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Industrial Marketing Management 37 (2008) 686 – 697
The B2B-RELPERF scale and scorecard: Bringing relationship marketing
theory into business-to-business practice
Luis Filipe Lages a,b,⁎, Andrew Lancastre a,c,✠ , Carmen Lages d
a
Faculdade de Economia, Universidade Nova de Lisboa, Campus de Campolide, 1099-032 Lisboa, Portugal
b
MIT's Deshpande Center for Technological Innovation, USA
c
IADE–Instituto de Artes Visuais, Design e Marketing, Av. D.Carlos I, no. 4, 1200, Lisboa, Portugal
d
ISCTE Business School-Lisbon, Av. das Forças Armadas 1649-026 Lisboa, Portugal
Received 23 August 2005; received in revised form 2 July 2006; accepted 7 May 2007
Available online 8 August 2007
Abstract
It is becoming increasingly important from both theoretical and managerial perspectives to measure Customer Relationship Management
(CRM) as a key intangible asset. This paper seeks to bring relationship marketing theory into practice by developing a new measure of relationship
performance between two firms, the business-to-business relationship performance (B2B-RELPERF) scale. Survey findings from a sample of
approximately 400 purchasing managers operating in a B2B e-marketplace reveal that relationship performance is a high-order concept, composed
of several distinct, yet related, dimensions: (1) relationship policies and practices, (2) relationship commitment; (3) trust in the relationship, (4)
mutual cooperation; and (5) relationship satisfaction. Findings reveal that the B2B-RELPERF scale relates positively and significantly with
customer loyalty. The paper also presents the B2B-RELPERF balanced scorecard, which combines tangible and intangible metrics. While existing
IT solutions usually focus exclusively on the use of tangible CRM indicators, this new tool includes the “voice of the customer”. At the managerial
level, both the scale and scorecard could act as useful instruments for short- and long-term management, controlling, planning, and improvement
of B2B relationships. Implications for relationship marketing theory are also presented.
© 2007 Elsevier Inc. All rights reserved.
Keywords: Relationship marketing theory; Balanced scorecard; Electronic markets; e-Commerce; e-Marketplace; Performance measurement; CRM; B2B; Purchasing
1. Introduction
“If you do not measure it, then you cannot manage it!”
(Jack Welch, former CEO of General Electric)
At the beginning of the 21st century it is widely accepted that
existing Customer Relationship Management (CRM) solutions
⁎ Corresponding author. Faculdade de Economia, Universidade Nova de
Lisboa, Campus de Campolide, 1099-032 Lisboa, Portugal. Tel.: +351
213801601; fax: +351 213886073.
E-mail addresses: [email protected] (L.F. Lages), [email protected]
(A. Lancastre), [email protected] (C. Lages).
URL: http://prof.fe.unl.pt/~lflages (L.F. Lages).
✠
Editor's note: Three days after this paper was accepted for publication,
Andrew Lancastre passed away due to leukemia. This issue of IMM is dedicated
to his memory.
0019-8501/$ - see front matter © 2007 Elsevier Inc. All rights reserved.
doi:10.1016/j.indmarman.2007.05.008
have much room for improvement. The main reason may be
found in a statement by Einstein: “everything that can be counted
does not necessarily count; everything that counts cannot necessarily be counted.” In order to measure what “cannot necessarily
be counted”, existing CRM Information Technologies (IT) solutions employ tangible metrics to assess intangible dimensions
(e.g. trust and cooperation). Many firm's intangible assets
constitute unique opportunities for economic (added) value,
such as customer relationships, and these can and should be
scientifically assessed (Hunt, 1997).
As managers and researchers observe that good versus poor
relationships significantly affect business performance, there is an
increasing concern with achieving a better understanding of
relationship development with business partners (Lages, Lages, &
Lages, 2005a; Lages, Lages, & Lages, 2005b; Lemon, White, &
Winer, 2002). Although intangible metrics are of interest to
academics and managers, these two worlds discuss the issue quite
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differently (Melnyk, Stewart, & Swink, 2004; Likierman, 2004).
While most managers do not have the scientific knowledge to
develop reliable measures, most academic researchers are not
concerned with the development of scientific metrics for
application at the managerial level (Weick, 2001). Within this
context, the development of business performance metrics
requires the exploration of synergies between researchers and
managers in order to develop scientific and reliable measures that
might be of interest to practitioners, bridging the gap. Moreover,
although marketing academics and practitioners have been
examining relationship marketing since the mid-1980s, a
significant criticism of most relationship marketing studies is
the fact that many studies are based on a single dimension or a
single financial indicator, intended to capture the nature of
complex relationships between buyers and suppliers (Yau et al.,
2000).
A major priority for the upcoming years is the development
of B2B metrics, namely in an e-commerce environment
(Parasuraman, Zeithaml, & Malhotra, 2005; Parasuraman &
Zinkhan, 2002). Despite both managers and academics' interest
in understanding relationships in e-commerce, concerted efforts
have not materialized (Grewal, Comer, & Mehta, 2001; Klein &
Quelch, 1997). This article attempts to help bridge the gap by
scientifically developing a new scale that enables, from a
customer perspective, the assessment of relationship performance in business-to-business (B2B) relationships — named
the “B2B-RELPERF scale”. Furthermore, the authors use the
B2B-RELPERF scale to suggest the development and testing of
the respective relationship performance scorecard for inclusion
in periodic business reports and/or existing CRM IT solutions. It
is believed that the scale (and future scorecard) might help firms
to administer resources more efficiently, by allocating them to
different customers, and identifying deviations from objectives.
Given the development of different customer relationship processes, this can also help a firm to establish its annual priorities
in terms of marketing efforts. Moreover, a firm can use relationship performance metrics as a motivation and reward tool
for managers and their teams (e.g., bonus, promotion) by
relying on comprehensive data. Finally, these metrics can support the development, monitoring, improvement and benchmarking of customer relationship processes (see Lages et al.,
2005a,b).
