spin dryer speed
Brand
Water consumption

35.48
28.39
17.08
16.93
2.13

Table I Characteristics of the product sample
Sample
(n 5 129)
Mean
Std dev.
Price (e)
Energy consumption (KwH/cycle)
Water consumption (l/cycle)
Spin dryer speed (turns/minute)

Brand A
(n 5 35)
Mean
Std dev.

Brand B
(n 5 21)
Mean
Std dev.

380.63
0.96
52.12
953.49

387.97
1.00
55.57
948.57

400.17
0.95
46.67
857.14

103.77
0.10
6.97
209.93

100.39
0.09
6.54
229.28

541

115.94
0.06
115.94
180.48

Brand C
(n 5 35)
Mean
Std dev.
412.09
0.95
48.89
1,000.00

99.48
0.11
99.48
187.87

Brand D
(n 5 38)
Mean
Std dev.
334.08
0.95
54.92
968.42

90.55
0.10
90.55
215.74




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iii) Submission of product profiles to a sample of customers
Thus, the 16 full product profiles were submitted to a random
sample of 97 owners or users of washing machines. The
sample is made by 54 percent of men and 46 percent of
women, while the age distribution is the following: 36 percent
are 20-39 years old, 42 percent are 40-59 and 22 percent over
60 years old. According to their employment, the interviewed
persons can be described as factory workers (17 percent),
housewives (14 percent), office workers (12 percent), students
(12 percent), retired people (12 percent), company managers
(8 percent), people working in education (8 percent) and
other (17 percent).
Respondents were washing machine owners and users and
have purchased washing machines in the past. They are also
prospective customers since they are going to make
purchasing decisions in the future. However they were not
going to purchase a new washing machine in the very period
in which they were surveyed (next three months).
The survey was administered through personal interviews.
The interviewed users were asked to express a likelihood of
purchase for each profile rating on a scale going from 1 (very
unlikely) to 9 (very likely). The questionnaire used for the
interviews reported all the profiles in a single page. Similar to
other works concerning household appliances (Sammer and
Wustenhagen, 2006; Deutsch, 2010; Ward et al., 2011), the
study follows the stated preference approach – rather than
observing the actual customer decisions (revealed preference
approach).

Table III Attributes, levels and utility estimates
Attributes and levels

IRj ¼ Pk

i¼1

ðMax½UjWji Š 2 Min½UjWji Š Þ

Standard error

Brand
B
D
C
A

2 0.183
0.263
0.039
2 0.119

0.190
0.190
0.190
0.190

Spin dryer speed
Low 600-800
Medium 900-1,100
High 1,200-1,600

2 0.215
2 0.038
0.253

0.146
0.171
0.171

Energy consumption
0.6
1.1
1.6

2 0.374
2 0.748
2 1.122

0.132
0.264
0.397

Water consumption
40
70

2 0.057
2 0.113

0.219
0.439

Price (euro)
149
299
499
949
(Constant)

2 0.312
2 0.624
2 0.936
2 1.247
7.204

0.098
0.196
0.294
0.392
0.485

Price is the most important attribute for purchasing decisions,
with a relative importance higher than 35 percent, followed by
the energy consumption, that is confirmed as an important
factor in customer choices. The spin dryer speed and brand
assume a moderate importance, while water consumption has
a very low importance.

iv) Utility estimate and relative importance of the attributes
The results of the interviews were elaborated using PASW
conjoint 18 of SPSS to obtain the utility coefficients for each
attribute, reported in Table III along with the standard error.
The utility coefficients allow to calculate the relative
importance of each attribute.
Results of correlation tests using R of Pearson and Tau of
Kendall, (Pearson’s R ¼ 0:878, with p-value 0.000; Kendall’s
Tau ¼ 0:650, with p-value 0.000) point out the existence of
significant correlation among the estimate and the observed
preferences.
The utility estimates provided in Table III express the value
assigned by the interviewed sample to the specific levels of
each attribute. Based on that, we can compute the importance
of each attribute, expressed as its “part-worth”, that is the
percentage of the total decision ascribed to that attribute. In
other words, the gaps emerging from the different utilities give
a measure of the value perceived by the customer moving
from one level to another of the same attribute. The relative
importance of each attribute is calculated by equation
(Molteni, 1993) (1):
Max½UjWji Š 2 Min½UjWji Š

