Journal of Business & Industrial MarketingCustomer‐perceived value in industrial contextsJozée Lapierre

Article information:To cite this document:Jozée Lapierre, (2000),"Customer#perceived value in industrial contexts", Journal of Business & Industrial Marketing, Vol. 15 Iss2/3 pp. 122 - 145Permanent link to this document:http://dx.doi.org/10.1108/08858620010316831

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Customer-perceived value inindustrial contextsJozeÂe LapierreAssociate Professor, EÂ cole Polytechnique de MontreÂal, QueÂbec,Canada

Keywords Business-to-business marketing, Value, Consumer behaviour,Information technology

Abstract Although customer-perceived value is discussed widely in the literature, fewempirical studies have been conducted due to an absence of operational measures.Reports on the development of measures and tests two customer-perceived valuestructures using data collected from industrial customers of the information technologyindustry. The findings generally support both structures and provide empirical supportfor a value proposition with 13 value drivers. Furthermore, results indicate that most ofthe 13 drivers are assessed in a similar way by industrial customers of three servicesectors surveyed, ICE (information, communication, entertainment), distribution andfinance. Flexibility and responsiveness ± two service-related benefits ± are importantvalue drivers for all the business customers surveyed. Relationship value drivers areassessed the most differently in two of the three sectors studied, finance and ICE(information, communication, entertainment).

IntroductionMuch attention in recent years has been given to value and its provision to

customers. However, remarkably few firms have the knowledge and

capability to actually assess value and gain an equitable return for the value

they deliver to customers (Anderson and Narus, 1999, p. 3). In business

markets, where knowledge of value is considered critical and can be thought

of as the cornerstone of business market management (Anderson et al., 1993),

it is critical for organizations to understand their offerings and learn how they

can be enhanced to provide value to their industrial customers. Organizations

therefore need to understand what drivers create value for customers in order

to build a competitive advantage (Lichtenthal et al., 1997).

A major issue was therefore the development of customer value measures,

specifically in the business-to-business service marketing field

(Parasuraman, 1998) because activity in business (Reid and Plank, 1995)

service (Quinn, 1996) sectors continues to grow. An example is information

technology (IT). Expenditures in this business sector are now recognized as a

significant balance-sheet item (Sullivan-Trainor, 1989). During the last

decade, service investments in IT grew by $700 billion (Quinn, 1996) and,

over the past three years, business spending on IT has risen by almost 45

percent (Mandel, 1997). To conduct empirical studies in this economic

sector, measures of customer value were therefore required. Specially in the

IT business sector, measures were needed to demonstrate and justify the

value of IT providers to top management because two-thirds of Fortune 100

companies' chief executive officers believed that their firms were not getting

the most out of their IT investments (Rifkin, 1989).

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

http://www.emerald-library.com

The author thanks the anonymous reviewers and the special issue editor for theirconstructive comments and useful suggestions.

Customer value measures

122 JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 15 NO. 2/3 2000, pp. 122-140, # MCB UNIVERSITY PRESS, 0885-8624

An executive summary formanagers and executivereaders can be found at theend of this issue

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In view of the construct's complexity and richness, a research program on

customer-perceived value was set up. The first phase addressed the

measurement of the customer-perceived value construct in one industrial

area, that of information technology (IT). The importance of doing this was

emphasized in a special issue of the Journal of the Academy of Marketing

Science (Woodruff, 1997; Parasuraman, 1997; Slater, 1997). Furthermore,

the huge, rapidly growing US service sector could not possibly have operated

at even a fraction of its current size, complexity, responsiveness and

reliability without modern IT systems (Quinn, 1996). Finally, the research

area of business-to-business marketing has also been declared to be highly

important, as evidenced in a special issue of the Journal of Business

Research (Woodside, 1997; LaPlaca, 1997; Johnston and Lewin, 1997;

Lichtental et al., 1997; Plank, 1997). The goal of the first phase was to

demonstrate the existence of customer value in the IT sector.

The primary objectives of the second phase, from which the present article

derives, are to provide more information about customer-perceived value

structure and to test two structures with three segments of industrial service

customers that are big users of information technology (IT).

The article is divided into six sections. The first provides the theoretical

background of the customer value construct. In the second section, a

summary of the first phase of this research is presented and key drivers of

customer-perceived value are identified. The research method is presented in

the third section. The fourth section outlines the measurement properties of

the scale used to measure customer value. Finally, the last sections present

and discuss the results, and outline a set of managerial implications and

future research directions.

Theoretical backgroundThe conceptual domain of customer-perceived value is delineated by

anchoring it with two theoretical issues central to this stream of research.

Domain

Should the domain of the customer-perceived value construct be restricted to

some parts or cover a broader perspective? As Mazumdar (1993) states:

`̀ Today's value-conscious customers are neither impressed by the best

product nor persuaded by the lowest price alone. Instead, customer purchase

decisions are often guided by a careful assessment of what benefits they

obtain in exchange for the costs they incur to acquire and consume the

product.'' Customer-perceived value can, therefore, be defined as the

difference between the benefits and the sacrifices (e.g. the total costs, both

monetary and non-monetary) perceived by customers (Slater, 1997; Berry

and Yadav, 1996; Ravald and GroÈnroos, 1996; Slater, 1996; Haas, 1995;

Mazumdar, 1993; Slater and Narver, 1992; Narver and Slater, 1990; Day,

1990; Zeithaml, 1988) in terms of their expectations, i.e. needs and wants.

Customer sacrifices are the overall monetary and non-monetary costs the

customer invests or gives to the supplier in order to complete a transaction or

to maintain a relationship with a supplier. Non-monetary costs can be

defined as the time/effort/energy and conflict invested by the customer to

obtain the products or services or to establish a relationship with a supplier.

Non-monetary costs are important, since, as reported by Carothers and

Adams (1991): `̀ Many customers count time rather than dollar cost as their

most precious asset.'' Other researchers argue that perceived value is made

of only benefits (Hunt and Morgan, 1995; Hamel and Prahalad, 1994). In this

Primary objectives

Domain

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research, we concur with the majority of researchers who define customer

value in terms of get (benefit) and give (sacrifice) components.

Scope

Should customer value be defined for the different parts of an organization?

One critical aspect of the customer value theory that is not yet fully

developed concerns the sources from which customers may derive value

(Parasuraman, 1997). Much of the current theory focuses on attributes

related to product and service offerings and customer value is inherent in or

linked through the use of some products (Woodruff, 1997). In a hyper-

competitive environment, where sources of both product-based and process-

based competitive advantages are quickly imitated by competitors (Jacobson,

1992; Dickson, 1992; Ghemawat, 1986), a commitment to customer-value

innovation is essential to sustaining a competitive advantage. One way to

conceive of innovation in relation to customer value is to look at relational

value-based drivers in addition to product- and service-related drivers. This

idea has been brought to the forefront by Ravald and GroÈnroos (1996), who

argue that value may also be relationship related. Other authors support this

vision (Sheth and Sharma, 1997). Measuring the value of customer

relationships and how customers perceive the `̀ total'' value proposition (e.g.

products, services, channels, ideas) have been identified as two of the highest

priorities by The Marketing Science Institute for 1996-1998. We identify the

IT sector as an area where products and services do not adequately define the

range of resources and activities that appear to create customer value.

The two issues, domain and scope, serve to delineate the customer value

construct. The next task is to identify critical drivers within this domain.

Key drivers of customer-perceived valueThe objective of the first phase was to develop a scale to measure customer-

perceived value in a business-to-business context. Measures of the customer-

perceived value construct were developed in several stages. First, since the

information technology (IT) industry appeared to be a relevant sector to

study, the researcher met three vice-presidents of a large Canadian IT

enterprise. After a discussion about the objectives of the research, two

business sectors were chosen to study the value of IT suppliers. The sectors

identified were ICE (information, communication, entertainment) and

distribution because they account for a large part of business expenses

(Quinn, 1996). It was also decided that business customers from the two

sectors and managers from the supplier side should be interviewed.

