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“Leveraging Customer Information
for Competitive Advantage.”
Raji Srinivasan
and
Gary LilienThe Pennsylvania State University
ISBM Report 17-1999
Institute for the Study of Business MarketsThe Pennsylvania State University
402 Business Administration BuildingUniversity Park, PA 16802-3004
(814) 863-2782 or (814) 863-0413 Fax
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ii
Abstract
Internet and database technologies enable marketers to collect ever more extensive information
on their customers’ needs, preferences and past behaviors, but marketers often claim that they arechallenged to make effective use of the information. Despite the substantial literature on marketinformation utilization, the topic of customer information has received limited attention frommarketing researchers. Does the generation and use of customer information lead to higher levelsof customer satisfaction and improved firm performance? And do these relationships hold ingeneral, or are there any environments under which these relationships are different?
We seek to address this gap in the literature by i) developing the customer informationmanagement construct and ii) studying the effects of customer information management oncustomer satisfaction and firm performance. In this paper, we also examine the moderatingeffects of a firm’s customer environments on the relationship between customer information
management and customer satisfaction and performance respectively.
Data from a national survey of 218 marketing executives provides strong support for a positiverelationship between customer information management and customer satisfaction and firmperformance that is robust to contexts characterized by varying levels of customer heterogeneityand customer relationship intensity. In sum, this research suggests that customer information is aknowledge asset that can be leveraged to improve firm performance.
Key Words
Market informationCustomer informationKnowledge Management
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INTRODUCTION
Internet and database technologies enable marketers to collect ever more extensive information
on their customers’ needs, preferences and past behaviors, but marketers often claim that they are
challenged to make effective use of the information. Indeed, marketers implicitly assume that if
they have extensive information about their customers and use that information to guide their
actions, then they will be rewarded in the marketplace with greater market share, higher profits
and the like. But is this assumption true? Does the generation and use of customer information
lead to higher levels of customer satisfaction and improved firm performance? And do these
relationships hold in general, or are there any environments under which these relationships are
different?
Despite the substantial literature (Deshpande, Farley and Webster 1993; Kohli and Jaworski
1990; Jaworski and Kohli 1993; Moorman 1995; Narver and Slater 1990; Sinkula 1994) on
market information utilization, the use of customer information has not received academic
attention1. This paper addresses the relationship between customer information management and
firm performance. Consistent with past research on organizational utilization of information
(Menon and Varadarajan 1992; Moorman 1995), we define customer information management to
include both the generation and use of customer information. We model the link between
customer information management, customer satisfaction and firm performance and examine
how these relationships are modified by two aspects of the firm’s environment – customer
heterogeneity and customer relationship intensity.
The paper is structured as follows. We first define the customer information management
construct. We then suggest a conceptual framework and a related set of models linking customer
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information management, customer satisfaction and firm performance. Based on this framework,
we develop hypotheses about the effects of customer information management on customer
satisfaction and firm performance and the moderating effects of customer environment
characteristics on these linkages. Following that, we describe the sample, methodology and the
results of the tests of hypotheses. We conclude with a discussion of the managerial implications
and then identify the limitations of this research and outline future research opportunities in this
area.
Our results show that customer information management has a strong direct positive effect both
on customer satisfaction and firm performance. Our data also provides support for a strong
indirect positive effect of customer information on firm performance, through its effect on
customer satisfaction. These relationships show no significant contingencies, suggesting that our
results are robust to varying levels of customer heterogeneity and customer relationship intensity.
CUSTOMER INFORMATION MANAGEMENT DEFINED
We define customer information as information2 about the attitudes and behaviors of the firm’s
current, past and prospective customers. The term “customer” in this definition includes both
end-users of the products and services and channel members including distributors, wholesalers
and retailers (Jaworski and Kohli 1993). Customer information may be collected and used either
at the aggregate market level, at the segment level or at the level of the individual customer.
Consider, Staples Direct, a division of Staples Inc., the office products superstore, which targets
small to medium sized-firms with between 5 and 50 employees. Although, the company’s private
label credit card and call center make it possible to know each customer individually, the firm
manages customer information at the segment level because of the small economic value of each
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customer. However, Staples National, another division aimed at large corporate procurement
departments has average account sizes in excess of $ 1 million and manages customer
information at the individual account level.
The extant literature conceptualizes organizational information activities as a series of processes
that include i) information generation (Kohli and Jaworski 1990; Moenaert and Souder 1996;
Moorman 1995) and ii) information utilization (Menon and Varadarajan 1992). Consistent with
past research, we conceptualize the construct of customer information management at the
organizational level as a two-dimensional construct that includes both customer information
generation and customer information utilization.
Customer information generation
A firm must generate customer information before it can use the information. Some researchers
suggest that if more information is available, then executives are more likely to use it
(Shrivastava 1987). However, usability assessments of information must be made prior to its
utilization and these assessments are important in affecting the usage of information (Day 1994;
Menon and Varadarajan 1992; Moenaert and Souder 1996). Hence, we define customer
information generation to include: 1) customer information availability and 2) customer
information interpretation.
Information availability refers to the availability of information in an organization and
encompasses the two processes of information acquisition and information transmission (Kohli
and Jaworski 1990; Moorman 1995). The firm must have the necessary information systems to
collect information about its customers at the right time and at the right level of aggregation for
subsequent use.
