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A STRATEGIC MARKETING EXAMINATION OF
STAKEHOLDERS, CUSTOMER SATISFACTION, AND PERFORMANCE
OF FIRMS EMBEDDED IN MULTI-ENTITY SUPPLY CHAINS

By

Jeannette A. Mena

A DISSERTATION
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY

Marketing

2010

 

ABSTRACT
A STRATEGIC MARKETING EXAMINATION OF
STAKEHOLDERS, CUSTOMER SATISFACTION, AND PERFORMANCE
OF FIRMS EMBEDDED IN MULTI-ENTITY SUPPLY CHAINS
By
Jeannette A. Mena

In today’s competitive business environment, ﬁrms are increasingly adopting a
stakeholder approach to doing business, where they seek to create value to multiple
stakeholders. Interestingly, for the most part, researchers in the marketing ﬁeld have been
slow to respond to this trend, concentrating almost exclusively on the customer as the
sole stakeholder group. To thoroughly understand how attending to the needs of multiple
stakeholders inﬂuences marketing phenomena, this three-essay dissertation examines the
importance of primary stakeholders (i.e., customers, employees, suppliers, shareholders,
communities, and regulators) across multiple levels and focus areas.

Essay 1 examines whether a stakeholder-focused approach to developing
marketing strategies is more effective than a market-driven one. A multilevel model of
the relative inﬂuences of the ﬁrm, strategic group, and industry effects on ﬁrms’ market
performance is developed. Emphasis is placed on two sets of strategic groups derived
from classical marketing strategy along with the recent marketing strategy focus on
stakeholders. The model is tested using data obtained ﬁ'om the Kinder Lydenburg Domini
Statistical Tool for Analyzing Trends in Social and Environmental Performance and
Standard & Poor’s Compustat North America databases, involving 1,716 ﬁrms over a
four-year period. The results reveal that, in general, placing more emphasis on a broad set

of stakeholders when developing marketing strategies is relatively more important for

market performance than adopting a more limited, market-driven focus which
concentrates on customers, employees, and suppliers, while paying comparatively less
attention to shareholders, communities, and regulators.

Essay 2 studies the antecedents and consequences of a focal ﬁrm’s stakeholder
focus (i.e., the amount of attention, resources, and time the ﬁrm devotes to addressing the
interests of multiple stakeholder groups). The conceptual model was tested with
secondary data obtained ﬁ'om four different databases spanning the years of 2004 to
2007. The results indicate that the stakeholder focus of the focal ﬁrm’s business-to-
business customers, primary suppliers, and major competitors has a direct or moderated
effect on the focal ﬁrm’s stakeholder focus. In addition, an inverted U-shaped
relationship is found between aspects of the focal ﬁrm’s stakeholder focus and customer
satisfaction. This implies that stakeholder management is a zero sum game — where if the
goal is to satisfy the customers, it may be achieved at the expense of other stakeholders.

Essay 3 studies the effects of organizational learning about stakeholders on the
ﬁrm’s responsiveness and on the extent of innovation and imitation of stakeholder
practices. The hypotheses were tested with data obtained from 349 marketing and supply
chain executives representing the strategic business units (SBUS) of 285 ﬁrms across all
economic sectors. The results indicate that four organizational learning processes (i.e.,
knowledge acquisition, information distribution, information interpretation, and
organizational memory) have a direct effect on stakeholder-focused responsiveness. In
addition, while experiential knowledge acquisition is related to innovative stakeholder

practices, vicarious knowledge acquisition is related to imitative ones.

Copyright © by
JEAUVhH?FTErA.thhU\

2010

To my parents,
Agnes Mercado and Luis Mena,
for their continued support

ACKNOWLEDGEMENTS

This dissertation would not have been possible without the support of various
individuals for whom I am extremely grateful. First and foremost, I would like to thank
my advisor and dissertation committee chair, Professor Tomas Hult (Professor of
Marketing, International Business, and Strategic Management; Director of the MSU
International Business Center; and Executive Director and Fellow of the Academy of
International Business), for his invaluable mentoring and countless hours throughout my
journey as a Ph.D. student at Michigan State University. I feel very fortunate to have had
him as my advisor, as he is a great example of the type of academician I aim to become.
His immense patience and helpful guidance were instrumental in the successful
completion of this dissertation, and for this, I will be forever indebted to him. I could not
have asked for a better advisor.