Interviews were therefore conducted with 16 individuals, eight on the

supplier side and eight on the customer side. On the customer side, eight

individuals from four companies in the ICE sector and four companies in the

distribution sector were selected as follows: three broadcasters (two from

communication and one from entertainment), and one person from

information, wholesale, courier, retail and energy distribution. All the

customers hold the title of vice-president. On the supplier side, six sales

directors (three from ICE and three from distribution) and two assistant vice-

presidents of the sectors were interviewed.

The interviews were conducted based on interview guidelines. First, the

interviewees were asked to talk broadly about customer value: about the

differences between suppliers' and customers' perceptions of customer

value; about the sources of customer value, about current strengths and

weaknesses; about how to provide more value to customers; about the

competition's offerings and customer needs and expectations. They were

Scope

The first phase

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also asked if customers expect more than products and services. Additional

questions were asked about each value-based driver identified by the

interviewees. For example, they were asked to define quality and trust. These

questions were asked to reaffirm the meaning of the drivers and associated

dimensions, i.e. benefit and sacrifice.

In the second stage, 13 value-based drivers were selected based on an in-

depth analysis of the interviews and on an extensive review of the literature.

The 13 drivers are product, service and relationship related (Figure 1). Of

these, ten value-based drivers are identified as benefits:

(1) alternative solutions ± product related;

(2) product quality ± product related;

(3) product customization ± product related;

(4) responsiveness ± service related;

(5) flexibility ± service related;

(6) reliability ± service related;

(7) technical competence ± service related;

(8) supplier's image ± relationship related;

(9) trust ± relationship related;

(10) supplier solidarity with customers ± relationship related.

Three value-based drivers are identified as sacrifices:

(1) price ± product and service related;

(2) time/effort/energy ± relationship related;

(3) conflict ± relationship related.

See Appendix 1 for a detailed description of the 13 drivers.

In the third stage, a pretest was conducted with eight target respondents: two

former vice-presidents from a large IT enterprise, one sales manager in the

entertainment area, one assistant vice-president in the distribution area, one

director for global solutions in the distribution area, and three professors

involved in the marketing and management of technology.

The content validity of the proposed customer value dimensions (benefit and

sacrifice) may be justified by comparing them with models proposed in the

literature (e.g. Zeithaml, 1988). The proposed model represents a practical

and tenable starting point for empirical testing. The content validity of the

Figure 1. Total value proposition

The second stage

Value dimensions

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measures for each driver, on the other hand, may be justified based on a close

adherence to guidelines for item generation (Loevinger, 1976): the pool must

be scrutinized for their logical relationship to the construct and the rationale

for selecting them must be made explicit. In choosing dimensions, drivers

and items, this study relied on 16 interviews with IT experts, customers and

suppliers on what they regard as typical trait manifestations as well as on

existing literature on value. Items from other studies were also adapted for

this study (Angleitner et al., 1986). These methods were all found to be

complementary and applicable because of the absence of the customer value

operational measures. The purpose was to ensure adequate coverage of the

conceptual domain for each dimension and conformity of the items to the

position taken in relation to the theoretical issues.

Data collection procedure and sample characteristicsEmpirical verification of the customer value construct was done using a

mailed questionnaire. Subjects were asked to assess the value of 13 drivers

(several items) identified a priori in terms of their expectations. Measures

were phrased on seven-point scales. The descriptors ranged from much

worse than expected, somewhat worse, slightly worse, as expected, slightly

better, somewhat better and much better than expected (Brown et al., 1993).

Even though there is some empirical evidence showing that a scale with

fewer response categories is considered easier to rate by subjects

(Diefenbach et al., 1993), a seven-point scale is a relevant choice given the

knowledge of the customers surveyed.

For the first phase, the questionnaire was sent to a sample of ICE and

distribution executives drawn randomly from the 1996 Dunn & Bradstreet

database in Canada (Quebec and Ontario). For the second phase, the same

questionnaire was sent to a sample of executives from the finance sector

drawn randomly from the 1998 Dunn & Bradstreet database in Canada

(Quebec and Ontario). Table I outlines information about the samples. The

unit of analysis is one executive on the customer side giving his/her own

perception of the value provided by his/her major IT supplier. Why

executives? They are key informants who are part of the decision-making

process in the buying center and have decision-making power. Moreover, IT

is strategic to organizations (Bharadwaj et al., 1993).

Because the primary focus of the study was to investigate customer-

perceived value, we had to collect data in an industry where value is a

primary concern. As shown above, the information technology (IT) sector

seems very relevant. The industrial service sectors chosen for the first phase

were ICE (information, communication, entertainment) and distribution, and

finance was chosen for the second phase. These choices were prompted by

the fact that firms in these service sectors depend heavily on technology

(Alic, 1994), specially on information technology, and most of these services

1996 1998

2,400 executives 1,397 executives

Sectors: ICE (Information, communication and

entertainment) and distribution

Sector: Finance

Telephone contact

Telephone contact 801 (Knowledgeable/willing to participate)

1184 (Knowledgeable/willing to participate) Response rate

Response rate 143 = 17.85% of 801

239 = 20.2% of 1,184 129 usable

209 usable

Table I. Samples

Questionnaire

Primary focus

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account for about 74 percent of the Gross Domestic Product and about

79 percent of all national employment (Quinn, 1996). It is of great interest to

understand how service organizations perceive products and services

supplied by an IT supplier and, therefore, what creates value for them given

that the large, rapidly growing US service sector could not possibly have

operated without modern IT systems (Quinn, 1996). Picking which drivers to

improve should be driven by customers (Woodruff, 1997).

Assessment of measurement propertiesAccording to Baggozzi (1980), the following measurement properties are

considered important for assessing the measures developed here: internal

consistency of operationalization (reliability and unidimensionality),

convergent validity and discriminant validity.

Results of the first phase indicate that in the area of information technology,

value, as perceived by industrial customers from the distribution and ICE

(information, communication, entertainment) industrial sectors, is

represented by the two dimensions ± benefit and sacrifice ± and 13 value-

based drivers that are product, service, and relationship related. The results

indicate that the operational measures are reliable and valid (Lapierre, 1999).

The model was therefore used to measure customer value in another business

area ± the finance sector. Since we used the same validation process for the

data obtained from customers in the finance sector as the one used in the first

phase of the study, we report on the results of the measurement properties for

the combined sample of the two phases.

Unidimensionality, convergent validity and reliability

Unidimensionality is defined as the existence of one latent construct

underlying a set of measures (Anderson, 1987). The model for unidimensionality

and convergent validity follows the work of JoÈreskog and SoÈrbom (1993)

and the convention of structural equation modeling. Table II outlines the

results of unidimensionality assessment for the 13 drivers. Based on columns

(3) to (8), that is, on the typical goodness-of-fit indices and on column (9), a

comparative goodness-of-fit index assessing t in relation to the fit of a more

restrictive model (Bentler, 1990), it can be concluded that each of the drivers

achieves unidimensionality and convergent validity. Reliability of the 13

value drivers is very good. All Cronbach alpha coefficients have a value

larger than 0.70 (Table II).