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Information interpretation refers to processes by which information is given meaning (Daft and
Weick 1984). Extant empirical research on information utilization in organization (Moenaert and
Souder 1996) suggests that information is assessed on the basis of 1) relevance 2)
comprehensibility and 3) timeliness and 4) accessibility before it is incorporated in managerial
decision making.
Customer information utilization
Customer information utilization3
is the extent to which customer information is used to guide
marketing strategies and decisions (John and Martin 1984). Menon and Varadarajan (1992) and
Moorman (1995) proposed a multi-dimensional conceptualization of information utilization
including direct (instrumental) use and indirect (conceptual) use of information4.
Instrumental use of customer information refers to the use of customer information in problem
solving and operations (Caplan, Morrison and Stambaugh 1975). For example, customer
information may be used in order to customize product offerings to customers based on customer
preferences and needs.
Conceptual use of customer information refers to the indirect use of information for general
understanding that has an indirect influence on managerial decision making (Menon and
Varadarajan 1992; Moorman 1995). For instance, customer information that is generated and
available to a firm’s managers may be used in less direct ways to stimulate the planning of new
product platforms or to help understand and react to general market trends.
In the next section, we present our conceptual framework, develop our hypotheses and present a
model of the proposed relationships between customer information management, customer
satisfaction and firm performance.
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CONCEPTUAL FRAMEWORK, HYPOTHESES AND MODEL
Conceptual Framework and Hypotheses
We examine the relationship between customer information management (i.e. generation and
usage of information) and customer satisfaction and firm performance within a conceptual
framework as shown in Figure 1. In the following paragraphs, we describe the relationships in
this framework and formally state our hypotheses.
(Figure 1 here)
Effect of Customer Information Management on Customer Satisfaction
Using customer information, a firm can develop and implement better targeted marketing
programs for its different customer segments (or individual customers) in terms of customized
product offerings, communications, pricing and distribution. For example, Staples National uses
customer information at the level of the individual customer to design customized product
offerings, pricing and delivery terms for each account based on customer preferences. These
targeted offerings should result in higher customer satisfaction among Staple National’s
customers. Firms that do not use customer information, on the other hand, will be more likely to
implement a common marketing strategy across all customers resulting in lower customer
satisfaction. Hence,
H1: The greater the level of customer information management in an organization, the more
satisfied its customers.
Direct Effect of Customer Information Management on Firm Performance
According to the resource-based view of the firm (Peteraf 1993; Wernerfelt 1984), resources are
firm-specific assets that are difficult to imitate. Knowledge assets are strategic resources that are
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sources of economic profits to the firm (Itami and Roehl 1987; Teece, Pisano and Shuen 1997).
Therefore, a firm’s customer information management capability is a resource that potentially
represents a source of competitive advantage. For e.g. Staples Direct, uses the customer
information it has on different customer segments to determine the deployment of marketing
resources for acquisition, development and retention strategies. Hence, all other things equal, a
firm that uses a differentiated marketing strategy should perform better than a firm that uses a
“one product/price fits all” approach. Secondly, the bonds between the marketer that effectively
uses customer information and its customers can create “an informational barrier to entry”
against competitors who do not have access to the same customer information base (Glazer 1991;
p. 15). However, some researchers (Christensen 1997; Hamel and Prahalad 1991, p. 83) argue
that excessive customer orientation can cause myopia resulting in a tyranny of the served market
so that these firms may miss opportunities/threats from outside their served market. For example,
Christensen (1997) argues that firms in the disk drive industry were so focused on meeting
existing customer needs that they missed new product opportunities because these new products
did not originally meet the needs of their existing customers. Hence, according to this view,
using customer information may negatively affect firm performance. On net, however, we
hypothesize a direct positive relationship between a firm’s customer information management
capability and its business performance. Hence,
H2: The greater the level of customer information management in an organization, the better its
business performance.
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Indirect Effect of Customer Information Management on Firm Performance through
Customer Satisfaction
In addition to the direct effect of customer information on firm performance (H2), we
hypothesize that customer information will have an indirect positive effect on firm performance
mediated through customer satisfaction. The greater the satisfaction of a firm’s customers, the
more loyal its customers (Gale 1994). Further, the costs to the firm of serving repeat customers
are lower than the costs of acquiring new customers (Blattberg and Deighton 1996). Hence,
H3: The higher the level of customer satisfaction of a firm’s customers, the better its business
performance.
In sum, we hypothesize the main effects of customer information management on firm outcomes
of customer satisfaction and firm performance in the following three ways (Table 1):
1. Customer information management will be positively related to customer satisfaction
(H1).
2. Customer information management will be positively related to firm performance (H2).
3. Customer information management will have an indirect positive effect on performance
mediated by customer satisfaction (H3).
(Table 1 here)
Moderating Effects of Customer Environments
The environmental context of an organization is likely to influence its structure, conduct and
consequences (Bourgeois 1980; Slater and Narver 1994). It is therefore likely that customer
information management may have a stronger positive effect on firm outcomes of customer
satisfaction and firm performance under some environmental conditions than others may.
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Specifically, we consider the effects of two characteristics of the firm’s customer environment,
the extent of customer heterogeneity and the intensity of customer relationships as factors that
may moderate the relationship between a firm’s use of customer information and its customer
satisfaction and performance. As the direct effects of these variables are not the main focus of
this research, we consider only interaction effects in our model5.