I would also like to thank the other members of my dissertation committee for
their insightful comments and suggestions. Speciﬁcally, I am deeply appreciative of
Professor David J. Closs’s (Chair of the Department of Supply Chain Management and
John H. McConnell Chair in Business Administration) unique insights and intellectual
thoughts regarding the intersection between supply chain phenomena and marketing
strategy issues in the dissertation. Thanks to Professor Ahmet Kirca (Assistant Professor
of Marketing and International Business) for his valuable advice, especially in the areas
of market orientation and institutional theory. I would like to express my gratitude to
Professor Robert Nason (Chair and Professor of Marketing Emeritus), who both offered
insights in macromarketing issues that pertained to the stakeholder concept, and guided

me on crispness and thoughtfulness in composing the dissertation.

vi

I also appreciate the opportunity to have had two members on my committee who
are not on the faculty at Michigan State University. First, Professor Claes Fomell
(Donald C. Cook Professor of Business Administration, Professor of Marketing, and
Director of the National Quality Research Center in the Ross School of Business at the
University of Michigan) provided intellectual guidance to the dissertation in the areas of
customer satisfaction, methods, and methodology. Second, Professor 0. C. Ferrell
(Creative Enterprise Scholar and Professor of Marketing in the Anderson School of
Management at the University of New Mexico) provided useful insights in the areas of
stakeholder theory and marketing strategy issues. It was truly an honor to have worked
with such dedicated scholars.

I extend my appreciation to the professors in the Department of Marketing and
the Department of Supply Chain Management for instilling in me rich knowledge about
the different areas of research within the ﬁelds of marketing and supply chain
management. In addition, I would like to acknowledge the MSU International Business
Center and the Broad School’s Dean’s Office for fully funding this dissertation.

Finally, I wish to thank my loving family, especially my parents, Agnes Mercado

and Luis Mena, for their continued support and encouragement over the years.

vii

TABLE OF CONTENTS

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

LIST OF TABLES ............................................................................................................ x
LIST OF FIGURES ........................................................................................................ xii
INTRODUCTION ......................... ........................................ 1
Overview of Essays ............................................................................ 12
Essay 1 ................................................................................................................ l2
Essay 2 - - ................. -- ..................................... 14
Essay 3 -- .................................................... 16
Organization of the Dissertation ....................................................... _ 18
References ........................................... - 19
ESSAY l
MARKETING STRATEGY AND PERFORMANCE:
COMPARJN G MARKET AND STAKEHOLDER APPROACHES ............................ 25
Systems Theory and Multilevel Inﬂuences ...................... 28
Firm-Level Effects on Market Performance .................... 29
Strategic Group-Level Effects on Market Performance ..................................... 32
Industry-Level Effects on Market Performance ..................... 41
Method . ..................................... 42
Data Collection .. .................................................... 42
Deﬁning Strategic Groups ............................ 45
Inclusion of Segmentation Variables ........................... 47
Market Performance - ................. 49
Hierarchical Linear Modeling ............................................................................. 50
Results .......................................... 52
Firm Effects on Market Performance .................................................................. 53
Strategic Group Effects on Market Performance .. 54
Industry Effects on Market Performance ................................. 55
Discussion - ....................... 56
Research Implications ......................................................................................... 57
Managerial Implications ..................................................................................... 61
Appendix A - .. .................................................... 64
References ................................................................................................................. 72
ESSAY 2
STAKEHOLDERS, CUSTOMER SATISFACTION, AND PERFORMANCE ........... 81
Conceptual Framework and Hypotheses .................... 83
Supply Chain Members’ Stakeholder Focus and the Effects on the
Focal Firm .......... - ..... __ ................... 92
Competitors’ Stakeholder Focus and the Effect on the Focal Firm .................... 94
The Moderating Effect of R&D on the Stakeholder Relationships .................... 96

viii

The Relationship between Stakeholder Focus and Customer

 