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Dimensions n w2(df) p-level D RMR GFI AGFI CFI a

Alternative solutions 301 0.03(1) 0.87 1.00 0.00 1.00 1.00 1.00 0.81

Product quality 309 0.59(1) 0.44 0.99 0.01 0.99 0.99 1.00 0.91

Product customization 287 1.85(1) 0.17 0.99 0.01 0.99 0.97 0.99 0.88

Responsiveness 306 1.15(1) 0.28 0.99 0.04 0.99 0.98 1.00 0.83

Flexibility 308 0.29(1) 0.59 1.00 0.01 1.00 0.99 1.00 0.88

Reliability 309 0.86(2) 0.65 0.99 0.01 0.99 0.99 1.00 0.91

Technical competence 298 5.33(4) 0.22 0.99 0.02 0.99 0.97 0.99 0.89

Image 310 0.30(1) 0.58 0.99 0.02 0.99 0.99 1.00 0.93

Trust 302 4.18(3) 0.24 0.99 0.02 0.99 0.97 0.99 0.92

Solidarity 301 0.39(1) 0.53 0.99 0.01 0.99 0.99 1.00 0.91

Price 291 1.18(4) 0.88 0.99 0.01 0.99 0.99 1.00 0.79

Time/effort/energy 304 1.03(3) 0.79 0.99 0.00 0.99 0.99 1.00 0.84

Conflict 295 0.02(1) 0.89 1.00 0.00 1.00 1.00 1.00 0.95

Table II. Assessment of unidimensionality, convergent validity and reliability for

the three sectors

Results

The model

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Discriminant validity

Discriminant validity refers to the degree to which measures of different

model dimensions are unique. Following Venkatraman (1989), discriminant

validity for customer value was assessed by testing if correlations between

pairs of drivers are significantly different from unity. A model in which this

correlation was constrained to one was compared with an unconstrained

model. A w2 difference value with an associated p-value of less than 0.05

(JoÈreskog and SoÈrbom, 1993) supports the discriminant validity criterion.

Results of the 78 pairwise tests among the 13 customer value drivers indicate

that all chi-square differences are significant at 0.001 level except the one

associated with `̀ alternative solutions'' and `̀ solidarity.'' It is significant at

0.01. Moreover, results indicate that the conflict driver correlates slightly

with all value-based drivers except for time/effort/energy. In general, there is

less correlation between the benefit and sacrifice value-based drivers than

between the benefit value-based drivers. The results of the 78 pairwise tests

among the 13 value-based drivers indicate strong support for the

discriminant validity criteria (see Appendix 2).

Exploration of the structureThe primary objective of the first phase of this research was to demonstrate

the existence of customer-perceived value instead of its exact structure. In

the present research, i.e. the second phase, the objective is to provide more

information with regard to structure. In order to do this, confirmatory factor

analyses are used to test two structures with different samples. The first

structure is associated with the domain of the construct, i.e. a solution with

two factors ± benefit and sacrifice. The second structure is associated with

the scope of the construct, i.e. a solution with three factors ± product, service

and relationship.

Two-factors structure

In order to test the first structure of the customer-perceived value construct,

i.e. the domain, the 13 value drivers were modeled as measure variables

determined by the two latent variables, benefit and sacrifice, the underlying

theory proposed by several authors (e.g. Slater, 1997; Berry and Yadav,

1996; Zeithaml, 1988) (Figure 2). An overall score for each driver was

computed taking into account its unidimensionality. Summated scales were

also used because of the large number of parameters to be estimated, which

in turn led to an increase in the sample size/parameter (Bagozzi and

Baumgartner, 1994).

The two-factors structure was tested with different samples given that

customer value is expected to be context specific (e.g. Bolton and Drew,

1991; Holbrook and Corfman, 1985). Three models with independent

samples and one with a combined sample were tested. The first three models

are associated with the three following industrial sectors: finance,

distribution and ICE (information, communication, entertainment), and the

fourth model is a combination of the three independent samples. Results of

the four models are summarized in Table III. The goodness-of-fit measure

indicates that MO1 (finance) and MO4 (3 sectors) have a very good fit; MO2

(distribution) has an acceptable fit and MO3 (ICE) does not have a good fit

when the usual w2 is used to assess the overall fit of a model[1]. With the

exception of the conflict driver in MO2 (distribution), the loading (l) for

each driver on its respective dimension is positive and significant. The two

latent dimensions, benefit and sacrifice, are not independent in any of the

four models. The strongest correlation is found in MO1 (finance).

Primary objective

Structure

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MO1

Finance

(n = 125)

MO2

Distribution

(n = 72)

MO3

ICE

(n = 85)

MO1 + MO2 +

MO3 = MO4

(n = 282)

l T-value l T-value l T-value l T-value

Benefit

l11 Alternative solutions 0.79d (9.61) 0.76d (5.80) 0.94d (8.09) 0.82d (13.55)

l21 Product quality 0.50d (6.10) 0.54d (4.76) 0.66d (5.94) 0.56d (9.73)

l31 Product customization 0.65d (8.39) 0.76d (5.57) 0.93d (8.95) 0.79d (13.59)

l41 Responsiveness 1.02 ± 0.95 ± 1.13 ± 1.04 ±

l51 Flexibility 0.96d (13.05) 1.04d (7.61) 0.97d (9.33) 0.99d (17.42)

l61 Reliability 0.60d (7.28) 0.91d (6.27) 0.76d (6.56) 0.73d (11.74)

l71 Technical competence 0.70d (9.82) 0.78d (6.29) 0.90d (9.33) 0.78d (14.69)

l81 Image 0.64d (7.08) 0.77d (5.41) 0.94d (7.57) 0.77d (11.67)

l91 Trust 0.68d (8.86) 0.76d (6.14) 0.97d (8.51) 0.81d (13.86)

l101 Solidarity 0.89d (10.67) 0.83d (6.86) 1.02d (9.92) 0.91d (15.78)

Sacrifice

l112 Price 0.77d (5.23) 0.69d (3.34) 0.87d (4.01) 0.79d (7.29)

l122 Time/effort/energy 0.91 ± 0.68 ± 0.91 ± 0.86 ±

l132 Conflict 0.48c (2.77) ±0.11 (±0.32) 0.85c (3.33) 0.48c (3.62)

f21 Benefit-sacrifice 0.62d (5.46) 0.46c 0.56d 0.56d (7.56)

w2 = 41.73(41) w2 = 55.36(41) w2 = 73.29(41) w2 = 32.62(41)

p = 0.44 p = 0.07 p = 0.00 p = 0.82

RMR = 0.03 RMR = 0.05 RMR = 0.06 RMR 0.03

D = 0.96 D = 0.91 D = 0.91 D = 0.99

GFI = 0.95 GFI = 0.90 GFI = 0.89 GFI = 0.98

AGFI = 0.89 AGFI = 0.78 AGFI = 0.76 AGFI = 0.96

CFI = 1.00 CFI = 0.97 CFI = 0.96 CFI = 1.00

Notes:1 One lambda/latent variable was fixed at one2 Standardized solutions3 c p � 0.01, d p � 0.001

Table III. First-order factor models ± independent samples123

Figure 2. First-order factor model for customer value (2 dimensions, 13 drivers)

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Correlations are similar and moderate in MO3 (ICE) and MO4 (3 sectors).

MO2 (distribution) has the lowest correlation coefficient. All results

associated with the correlations between the benefit and sacrifice dimensions

show that they are not independent; they are actually quite dependent.

More specifically, results indicate that a value proposition implies much

more than a trade-off between product quality and price (Mazumdar, 1993).

With the exception of the conflict driver, which accounts for less variance

than the other drivers in MO1 (finance), MO2 (distribution) and MO4 (3

sectors), product quality is the driver that contributes the least to the value

proposition. This is true in the four models. The price driver, a monetary

cost, is an important value driver, especially in the ICE model. Therefore,

one cannot say that price is not taken into account in the assessment of the

value provided by major IT suppliers (Carothers and Adams, 1991). Even

though price is an important driver in the value proposition, other drivers are

more important. If we acknowledge that value is more than product quality

and price, it is of considerable interest for IT suppliers to be informed of

which driver is the most important in the eyes of the customers. Customers

from the three industrial sectors surveyed have their own specific first

choice. The responsiveness of IT suppliers is the most important value driver

in MO1 (finance), MO3 (ICE) and MO4 (3 sectors). The flexibility of IT

suppliers is the most important value driver in MO2 (distribution). The less

important contribution of the conflict driver in MO1 (finance) and MO2

(distribution) may be explained by the fact that this psychological sacrifice is

not always present. However, as confirmed by the answers of IT customers,

money, time/effort/energy are always spent and/or devoted. Overall, the

findings show that IT solutions are made up of what the customer gets, i.e.

all the benefits, and of what the customer gives, i.e. all monetary and non-

monetary costs associated with the sacrifice dimension.