Customer heterogeneity is the extent to which the customers of a given firm are different from
each other. We consider two sources of customer heterogeneity. First, customers differ in terms
of their needs and preferences for the firm’s products, so that the firm’s product offerings may
vary across different customers. Second, customers differ in terms of their size and profit
potential to the firm. The greater the extent of customer heterogeneity, the greater the need for
the firm to acquire more information about its different customers (who are more different from
each other) and the greater the likelihood that the use of customer information will lead to
superior customer value and higher firm performance. On the other hand, the returns to using
customer information in a firm whose customers are homogenous will be lower because of the
intrinsically lower need for information. Hence, the use of customer information is likely to be
more strongly related to customer satisfaction in firms that have greater customer heterogeneity
than in firms that have lower customer heterogeneity. By similar reasoning, firms with greater
customer heterogeneity that use customer information are more likely to have superior
performance than firms with lower customer heterogeneity. Hence,
H4a: The greater the heterogeneity of a firm’s customers, the stronger the relationship between
customer information management and the satisfaction of its customers.
H4b: The greater the heterogeneity of a firm’s customers, the stronger the relationship between
customer information management and its business performance.
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Customer relationship intensity is the extent of transaction intensity in a firm’s relationship with
its customers. Firms differ in the extent of their transaction intensity with their customers. Some
firms have high transaction-intensity in their customer relationships so that the firm’s sales
revenue is generated from many transactions with many customers covering many products
(Glazer 1991). On the other hand, some firms generate their sales value from a narrow product
range, serving few customers through a small number of business transactions. In firms with high
customer relationship intensity, each transaction is an opportunity for the firm to collect
information about its customers. The firm can use the customer information it has thus collected
to improve both customer and firm value in subsequent transactions over the customer’s lifetime.
If the customer relationship intensity is low, then there are fewer occasions for the firm to both
generate and use customer information. Hence, we hypothesize that the effects of customer
information utilization on both firm outcomes of customer satisfaction and business performance
will be higher in organizations that have higher customer relationship intensity. Hence,
H5a: The greater the firm’s customer relationship intensity, the stronger the relationship
between customer information management and the satisfaction of its customers.
H5b: The greater the firm’s customer relationship intensity, the stronger the relationship
between customer information management and its business performance.
Model Specification
Main Effects
As our sample includes firms spanning a number of different industries and markets with
different competitive environments, we include control variables to account for differences that
may exist in the customer satisfaction and performance standards of different industries. Strategy
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researchers (Porter 1985) argue that firm performance is influenced, in part, by the
characteristics of the competitive environment. We include control variables characterizing the
firm’s environment that have been considered by past literature to be important determinants of
performance (Boulding and Staelin 1990; Jacobson and Aaker 1987; Jaworski and Kohli 1993).
Because factors relating specifically to the competitive market environment are likely to affect
customer satisfaction levels in industries, we include competitive intensity (CINT ) and buyer
power (BPOWER) in the relationship between customer information management and customer
satisfaction. The control variables supplier power (SPOWER), barriers to entry (B2E ), pressure
from substitute products (SUBST ) and product quality (PQ) are included in the relationship
between customer information usage and firm performance. Given that these six variables are
being included only as controls in the model and do not constitute the variables of substantive
interest, we consider only their additive effects and do not consider the interaction terms between
these variables and customer information management. Hence, the proposed relationship
between customer information management and customer satisfaction and firm performance is
expressed as follows:
where
CSAT i = customer satisfaction measure for the ith firm
CIM i = customer information management measure for the ith firm
)1(13210 iiiii BPOWERCINT CIM CSAT ε α α α α ++++=
)2(
2
2654
3210
iiii
iiii
PQSUBST SPOWER
E BCSAT CIM PERF
ε β β β
β β β β
++++
+++=
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PERF i = performance of the ith firm
CINT i = competitive intensity of the ith firm
BPOWERi = buyer power of the ith firm
SPOWERi = supplier power of the ith firm
B2E i = barriers to entry of the ith firm
SUBST i = substitutability from competitors’ products of the ith firm
PQi = quality of products of the ith firm
ε 1i and ε 2i = error terms
and α s and β s are coefficients to be estimated.
We specify the relationships outlined in Figure 1 as a system of linear equations. Our path
diagram in Figure 1 forms a recursive system of equations with only one-way causal flows in the
system. Recursive models with the assumption of independent errors, fulfill the rank and order
conditions for identification with no additional restrictions 6 (Land 1973, p. 31 provides a formal
proof). We thus obtain consistent parameters of estimates in each equation. We tested several
nonlinear specifications and found no support for those functional forms in our data.
We tested hypotheses H1-H3 using the procedure recommended by Baron and Kenny (1986) by
studying the mediating effects of customer satisfaction on the relationship between customer
information management and firm performance. Hence, we estimated the following regression
equations: 1) regress customer satisfaction on customer information management 2) regress firm
performance on customer information management and 3) regress firm performance on customer
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information management and customer satisfaction. Thus, in addition to Eqs. (1) and (2), we will
also regress firm performance on customer information as shown in Eq. (3).
ε 3i is the error term and γ s are coefficients to be estimated.
Our three hypotheses are supported if the following conditions hold: i) customer satisfaction
must depend on customer information management in Eq.(1) ii) customer information
management must affect firm performance in Eq. (3) iii) customer satisfaction and customer
information management must affect firm performance in Eq. (2).