Satisfaction .......................................................................................................... 99
The Relationship between Customer Satisfaction and Firm
Performance ...................................................................................................... 102
Method .................................................................................................................... 104
Data Collection ................................................................................................. 104
Study Measures ................................................................................................. 107
Hierarchical Regression and Polynomial Regression ....................................... 112
Results ...................................................................................................................... 113
Antecedents, Moderators, and Stakeholder Focus of the Firm .......................... 113
Stakeholder Focus of the Firm and Satisfaction ............................................... 116
Satisfaction and Performance ............................................................................ 118
Discussion and Implications ................................................................................... 122
Appendix B .............................................................................................................. 128
References ............................................................................................................... 136
ESSAY 3
STAKEHOLDER-FOCUSED ORGANIZATIONAL LEARNING,
RESPONSIVENESS, AND INNOVATION / IMITATION ....................................... 144
Organizational Learning .......................................................................................... 146
Knowledge Acquisition ..................................................................................... 147
Information Distribution ................................................................................... 150
Information Interpretation ................................................................................. 151
Organizational Memory .................................................................................... 152
Hypotheses Development ........................................................................................ 154
Stakeholder-Focused Organizational Learning and Responsiveness ................ 156
Knowledge Acquisition Mechanisms, Innovation, and Imitation ..................... 160
Combinative Effects between Knowledge Acquisition Mechanisms ............... 164
Method .................................................................................................................... 165
Data Collection ................................................................................................. 165
Study Measures ................................................................................................. 167
Conﬁrmatory Factor Analysis and Hierarchical Regression ............................ 170
Results ...................................................................................................................... 171
Measurement Results ........................................................................................ 171
Hypothesis Results ............................................................................................ 175
Discussion and Implications ................................................................................... 181
Appendix C ...... - .................................................................................. 187
References ............................................................................................................... 189
CONCLUSION ............................................................................................................. 197
References ............................................................................................................... 205

ix

Table 1:

Table 2:

Table 3:

Table 4:

Table 5:

Table 6:

Table 7:

Table 8:

Table 9:

Table 10:

Table 11:

Table 12:

Table 13:

Table 14:

Table 15:

Table 16:

LIST OF TABLES

A Sample of Marketing Articles Addressing Stakeholder Groups

Sample Sizes of Firms, Strategic Groups, and Industries ..............
Demographics of the Firms in the Overall Sample for 2007 .........
Sample Size by Industry ................................................................

Sample Size by Product vs. Service Firm Classiﬁcation ...............

The Effects of the Firm, Strategic Group, and Industry Levels on
Market Performance (Tobin’s Q)

.................. 3

................ 43

................ 45

................ 45

................ 48

........... 53

 

The Relative Immrtance of Firm, Strategic Group, and Industry

 

Levels .........................

Average, Range, and Standard Deviation

of the Control Variables .................................................................

Hierarchical Regression Results with the Firm’s Stakeholder
Focus in 2005 as the Criterion Variable

................ 59

.............. 111

..... 115

 

 

Hierarchical Regression Results with Satisfaction of the Firm’s

B2C Customers in 2006 as the Criterion Variable .........................

Hierarchical Regression Results with Return-on-Sales in 2007
as the Criterion Variable . - - .......

.............. 117

........ 119

 

Hierarchical Regression Results with Retum-on-Assets in 2007

 

as the Criterion Variable

Hierarchical Regression Results with Tobin’s Q m 2007 as the
Criterion Variable ......... -

......... 120

.......... 121

 

Demographics of the F inns in the Sample (n=349) .......................

Means, Standard Deviations, Correlations, and Shared Variances
(n=349) -

.............. 167

............. 172

 

Composite Reliabilities, Average Variances Extracted, Factor

Loadings, and Fit Statistics (n=349) ..............................................

.............. 173

Table 17:

Table 18:

Table 19:

Table 20:

Table 21.