What is noteworthy from the findings associated with the domain of

customer-perceived value in industrial contexts is that, at least for the three

sectors surveyed, IT customers do not act much differently when they assess

the value provided by their major IT supplier. The most important

differences in the evaluation of the value drivers are found between the

finance and ICE (information, communication, entertainment) sectors. These

drivers are product customization, technical competence, image, trust and

conflict. The loadings of each of these five drivers are larger for the ICE

customers. The only differences between the finance and distribution sectors

concern the reliability, time/effort/energy and conflict drivers. Of these,

reliability received a much better evaluation from the distribution customers.

Trust, time/effort/energy and conflict are drivers that contribute more to the

value proposition for ICE customers than for distribution customers. In

summary, the above results show the relevance of the first theoretical

structure. The domain of the customer value construct is correctly

represented by a two-factors structure ± benefit and sacrifice.

Three-factors structure

For the second structure of the customer value construct, i.e. the scope, the

same four samples were used to test for the relevance of product-, service-

and relationship-related value drivers (Figure 3).

First, the 13 value drivers were modeled as measure variables determined by

the following three latent variables: product, service and relationship. An

overall score for each driver was computed taking into account its

unidimensionality. Summated scales were also used because of the large

Value proposition

The findings

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number of parameters to be estimated, which in turn led to an increase in the

sample size/parameter (Bagozzi and Baumgartner, 1994). Comparative

results of the standardized solutions are outlined in Table IV. Overall, the fit

of MO2 (distribution) and MO4 (3 sectors) is good. The solution of MO1

(finance) is acceptable. The solution of MO3 (ICE) has a good fit when the

Carmines and McIver's (1981) method is applied[2]. For the three industrial

sectors, almost all loadings (l) are significant at 0.001, except the ones

associated with the conflict driver, which is significant at 0.01 in MO1

(finance), MO3 (ICE) and MO4 (3 sectors); the loading is not significant in

MO2 (distribution). As expected, the three latent variables ± product, service

and relationship ± are not independent; all correlations are very high. This

indicates that product, service and relationship variables are dependent.

More specifically, this study reveals that a value proposition associated with

IT solutions is more than product only, more than service only, more than

relationship only. The findings suggest that IT solutions are made up of at

least three sources ± product, service, relationship ± at different levels

(Ravald and GroÈnroos, 1996; Bolton and Drew, 1991; Zeithaml, 1988). The

most striking result is that price, a value driver that is product and service

related, is not significant when it is associated with service and its

significance varies when it is associated with product. It is strongly

significant in MO1 (finance), moderately significant in MO4 (3 sectors),

slightly significant in MO2 (distribution) and not significant in MO3 (ICE).

Price is therefore the driver that differentiates the three industrial sectors

under study the most. In the eyes of IT customers, price, a monetary cost, is

not associated with service. A plausible explanation is that IT customers

perceive that a monetary cost, price, is only associated with tangible products

and not with the intangible service aspects.

The product quality driver is, with the exception of conflict and price

associated with service, the least important value driver in MO1 (finance).

Time/effort/energy, a relationship driver, is the one that contributes the least

Figure 3. First-order factor model for customer value (3 dimensions, 13 drivers)

Value proposition

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to the value proposition in MO2 (distribution) and MO3 (ICE). Price, a

monetary cost, and time/effort/energy, a non-monetary cost, do not

contribute much to the value proposition in MO2, MO3, MO4. The only

drivers that are less important are conflict and price related to service. Price,

as a product driver, is nonetheless the most important one only in MO1

(finance). Flexibility is the most important value driver in MO2

(distribution), and responsiveness is the most important value driver in MO3

(ICE). When the three samples are combined, flexibility is the most

important value driver.

Overall, this research shows that both structures associated with the domain

± benefit and sacrifice, and with the scope ± product, service, relationship,

are relevant representations of the total value proposition studied here. For

the two-factors structure, the most important differences are between finance

and ICE customers and are mostly associated with relationship-related

drivers. For the three-factors structure, price is the driver that differentiates

the three industrial sectors studied here the most. The product quality, time/

effort/energy and conflict drivers account the least for the value provided by

IT suppliers in the three sectors studied. Globally, IT customers do not assess

the total proposition tested in the current research very differently; however,

they assess some of the drivers differently. Finally, value drivers that

differentiate the three service sectors studied are not the same when value is

MO1

Finance

(n = 125)

MO2

Distribution

(n = 72)

MO3

ICE

(n = 85)

MO1 + MO2 +

MO3 = MO4

(n = 282)

l T-value l T-value l T-value l T-value

Product

l11 Alternative solutions 0.81d (8.13) 0.92d (5.72) 0.94d (7.75) 0.87d (12.75)

l21 Product quality 0.51d (5.59) 0.56d (4.56) 0.68d (5.90) 0.58d (9.45)

l31 Product customization 0.71 ± 0.78 ± 0.90 ± 0.81 ±

l111 Price 1.36d (2.06) 0.34b (2.10) 0.53 (1.53) 0.58c (3.53)

Service

l42 Responsiveness 1.02 ± 0.94 ± 1.14 ± 1.05 ±

l52 Flexibility 0.97d (12.91) 1.07d (7.74) 0.97d (9.31) 1.00d (17.50)

l62 Reliability 0.63d (7.94) 0.95d (6.77) 0.83d (7.50) 0.78d (13.18)

l72 Technical competence 0.70d (9.76) 0.77d (6.25) 0.88d (8.95) 0.77d (14.35)

l112 Price ±0.95 (±1.45) 0.03 (0.18) ±0.06 (±0.17) ±0.15 (±0.92)

Relationship

l83 Image 0.68d (6.93) 0.80d (6.37) 0.97d (8.10) 0.81d (12.51)

l93 Trust 0.75 ± 0.85 ± 1.06 ± 0.90 ±

l103 Solidarity 0.97d (10.134) 0.91d (8.60) 1.05d (11.33) 0.97d (17.32)

l123 Time/effort/energy 0.61d (7.53) 0.33d (4.23) 0.53d (5.77) 0.50d (10.31)

l133 Conflict 0.34c (2.99) ±0.04 (±0.25) ±0.48c (3.12) 0.29c (3.62)

f21 Product-service 0.94d (5.73) 0.78c (3.63) 0.93d (4.99) 0.89d (8.49)

f31 Product-relationship 0.93d (5.90) 0.81d (3.90) 0.98d (5.48) 0.91d (9.01)

f32 Service-relationship 0.91d (5.94) 0.90d (4.33) 0.93d (5.27) 0.91d (9.13)

w2 = 64.32(48) w2 = 52.96(48) w2 = 89.86(48) w2 = 53.88(48)

p = 0.06 p = 0.32 p = 0.00 p = 0.26

RMR = 0.04 RMR = 0.06 RMR = 0.07 RMR 0.03

D = 0.93 D = 0.91 D = 0.89 D = 0.97

GFI = 0.93 GFI = 0.90 GFI = 0.87 GFI = 0.97

AGFI = 0.87 AGFI = 0.82 AGFI = 0.76 AGFI = 0.95

CFI = 0.98 CFI = 0.99 CFI = 0.94 CFI = 0.99

Notes:1 One lambda/latent variable was fixed at one2 Standardized solutions3 b p � 0.05, c p � 0.01, d p � 0.001

Table IV. First-order factor models ± independent samples123

Structures

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conceptualized in terms of benefit and sacrifice and when it is conceptualized

in terms of product, service and relationship.