To investigate the extent to which customer information management explains variance in firm
outcomes of customer satisfaction and firm performance above that provided by industry and
market control variables, we perform model comparison using baseline models for customer
satisfaction and firm performances with only control variables as follows:
where ε 4i and ε 5i are error terms and the η s, and µ s are coefficients to be estimated.
Interaction Effects
We tested the moderating effects of customer heterogeneity and customer relationship intensity
using moderator regression analysis (MRA) within a regression framework (Pedhazur 1997) by
creating an interaction term that is a multiplicative product of each of the moderator variables
)4(4210 iiii BPOWERCINT CSAT ε η η η +++=
)5(2 543210 iiiiii PQSUBST E BSPOWERPERF ε µ µ µ µ µ +++++=
)3(
2
354
3210
iii
iiii
PQSUBST
SPOWERE BCIM PERF
ε γ γ
γ γ γ γ
++
++++=
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and the explanatory variables. Following accepted guidelines (Aiken and West 1991, p. 12), we
also included the main effects of the explanatory variables and the moderators in addition to the
interaction effects. The MRA analysis of the relationship between customer information
management and customer satisfaction involved five predictors (customer information
management, customer heterogeneity, customer relationship intensity, customer information
management × customer heterogeneity, customer information management × customer
relationship intensity) and two control variables. Likewise, the MRA analysis of the relationship
between customer information management and firm performance involved the same five
predictors and four control variables. All the variables were mean-centered before we
constructed the interaction terms to reduce the potential effects of collinearity (Cronbach 1987).
The interaction effects between the customer environmental variables of customer heterogeneity
and customer relationship intensity on the relationship between customer information
management and customer satisfaction and firm performance respectively are specified in the
following equations:
where
CHET i = customer heterogeneity of the ith firm
CRI i = customer relationship intensity of the ith firm
)7(2
)*()*(
79876
54
3211
iiiii
iiii
iiii
PQSUBST SPOWERE B
CIM CRI CIM CHET
CRI CHET CIM PERF
ε φ φ φ φ
φ φ
φ φ φ φ
+++++
++
++++=
)6(
)*()*(
676
54
3210
iii
iiii
iiii
BPOWERCINT
CIM CRI CIM CHET
CRI CHET CIM CSAT
ε λ λ
λ λ
λ λ λ λ
++
++
++++=
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ε 6i and ε 7i are error terms and the λ s, and φ s are coefficients to be estimated. All other variables
are as defined in Eqs. (1) and (2).
In the next section, we describe the method used to collect data and the results of our analysis.
METHOD
Data collection
We collected data for this research as part of a Customer Information Benchmarking Study
jointly conducted by Penn State’s Institute for the Study of Business Markets (ISBM) and the
Direct Marketing Association in Summer 1998. We drew the sample randomly from a list of
member firms from ISBM and a database from Dun and Bradstreet. We found no significant
differences between companies from the two different lists on key variables of the study.
Researchers from a professional marketing research firm called heads of marketing department
to request their participation in the study. Informants were promised a summary of the results in
return for their participation. All questions regarding the organization used the division or
strategic business unit (SBU) as the organizational unit of analysis. 2700 firms were contacted
out of which 217 firms responded resulting in a response rate of 8%. Non-response analysis
showed no differences in the demographic characteristics of companies of managers who
declined to participate from those that participated in the study. Callbacks indicated that the main
reason for non-participation was lack of time.
Measurement
We developed scales for the study using a multi-phase, iterative procedure. First, we generated a
large pool of items measuring each of the study’s constructs. From this pool of items, we
selected a subset using the criteria of uniqueness and the ability to convey “different aspects of
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meaning” to informants (Churchill 1979). We reverse coded some items to offset response set
bias. Responses were recorded on a 5-point Likert scale with 1 indicating strong disagreement
with the statement and 5 indicating strong agreement.
We pre-tested items for the different scales in three phases: 1) face-to-face interviews with 4
academic experts and 2 practitioner managers in a direct marketing company 2) telephone
interviews with 4 managers of marketing information systems and 3) a pilot survey of 8
managers. At each stage, participants were asked to identify items that were confusing, tasks that
were difficult to perform and any other problems that they encountered. We revised or
eliminated items that were problematic. The items used in the scales are provided in the
Appendix. A brief description of the scale items follows.
Customer information management (CIM) was measured by a 9-item scale. Five items pertained
to customer information generation and four items pertained to customer information utilization.
Representative items included “Customer information is accessible to all managers who need to
use it” (customer information generation) and “Customer information is a central input in our
business planning” (customer information utilization).
Customer heterogeneity (CHET) and customer relationship intensity (CRI) were measured by
two item and three item scales respectively. The items for customer heterogeneity assessed the
heterogeneity of the firm’s customers both in terms of their preferences and the sales and profit
potential to the firm. Customer relationship intensity scale assessed the extent of transaction
intensity in a firm’s relationships with its customers. Representative items were “Our customers
are very different from each other in terms of needs and preferences” (customer heterogeneity)
and “Once we get a customer, we do not have to invest a great deal of effort and time in
managing our customer relationships (reverse-coded for customer relationship intensity).