This paper starts by presenting the five dimensions of the
B2B-RELPERF scale. We then refine the preliminary scale
using qualitative research and testing it through a field survey
of approximately 400 purchasing managers of small and
medium-sized enterprises (SMEs) in an e-marketplace. We
also analyze the impact of the B2B-RELPERF scale on
customers' loyalty intentions. Finally, we present implications
for theory and practice, suggest a B2B-RELPERF scorecard,
point out research limitations and define directions for further
research.
2. The B2B-RELPERF scale
A recent meta-analysis of relationship marketing literature
(Palmatier, Dant, Grewal, & Evans, 2005) indicates that re-
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search in the field should follow a multidimensional perspective
because there is no single or best dimension able to capture the
full essence of this phenomenon. Indication of the main constituents of a relationship process can be found in the literature.
While building on past RM literature, the B2B-RELPERF scale
reflects the performance of a buyer–supplier relationship
marketing process at a specific point in time. This scale is a
higher-order construct composed of five distinct, yet related,
dimensions: (1) relationship policies and practices; (2) trust in
the relationship; (3) relationship commitment; (4) mutual cooperation; and (5) relationship satisfaction.
Following the findings of Dwyer, Schurr and Oh (1987),
supported some years later by Jap and Ganesan (2000), these
five dimensions are key ingredients of a relationship process.
Relationship policies and standards of conduct that mark a
relational contract take form during the exploration phase, and
go on building during the relationship process. The rudiments of
trust and joint satisfaction established during this phase develop
in the next stages of the process, leading to increased risk taking
within the dyad. The expansion process that follows the exploration phase “is a consequence of each party's satisfaction
with the other's role performance”. The resulting perceptions of
goal congruence and cooperation lead to interactions beyond
those that are strictly required at the outset, resulting in the
parties purposefully committing resources to maintain and
develop the relationship. These findings, supported by the
relationship marketing literature (Anderson & Narus, 1990;
Morgan & Hunt, 1994; Sirdeshmukh, Singh, & Sabol, 2002),
suggest the significance of the identified five dimensions to
relationship process performance.
2.1. Relationship policies and practices
Relationship policies and practices represent one of the
most important dimensions during a relationship process (Jap
& Ganesan, 2000). By establishing clear relationship policies
and practices, the supplier becomes motivated to behave in a
way that is beneficial to the relationship as a whole, and as a
result, “emerging exchange partners start setting the ground
rules for future exchange” (Dwyer et al., 1987: 17). Despite
the well-recognized significance of such policies, few studies
have examined company behaviors and practices specifically
or the mechanisms by which they strengthen a relationship
(Sirdeshmukh et al., 2002). Nevertheless, it is recognized that
relationship policies and practices should include ethical
values, such as the supplier showing respect for the customer,
because they contribute to the development of the relationship between firms and customers (Morgan & Hunt, 1994).
Indeed, the extent to which partners have common beliefs
about what behaviors and policies are important, appropriate, and right, is important to the process of relationship
development.
Strategic considerations motivate firms to serve their customers better, and technology is viewed as a means to build
competitive advantage. In e-markets, strategic considerations,
such as providing better customer service, are particularly
significant because the virtual world increases the possibility of
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suppliers acting opportunistically and customers perceiving
augmented risk and uncertainty (Grewal et al., 2001). Thus,
providing better service through quicker and easier problem
solving represents an added relationship value in the electronic
context and a contribution to the buyer–supplier relationship.
2.2. Trust
In interpersonal relationships trust is defined as the “willingness to rely on another party and to take action in circumstances
where such actions make one vulnerable to the other party”
(Doney, Cannon, & Mullen, 1998, 604). Trust has been extended
to relations between organizations since interorganizational exchanges are made by individuals from each organization
(Aulakh, Kotabe, Sahay, 1996). Thus, trust exists when one
party has confidence in an exchange partner's reliability and
integrity (Morgan & Hunt, 1994). Trust is an essential ingredient
in the creation, development, and maintenance of long-term
relationships between buyers and suppliers (Anderson & Narus,
1990; Ganesan, 1994).
According to the findings of Palmatier et al. (2005), trust is a
key dimension of (objective) performance. As trust is the cornerstone of the strategic partnership and relationship development process (Moorman, Deshpandé, & Zaltman, 1993;
Morgan & Hunt, 1994), it is widely accepted that trust plays a
central role in the development of relationship performance
(Dwyer et al., 1987; Morgan & Hunt, 1994). Trust becomes
even more important for relationship and loyalty development
purposes when perceived risk is more pronounced. Perceived
risk in an e-commerce environment is high when compared to
traditional commerce, due to spatial and temporal separation
between buyers and sellers. The internet transaction typically
takes place from different locations at different times, and goods
or services are delivered after a seller has verified payment.
When selecting a supplier, a buyer might consider the possibility that the supplier may be an expert in attracting customers
and cashing credit cards but not in actually delivering goods or
services (Smith, Bailey, & Brynjolfsson, 1999). Thus, given the
higher perceived risks and uncertainty in the “marketspace,”
trust may be even more critical in the buyer–supplier relationship development process than in the traditional marketplace.
tomer-oriented assortment (Anderson & Weitz, 1992), and
similarly buyers in an e-marketplace receive more relevant upto-date market and product information, a better assortment
choice, and order/payment automation (Smith et al., 1999;
Weiber & Kollman, 1998). Because both parties receive new
benefits from each other, each has a stronger motivation to
build, maintain and develop the relationship through renewed
committed efforts (Kumar et al., 1995). Thus, strong relationships are “built on the foundation of mutual commitment”
(Berry & Parasuraman, 1991: 139).
