The relationship between customer value and pricing strategies

Pricing strategy & practice

The relationship between customer value andpricing strategies: an empirical test

Anna Codini

University of Brescia, Brescia, Italy, and

Nicola Saccani and Alessandro SiccoDepartment of Industrial and Mechanical Engineering, Supply Chain and Service Management Research Centre,

University of Brescia, Brescia, Italy

AbstractPurpose – The paper seeks to fill a research gap that concerns empirical studies on value-based pricing in durable consumer goods. It aims to analysethe 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 analysistechnique. 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 subsamplespresents a positive dependence among the variables).Research limitations/implications – The study lacks explanatory power about the reasons for the misalignment between price and customer valuein the investigated sector. The results, moreover, refer to a specific product category and a specific national market, although their representativeness asa 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. Itprovides practitioners a deeper understanding of the consequences of misaligned pricing, and of the potential of understanding the actual valuesources for the customers.Originality/value – The study empirically assesses the relationship between value for the customer and market prices of a category of mature durablegoods. The results support the claim that value-based pricing, although believed to be superior to other pricing policies, is still not established as aprominent practice. Moreover, the findings contribute to the discussion on the value of environment-related attributes and their lifecycle monetaryimpact 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 addedto 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

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

www.emeraldinsight.com/1061-0421.htm

Journal of Product & Brand Management

21/7 (2012) 538–546

q Emerald Group Publishing Limited [ISSN 1061-0421]

[DOI 10.1108/10610421211276321]

This paper has been inspired by the activity of the ASAP ServiceManagement Forum, an Italian-based community where scholars andpractitioners from five Italian universities, and more than 50 leadingmanufacturing companies and service providers collaborate in developingresearch and dissemination in the product-services management field. Formore information see www.asapsmf.org/

538

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.

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 value-

based 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 value-

informed 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

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.

2.2 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

The relationship between customer value and pricing strategies

Anna Codini, Nicola Saccani and Alessandro Sicco


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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 cost-

plus 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 5kg 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.

4. Empirical findings

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

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.

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).

Table I Characteristics of the product sample

Sample Brand A Brand B Brand C Brand D

(n5 129) (n5 35) (n5 21) (n5 35) (n5 38)

Mean Std dev. Mean Std dev. Mean Std dev. Mean Std dev. Mean Std dev.

Price (e) 380.63 103.77 387.97 100.39 400.17 115.94 412.09 99.48 334.08 90.55

Energy consumption (KwH/cycle) 0.96 0.10 1.00 0.09 0.95 0.06 0.95 0.11 0.95 0.10

Water consumption (l/cycle) 52.12 6.97 55.57 6.54 46.67 115.94 48.89 99.48 54.92 90.55

Spin dryer speed (turns/minute) 953.49 209.93 948.57 229.28 857.14 180.48 1,000.00 187.87 968.42 215.74

Table II The relative importance of the five attributes

%

Price 35.48

Energy consumption 28.39

Spin dryer speed 17.08

Brand 16.93

Water consumption 2.13




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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).

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):

IRj ¼ Max UjWji½ �2 Min UjWji½ �Pk

i¼1 Max UjWji½ �2 Min UjWji½ �ð Þð1Þ

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.

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.

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,

1993):VFC*i ¼ b0þPk

j¼1 UjWji

VFC*i ¼ b0 þ

Xk

j¼1

UjW ji ð2Þ

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.

Table III Attributes, levels and utility estimates

Attributes and levels Utility estimate Standard error

BrandB 20.183 0.190

D 0.263 0.190

C 0.039 0.190

A 20.119 0.190

Spin dryer speedLow 600-800 20.215 0.146

Medium 900-1,100 20.038 0.171

High 1,200-1,600 0.253 0.171

Energy consumption0.6 20.374 0.132

1.1 20.748 0.264

1.6 21.122 0.397

Water consumption40 20.057 0.219

70 20.113 0.439

Price (euro)149 20.312 0.098

299 20.624 0.196

499 20.936 0.294

949 21.247 0.392

(Constant) 7.204 0.485




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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.

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

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,

Table IV Results of the regression analysis

5 kg capacity sub-sample 5.5-6 kg capacity sub-sample

(n5 66) (n5 63)

b1 p R2 Adjusted R2 b1 p R2 Adjusted R2

Price vs VFC * 0.441 ,0.0001 0.26 0.22 0.444 0.0011 0.16 0.11




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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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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.




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About the authors

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: [email protected]

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

(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.

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

Loading capacity

<5 kg 5 kg 5.5-6 kg 6.5-7 kg 8 kg >8 kg Overall sample

Number of models 36 126 130 88 52 18 450

MeanPrice 439.13 359.58 506.69 521.24 632.47 859.06 491.57

Energy consumption (KwH/cycle) 0.80 0.88 1.02 1.19 1.39 1.52 1.06

Water consumption (l/cycle) 47.33 49.13 52.41 57.66 63.12 75.17 54.26

Spin dryer speed (turns/minute) 893.06 878.17 1,056.15 1,152.27 1,228.85 1,244.44 1,039.56

Table AII Mean of the analyzed features for distinct loading capacity classes (initial sample)

Simple linear regressions Multiple linear regression

Loading capacity Energy consumption Loading capacity Energy consumption

b1 p b1 p b1 p b1 p

Price 74,114 ,0.0001 324.37 ,0.0001 134,41 ,0.0001 2 393.25 0.0003




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The relationship between customer value and pricing strategies

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