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Business performance (PERF) was measured using a 2-item scale of judgmental measures
(Jaworski and Kohli 1993; Slater and Narver 1994). Because the firms in our samples covered a
number of different industries characterized by different performance standards, we used
subjective measures of performance. Previous studies in firm performance have found a strong
correlation between subjective assessments and their objective counterparts (Dess and Robinson
1984; Slater and Narver 1994). The judgmental measure asked informants for their assessments
of the overall performance of the business and its performance relative to its major competitors,
rated on a five-point scale ranging from “poor” to “excellent”.
Customer satisfaction (CSAT) was also measured using a 2-item scale of subjective measure. The
judgmental measure asked informants for their assessment of the overall customer satisfaction of
a firm’s customers and the customer satisfaction of its customers relative to major competitors on
a five-point scale ranging from “poor” to “excellent”.
The six control variables of competitive intensity (CINT ), buyer power (BPOWER), supplier
power (SPOWER), entry barriers (B2E ), substitutability pressure from competitors’ products
(SUBST ) and product quality (PQ) were measured using subjective single-item measures adapted
from Jaworski and Kohli (1993).
RESULTS
Reliability Analysis
We assessed the reliability of each multi-item scale by computing its coefficient alpha (Table 2).
We eliminated items that exhibited low inter-item correlations to improve the internal
consistency of the scales. The refined scales generally have good reliability coefficients that
exceed the levels of 0.70 recommended by Nunnally (1978) for exploratory research except for
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customer heterogeneity and customer relationship intensity measures that had reliability
coefficients of 0.61 and 0.58 respectively. Given the need to test for moderating effects, we
retained these scales despite their lower reliabilities.
(Table 2 here)
The two components of customer information management – customer information generation
and customer information utilization have reliability coefficients of 0.81 and 0.74 respectively.
Further, exploratory factor analysis of the customer information management construct showed a
clear loading of the items on 2 distinct factors - customer information generation and customer
information utilization (Table 3) providing support for the two-dimensionality of the customer
information management construct. The correlation between the two sub-factors of customer
information generation and customer information usage was 0.59.
(Table 3 here)
Given that both customer information generation and customer information usage are essential
aspects of the customer information management construct, we computed the scores for the
customer information management measure (and other multi-item scores) by adding the
corresponding item scores7. The mean score of customer information management was 33.83
with a standard deviation of 6.05 and a range of 15 to 45 (out of a possible range of 9 to 45). The
coefficient alpha for the customer information management scale including the two components
of customer information generation and customer information usage is good at 0.84. Table 4
contains the descriptive statistics and the correlation matrix of the different constructs used in the
research.
(Table 4 here)
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Hypotheses Testing
The results of the regression for customer satisfaction indicate that customer information
management is positively related (α1 = 0.080, t = 4.25) to customer satisfaction in Eq. 1
providing support for H18 (Table 5). We find that customer information management has a
significant direct positive effect on firm performance in equation 3 (γ 1 = 0.086, t = 4.30) (Table
7) providing support for H2. Further, customer satisfaction has a significant positive effect on
firm performance in equation 2 (β2 = 0.552, t = 9.36) (Table 6) supporting H3. Thus, our data
support all our three hypotheses H1, H2 and H3.
(Table 5, 6 and 7 here).
When both customer satisfaction and customer information management are included in the
model for firm performance (Table 6), the coefficient for CIM drops from (γ 1= 0.086, t = 4.30)
(Table 7) to (β1= 0.051, t = 3.00), indicating that customer satisfaction partially mediates the
relationship between customer information management and firm performance.
These results suggest that customer information has two effects on firm performance - a direct
positive effect and an indirect positive effect mediated through customer satisfaction. The total
effect of customer information management on firm performance including both the direct and
indirect effects is (0.086 + 0.080 % 0.552 = 0.130). Not surprisingly, the direct effects of
customer satisfaction on firm performance are larger (β2 = 0.552) than the effect of customer
information management on firm performance (combined direct and indirect effect = 0.130).
(Table 8 here)
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We conducted model comparisons between models including customer information over baseline
models with only the control variables to check whether the inclusion of customer information
management is able to explain a significantly larger proportion of variance. The R-square for the
model for customer satisfaction (Eq. 1) improves by more than 100% over the baseline model
(Eq. 4) (from 0.044 to 0.116) and the results of the F-tests of difference in fit (Pedhazur 1997; p.
108) are significant (p< 0.01) (Table 8). We obtain similar results for the F-test of the difference
in fits between the model for firm performance that includes customer information management
(Eq. 3) and a baseline model with only the control variables (Eq. 5) (R-sq. increases from 0.064
to 0.141; p < 0.01). Finally, a model of firm performance that includes both customer
information management and customer satisfaction (Eq. 2) provides a considerably improved fit
over a baseline model with only control variables (Eq. 5) (R-sq. increases from 0.064 to 0.391; p
< 0.01).
(Table 9 and 10 here)
The tests of the hypothesized moderating effects of customer heterogeneity and customer
relationship intensity on the linkage between customer information management and customer
satisfaction (Hypotheses H4a, H5a) (Table 9) and firm performance (Hypotheses H4b, H5b)
(Table 10) are not significant (p < 0.05). Results of a differential F-test shows (p < 0.05) that
including the interaction effects of customer heterogeneity and customer relationship intensity
(Eqs. 6 and 7) does not provide any explanatory power in the model over a model that includes
customer information management and the control variables. (Eqs. 4 and 5) (Table 8).