In an organizational environment, commitment may be
affective (attachment to an organization), continued (perceived
cost of leaving an organization), or normative (perceived obligation to stay with an organization) (Meyer, Allen, & Smith,
1993). Of these three, only affective commitment influences the
degree to which the customer wants to maintain a relationship
with the firm (Roberts et al., 2003). Thus, in the electronic
setting, we assume that relationship commitment is the buyer's
attachment to the supplier and that it leads to the development of
stable, long-term relationships.
2.4. Mutual cooperation
Relationships require cooperative behavior (Morgan & Hunt,
1994). Cooperation between partners in a relationship exchange
process grows as each partner perceives greater benefits from
working together than from working independently. Thus,
mutual cooperation is defined as “similar or complementary
coordinated actions taken by firms in interdependent relationships to achieve mutual outcomes or singular outcomes with
expected reciprocation over time” (Anderson & Narus, 1990:
45).
“The e-marketplace is a networked information system that
serves as an enabling infrastructure for buyers and sellers to
exchange information, transact, and perform other activities”
(Varadarajan & Yadav, 2002: 297). Mutual cooperation in an
electronic environment is viewed in terms of the regularity of
the interactions and the openness in communication activities
between the buyer and the supplier (Hewett & Bearden, 2001;
O'Keefe, O'Connor, & Jung, 1998), and represents an
important element in relationship performance planning, management, and control.
2.3. Relationship commitment
2.5. Relationship satisfaction
Relationship commitment is defined as the “desire to develop a stable relationship, a willingness to make short-term sacrifices to maintain the relationship, and a confidence in the
stability of the relationship” (Anderson & Weitz, 1992: 19).
Commitment is essential for the development of long-term
relationships (Anderson & Narus, 1990; Kumar, Scheer, &
Steenkamp, 1995), and is an important indicator of both objective and relationship performance (Roberts, Varki, & Brodie,
2003). Moreover, relationship commitment is a means for differentiating successful relationships from unsuccessful ones
(Morgan & Hunt, 1994).
Suppliers in a committed relationship gain greater access to
market information, which enables them to select a better cus-
Relationship satisfaction refers to “the cognitive and affective evaluation based on personal experience across all …
episodes within the relationship” (Roberts et al., 2003: 175).
Thus, relationship satisfaction can be considered to be a positive
emotional and rational state resulting from the assessment of the
buyer's working relationship with the supplier (Geyskens,
Steenkamp, & Kumar, 1999). Relationship satisfaction summarizes the customer's previous interactions with the supplier,
which in turn influence expectations of future relationship
development (Roberts et al., 2003). Customer evaluation
of relationship satisfaction with the supplier is critical for
the development of future business exchanges (Cannon &
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Perreault, 1999). Positive interactions lead to long-term continuation of the relationship, thus emphasizing that relationship
satisfaction is essential to the improvement of relationship
performance.
3. Method
3.1. Unit of analysis and research setting
The unit of analysis for this research is a specific buyer's
relationship with a specific supplier. The analysis is conducted by
examining the buyer's perspective. It was decided to collect
customers' data because the customer ultimately decides whether
to purchase from the supplier, and the customer's perception of
the relationship is likely to predominantly determine its development and performance (Cannon & Perreault, 1999).
The research setting has two main characteristics we believe
to be worth examining when considering relationships:
business-to-business and electronic. Business-to-business because recent research findings (Palmatier et al., 2005) reveal that
customer relationships lead to higher levels of objective
performance in business markets versus consumer markets.
Electronic because the e-market environment might be
perceived as risky and uncertain, and thus relationship
development can be particularly relevant and difficult. Electronic business has added a new dimension to business
relationship discussions (Morgan & Hunt, 2003). In an emarket environment, business is conducted at a distance, and
risks and uncertainties are enhanced because the Internet, which
is a relatively new and complex technology from the
consumer's perspective, presents security problems. For
example, online suppliers can easily register and track customer
data, which increases the possibility for them to act opportunistically. Furthermore, many online buyers may not yet have
accumulated the necessary shopping experience and relevant
knowledge about potential market suppliers or other partners in
this new shopping channel (Einwiller, Ingenhoff, & Schmid,
2003), which might undermine their confidence in using it.
3.2. The online business center
The online business center pmelink.pt, selected for this
research, sells office goods and services (e.g., office paper,
consumables, computers, office furniture, recruitment services,
customer credit reports) to SMEs in order to support their core
businesses. We use pmelink.pt to analyze the extent to which it
might be possible to monitor marketing relationship performance in a non-critical products' context. The purchasing literature suggests that non-critical items do not have a significant
impact on the finances of most firms, representing a low supply
risk to most B2B buyers (Kraljic, 1983). It has been argued that
for routine products, value added strategies play a secondary
role because the buyer normally relies on exploitation of full
purchasing power, order volume optimization, and targeted
pricing strategies. As long as key purchasing variables (e.g.
lowest price and availability/delivery) are addressed by the
seller, the buyers will become committed, and all other “added-
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value” activities will become secondary.1 The pmelink.pt
supplier delivers non-critical products to SMEs by focusing
on added-value services, such as offering management relevant
information, customer problem solving and advice, product
portfolio access and payment convenience, customization and
personalization, following retention and loyalty strategies.
Interestingly, findings from a previous study (see Lancastre
& Lages, 2006) revealed that although pmelink.pt sells routine,
or at best non-critical products, the key determinants of buyers'
cooperation (a key dimension in a relationship process) with
this seller were essentially marketing relationship dimensions.
Surprisingly, product prices (− .05, P b.05) resulted in a minor
determinant of cooperation, and product delivery showed a nonsignificant association with that relationship dimension. Hence,
by using a sample of pmelink.pt's business customers, it is our
goal to better understand how it is possible for a firm to monitor
and strengthen relationship performance with its customers
even when dealing with non-critical products.