DiscussionThis research has been directed at testing two structures ± domain and scope

± of the customer value construct in three industrial service sectors. The data

analyzed in this study support the theory that the drivers are divided in terms

of benefit, i.e. what the customer gets, and sacrifice, i.e. what the customer

gives. The results refer to the first theoretical issue, the domain, and concurs

with the majority of researchers interested in the customer value construct

(e.g. Slater, 1997; Zeithaml, 1988). On the other hand, the data also support

the theory that value drivers are product, service and relationship related.

Specifically, the results outlined in this research indicate an extremely good

fit between the two theoretical issues ± scope and domain ± in explaining the

data. The results show that each driver loads on the expected dimension.

Even though not many differences were found, the finance and ICE

customers differ the most. Results indicate that flexibility and responsiveness

are important value drivers in the models associated with the two structures.

Flexibility and responsiveness are already recognized as highly important

service aspects. Specifically, Quinn (1996, p. 73) writes that `̀ that the huge

service sector could not possibly have operated at even a fraction of its

current size, complexity, responsiveness and reliability without IT systems.''

Again, Quinn (1996, p. 74) concurs that IT's most important quality gains

are, for example, greater safety, convenience, accuracy, flexibility, variety

and reliability. Finally, results are indicative that, relationship value drivers

act as important differentiators.

ImplicationsGiven that, specifically in the service area, Bolton and Drew (1991) found

that the customer's assessment of value depends on the customer's frame of

reference, it was expected that customers from the three industrial sectors

would assess the value provided by their major IT supplier differently.

Results of this research show that customers in different actionable segments

assess most of the value drivers similarly. Globally, IT customers surveyed

act much more in concert than expected from the previous literature on

customer-perceived value.

Furthermore, managers should be interested in the two structures and in the

operational measures that allow distinctions to be made between the benefit

and the sacrifice dimensions (Slater, 1997; Berry and Yadav, 1996; Ravald

and GroÈnroos, 1996; Slater, 1996; Haas, 1995; Mazumdar, 1993; Slater and

Narver, 1992; Narver and Slater, 1990; Day, 1990; Zeithaml, 1988).

Managers should also recognize that a realistic view of customer value must

make a distinction between the products and the services offered by the

supplier (Woodruff, 1997) and the relationship (Sheth and Sharma, 1997;

Ravald and GroÈnroos, 1996) experienced with the major IT supplier. The

three sources are helpful for articulating more realistic customer strategies.

They offer some practical guidelines for designing value-based competitive

strategies. A realistic value proposition for IT providers is therefore made up

of several aspects that deserve specific attention in terms of customer value

dimensions and drivers (Woodruff, 1997). Results clearly show that the

value of IT systems depends not only on computer and telecommunications

hardware and software, but on the employees ± their responsiveness,

flexibility, reliability, competence, as well as on the relationship between the

Finance and ICE

Distinctions

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customer and the supplier and on what is given up by the customer's

organization itself.

In our view, this article's most important contribution is to provide a broad

perspective and diversity of value drivers. This is important considering the

information and insights necessary to deal with the numerous issues and

problems in the world of business-to-business relations. We hope that this

research will convince service managers that a customer value proposition is

a complex one (Marketing Science Institute, 1996-1998). Rather than merely

providing an overall evaluation of XYZ organization's value, the two

conceptualizations provide rich insights into how customers perceive each

type of driver in the total value proposition. In this context, managers will be

better equipped to question their value strategy and improve it as necessary.

Future researchGiven the complexity and the richness of the customer-perceived value

construct, several research avenues might be explored. First, as customer

value is a source of competitive advantage (Woodruff, 1997; Parasuraman,

1997), a relevant use of the scale developed in the first phase would be to

evaluate competitors' value drivers. An assessment of competitors based on

the 13 proposed drivers could reveal the reasons for their success. The results

can form the basis for improvements in IT products, services, relationships.

If the driver does not create value for customers, it should be eliminated; if

the driver is not a source of competitive advantage for the company, the

company should consider outsourcing the activity. Second, because there is a

rich literature pertaining to the situational character of customer-perceived

value (e.g. Bolton and Drew, 1991; Holbrook and Corfman, 1985), other

segmentation variables might be tested and other analyses performed.

Examples of segmentation variables are the size of the firm, its annual

revenue, IT expenses, managers and employees from different functions.

Third, other measures must be formulated and compared with the results of

this study to clarify the theoretical foundations of the construct[3]. An

interesting research avenue would be to increase the number of sacrifice

drivers. For example, we know that the costs can be broken down into cost of

use, cost of service, starting cost and other psychological costs such as

complexity. Specifically, it would be interesting to check for the relevance of

cost of service because the results indicate that the price driver is more

relevant for product than for service. More research is necessary, specially on

the conflict driver because of the number of different results compared to

other drivers and because, in some cases, it is perceived as a positive value

driver and, in other cases, it is perceived as a negative value driver. Fourth,

because the technologies of service industries and manufacturing draw from

the same storehouse of knowledge, particularly when it comes to information

technology (IT) systems (Alic, 1994), it would be of interest to replicate this

study in manufacturing sectors in order to discover similarities and

differences, if any. Fifth, customer value measures should also be

incorporated in a causal model to measure the impact of the antecedents of

customer value, market orientation (Kohli et al., 1993; Narver and Slater,

1990) and technological orientation (Gatignon and Xuereb, 1997) and to

assess the impact of customer value on business performance (Narver and

Slater, 1990) because the increasingly competitive nature of many service

industries serves to emphasize the importance of a value orientation (Slater,

1997). Finally, as customer value is a dynamic concept, we should not expect

those value drivers to remain the same over time (Parasuraman, 1997). In

other words, the drivers that motivate a customer's initial purchase of a

Research

Theoretical foundations

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service and/or product may differ from the criteria that connote value during

use right after purchase, which in turn may differ from the determinants of

value during long-term use (Parasuraman, 1997).

Notes

1. Because the chi-square test is sensitive to sample size, Carmines and McIver (1981)

provide another way to check for the overall goodness-of-fit measure. The calculation is

as follows: chi-square/degrees of freedom. If the value is smaller than 3, the fit is very

good, whereas if the value is between 3 and 5, the fit is acceptable. Then, in MO3, for a

chi-square of 73.29 and 41df, we obtain a value of 1.77, which demonstrates a very good

fit. Bentler and Bonnett (1980, pp. 599-600) suggested another index that is not

influenced by sample size. It refers to the D in Tables III and IV. A value close to one is

very good and a value greater than 0.90 is an acceptable value.

2. Chi-square/degree of freedom: 89.86/48 = 1.87.

3. This is particularly critical because the model was developed as well as tested using the

same data set (Sethi and King, 1994, p. 1618).

References

Alic, J.A. (1994), `̀ Technology in the service industries'', International Journal of Technology

Management, Vol. 9 No. 1, pp. 1-14.

Anderson, J.C. (1987), `̀ An approach for confirmatory measurement and structural modeling

of organizational properties'', Management Science, Vol. 33 No. 4, pp. 525-41.

Anderson, J.C. and Narus, J.A. (1999), Business Market Management, Prentice-Hall, NJ.

Anderson, J.C., Jain, D.C. and Chintagunta, P.K. (1993), `̀ Customer value assessment in

business markets: a state-of-practice study'', Journal of Business-to-Business Marketing,

Vol. 1 No. 1, pp. 3-29.

Angleitner, A., John, O.P. and Lahr, J. (1986), `̀ It's what you ask and how you ask it: an

intermetric analysis of personality questionnaires'', in Angleitner, A. and Wiggins, J.

(Eds), Personality Assessment via Questionnaire, Springer-Verlag, Berlin, Germany,

pp. 61-108.

Bagozzi, R.P. (1980), Causal Models in Marketing, Wiley, NY, USA.

Bagozzi, R.P. and Baumgartner, H. (1994), `̀ The evaluation of structural equation models and

hypothesis testing'', in Bagozzi, R.P. (Ed.), Principles of Marketing Research, Blackwell,

Cambridge, MA, USA.