Utility estimate

4.2 Step 2: value for the customer of the washing
machine sample
Given the utility estimates computed in Step 1, we calculated
the value assigned to the actual products available on the
market, i.e. the 129 washing machine models in our sample.
We substituted to the actual levels of the different
attributes, except price, the utility values in order to
calculate the value assigned to the real product profiles. The
computation is made thanks to equation (2) (Molteni,
P
1993):VFC* i ¼ b0 þ k UjWji
j¼1
VFC* ¼ b0 þ
i

k
X

U j W ji

ð2Þ

j¼1

where VFC *i is the global utility of the washing machine i in
the sample, b0 is the constant; k is the total number of the
offering’s attributes (4, since price is excluded), Wij is the
level of the “j” attribute of the “i” product profile and UjWij
represents the utility level associated to the specific attribute
assumed by the specific product profile. In our case the value
for the customer of product i is given by the sum of the Brand
utility, Energy consumption utility, Water consumption utility
and Spin dryer speed utility. Our global utility indicator does
not include the price utility, therefore it can be compared with
the actual market price of the products.

ð1Þ

where IRj is the relative importance of the j attribute; k is the
number of the attributes included in the analysis; Max
[UjWji ] is the maximum utility value associated to the Wji
level of the “j” attribute of the “i” product profile; Min
[UjWji ] is the minimum utility value associated to the Wji
level of the “j” attribute of the “i” product profile. The IRj
calculated as in (1) are reported in Table II.
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4.3. Step 3: exploring the relationship between price and
value for the customer
In order to test whether the price of washing machines is
consistent with the value for the customers, we performed a
regression analysis of the price versus VFC * for the 129
washing machine models in the sample. We carried out the
analysis separately on two subsamples according to the
loading capacity (5 kg and 5.5-6 kg), to avoid any possible bias
in the results, as explained in the Appendix. Table IV shows
the results of the regression analysis.
Table IV suggests that a positive linear relationship between
price and VFC * (value for the customer considering all the
attributes except price) exists in one subsample (loading
capacity equal to 5 kg), where the level of p and adjusted R2
support a statistical significance of the results, but not in the
other (low value of p and adjusted R2).
The results reported in this section suggest that the
alignment between price and value for the customer in the
empirical sample is unclear, or at least partial: this seems in
line with the limited adoption of value based pricing pointed
out in the literature review section.

out in Table II. Besides price, customers give a high
importance to the energy consumption, showing an increased
awareness about both the environmental and financial lifecycle
impact of durable goods: energy-efficient washing machines,
indeed, will consume less environmental resources and
generate lower usage costs during their lifecycle compared to
less efficient ones. Although specific of one kind of product,
our findings contribute to the discussion about the role of
environmental attributes in customer choices and their relation
with pricing policies. In line with other studies (Sammer and
Wustenhagen, 2006; Mills and Scleich, 2010; Deutsch, 2010,
Ward et al., 2011) our work shows that customers attach value
to environmental factors when purchasing durables and this
should be taken into consideration when adopting value-based
pricing policies. Moreover, customers trade-off the
environmental impact of product attributes with their
economic impact over the lifecycle. On the latter aspect
results from previous research are contradictory (do customers
value attributes such as energy efficiency less or more than the
monetary savings achievable during the product lifecycle?, see
e.g. Ward et al., 2011; Deutsch, 2010). Interpreting the results
of this study we can raise an observation about the relation of
environmental attributes with the perceived product quality.
Customers give a very different importance to energy and
water consumption of washing machines: they seem to attach
both environmental and financial savings (with no impact on
product quality) to reduced energy consumption. On the other
hand, they do not attach monetary savings to lower water
consumption (due to the low cost of water) and trade-off the
environmental savings with a perceived reduction in product
quality: in fact, 46 percentof the customer sample attached a
lower value to lower water consumption. This point leads to
another issue: the role of information/communication in
influencing the customers’ perceived value (Ward et al.,
2011) and thus perceived price fairness (Cockrill and Goode,
2010). Increasing customer awareness on e.g. the impact on
product quality of lower water consumption, or the energy
label or consumption knowledge and its cost saving effects
(Mills and Scleich, 2010), may influence the value for the
customer of such attributes.
Finally, interpreting the findings from this study, we can
suggest an additional obstacle to value-based pricing to the
ones evidenced by the literature. The misalignment between
prices and value for the customer could derive from a limited
market sensing ability, but also from the very market
structure. In an industry characterized by intermediation
(retail chains sell to final customers washing machines made
by manufacturers) and concentration at both the
manufacturing and retail level, price pressures are induced
and price promotions at the retail level are very common.
These factors influence substantially the actual market prices,