Essentially, pmelink.pt operates among diverse businesses
and suppliers: a customer places an order with pmelink.pt,
which is forwarded to one of its 30 suppliers, with whom they
have previously established contractual agreements; an express
cargo carrier takes care of delivery logistics, and pmelink.pt
bills the customer. The pmelink.pt online business center was
formed when three major Portuguese groups (Portugal Telecom
and two of the main national Banks) recognized an opportunity
to market a variety of goods and services to their common client
base. Portugal Telecom's market penetration is almost 100%
and the other two partners together retain around 65% of all
SMEs operating in the country as clients. The two banks
realized that during their day-to-day relationships with their
SME's customers, the SMEs were generally strong concerning
their core businesses but relatively poor in providing support
areas – clerical, office supplies and purchasing, IT, marketing,
logistics and so on – vital for relationship development. The
company pmelink.pt offered an opportunity through technology
to offer those supporting tools to their SME's customers as well
as to increase their knowledge and business with them.
The online business center pmelink.pt uses an Internet and
CRM electronic platform to formulate an integrated e-business
infrastructure that allows the provision of adequate and reliable
information, a variety of added-value services, and personalization. In addition to products, pmelink.pt offers a range of
business expertise, advice and information through the same
online connection. As an example, pmelink.pt realized that
SMEs often had difficulty dealing with various legal requirements. In response, it developed a package of core services that
includes a search engine for all types of legal documents, a
simulator for various fees and taxes, a fiscal calendar with
reminders of major dates, and a library of printable official
forms. Not only does the supplier promise fast and efficient
delivery of goods (pmelink.pt promises a 99% delivery success
rate within a 24-h timeframe), it also leverages direct cost
reductions to its clients through bulk ordering and strategic
sourcing of materials from key suppliers.
1
We acknowledge an anonymous reviewer for this insight.
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Monitoring user activities on the site allows individual
visitors to be segmented, so that campaigns and newsletters can
be targeted more effectively, and loyalty programs can be
developed. Based on its unique relationship with its investors
and customers, pmelink.pt aimed to contact and start business
with around 10% of the 200,000 SMEs operating in Portugal by
the end of its third year of existence.
3.3. Survey instrument development and test
Scale development follows Churchill's (1979) traditional
approach. On the basis of the literature review and preliminary
findings, we specified the domain of the construct to include
five Business-to-Business Relationship Performance (B2BRELPERF) dimensions — relationship policies and practices,
trust, relationship commitment, mutual cooperation, and relationship satisfaction. Fifty-two items were developed to measure these five dimensions based on CRM and electronic
marketing literature. These items were then refined through indepth interviews with the five managers of pmelink.pt involved
in the e-business operations. On the basis of their feedback,
some items were removed so that the measures became better
adjusted to the reality of e-markets. Three SME customers then
assessed the final set of items of the B2B-RELPERF scale for
content and face validity. To pre-test the B2B-RELPERF scale,
data were collected through an initial online survey targeting 40
SMEs previously selected by the supplier pmelink.pt, considering loyal, regular and infrequent buyers. At the end of this
process the scale included the 5 dimensions considered
previously. As a consequence of the above-mentioned stages
of the refinement process, the final solution included 38 of the
52 initial items.
3.4. Data collection
The exploratory stage was followed by a survey based on an
online questionnaire that we administered to a sample of the
SME customers who were responsible for purchasing operations. In line with previous studies conducted by pmelink.pt, the
online survey attached to the firm's periodic online newsletter,
resulted in 395 valid questionnaires, which is greater than the
minimum number (i.e., 381) required for a 95% confidence
level. Stratified sampling was accomplished and controlled
through the data collecting process, using the supplier's customers' loyalty degree strata grouping — loyal, regular, infrequent
and new buyers.
3.5. Sample profile and nonresponse bias
Respondents were representative of the main industry and
economic activities in the primary sector (5%), industrial sector
(21%), and services sector (74%). They also regularly purchased main product categories (as classified by the supplier):
paper (74%), consumable goods (73%), other office products
(57%), systems equipment (29%), office furniture (5%), and
services (6%). On the basis of the supplier database, we directed
the survey to individuals who were primarily responsible for
the SMEs' buying centers. Although the job titles of the respondents ranged from general manager to financial manager,
purchasing manager, and administrative manager, all of the
respondents were the main person responsible for their SME's
purchasing operations and most interacted with the supplier on a
daily basis. In terms of profile, 20% of the respondent firms had
fewer than 6 months of business experience with the supplier,
30% varied between 6 and 12 months, and the remaining 50%
had more than 12 months of experience. In this last group, 70%
had more than 2 years of experience. This indicates that though
the title of the respondents' positions may be wide ranging, all
appear to have significant knowledge about the specific
purchasing activities of the firm. Although the respondents'
profile suggests a high degree of confidence in the sample, we
considered it important to test for possible nonresponse bias. By
assessing the differences between the early and late respondents
(Armstrong & Overton, 1977), we found no significant
differences.2
Exploratory Factor Analysis (EFA) was conducted to
examine the factor structure of each variable presented in the
conceptual model. EFA is a data simplification technique particularly useful for reducing the number of indicators to a
manageable set. EFA with principal components analysis (PCA)
and varimax rotation (when Kaiser's rule is used for factor
selection) was performed on the survey data. Varimax rotation is
the most widely used analytical format to analyze orthogonal
factors. This method was selected because although the subdimensions may be related, the relationships cannot be anticipated a priori. The use of an orthogonal rotation has the
advantage of allowing a more intuitive interpretation of the
results (Hair, Anderson, Tatham, & Black, 1998). Coefficient
alpha was also computed for the emerged factors in order to
assess the validity of the initial scales and further purify the
number of items for each factor. Five items were removed
during this initial process, two from the factor relationship
policies and practices (Sirdeshmukh et al., 2002) and three from
the factor relationship commitment (Anderson & Weitz, 1992).