Hierarchical Regression Results with Stakeholder-Focused
Responsiveness as the Criterion Variable (HI) ........................................... 176

Hierarchical Regression Results with Innovativeness as the
Criterion Variable (112a) ....... - -- . ........................ 177

 

Hierarchical Regression Results with Irnitativeness as the
Criterion Variable (HZb) .............................................................................. 179

Interaction-Based Hierarchical Regression Results with
Responsiveness as the Criterion Variable (H3) ........................................... 180

Summary of the Main Findings and Implications of the
Dissertation - -- - - ...................................... 199

 

xi

LIST OF FIGURES

Figure 1: Stakeholder Focus at Multiple Levels ............................................................. 12
Figure 2: A Vertical Focus on Stakeholders .................................................................. 14
Figure 3: A Horizontal Focus on Stakeholders .............................................................. 16
Figure 4: An SBU Focus on Stakeholders .......................... 18

 

 

Figure 5: Hierarchical Linear Model of the Effects of the Firm, Strategic
Group, and Industry Levels on Market Performance (Tobin’s Q) .................. 29

Figure 6: A Model of Stakeholder Focus, Customer Satisfaction, and
Performance - - ........... 91

 

Figure 7: Sample Size of Focal Firms and their Connections to Entities in
the Supply Chain Environment and Marketplace ......................................... 107

xii

INTRODUCTION

“The strength of a company is often measured in the kinds of relationships it
develops with its customers, employees, suppliers and communities. [. . .] a
commitment to strong and dynamic relationships remains an important element of
conducting business” (Lockheed Martin 2009).

In today’s highly competitive business environment, the stakeholder relationships
a ﬁrm develops and maintains are critical to its success, as the opening quotation
suggests. AS a result, ﬁrms are increasingly adopting a stakeholder approach to doing
business, where they seek to create value to multiple stakeholder groups. Speciﬁcally, a
stakeholder refers to “any group or individual who can affect or is affected by the
achievement of the organization’s objectives” (Freeman 1984, p. 46) and includes
customers, suppliers, employees, and shareholders (Clarkson 1995). The signiﬁcance of
this widespread attention to stakeholders is further illustrated by the proliferation of
rankings in the media that evaluate ﬁrms based on how effectively they deal with their
stakeholders (e.g., Fortune magazine’s “World’s Most Admired Companies,” Business
Ethics’ “100 Best Corporate Citizens,” and Forbes’ “America’s Most Reputable
Companies”).

Surprisingly, even though the practical reality reﬂects that ﬁrms are increasingly
paying attention to multiple stakeholders, for the most part, researchers in the marketing
ﬁeld have been slow to respond to this trend (e.g., Ferrell, Gonzalez-Padron, Hult, and
Maignan 2010). For example, despite the fact that early advocates of market orientation
stressed that a ﬁrm’s market orientation not only includes efforts directed at customers,

but also at a broader set of stakeholders since these additional groups may have an effect

on the ﬁrm’s long-term performance (e. g., Narver and Slater 1990), research in this

stream has mostly maintained a narrow focus where interest has laid on the customer as
the sole stakeholder group.

To investigate how inclusive marketing studies have been of the different
stakeholder groups, an extensive literature review of articles addressing stakeholder
groups in the top marketing journals — Journal of Marketing, Journal of Marketing
Research, Journal of Consumer Research, Marketing Science, Journal of the Academy of
Marketing Science, Journal of Retailing, International Journal of Research in Marketing,
and Journal of Public Policy and Marketing - was conducted for the period of 1985-
2009. The search focused on the primary stakeholders, who are those that are essential for
the ﬁrm’s survival and continued market success, and normally consist of customers,
suppliers, employees, regulators, shareholders, and the local community (Clarkson 1995).
As can be noted ﬁom Table 1, the vast majority of the papers in this review concentrate
on one or two stakeholders individually, while very few (e.g., Greenley and Foxall 1998;
Maignan and Ferrell 2004) study multiple stakeholders simultaneously. Hence, given the
growing importance of stakeholder relationships and the lack of marketing studies
capturing this practical reality, it is imperative to examine from a holistic perspective the
marketing implications of paying attention and responding to the demands of multiple

stakeholder groups.