In other words, the positive relationships between customer information management and firm
outcomes of customer satisfaction and business performance appear to be robust across
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environments characterized by varying levels of customer heterogeneity and customer
relationship intensity.
DISCUSSION
Managerial Implications
Our results have important implications for practice. First, our results suggest that customer
information management is a two-dimensional construct covering customer information
generation and customer information utilization. The two-dimensional nature of the construct
suggests that it is not only important for managers to invest in the generation of customer
information, but also to ensure that systems exist to ensure the effective utilization of customer
information. Second, the tests of hypotheses suggest that customer information management of a
firm is related to two important firm outcomes – customer satisfaction and business performance.
Hence, all things being equal, firms that implement customer information management are likely
to not only have more satisfied customers, but are also likely to perform better than those that do
not. Finally, the null results of the tests of moderating effects suggest that the positive
relationship between customer information management on firm outcomes are robust and
generally applicable regardless of the two characteristics, customer heterogeneity and customer
relationship intensity of the firm’s customer environment.
Limitations and Future Research Directions
In this section, we discuss the study’s limitations and identify some opportunities for future
research.
1. Methodological limitations. The cross-sectional nature of data in our study restricts
conclusions to those of association, not of causation. Hence, a fruitful extension of this
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research would be a longitudinal study where customer information usage in period 1 is
related to outcome measures in period 2. Such a methodology will more strongly establish
causality between customer information usage and business performance. Additionally, a
longitudinal study will provide a more rigorous test of our hypotheses as it may be argued
that effects of customer myopia, if any, are more likely to be observed on long term firm
performance measures that are better captured in data from a longitudinal study. Second, the
data for our study is provided by a single informant, the head of marketing, and therefore
suffers from limitations of single-source data (Kumar, Stern and Anderson 1993). Hence,
future researchers may try to use multiple informants that may improve the overall reliability
of the analyses and enhance confidence in theory testing.
2. Antecedents of customer information management system. In this study we did not identify
the antecedents of a effective customer information management system. For example, what
should firms do in order to ensure that customer information is being generated and used in
their firms? It is important for managers to know the factors that limit or enhance customer
information generation and usage if they are to develop optimal customer information
management strategies. Future research could examine the managerial variables that facilitate
or hinder customer information management systems.
3. Relationship between customer information availability and customer information usage. In
this research, we did not examine the relationship between customer information generation
and customer information utilization. In other words, do firms that use customer information
generate customer information or do firms that generate customer information use it? It may
be important for managers to disentangle the causality between customer information
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generation and customer information utilization because resource implications for generation
and usage of customer information are different.
Conclusions
In this research, we define the construct of customer information management and develop a
reliable measure for it and empirically validate the measure among a sample of managers. We
demonstrate empirically that customer information management is positively related to two firm
outcomes of customer satisfaction and business performance. Further, the null results of the tests
of the moderating effects of the firm’s customer heterogeneity and customer relationship
intensity suggest that the effects of customer information management on customer satisfaction
and business performance are robust across different customer environments. In sum, this
research provides evidence that customer information is a knowledge asset that can be leveraged
to improve business performance.
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Figure 1- Effects of Customer Information Management on Customer Satisfaction and F
Customer
Information
Customer
Satisfaction
Firm
Pe
Customer Environments
• Customer Heterogeneity
• Customer Relationship
Intensity
In
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Table 1
Summary of Hypotheses
Hypotheses Relationship Sign Support from theLiterature
H1 Customer informationmanagement on customersatisfaction
Positive -
H2 Customer informationmanagement onfirm performance
Positive Glazer 1991;Itami and Roehl1987;
H3 Customer satisfaction on firm
performance
Positive Blattberg and
Deighton 1996;Gale 1994;
H4a,H4b Moderating effects of customerheterogeneity on the relationshipbetween customer informationmanagement and customersatisfaction and firm performance
Positive -
H5a, H5b Moderating effects of customerrelationship intensity on therelationship between customerinformation management and