The other 19 items were removed as a consequence of CFA,
Fornell and Larcker's (1981) test of variance extracted, and
Bagozzi's (1980) test of composite reliability.
4. Data analysis
4.1. CFA
CFA was performed to assess the measurement properties of
the scales, using full-information maximum likelihood estimation procedures in LISREL 8.3 (Jöreskog & Sörbom, 1993). In
this model, each item is restricted to load on its prespecified
factor, with the five first-order factors correlating freely. The
chi-square for this model is significant (χ2 = 143.58, d.f. = 67,
P b.05). Because the chi-square is sensitive to sample size, we
2
Early respondents were defined as the first 75% of the returned
questionnaires, and the last 25% were considered to be late respondents. These
proportions approximate the actual way in which questionnaires were filled in.
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also assessed additional fit indices: (1) non-normed fit index
(NNFI), (2) comparative fit index (CFI), incremental fit index
(IFI), and standardized root mean square residual (SRMR). The
NNFI, CFI, and IFI, of this model are .99, and the SRMR is .41.
Because fit indices can be improved by allowing more terms to
be freely estimated, we also assessed the root mean square error
of approximation (RMSEA), which, for our measurement
model, is .054. A full listing of the 14 final items and their
scale reliabilities appear in Table 1. The average internal reliability (Cronbach α) was .86.
The significant standardized loadings of each item on its
intended construct (average loading size was .83) evidenced
unidimensionality. Table 1 shows that all constructs present
the desirable levels of composite reliability (Bagozzi, 1980).
Table 1
The B2B-RELPERF scale: scale dimensions and items, reliabilities, and
variance extracted
RPP:
V1
V2
V3
RCO:
V4
V5
V6
Please rate your agreement with each of the
following statements, regarding your relationship
with Firm X:
α /ρvc(n) /ρ
Relationship policies and practices
(Adapted from Sirdeshmukh et al., 2002)
Firm X has policies that show respect for the customer.
Firm X has practices that make solving problems easy.
Firm X solves my firm's problems quickly.
.81/.61/.82
Relationship commitment
.86/.69/.87
(Adapted from Kumar et al., 1995)
Our relationship with Firm X is a long-term partnership.
We would not drop Firm X because we like being
associated with it.
We want to remain as a customer of Firm X because we
have pride in being associated with a firm that carries a
technological image.
TRUST: Trust in the relationship
(Adapted from Morgan & Hunt, 1994)
In our relationship, Firm X…
V7
is someone to whom I give my confidence.
V8
has high integrity.
V9
gives us reliable information and advice.
.91/.78/.91
MCO:
Mutual cooperation
(Adapted from Hewett & Bearden, 2001)
My firm and Firm X regularly interact.
There is an open communication between our firms.
.82/.71/.83
Relationship satisfaction
(Adapted from Cannon & Perreault, 1999)
Overall, we are satisfied with Firm X.
We are pleased with what Firm X does for us.
If we had to do it again, we would still choose to
use Firm X.
.88/.71/.88
V10
V11
SAT:
V12
V13
V14
Notes: Firm X = “The supplier” (i.e., pmelink.pt). For purposes of replication of
this study in other types of B2B relationships, Firm X may be considered any
type of firm (e.g., a manufacturer, buyer, distributor, franchisor, franchisee,
exporter, importer, multinational).
Cronbach's α = internal reliability (Cronbach, 1951); ρvc(n) = variance extracted
(Fornell & Larcker, 1981); and ρ = composite reliability (Bagozzi, 1980).
All items are 7-point Likert scales, anchored by “strongly disagree” and
“strongly agree.”
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Table 1 also shows that Fornell and Larcker's (1981) index of
variance extracted was greater than the recommended level of
.50 for all five constructs.
The construct intercorrelations, which are significantly different from 1, reveal evidence of discriminant validity, and the
shared variance between any two constructs (i.e., the square of
their intercorrelation) is less than the average variance explained
in the items by the construct (Fornell & Larcker, 1981;
MacKenzie, Podsakoff, & Rich, 1999). The largest squared
multiple correlation between any two constructs was .53 (.73
was the highest correlation [between satisfaction and commitment and between satisfaction and trust]), whereas the variance
extracted ranged from .61 to .78.
To assess nomological validity, the measures were tested
with respect to a dimension to which the constructs are supposed to be theoretically related (Churchill, 1995). Expecting a
positive relationship between relationship performance and
loyalty is well grounded in theoretical reasoning. Buyer loyalty
is the intention to perform a set of behaviors that indicate a
motivation to maintain a relationship with a supplier, including
allocating a higher share of wallet, engaging in positive word of
mouth, and repeat purchasing (Zeithaml, Berry, & Parasuraman,
1996). When suppliers are oriented to the relationship and act in
a way that strengthens relationship policies and practices, trust,
relationship commitment, cooperative activities, and satisfaction, the perceived risk with the specific service supplier is
likely to be reduced, enabling the customers to make confident
predictions about the supplier's future behaviors (Morgan &
Hunt, 1994). This helps shape the customers' perceptions about
their relationship with suppliers, and enhances their loyalty
(Sirdeshmukh et al., 2002). According to recent findings
(Palmatier et al., 2005), relationship marketing may build
customer loyalty more effectively in this type of research setting
(i.e. service offering and business markets).
We used three items for measuring buyer loyalty (α = .93):
(1) the buyer's intention to make most future purchases from the
supplier, (2) the buyer's intention to recommend the supplier to
other firms, and (3) the buyer's intention to use the supplier the
next time he/she needs to purchase products or services. We
measure all items on 7-point Likert scales, anchored by “strongly disagree” and “strongly agree” (Sirdeshmukh et al., 2002).
