leveraging CIM for competitive advantage

8/7/2019 leveraging CIM for competitive advantage

http://slidepdf.com/reader/full/leveraging-cim-for-competitive-advantage 1/42

“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



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



1

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



2

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



3

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.



4

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.



5

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



6

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.



7

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.



8

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.



9

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



10

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

++++

+++=



11

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



12

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

++

++++=



13

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

++

++

++++=



14

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



15

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).



16

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



17

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)



18

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)



19

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



20

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



21

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



22

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.



23

References

Aiken, Leona S. and Stephen G. West (1991), Multiple Regression: Testing and Interpreting

Interactions. Newbury Park, CA: Sage Publications.

Baron, Reuben M. and David A. Kenny (1986), “The Moderator-Mediator Variable Distinction

in Social Psychological Research: Conceptual, Strategic and Statistical Considerations,” Journal

of Personality and Social Psychology, Vol. 51(6), 1173-1182.

Blattberg, Robert C. and John Deighton (1996), “Manage Marketing by the Customer Equity

Test,” Harvard Business Review, Vol. 74(4), 136-14

Boulding, W. and Staelin, R. (1990), “Environment, Market Share and Market Power’,

Management Science, 36, 1160-1177.

Bourgeois, Leonard Jay, III (1980), “Strategy and Environment: A Conceptual Integration,”

Academy of Management Journal, 1 (January), 25-39.

Caplan, Nathan, Andrea Morrison and Russell Stambaugh (1975), The Use of Social Science

Knowledge in Policy Decisions at the National Level. Ann Arbor, MI: Institute for Social

Research.

Christensen, Clayton M. (1997), Innovators’ Dilemma. Boston, MA: Harvard Business School

Press.

Churchill, Gilbert, A., Jr. (1979), “ A Paradigm for Developing Better Measures of Marketing

Constructs,” Journal of Marketing Research, 16 (February), 64-73.

Cronbach, Lee J. (1987), “Statistical Tests for Moderator Variables: Flaws in Analyses Recently

Proposed,” Psychological Bulletin, 1(3), 414-17.

Daft, Richard, L. and Karl E. Weick (1984), “Toward a Model of Organizations as Interpretation

Systems,” Academy of Management Review, 9 (April), 284-95.



24

Day, George S. (1994), “ The Capabilities of Market-Driven Organizations,” Journal of

Marketing, 58 (October), 37-52.

Deshpande, Rohit, John U. Farley and Frederick E. Webster (1993), “ Corporate Culture,

Customer Orientation and Innovativeness in Japanese Firms: A Quadrad Analysis,” Journal of

Marketing, 57 (January), 23-37.

Dess, G.G. and Richard B. Robinson, Jr. (1984), “Measuring Organizational Performance in the

Absence of Objective Measures: The Case of the Privately held Firm and Conglomerate

Business Unit,” Strategic Management Journal, 5(July-September), 265-273.

Gale, Bradley T. (1994), Managing Customer Value. New York, NY: The Free Press.

Glazer, Rashi (1991), “Marketing in an Information-Intensive Environment: Strategic

Implications of Knowledge as an Asset,” Journal of Marketing, 55 (October), 1-19.

Hamel, Gary and C. K. Prahalad (1991), “Corporate Imagination and Expeditionary Marketing,”

Harvard Business Review, July-August, 1991, 81-92.

Itami, Hiroyuki and Thomas W. Roehl (1987), Mobilizing Invisible Assets. Cambridge, MA:

Harvard University Press.

Jacobson, Robert and David A. Aaker (1987), “The Strategic Role of Product Quality,” Journal

of Marketing, 51(4), 31-44.

Jaworski, Bernard J. and Ajay K Kohli (1993), “Market Orientation: Antecedents and

Consequences,” Journal of Marketing, 57 (July), 53-71.

John, George and John Martin (1984), “Effects of Organizational Structure of Market Planning

on Credibility and Utilization of Plan Output,” Journal of Marketing Research, 21 (May), 170-

183.



25

Kohli, Ajay. K. and Jaworski, Bernard J. (1990), “Market Orientation: The Construct, Research

Propositions, and Managerial Implications,” Journal of Marketing, 54 (April), 1-18.

Kumar, Nirmalya, Loius W. Stern and James C. Anderson (1993), “Conducting

Interorganizational Research using Key Informants,” Academy of Management Journal, Vol.

36(6), 1633-1651.

Land, Kenneth (1973), “Identification, Parameter Estimation and Hypothesis Testing in

Recursive Sociological Models,” in Structural Equation Models in the Social Sciences, Arthur S.

Goldberger and Otis D. Duncan, eds. New York: Seminar Press Inc., 19-48.

Menon, Anil and Rajan P Varadarajan (1992), “A Model of Marketing Knowledge Use Within

Firms,” Journal of Marketing, 56 (October), 53-71.

Moenaert, Rudy. K. and William E. Souder (1996), “Context and Antecedents of Information

Utility at the R&D/Marketing Interface,” Management Science, 42(November), 1592-1610.

Moorman, Christine (1995), “Organizational Market Information Processing: Cultural

Antecedents and New Product Outcomes,” Journal of Marketing Research, 32 (August), 318-

335.

Narver, John, C. and Stanley F.Slater (1990), “The Effect of a Marketing Orientation on

Business Profitability,” Journal of Marketing, 54 (October), 20-35.

Nunnally, Jim C. (1978), Psychometric Theory. 2nd ed. New York, NY: McGraw-Hill.

Pedhazur, Elazar (1997), Multiple Regression in Behavioral Research: Explanation and

Prediction. 3rd

ed. Orlando, FL: Harcourt Brace Publishers.

Peppers, Don and Martha Rogers (1996). The 1:1 Future: building relationships one customer at

a time. New York: Bantam Doubleday Publishing Group Inc.



26

Peteraf, Margaret, A. (1993), “The Cornerstones of Competitive Advantage: A Resource-Based

View,” Strategic Management Journal, 14 (March), 179-91.

Pine, B. Joseph, Don Peppers and Martha Rogers (1995), “Do You Want to Keep Your

Customers Forever,” Harvard Business Review, (March/April), 103-114.

Porter, Michael, (1985), Competitive Advantage. New York, NY: The Free Press.

Shrivastava, Paul (1987), “Rigor and Practical Usefulness of Research in Strategic

Management,” Strategic Management Journal, 8 (January-February), 77-92.

Sinkula, James, M. (1994), “ Market Information Processing and Organizational Learning,”

Journal of Marketing, 58 (January), 35-45.

Slater, Stanley F. and John C. Narver (1994), “Does Competitive Environment Moderate the

Market Orientation-Performance Relationship?” Journal of Marketing, 58 (January), 46-55.

Teece, David, J., Gary Pisano and Amy Shuen (1997), “Dynamic Capabilities and Strategic

Management,” Strategic Management Journal, 18(7), 509-533.

Wayland, Robert E. and Paul M. Cole (1997). Customer Connections: New Strategies for

Growth. Boston, MA: Harvard Business School Press.

Wernerfelt, Birger (1984), “A Resource-Based View of the Firm,” Strategic Management

Journal, 5 (March), 171-80.



27

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



28

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 -



29

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



30

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



31

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.



32

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



33

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



34

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



35

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)



36

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


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


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



37

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


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


Management × Customer

Heterogeneity (CIM × CHET)

(φ 4)

-0.005(0.011) -0.46


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



38

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.



39

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.



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).

Documents

leveraging CIM for competitive advantage