Bentler, P.M. (1990), `̀ Comparative fit indexes in structural models'', Psychological

Association, Vol. 107 No. 2, pp. 238-46.

Bentler, P.M. and Bonnett, D.G. (1980), `̀ Significance tests and goodness of fit in analysis of

covariance structures'', Psychological Bulletin, Vol. 88, pp. 588-606.

Berry, L.L. and Yadav, M.S. (1996), `̀ Capture and communicate value in the pricing of

services'', Sloan Management Review, Summer, pp. 41-51.

Bharadwaj, S.G., Varadarajan, P.R. and Fahy, J. (1993), `̀ Sustainable competitive advantage in

service industries: a conceptual model and research propositions'', Journal of Marketing,

Vol. 57 No. 4, pp. 83-99.

Bolton, R.W. and Drew, J.H. (1991), `̀ A multistage model of customers' assessments of

service quality and value'', Journal of Consumer Research, Vol. 17, March, pp. 375-84.

Brown, T.J., Peter, J.P. and Churchill, T.J. (1993), `̀ Improving the measurement of service

quality'', Journal of Retailing, Vol. 69 No. 1, Spring, pp. 127-39.

Carmines, E.G. and McIver, J.P. (1981), `̀ Analysing models with unobservable variables'', in

Bohrnstedt, G.W. and Borgata, E.F. (Eds), Social Measurement, Current Issues, Beverly

Hills, Sage, pp. 65-115.

Carothers, G.H. and Adams, M. (1991), `̀ Competitive advantage through customer value: the

role of value-based strategies'', in Stahl, M. and Bounds, G.M. (Eds), Competing Globally

through Customer Value, Quorum Books, Westport, CT, pp. 32-66.

Day, G.S. (1990), Market Driven Strategy: Processes for Creating Value, The Free Press, New

York, NY.

Dickson, P.R. (1992), `̀ Toward a general theory of competitive rationality'', Journal of

Marketing, Vol. 56, pp. 69-83.

JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 15 NO. 2/3 2000 135

Dow

nloa

ded

by U

FPB

At 0

5:54

09

Nov

embe

r 20

14 (

PT)

Diefenbach, M.A., Weinstein, N.D. and O'Reilly, J. (1993), `̀ Scales for assessing perception

of health hazard susceptibility'', Health-Education Research, Vol. 8, June, pp. 187-92.

Gatignon, H. and Xuereb, J.M. (1997), `̀ Strategic orientation of the firm and new product

performance'', Journal of Marketing Research, Vol. 34, pp. 77-90.

Ghemawat, P. (1986), `̀ Sustainable advantage'', Harvard Business Review, Vol. 64,

September-October, pp. 53-8.

Haas, R.W. (1995), Business Marketing, South-Western College Publishing, Cincinnati, OH.

Hamel, G. and Prahalad, C.K. (1994), Competing for the Future, Harvard Business School

Press, Boston, MA.

Holbrook, M.B. and Corfman, K.P. (1985), `̀ Quality and value in the consumption experience:

phaedrus rides again'', in Jacoby, J. and Olson, J. (Eds), Perceived Quality, Lexington

Books, Lexington, MA, pp. 31-57.

Hunt, S.D. and Morgan, R.M. (1995), `̀ The competitive advantage theory of competition'',

Journal of Marketing, Vol. 59 No. 2, April, pp. 1-15.

Jacobson, R. (1992), `̀ The Austrian school of strategy'', Academy of Management Review,

Lexington, MA, Vol. 17 No. 4, pp. 782-807.

Johnston, W.J. and Lewin, J.E. (1997), `̀ Advances in industrial marketing theory and research

from the journal of business and industrial marketing'', Journal of Business Research,

Vol. 38, pp. 199-209.

JoÈreskog, K.G. and SoÈrbom, D. (1993), `̀ Lisrel 8: a guide to the program and applications'',

SPSS Inc., Chicago, IL.

Kohli, A.K., Jaworski, B.J. and Kumar, A. (1993), `̀ MARKOR: a measure of market

orientation'', Journal of Marketing Research, Vol. 30, November, pp. 467-77.

Lapierre, J. (1999), `̀ Development of measures to assess customer value in a business-to-

business context'', in Woodside, A.G. (Ed.), Advances in Business Marketing and

Purchasing, Vol. 9.

LaPlaca, P.J. (1997), `̀ Contributions to marketing theory and practice from industrial

marketing management'', Journal of Business Research, Vol. 38, pp. 179-98.

Lichtenthal, J.D., Wilson, D.T. and Long, M.M. (1997), `̀ Scientific contributions to the field

from the Journal of Business-to-Business Marketing'', Journal of Business Research,

Vol. 38, pp. 211-33.

Loevinger, J. (1976), `̀ Objectives tests as instruments of psychological theory'', in Jackson,

D.N. and Messick, S. (Eds), Problems in Human Assessment, McGraw-Hill, NY, pp. 78-

123.

Mandel, M.J. (1997), `̀ The new business cycle'', Business Week, March 31, pp. 58-68.

Marketing Science Institute (1996-1998), Research Priorities. A Guide to MSI Research

Programs and Procedures.

Mazumdar, T. (1993), `̀ A value-based orientation to new product planning!'', Journal of

Consumer Marketing, Vol. 10 No. 1, pp. 28-41.

Narver, J.C. and Slater, S.F. (1990), `̀ The effect of a market orientation on business

profitability'', Journal of Marketing, Vol. 54 No. 4, October, pp. 20-35.

Parasuraman, A. (1997), `̀ Reflections on gaining competitive advantage through customer

value'', Journal of the Academy of Marketing Science, Vol. 25 No. 2, pp. 154-61.

Parasuraman, A. (1998), `̀ Customer service in business-to-business markets: an agenda for

research'', Journal of Business & Industrial Marketing, Vol. 13 No. 4-5, pp. 309-21.

Plank, R.E. (1997), `̀ Theory, practice and empirical development contributions'', Advances in

Business Marketing and Purchasing, Vol. 38, pp. 235-41.

Quinn, J.B. (1996), `̀ The productivity paradox is false: information technology improves

services performance'', in Swartz, T.A., Bowen, D.E. and Brown, S.W. (Eds), Advances in

Services Marketing and Management, Vol. 5, JAI Press Inc., Greenwich, CT, pp. 71-84.

Ravald, A. and GroÈnroos, C. (1996), `̀ The value concept and relationship marketing'',

European Journal of Marketing, Vol. 30 No. 2, pp. 19-30.

Reid, J.A. and Plank, R.E. (1995), `̀ Has industrial marketing come of age?'', Working Paper,

College of Business Administration, University of Toledo, Toledo, OH.

Rifkin, G. (1989), `̀ CEOs give credit for today but expect more for tomorrow'', Computer-

world, 239160, April 17, pp. 75-82.

136 JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 15 NO. 2/3 2000

Dow

nloa

ded

by U

FPB

At 0

5:54

09

Nov

embe

r 20

14 (

PT)

Sethi, V. and King, W.R. (1994), `̀ Development of measures to assess the extent to which an

information technology application provides competitive advantage'', Management

Science, Vol. 40 No. 12, pp. 1601-27.

Sheth, J.N. and Sharma, A. (1997), `̀ Supplier relationships: emerging issues and challenges'',

Industrial Marketing Management, Vol. 26, pp. 91-100.

Slater, S.F. (1996), `̀ The challenge of sustaining competitive advantage'', Industrial Marketing

Management, Vol. 25, pp. 79-86.

Slater, S.F. (1997), `̀ Developing a customer value-based theory of the firm'', Journal of the

Academy of Marketing Science, Vol. 25 No. 2, pp. 162-7.

Slater, S.F. and Narver, J.C. (1992), `̀ Superior customer value and business performance: the

strong evidence for a market-driven culture'', Report, Marketing Science Institute,

Cambridge, MA, pp. 92-125.

Sullivan-Trainor, M. (1989), `̀ Building competitive advantage by extending information

systems'', Computerworld, Vol. 23 No. 41, SR/19.