We found that loyalty is positively correlated with relationship
orientation (r = .50), relationship commitment (r = .64), trust
(r = .53), mutual cooperation (r = .58), and relationship satisfaction (r = .80). Given that all of the coefficients are positive and
significant (P b.01 or better), we conclude that the performance
of the buyer–seller relationship has a positive impact on loyalty.
Thus, we have support for the nomological validity of the five
proposed measures (Cross & Chaffin, 1982).
4.2. Higher-order factor
We also estimate a second-order factor model of B2BRELPERF. In addition to obtaining respondents' evaluations on
the five dimensions, the second-order factor model captures the
common variance among them, which reflects an assessment of
the B2B relationship performance process. This model includes
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L.F. Lages et al. / Industrial Marketing Management 37 (2008) 686–697
Fig. 1. The B2B-RELPERF scale: CFA standardized coefficients for higher-order model. Legend: RP:Relationship performance; RPP:Relationship policies and
practices; RCO:Relationship commitment; TRUST:Trust in the relationship; MCO:Mutual cooperation; SAT:Relationship satisfaction.
the five first-order factors along with their standardized coefficients, observable indicators, and measurement errors (see
Fig. 1). Each of the first-order factors has significant (P b.01)
loadings of .54, .80, .79, .74, and .94, respectively, on the
second-order factor. Although the chi-square for the secondorder model is significant (χ2 = 152.66, d.f. = 72, P b.05), the
NNFI, CFI, and IFI are .99; the SRMR is .41; the RMSEA is
.053; and the chi-square difference test between the first-order
and second-order models is not significant (▵χ2 = 9.08, ▵d.f. =
5, P N.10). Overall, this suggests that the higher-order model
accounted for the data well. Further evidence is demonstrated in
the correlations between the five constructs. All correlations are
significant (P b.01), and the coefficients are large and positive,
indicating that the five scales converge on a common underlying construct (Cadogan, Diamantopoulos, & de Mortanges,
1999; Lages & Fernandes, 2005).
5. Main findings and discussion
In this study we propose the B2B-RELPERF scale as a broad
conceptualization and specific operationalization of relationship
performance. Although we cannot claim to have captured the
dimensions of this concept fully, we argue that we have come
close to doing so because the second-order factor extracts the
underlying commonality among the five dimensions, as CFA
results suggest. The hierarchical structure of the B2BRELPERF scale reveals that relationship performance is
composed of five elements: relationship orientation, relationship commitment, trust, mutual cooperation and relationship
satisfaction. The B2B-RELPERF scale reflects an entire set of
relationship marketing metrics that might be influenced and, to
a certain extent, are within the control of management. For
example, our findings reveal that all five dimensions are
positively correlated with loyalty, which measures the buyer's
intention to repeat and make most future purchases from the
supplier, beyond recommending it to other firms. Moreover,
CFA results suggest that the second-order factor model captures
the common variance among the five dimensions, which
reflects an assessment of the B2B relationship performance
process, following retention and loyalty strategic orientations.
Earlier studies on B2B relationship determinants (e.g.
Lancastre & Lages, 2006) suggest that relationship marketing
activities offer added-value to the customer – even when selling
routine products – and are worth investing in as they promote
customer loyalty. Moreover, the non-critical products' context
represents an excellent opportunity to develop cross-selling and
up-grading business activities with the buyers, outcomes that
normally follow the implementation of retention and loyalty
strategies. Development of tools to assess the performance of a
long-term relationship process between two firms becomes
crucial for managers to better understand and efficiently handle
their relationships. Moreover, a gap between academics and
practitioners has been identified (see 2001 special issue of the
British Journal of Management). By developing the B2BRELPERF scale, we are striving to help bridge that gap. We
believe that such a research tool helps to enhance relationship
performance at the individual customer level in order to better
guide the respective relationship effort.
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L.F. Lages et al. / Industrial Marketing Management 37 (2008) 686–697
One of the implications of these concerns is the suggestion
that from a managerial perspective, the possibility of grouping
the 5 dimensions into a single score allows managers to have a
starting point to more easily recognize and monitor the overall
relationship performance with each customer. Indeed, the function of the customer relationship development process is to
build relationships with preferred customers (Knox, 1998). Not
all customers are willing to join a relationship process with the
supplier, nor are they all-important for the supplier to add value
to his business (Christy, Oliver, & Penn, 1996; Grönroos, 1997).
In fact, the supplier may expend significant sums of money to
retain an unprofitable customer, or to establish and maintain a
relationship when the customer does not wish for one. Segmentation strategies, based on customer requirements, profitability, and customer loyalty are essential (Knox, 1998; Payne
& Frow, 1999). This will lead to more productive marketing
activities by developing and increasing individual customer
loyalty (Palmatier et al., 2005).
This research develops a buyer–seller relationship measure
in an e-business and routine products context. The e-marketplace is a particularly interesting environment in which to
develop these metrics because e-business relationships are
conducted at a distance, and risks and uncertainties are magnified. Despite purchasing literature suggestions that the buyer
normally relies on order volume optimization and pricing strategies, the routine and non-critical products context represents an
opportunity for the supplier to follow added-value relationship
strategies, in order to develop up-selling, up-grading and crossselling of its products and services. Moreover, because the
relationship process develops through various stages in which
different dimensions contribute in different ways during the
process (Dwyer et al., 1987; Jap & Ganesan, 2000) we suggest
that the use of a balanced scorecard that weights dimensions
differently may capture this process.
6. The B2B-RELPERF scorecard
At a time when B2B relationships are instrumental in the
determination of enterprises' value and performance, the B2BRELPERF scale has the potential to help practitioners plan,
manage, monitor, and improve their ongoing B2B relationships
if properly integrated with objective metrics through a balanced
scorecard – often available through existing Enterprise Resource Planning (ERP) and traditional CRM solutions – (see
Appendix).3 This approach of matching objective and subjective metrics has the potential to provide a better understanding
of relationship performance constituents, and to increase return
on relationship marketing investments (see: Bolton, 2004;
Lehmann, 2004).