5. Conclusion
Although the literature points out the benefits of value-based
pricing policies (Cannon and Morgan, 1990; Monroe, 2003;
Ingenbleek et al., 2003; Docters et al., 2004), their diffusion is
still limited in business practice: as well, very few empirical
studies in durable consumer goods are reported in the
literature assessing value for the customer and pricing
policies. We aim to contribute in filling this gap and add to
the body of research on value based pricing in durable
consumer goods with an empirical study in the washing
machines market.
To our knowledge, this study is among the first attempts to
assess the alignment between the value for the customer and
the actual market prices on a large sample of durable
consumer products. Our methodology is based on an
estimation of the value for the customer, through the
conjoint analysis technique, of attributes other than price
(brand, energy consumption, water consumption, spin dryer
speed) and on a regression analysis to assess the relationship
between actual prices and value for the customer.
The empirical results show some alignment in one
subsample (5 kg loading capacity), that is not confirmed in
the other subsample (6 kg). This constitutes additional
evidence supporting the claim that the customer-value based
approach, despite the benefits acknowledged, is still not
established as a prominent practice in durable goods markets
(Hinterhuber, 2008; Carricano et al., 2010).
Moreover, our study sheds some light on the sources of value
for the customer in the product category studied, as pointed

Table IV Results of the regression analysis
5 kg capacity sub-sample
(n 5 66)
b1
p
Price vs VFC *

R2

0.441

0.26

,0.0001

Adjusted R2

5.5-6 kg capacity sub-sample
(n 5 63)
b1
p
R2

Adjusted R2

0.22

0.444

0.11

543

0.0011

0.16




Volume 21 · Number 7 · 2012 · 538 –546

even in presence of a declared “value pricing orientation” by
manufacturers.
This study also has some managerial implications. First, as
remarked in section 2.1, one of the main obstacles to the
implementation of value-based pricing policies is given by
value assessment. Conjoint analysis, a rigorous technique to
assess value for the customer, has a relatively low diffusion,
due to its complexity, the perceived difficulty in administering
the survey and the limited market orientation of companies.
This study provides an example and guidelines to
practitioners on how to implement a customer value
assessment and check the alignment of their companies’
pricing policies to customers’ value perceptions.
Moreover, our findings give to practitioners in the studied
industry a picture of the alignment of prices with customer
value, providing them a deeper understanding of the
consequences of misaligned pricing. In fact, setting prices
without considering the customer value, could bring either to
lost sales (effect of overpriced products) or to lost margins
(underpriced products): understanding the value attached to
the different product attributes allow “value for money” to be
given to the customers, better exploiting the profit potential of
the products and eventually increasing customer satisfaction
and market shares.
Besides its merits, this paper presents some important
limitations, too. First of all, the results of the study refer to a
specific product category and a specific national market,
although their representativeness as a mature durable in a
mature market suggests a broader value of the findings.
Moreover, both the conjoint analysis and the regression
analysis are based on limited samples, that prevent the
discerning of a statistical basis if the different brands adopt
different approaches to pricing, as suggested by our analyses.
Finally, it is important to notice that our empirical research
lacks explanatory power about the reasons of the
misalignment between price and value for the customer in
the investigated sector. Some interpretations of results in this
sense are provided, but they are based on the knowledge of
the industry by the authors and their personal judgment.
These research limitations clearly indicate some directions
for future research: to increase the sample sizes, the product
categories and geographical markets investigated, to involve
company managers in qualitative research to investigate the
pricing policies and processes they adopt. Moreover, the
research implications reported above also pave the way for
future research on the perceived value of product attributes in
durables, with particular emphasis on their environmental and
life-cycle cost impacts.