customer satisfaction and firmperformance
Positive -
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Table 2
Reliabilities of Multi-item Scales used in the Study
MeasuresReliabilityCoefficient
(Cronbach’s
Alpha)
Customer Information Management (CIM)
Customer information generation (CIG)ComprehensivenessAccuracyAccessibilityRelevantPerceived quality
Customer information usage(CIU)Used for managing operationsWell-integrated into operationsUsed in planningUsage relative to competitors
0.84
0.81
0.74
Firm performance (PERF)OverallRelative to competitors
0.74
Customer satisfaction (CSAT)OverallRelative to competitors
0.76
Customer heterogeneity (CHET)Difference in sales and profit to firmDifference in preferences and needs
0.61
Customer relationship intensity (CRI)Customer servicing calls for ongoing effortInvestments in managing customerrelationshipsCustomer relationships require ongoingcommunications
0.58
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Table 3Exploratory Factor Analysis of Customer Information Management Showing Two Factors
of Customer Information Generation (CIG) and Customer Information Utilization (CIU)
Variable Factor 1 Factor 2
Customer Information
Generation (CIG)
Comprehensiveness 0.588 0.177
Accuracy 0.813 0.040
Accessibility 0.445 0.227
Relevance 0.562 0.052
Perceived quality 0.711 -0.023
Customer Information Usage
(CIU)
Used for managing operations 0.095 0.602
Well-integrated into operations 0.328 0.530
Used in planning -0.106 0.851
Usage relative to competition 0.227 0.283
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Table 4: Correlation Matrix of Measures Used in the Research
Range Means
(sd) CIMPERF
CSAT CHET CRI CINT BPOW
1. Customer Information
Management (CIM)
9-45 33.831
(6.047)
1.000
2. Performance (PERF)
2-10 7.63
(1.766)
0.316 1.000
3. Customer Satisfaction
(CSAT)
2-10 7.720
(1.483)
0.266 0.601 1.000
4. Customer heterogeneity
(CHET)
2-10 7.017
(2.043)
0.055
(ns)
0.081
(ns)
-0.034
(ns)
1.000
5. Customer relationshipintensity (CRI)
3-15 11.684(2.410)
0.236 0.045(ns)
-.045(ns)
.289 1.000
6. Competitive intensity (CINT) 1-5 3.679
(1.213)
-0.049
(ns)
-0.135* -0.161* 0.165* 0.354 1.000
7. Buyer power (BPOWER) 1-5 3.259
(1.180)
0.039
(ns)
-0.132
(ns)
-0.169* 0.104
(ns)
0.325 0.229 1.000
8. Supplier power (SPOWER) 1-5 3.508
(1.111)
0.122
(ns)
0.018
(ns)
-0.027
(ns)
0.191 0.187 0.088
(ns)
0.066
(ns)
9. Barriers to entry (B2E) 1-5 2.376
(1.285)
0.003
(ns)
0.013
(ns)
0.072
(ns)
-0.026
(ns)
-0.222 -.080
(ns)
0.002
(ns)
10. Pressure from substitute
products (SUBST)
1-5 2.646
(1.249)
-0.151* -0.017
(ns)
0.008
(ns)
0.051
(ns)
0.025
(ns)
0.097
(ns)
0.050
(ns)
11. Product quality (PQ) 1-5 4.249
(0.760)
0.222 0.253 0.376 -0.013
(ns)
0.049
(ns)
-0.168* -0.02
(ns)
* denotes significant at p < 0.05. All others significant at p < 0.001.
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Table 5
Regression Analyses showing the Effects of Customer Information Management
on Customer Satisfaction (Equation 1)
Variables Parameterestimates
(se)
t-value
Customer information
Management (CIM) (α 1)0.080(0.019) 4.25
Competitive intensity (CINT) (α 2) -0.203(0.119) 1.71
Buyer power (BPOWER) (α 3) -0.275(0.119) -2.31
R-sq. 0.116
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Table 6
Regression Analyses Showing the Effects of both Customer Information Management and
Customer Satisfaction on Performance (Equation 2)
Variables Parameter
estimates
(se)
t-value
Customer informationmanagement
(CIM) (β 1)
0.051(0.017) 3.00
Customer satisfaction (CSAT) (β 2) 0.552(0.059) 9.36
Barriers to entry (B2E) (β 3) -0.047(0.096) -0.49
Supplier power (SPOWER) (β 4) 0.018(0.098) 0.18
Substitutability of products
(SUBST) (β 5)0.009(0.098) 0.09
Product quality (PQ) (β 6 ) 0.009(0.105) 0.09R-sq. 0.391
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Table 7
Regression Analyses Showing the Effects of Customer Information Management onPerformance (Equation 3)
Variables Parameterestimates
(se)
t-value
Customer informationmanagement
(CIM) (γ 1)
0.086(0.020) 4.30
Barriers to entry (B2E) (γ 2) 0.004(0.114) 0.04
Supplier power (SPOWER) (γ 3) -0.072(0.115) -0.63
Substitutability of products
(SUBST) (γ 4)
0.086(0.116) 0.74
Product quality (PQ) (γ 5) 0.350(0.117) 2.99
R-sq. 0.141
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Table 8
Model Comparisons to Examine the Explanatory Power of Customer Information Managementon Customer Satisfaction and Firm Performance showing Improved Performance over a
Baseline Model that Includes only Control Variables
Model (Equation no.) R-
square
Overall
F
Degrees
of freedom
Results of Differential
F-test(F and p values)
Customer Satisfaction
Only control variables (4)0.044 4.982 2, 215
Customer informationmanagement +controlvariables (1)
0.116 9.355 3, 214 17.43(0.01)
(Eq. 1 vs. Eq. 4)
Customer informationmanagement +controlvariables +interaction effects
of environmental variables(6)
0.118 3.996 7, 2100.48(ns)
(Eq. 6 vs. Eq. 1)
Firm Performance
Only control variables (5)0.064 3.622 4, 213
Customer informationmanagement +Controlvariables (3)
0.141 6.965 5, 212 19.00(0.01)
(Eq. 3 vs. Eq. 5)
Customer informationmanagement + Customer
satisfaction + Controlvariables(2)
0.391 22.57 6, 211 56.64(0.01)
(Eq. 2 vs. Eq. 5)
Customer informationmanagement +controlvariables +interaction effectsof environmentalvariables(7)
0.152 4.128 9, 208 0.61(ns)
(Eq. 7 vs. Eq. 3)
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Table 9
Interaction Effects of Customer Heterogeneity and Customer Relationship Intensity on theRelationship between Customer Information Management and Customer Satisfaction
(Equation 6)
Variable ParameterEstimate (se)
t-value
Customer Information
Management (CIM)(λ 1)0.083(0.020) 4.15
Customer Heterogeneity (CHET)