Managers are often judged not only by the performance of
different relationship processes, but also by the priorities they
assign to the relationships. The recommendation is to compare
3
An IT solution is also being developed while using as a basis the B2BRELPERF Scale and the B2B-RELPERF Scorecard. For further information
please contact the first author.
693
each current year (Y) weighted score with that of the previous
year (Y − 1) because problems are easier to detect by the size of
the gap between a current year's metrics and the base score from
the previous year. This annual feedback enables marketers to
make corrections and helps them define goals and priorities for
the relationships in the following year. To define each relationship process goal and degree of priority, firms might also
consider existing objective metrics per customer and industry
benchmarks, such as sales or margins by product category,
frequency of sales and respective deviation standard, promotion
sales and costs (in the case of pmelink.pt). Another possibility is
to set sub-goal intervals for the B2B-RELPERF scorecard (e.g.
on a quarterly or half-year basis). The major advantage of this is
that quarterly or half-year feedback would enable marketers to
review relationship performance trends and to make corrections
more frequently, in response to the changing environment
(Abernathy, 1997). In this way, periodic scheduled reviews with
the B2B-RELPERF scorecard could be extremely useful in
monitoring and improving relationship and loyalty strategies.
Regarding the B2B-RELPERF scorecard development and
implementation, data from longitudinal studies are required to
test it. We present some recommendations below. The first step
is to enter under “customer description” the name of the customers to be analyzed. Second, under the “objective metrics”
column(s), identify existing financial or other objective metrics
(e.g. sales, profits, sales frequency, operational costs, lifetime
value) and establish a ranking for each customer. Then, ask
customers to assess the 14 items in the B2B-RELPERF scale on
a 7-point Likert scale (see Table 1). The next step is to assess the
average score for each of the five B2B-RELPERF dimensions
and multiply them by their weights. These results should be
presented under the “relationship performance dimensions”
column. The relationship process develops through mutual
learning stages in which relationship policies and practices,
trust, relationship commitment, mutual cooperation, and
relationship satisfaction are critical issues. Depending on the
relationship stage, different factors might present different
weightings. Weightings should differ across the various customers' relationship processes, depending on the different
phases of relationship development. Hence, it is important that
the management team agrees on the weightings of each dimension before its implementation and future assessment. The sum
of the five weighted dimensions represents the final RPScore per
customer. The RPWeightedScore is the result of multiplying the
final RPScore by the weight of each customer to overall relationship performance. Each RP weight to overall relationship
performance must take into consideration different factors, such
as the firm's mission and objectives, the firm's strategy, and
different insights from managers. A final RELPERFScore results
from the sum of all individual RPWeightedScores.
7. Limitations and further relationship marketing research
Although this study provides a number of new insights, it
is important to note its limitations. A first limitation regards the
specificities of this study's research context and respondents —
Portuguese SMEs' relationships with an internet-based supplier.
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L.F. Lages et al. / Industrial Marketing Management 37 (2008) 686–697
While enhancing the focus of this research, it may limit the
generalization of the results to some degree, at the same time
creating the need for further research, in this environment and
other contexts. Another limitation results from the fact that
research relies only on the responses of the buyers. Ideally,
researchers should collect data from both sides of the dyad.
However, as research in relationship marketing is still at an early
stage of development, we begin with the buyer's view to better
understand the components of B2B relationship performance, as
in many situations the buyer is the one that decides if he wishes
or not to continue the relationship, while evaluating the addedvalue that results from his relationship with the supplier. A third
limitation is that our research instrument (i.e., the questionnaire)
may have created common method variance. This could be
particularly problematic if the respondents were aware of the
conceptual framework of interest. However, we did not explain
the specific purpose of the study to them, and we mixed and
separated the construct items (Jap, 2001; Lages & Montgomery,
2005). Furthermore, we guaranteed confidentiality to all survey
participants for self-presentation reasons, which also helps to
reduce the possibility of bias in issues such as relationship
policies and practices, trust, commitment, cooperation, and
satisfaction (Singh, 2000). In addition, if common method bias
exists, a CFA containing all constructs should produce a singlemethod factor (Podsakoff and Organ, 1986). The goodness-offit indices (NNFI = .85, CFI = .87, IFI = .87, and RMSEA = .191)
indicate a poor fit, which suggests that biasing from common
method variance is unlikely (Lages and Lages, 2004). Hence,
due to the potential pitfalls of this tool, our efforts to develop the
B2B-RELPERF scale should be seen as a starting point,
considered as part of a larger effort to help managers and
researchers capture relationship performance. Nevertheless,
future operationalization of the B2B-RELPERF scorecard is
encouraged, through a combination of objective and intangible
metrics, as earlier discussed, to help overcome the issue of
common method bias.4
This study provides a foundation for significant research
endeavors to advance the field. Additional research is required
that analyzes the antecedents and consequences of the B2BRELPERF scale (see Lages et al., in press). In addition to its
relationship with loyalty, further research must investigate how
this scale is related to other established constructs in the
relationship marketing field, such as transaction-specific
investments. We recommend further research that tests the
B2B-RELPERF scale on the suppliers' side and on both sides of
the dyad. Consider testing the scale in different settings and in a
wide range of activities, such as exporter–importer and
franchiser–franchisee relationships, and whether findings are
different depending on the buying situation of the customers or
on the customer profile. Relationships in an international
context transcend national boundaries, and are particularly
complex because they are affected by socio-cultural, and other
environmental differences. When applying the scale to different
4
We acknowledge an anonymous reviewer for this insight.
contexts, we encourage researchers to add new items and factors
in order to continue refining the B2B-RELPERF scale.