Venkatraman, N. (1989), `̀ Strategic orientation of business enterprises and the construct,

dimensionality and measurement'', Management Science, Vol. 35 No. 8, August, pp. 942-

62.

Woodruff, R.B. (1997), `̀ Customer value: the next source for competitive advantage'', Journal

of the Academy of Marketing Science, Vol. 25 No. 2, pp. 139-53.

Woodside, A.G. (1997), `̀ Contributions of business-to-business marketing journals:

introduction to the special issue'', Journal of Business Research, Vol. 38, p. 178.

Zeithaml, V.A. (1988), `̀ Consumer perceptions of price, quality and value: a means-end model

and synthesis of evidence'', Journal of Marketing, Vol. 52 No. 3, July, pp. 2-22.

Appendix 1

Alternative solutions

The range of alternatives offered by the supplier

The supplier's capability to tailor their offerings to match your needs

The supplier's helpfulness in terms of assisting you in solving your problems

Product quality

The durability of products you buy

The reliability of the products you buy over the years

The performance of the products you buy

The consistent improvement in product quality over the years

Product customization

The customization of products for your firm

The ability to meet unique specifications for products not offered by your IT supplier's

competitors

The supplier's ability to offer different products from (not similar to) many of their customers

The ability to provide custom-built products for your firm

Responsiveness

Provide quick answers and solutions to your problems

Listen to your problems

Visit your locations to better understand your business

Flexibility

Their flexibility in responding to your requests

Their ability to adjust their products and services to meet unforeseen needs

The way they handle change

Their ability to provide emergency product and service deliveries

Reliability

The accuracy and clarity of the billing

Their ability to do things right the first time

The overall competence of employees with whom you do not have face-to-face contact

Their ability to keep promises

The accuracy of transactions

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Technical competence

Their creativity

Their specialized expertise in your activity sector

Their ability to demonstrate comprehensive process knowledge of your business

The way they use new technology to generate solutions

Their ability to provide system solutions in response to your problems

Image

Its reputation

Its credibility

Trust

Your confidence that the supplier is telling the truth, even when your supplier gives you a

rather unlikely explanation

The accuracy of the information provided by your major supplier

The supplier's fulfillment of promises made to your organization

The judgment or advice on your business operations that your supplier is sharing with you

The sincerity of your supplier

Solidarity

The help provided by your major supplier when you run into problems

The supplier's problems sharing that arise in the course of your relationship with them

The supplier's commitment to improvements which may benefit your overall relationship with

them (not only of benefit for their own sakes)

The supplier's willingness to meet your needs beyond the contract terms

Price

Most prices of the products and services you buy

Most prices you pay in relation to your major IT supplier's profitability

The impact of competition on the prices you pay

The justification of your major IT supplier in the prices they charge

The fairness of most prices you pay

Time/effort/energy

The number of meetings with the supplier's staff

The bargaining effort with the supplier's staff in reaching an agreement

Your time and effort spent for training a number of your employees

Your time and effort spent in developing a working business relationship with your major IT supplier

Your energy invested with your major IT supplier

Conflict

The frequent arguments you have with your supplier about business issues

The controversial arguments you have with your supplier

The disagreements you have with your supplier about how you can best achieve your

respective goals

Appendix 2

MLestimated

Phi t-valuew2 constrained

model

w2

unconstrainedmodel Dw2

Alternative solutions withProduct quality 0.42 7.82d 44.02 303.89 259.87d

Product customization 0.67 16.09d 21.59 159.38 138.79d

Responsiveness 0.65 14.89d 55.86 193.82 137.95d

Flexibility 0.71 18.65d 19.50 156.84 107.24d

Reliability 0.49 9.95d 38.68 278.30 239.61d

Technical competence 0.61 13.47d 46.48 219.15 172.67d

Image 0.55 11.55d 12.93 229.46 216.53d

Trust 0.64 14.84d 57.76 224.06 166.29d

Solidarity 0.63 3.20c 2.27 10.20 7.92c

Price 0.54 10.39d 16.88 197.97 180.09d

Time/effort/energy 0.48 8.25d 54.94 192.50 137.71d

(Continued)

Table AI. Discriminant validity analysis for the three sectors

138 JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 15 NO. 2/3 2000

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Phi t-valuew2 constrained

model

w2

unconstrainedmodel Dw2

Conflict 0.17 2.69c 78.40 349.54 271.14d

Product quality withProduct customization 0.39 7.22d 69.70 517.15 447.45d

Responsiveness 0.43 8.16d 41.69 404.60 362.90d

Flexibility 0.37 6.97d 56.95 395.45 338.50d

Reliability 0.51 11.21d 47.51 484.81 437.29d

Technical competence 0.46 9.20d 62.73 786.05 423.32d

Image 0.56 14.78d 37.68 356.99 319.31d

Trust 0.50 10.50d 57.76 463.40 405.64d

Solidarity 0.40 7.78d 63.27 392.07 328.80d

Price 0.33 5.54d 38.91 303.47 264.55d

Time/effort/energy 0.28 4.63d 59.05 317.84 258.76d

Conflict 0.02 0.39 58.38 388.87 330.49d

Product customization withResponsiveness 0.62 14.15d 21.44 243.31 224.87d

Flexibility 0.74 21.47d 45.68 166.31 120.63d

Reliability 0.52 11.00d 17.39 371.36 353.96d

Technical competence 0.72 20.55d 24.46 225.96 201.50d

Image 0.50 10.01d 14.00 33.49 359.49d

Trust 0.55 11.61d 41.22 353.32 312.10d

Solidarity 0.61 13.73d 20.97 247.59 226.62d

Price 0.55 10.59d 31.47 214.41 182.94d

Time/effort/energy 0.39 6.34d 44.17 210.36 166.19d

Conflict 0.13 2.04b 58.95 349.86 290.91d

Responsiveness withFlexibility 0.80 27.04d 49.58 134.04 84.46d

Reliability 0.64 16.57d 25.56 286.15 260.59d

Technical competence 0.68 17.41d 50.45 252.54 202.08d

Image 0.58 13.25d 6.20 323.58 317.37d

Trust 0.71 20.20d 62.91 248.70 185.78d

Solidarity 0.72 20.06d 22.51 187.29 164.77d

Price 0.40 6.86d 8.98 253.59 244.60d

Time/effort/energy 0.50 8.92d 65.73 209.37 143.64d

Conflict 0.15 2.32d 97.15 362.41 265.25d

Flexibility withReliability 0.72 21.18d 52.50 217.08 164.58d

Technical competence 0.82 29.11d 81.38 163.78 82.39d

Image 0.61 14.80d 23.93 252.00 228.07d

Trust 0.76 23.43d 56.28 174.15 117.87d

Solidarity 0.77 24.11d 49.87 156.40 106.53d

Price 0.54 10.89d 20.17 219.78 199.61d

Time/effort/energy 0.53 10.02d 60.48 192.44 131.96d

Conflict 0.20 3.38c 111.02 365.26 254.24d

Reliability withTechnical competence 0.67 17.73d 48.24 368.72 320.48d

Image 0.69 20.73d 11.19 282.45 293.64d

Trust 0.77 27.05d 63.07 319.23 256.16d

Solidarity 0.63 16.00d 58.33 277.54 219.21d

Price 0.50 9.88d 25.88 257.98 232.10d

Time/effort/energy 0.37 6.37d 31.06 222.68 191.62d

Conflict 0.14 2.25b 131.42 380.47 249.05d

Technical competence withImage 0.59 13.46d 22.04 329.92 307.86d

Trust 0.68 17.96d 66.69 337.02 270.32d

Solidarity 0.72 20.34d 37.22 209.84 172.62d

Price 0.47 8.59d 28.80 248.69 219.89d

Time/effort/energy 0.44 7.51d 65.55 225.52 159.97d

Conflict 0.19 2.96b 80.66 350.63 269.97d

Image withTrust 0.67 18.34d 28.81 299.92 271.11d

Solidarity 0.60 14.21d 17.46 264.36 247.89d

Price 0.45 8.19d 10.18 246.16 235.98d

Time/effort/energy 0.40 6.94d 15.94 199.65 183.71d

Conflict 0.12 1.89a 87.14 497.90 410.76d

(Continued)