(λ 2)-0.013(-0.072) -0.18
Customer Relationship Intensity
(CRI) (λ 3)
-0.007(0.063) -0.11
Customer Information
Management × Customer
Heterogeneity (CIM × CHET)
(λ 4)
-0.003(0.012) -0.25
Customer InformationManagement × CustomerRelationship Intensity
(CIM × CRI) (λ 5)
0.004(0.007) 0.57
Competitive Intensity (CINT)
(λ 6 )
-0.195(0.128) -1.52
Buyer power (BPOWER) (λ 7 ) -0.261(0.125) -2.09
R-sq. 0.118
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Table 10
Interaction Effects of Customer Heterogeneity and Customer Relationship Intensity on theRelationship between Customer Information Management and Firm Performance
(Equation 7)
VariableParameterEstimate
(se)
t-value
Customer Information
Management (CIM) (φ 1)0.092 (0.021) 4.38
Customer Heterogeneity (CHET)
(φ 2)
0.093(0.071) 1.31
Customer Relationship Intensity
(CRI) (φ 3)
-0.038(0.058) -0.66
Customer Information
Management × Customer
Heterogeneity (CIM × CHET)
(φ 4)
-0.005(0.011) -0.46
Customer Information
Management × CustomerRelationship Intensity
(CIM × CRI) (φ 5)
-0.006(0.007) 0.86
Barriers to entry (B2E) (φ 6 ) -0.023(0.118) -0.20
Supplier Power (SPOWER) (φ 7 ) -0.097(0.119) -0.82
Substitutability of Products
(SUBST)(φ 8)
0.088(0.116) 0.76
Product quality (PQ) (φ 9) 0.348(0.118) 2.95
R-sq. 0.152
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Appendix
Items of Scales Used in the Research
I. Customer Information Management (CIM) (αααα=0.84)
Customer Information Generation (CIU) (α =0.81)
1. Our customer information is detailed, comprehensive and reliable.2. Our customer information is accurate and up-to-date.3. Our customer information is accessible to all managers who need to use it.4. We have relevant and necessary information about our customers.5. The quality of our customer information is not good (R).
Customer Information Utilization (CIU) (α =0.74)
1.
We use customer information for managing our current business operations.
2. Our customer information system is well-integrated into our business systems and forms thebackbone of our business operations.
3. Our customer information is a central input in our business planning.4. The integration of customer information in our business processes and planning is better than
that of our major competitors.
II. Firm Performance (PERF) (α = 0.74)
1. Overall performance of our business unit.2. Performance of our business unit relative to that of our major competitors.
III. Customer satisfaction (CSAT) (α =0.76)
1. Overall satisfaction of our business unit’s customers.2. Satisfaction of our business unit’s customers with us, relative to their satisfaction with our major
competitors.
IV. Customer heterogeneity (CHET) (α =0.61)
1. Our customers differ substantially from each other in terms of sales and profit potential to us.2. Our customers are very different from each other in terms of needs and preferences.
V. Customer relationship intensity (CRI) (α =0.58)
1. The servicing of our customers calls for ongoing selling and marketing effort on our part.2. Once we get a customer, we do not have to invest a great deal of effort and time in managing our
customer relationships (R).3. Transactions with our business unit’s customers requires a lot of ongoing communications.
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VI. Control Variables #
1. Buyer power(BPOWER)
Our major customers are in a strong bargaining position with our business unit/company.
2. Competitive intensity(CINT)
Our customers see little difference between our products (or services) and those of competitors.
3. Barriers to entry (B2E)
It is easy for new players to enter our industry.
4. Supplier power(SPOWER)
Major vendors/suppliers have the power to dictate prices to us.
5. Substitutability of products(SUBST)
Competitors outside of our industry offer viable substitutes for products (or services).
6. Product quality(PQ)
Our customers often praise our product’s (or service’s) quality.
Note:
1. All items were scored using a 5-point scale where 1 corresponds to strongly disagree and 5 tostrongly agree.
2. (R) indicates an item that is reverse-coded.
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Footnotes
1 Indicative of its importance to managerial practice, the topic of customer information has received substantial attentionin the business press (Peppers and Rogers 1995; Pine 1995; Wayland and Cole 1996).
2 While some scholars (Davenport 1997; Glazer 1991; Itami and Roehl 1987) make a distinction between data,
information and knowledge, we use the terms interchangeably in this paper.3 We use the terms utilization, use and usage interchangeably in this paper.
4 Some researchers (Menon and Varadarajan 1992) also consider the symbolic and affective uses of information byindividual managers. Because the focus of this research is on the effects of such usage on performance outcomes and noton the socio-psychological factors of information usage, we do not consider the symbolic and affective use of customerinformation.
5 Analysis of our data did not provide support for main effects of these environmental variables on both customersatisfaction and firm performance.
6 An analysis of the residuals from each of the equations estimated in the study supports our assumption.
7 Multiplicative formulation of customer information generation and customer information usage did not providesignificant improvement in fit over the additive form.
8 The effects of the control variables are also reported in Table 5a and 5b. As might be expected, buyer power has a
negative effect on customer satisfaction (α 3 = -0.275, t =-2.31) and product quality has a positive effect on performance
(γ 5 = 0.35, t = 2.99) and. All other control variables are not significant (p <0.05).
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