8. Conclusions
In this economic environment when corporate budgets are
being squeezed, Chief Marketing Officers are kept up at night
by worry, trying to justify their expenditures and their existence.
They believe that what they are doing has value, and they have
to figure out how to demonstrate that value to skeptical CEOs
and CFOs (Reibstein, 2004).
As a direct response to a recent observation in the literature
(Morgan & Hunt, 2003), we hope that this paper will help
cultivate knowledge on relationship management theory while
shedding light on B2B relationships that are supported by new
information and communication technologies. Moreover, at
a time when researchers are challenged to present studies with
managerial implications (Marketing Science Institute, 2004), the
B2B-RELPERF scale and the B2B-RELPERF scorecard might
be used to address the managerial needs of B2B relationship
performance planning, implementation, and control. By using
the B2B-RELPERF scale and the B2B-RELPERF scorecard to
assess the performance of a buyer–supplier relationship process,
managers can better understand the constituent elements of the
relationship process, which in turn aids managers in selecting,
using, and controlling the most adequate marketing tools for
each of these elements. These tools may also help managers
understand differences that exist in the relationship process
development phases among different customers or groups and
can enable managers to handle such differences more efficiently
and effectively. Moreover, by defining actions that address
potential problems during the relationship marketing process
development, managers can ultimately influence their firm's
relationship orientation, retention, and loyalty strategies. Finally,
by balancing and complementing financial and other operational
measures with intangible customer metrics, the B2B-RELPERF
balanced scorecard allows for better monitoring of relationship
performance at both the customer and corporate levels, while
helping to focus the entire organization on long-term strategy
and improving shareholder value (Kaplan & Norton, 1992,
1996, 2000). As a whole, in addition to addressing the relationship marketing area, this paper may also enrich knowledge in the
e-commerce and purchasing literature.
Acknowledgements
This research was funded by the following research grants to
the first author: NOVA FORUM, NOVA EGIDE, and 6th
European Framework Program while affiliated with Universidade
Nova de Lisboa; FCT-EU while a Visiting Scholar at London
Business School and MIT's Deshpande Center for Technological
Innovation. Andrew Lancastre thanks UNIDCOM/IADE, and
Carmen Lages thanks UNIDE/ISCTE. The authors acknowledge
IMM editor, three IMM anonymous reviewers, and the
participants and reviewers of 2005 CRM (Russia), 2005 ICRM
(Canada) and 2007 EMAC (Iceland) conferences for comments
on earlier versions of this manuscript.
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Appendix A
Customer
description
Objective metrics
Relationship performance dimensions
Objective metrica
Customer1
Customer2
Customer3
Customern
_____
_____
_____
…
Subjective metrics
____
____
____
…
Rankinga
____
____
____
….
Year Y − 1
Year Y + 1
RPScore ×
Weight to overall
relationship perform.(%)
= RPWeightedScore
Base(s)b
Goal(s)b
Priorityb
_______
_______
_______
…
×_______
×_______
×_______
…
Sum =
=_______
=_______
=_______
…
RELPERFScore
______
______
______
…
______
______
_____
…
______
______
_____
…
Relationship performance
RPPScore:
RCOScore:
TRUSTScore:
MCOScore:
SATScore:
3-item
average ×
weight (%)
3-item
average ×
weight (%)
3-item
average ×
weight (%)
2-item
average ×
weight (%)
3-item
average ×
weight (%)
___ × ___
___ × ___
___ × ___
…
___ × ___
___ × ___
___ × ___
…
___ × ___
___ × ___
___ × ___
…
___ × ___
___ × ___
___ × ___
…
___ × ___
___ × ___
___ × ___
…
Notes: RPScore = RP1WeightedScore + RP2WeightedScore + RP3WeightedScore + RP4WeightedScore + RPnWeightedScore
RPScore = RPPScore + TRUSTScore + RCOScore + MCOScore + SATScore
RELPERFScore = overall relationships performance, RPP = relationship policies and practices, RCO = relationship commitment, TRUST = trust in the relationship, MCO = mutual cooperation, and SAT = relationship
satisfaction.
a
This column should be repeated according to the number of existing objective performance metrics (e.g., sales volume, profit, costs). If more than one objective metric is available, a final ranking of objective relationship
performance should be created while taking into consideration different weights for each objective metric.
b
These fields should take into consideration the RPWeightedScore and, if possible, existing objective metric(s).
L.F. Lages et al. / Industrial Marketing Management 37 (2008) 686–697
The B2B-RELPERF Scorecard
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Luis Filipe Lages (PhD, Warwick) is Associate Professor of Marketing and
International Business at Faculdade de Economia, Universidade Nova Lisboa,
Portugal and Visiting Scholar at MIT's Deshpande Center for Technological
Innovation, USA. His research interests include measurement and monitoring of
intangibles, international marketing, innovation/disinnovation strategy, and
technology-market transfer. His publications appeared in Industrial Marketing
Management, Journal of International Business Studies, Journal of International
Marketing, Journal of Business Research, European Journal of Marketing, MSI
Reports, among others.
Andrew Lancastre (PhD, ISCTE) is Associate Professor of Marketing at IADEInstituto de Artes Visuais, Design e Marketing in Lisbon, and Visiting Professor of
Marketing Strategy at Faculdade Economia, Universidade Nova Lisboa, Portugal.
Previously, he was a marketing manager at British Petroleum and Renault. Main
areas of interest cover relationship marketing, B2B activities, SMEs, and
international marketing. His work is published in Industrial Marketing Management and in the proceedings of leading conferences in the marketing field.
Carmen Lages (PhD, Warwick) is Assistant Professor of Marketing at ISCTE
Business School-Lisbon, Portugal. Her research interests include relationship
marketing, social marketing, and marketing communications. She has
published in the Journal of Business Research, European Journal of Marketing,
Journal of International Marketing, among others.