Table AI. Discriminant validity analysis for the three sectors

JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 15 NO. 2/3 2000 139

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MLestimated

Phi t-valuew2 constrained

model

w2

unconstrainedmodel Dw2

Trust withSolidarity 0.86 38.56d 112.95 180.76 67.81d

Price 0.62 13.95d 22.02 194.31 172.29d

Time/effort/energy 0.53 10.04d 71.52 212.10 140.58d

Conflict 0.24 4.04d 308.10 409.95 101.85d

Solidarity withPrice 0.58 11.77d 35.67 215.02 179.35d

Time/effort/energy 0.60 12.48d 39.16 153.17 114.01d

Conflict 0.23 3.90d 104.56 351.98 249.42d

Price withTime/effort/energy 0.28 4.18d 37.53 229.58 192.04d

Conflict 0.07 1.04 89.24 363.32 274.08d

Time/effort/energy withConflict 0.51 9.83d 50.04 214.06 164.02d

Notes: ap < 0.10; bp < 0.05; cp < 0.01; dp < 0.001

Table AI.

140 JOURNAL OF BUSINESS & INDUSTRIAL MARKETING, VOL. 15 NO. 2/3 2000

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96. Mika Westerlund, Senja Svahn. 2008. A relationship value perspective of social capital in networks of software SMEs. IndustrialMarketing Management 37, 492-501. [CrossRef]

97. Jeffrey Lewin, James Barry, Tamara S. Terry. 2008. Empirical study of relationship value in industrial services. Journal of Business& Industrial Marketing 23:4, 228-241. [Abstract] [Full Text] [PDF]

98. Jeffrey Lewin, Tracy R. Harmon, Merlyn A. Griffiths. 2008. Franchisee perceived relationship value. Journal of Business & IndustrialMarketing 23:4, 256-263. [Abstract] [Full Text] [PDF]

99. Albert Graf, Peter Maas. 2008. Customer value from a customer perspective: a comprehensive review. Journal für Betriebswirtschaft58, 1-20. [CrossRef]

100. Stephan C. Henneberg, Stefanos MouzasFinal customers’ value in business networks 99-127. [Abstract] [Full Text] [PDF] [PDF]101. Roger BaxterIntangible value in buyer–seller relationships 27-98. [Abstract] [Full Text] [PDF] [PDF]102. Tuomas Ahola, Eino Laitinen, Jaakko Kujala, Kim Wikström. 2008. Purchasing strategies and value creation in industrial turnkey

projects. International Journal of Project Management 26, 87-94. [CrossRef]103. J.A. López Sánchez, M.L. Santos Vijande, J.A. Trespalacios Gutiérrez. 2008. LA INFLUENCIA DE LA CONFIANZA Y EL

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104. Miguel A. Moliner, Javier Sánchez, Rosa M. Rodríguez, Luís Callarisa. 2007. Perceived relationship quality and post‐purchaseperceived value. European Journal of Marketing 41:11/12, 1392-1422. [Abstract] [Full Text] [PDF]

105. Jillian C. Sweeney, David A. Webb. 2007. How functional, psychological, and social relationship benefits influence individual andfirm commitment to the relationship. Journal of Business & Industrial Marketing 22:7, 474-488. [Abstract] [Full Text] [PDF]

106. Lesley Ledden, Stavros P. Kalafatis, Phillip Samouel. 2007. The relationship between personal values and perceived value ofeducation. Journal of Business Research 60, 965-974. [CrossRef]

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108. Katherine Tyler, Mark Patton, Marco Mongiello, Derek Meyer, Graham Whittaker, Lesley Ledden, Stavros P. Kalafatis. 2007.A re‐examination of the relationship between value, satisfaction and intention in business services. Journal of Services Marketing21:5, 345-357. [Abstract] [Full Text] [PDF]

109. Christopher P. Blocker, Daniel J. Flint. 2007. Customer segments as moving targets: Integrating customer value dynamism intosegment instability logic. Industrial Marketing Management 36, 810-822. [CrossRef]

110. Christopher P. Blocker, Daniel J. Flint. 2007. Exploring the dynamics of customer value in cross‐cultural business relationships.Journal of Business & Industrial Marketing 22:4, 249-259. [Abstract] [Full Text] [PDF]

111. Djoko Setijono, Jens J. Dahlgaard. 2007. The Added‐Value Metric ‐ A Complementary Performance Measure for Six Sigma andLean Production. Asian Journal on Quality 8:1, 1-14. [Abstract] [PDF]

112. Javier Sánchez-Garcia, Miguel A. Moliner-Tena, Luís Callarisa-Fiol, Rosa M. Rodríguez-Artola. 2007. Relationship Quality ofan Establishment and Perceived Value of a Purchase. The Service Industries Journal 27, 151-174. [CrossRef]

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116. Paolo Guenzi, Gabriele Troilo. 2006. Developing marketing capabilities for customer value creation through Marketing–Salesintegration. Industrial Marketing Management 35, 974-988. [CrossRef]

117. Juan Carlos Fandos Roig, Javier Sanchez Garcia, Miguel Angel Moliner Tena, Jaume Llorens Monzonis. 2006. Customer perceivedvalue in banking services. International Journal of Bank Marketing 24:5, 266-283. [Abstract] [Full Text] [PDF]

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118. Marianna Sigala, Evangelos Christou, Marianna Sigala. 2006. Mass customisation implementation models and customer value inmobile phones services. Managing Service Quality: An International Journal 16:4, 395-420. [Abstract] [Full Text] [PDF]

119. Javier Sánchez, Luís Callarisa, Rosa M. Rodríguez, Miguel A. Moliner. 2006. Perceived value of the purchase of a tourism product.Tourism Management 27, 394-409. [CrossRef]

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129. John Noonan, Michael Wallace. 2004. Building responsive contract manufacturers through value‐focused strategies. Supply ChainManagement: An International Journal 9:4, 295-302. [Abstract] [Full Text] [PDF]

130. Yonggui Wang, Hing Po Lo, Renyong Chi, Yongheng Yang. 2004. An integrated framework for customer value and customer‐relationship‐management performance: a customer‐based perspective from China. Managing Service Quality: An InternationalJournal 14:2/3, 169-182. [Abstract] [Full Text] [PDF]

131. Yonggui Wang, Hing‐Po Lo. 2004. CUSTOMER‐FOCUSED PERFORMANCE AND ITS KEY RESOURCE‐BASEDDETERMINANTS: AN INTEGRATED FRAMEWORK. Competitiveness Review 14:1/2, 34-59. [Abstract] [PDF]

132. Daniel J. Flint. 2004. Strategic marketing in global supply chains: Four challenges. Industrial Marketing Management 33, 45-50.[CrossRef]

133. Wolfgang Ulaga. 2003. Capturing value creation in business relationships: A customer perspective. Industrial MarketingManagement 32, 677-693. [CrossRef]

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135. Yonggui Wang, Hing‐Po Lo. 2003. Customer‐focused performance and the dynamic model for competence building andleveraging. Journal of Management Development 22:6, 483-526. [Abstract] [Full Text] [PDF]

136. Yonggui Wang, Hing‐Po Lo. 2002. Service quality, customer satisfaction and behavior intentions. info 4:6, 50-60. [Abstract][Full Text] [PDF]

137. Daniel J. Flint, Robert B. Woodruff, Sarah Fisher Gardial. 2002. Exploring the Phenomenon of Customers’ Desired Value Changein a Business-to-Business Context. Journal of Marketing 66, 102-117. [CrossRef]

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