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Management: Understanding, Creating and Delivering Value,
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Appendix. Preliminary sample analysis
The initial sample concerned 450 washing machines of 17
different brands. As a preliminary analysis, we wanted to
check the influence of loading capacity on the appliances’
price and on the other features, as emerged from results of
previous literature (Barbiroli and Focacci, 2003). To test this
assumption, we used a non-parametric ANOVA on the
subgroups, and we set up a mediation model (Baron and
Kenny, 1986) to test the effect of the capacity on the
dependence between price and the other technical features.
Table AI shows the mean for all the considered features on
the subgroups of models with similar capacity:
The ANOVA tests (not provided here) suggested a
statistically significant difference among the different
capacity classes regarding price, energy consumption, water
consumption and spin dryer speed. Moreover, as reported in
Table AI, the loading capacity acts as a mediator on the
dependence between price and the other technical features.
Table AII reports on the left the results of simple linear
regressions between price and capacity, price and energy
consumption, and on the right the results of a multiple
regression on price vs capacity and energy consumption.
Introducing the capacity variable in the regression, the
dependence between price and energy consumption changes
direction, thus demonstrating the mediation effect.
The analysis in Table AII suggests that the relation between
energy consumption and price is indeed influenced by the
loading capacity. When we consider homogeneous capacity
subsamples, in fact, the relation between price and the other
technical characteristics change or disappear as reported in
section 3.2. Therefore, to avoid any bias introduced by the
loading capacity, we decided to carry out the analysis dividing
the final sample into two different capacity classes.
545




Volume 21 · Number 7 · 2012 · 538 –546

Table AI Mean of the analyzed features for distinct loading capacity classes
< 5 kg

Number of models
Mean
Price
Energy consumption (KwH/cycle)
Water consumption (l/cycle)
Spin dryer speed (turns/minute)

36
439.13
0.80
47.33
893.06

5 kg

Loading capacity
5.5-6 kg
6.5-7 kg

126
359.58
0.88
49.13
878.17

8 kg

> 8 kg

Overall sample

130

88

52

18

450

506.69
1.02
52.41
1,056.15

521.24
1.19
57.66
1,152.27

632.47
1.39
63.12
1,228.85

859.06
1.52
75.17
1,244.44

491.57
1.06
54.26
1,039.56

Table AII Mean of the analyzed features for distinct loading capacity classes (initial sample)
Simple linear regressions
Loading capacity
Energy consumption
b1
p
b1
p
Price

74,114

,0.0001

324.37

Multiple linear regression
Loading capacity
Energy consumption
b1
p
b1
p
134,41

,0.0001

About the authors

, 0.0001

2 393.25

0.0003

(www.scsm.it). He is also part of the ASAP Service
Management Forum (www.asapsmf.org). His research and
publications concern mainly service operations management,
buyer-supplier relationships and demand and inventory
planning for spare parts.
Alessandro Sicco is Post-doc Fellow at the University of
Brescia where he is a member of the Supply Chain and
Service Management Research Centre (www.scsm.it). He is
also part of the ASAP Service Management Forum
(www.asapsmf.org). His main research ﬁeld concerns
information systems and their implementation (evaluation,
impact on performances) on SMEs.

Anna Codini is Researcher and Aggregate Professor at the
University of Brescia where she teaches Innovation and
Operations Management. She is a member of the scientiﬁc
committee of the Supply Chain and Service Management
Research Centre (www.scsm.it). Her research activity and
publications concern mainly purchasing and innovation
management. Anna Codini is the corresponding author and
can be contacted at: codini@eco.unibs.it
Nicola Saccani is Researcher and Aggregate Professor at the
University of Brescia where he is a member of the Supply
Chain and Service Management Research Centre

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