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10

To thoroughly understand how attending to the needs of multiple primary
stakeholders inﬂuences marketing phenomena, this dissertation studies the different
components of Figure 1 over three integrated essays. Brieﬂy, Essay 1 (Marketing
Strategy and Performance: Comparing Market and Stakeholder Approaches) investigates
the vertical levels of the industry, strategic group, and ﬁrm and their relative effects on
performance. The key stakeholder focus in this essay is at the strategic group level, where
it examines the importance of developing a ﬁrm’s strategy based on a stakeholder-
focused approach vis—a-vis a market-driven approach to doing business. Essay 2
(Stakeholders, Customer Satisfaction, and Performance) rests at the ﬁrm level but
horizontally spans multiple entities. In particular, this essay examines how the ﬁrm’s
stakeholder focus is shaped by entities in its supply chain environment (primary
suppliers, business-to-business customers, and strategic partners) and marketplace (major
competitors) and how a stakeholder focus impacts customer satisfaction and
performance. Lastly, Essay 3 (Stakeholder-Focused Organizational Learning,
Responsiveness, and Innovation/Imitation) lies at the strategic business unit (SBU) level
to investigate how knowledge development comes into play when dealing with
stakeholders. It studies the direct and combinative effects of organizational leaming about
stakeholders on the ﬁrm’s market responsiveness and on the extent of innovation and
imitation. As such, this dissertation examines the importance of primary stakeholders
across multiple levels and focus areas. The next three sections provide an overview of

what each essay seeks to accomplish.

11

FIGURE 1

Stakeholder Focus at Multiple Levels

  

  

Strategic Group

Finn’s 828
Customers

    
    
 

  
 
  

  
   
 

Firm’s Major

Suppliers Firm-Level

Finn’s Strategic ”rm“

Partners

Firm’s Major

Competitors SBU-Level
Business Unit Performance

 

OVERVIEW OF ESSAYS

Essay 1

Strategic marketing researchers have long been interested in determining the
performance implications of different approaches to marketing strategy development. For
example, research stemming from the market orientation stream has centered on the
beneﬁts that ﬁrms adopting a market-driven approach to developing strategies obtain
(e.g., Day 1994). Researchers in this area have consistently found that those ﬁrms that
acquire information about the needs and wants of their customers and that respond to this
information by developing and implementing strategies that target those needs and wants
outperform ﬁrms that do otherwise (e.g., Jaworski and Kohli 1993; Rueckert 1992).
However, changes in the business environment have caused a number of ﬁrms to
embrace a newer approach — the stakeholder-focused approach to strategy development —
which focuses on developing strategies that create value to multiple stakeholder groups

12

(e.g., Walker and Mart 2001). Advocates of this approach argue that ﬁrms that develop
and implement strategies that are focused on meeting stakeholder claims obtain a
competitive advantage (e.g., Jones 1995) and achieve superior performance (Berrnan et
al. 1999).

Interestingly, even though the performance implications of these different
approaches to marketing strategy development have been examined independently (e. g.,
Greenley and Foxall 1998; Jaworski and Kohli 1993), it is still unclear which of the two
approaches is more important for performance. Unless the market-driven approach and
the stakeholder-focused approach are considered simultaneously, researchers and
executives alike are left unsure about their relative merit in explaining performance.
Essay 1 is intended to alleviate this important knowledge gap. As such, a key objective of
this essay is to assess which marketing strategy development approach is relatively more
important to achieve superior market performance. To reach this objective, a multilevel
model of the inﬂuences of the ﬁrm, strategic group, and industry effects on ﬁrms’ market
performance is developed (see Figure 2). The multilevel modeling approach serves to
tease out the ﬁrm- and industry-level effects. Emphasis is placed on two sets of strategic
groups derived from classical marketing strategy along with the recent marketing strategy
focus on stakeholders to determine the effectiveness of ﬁrms developing marketing
strategy based on a market-driven and a stakeholder-focused approach. The model is
tested using data obtained from the Kinder Lydenburg Domini Statistical Tool for
Analyzing Trends in Social and Environmental Performance and Standard & Poor’s

Compustat North America databases, involving 1,716 ﬁrms over a four-year period.

13

FIGURE 2

A Vertical Focus on Stakeholders

 

 

 

 

 

 
      
   
    

 

 

 

 

 

 

 

 

 

 

 

 

   

shag: Group

Finn’s 3213 r1. 2:. -- . -. "l
Customers l
Finn’s Major .
Suppliers . - ‘

. . L. m ‘ ..-~-'- Lute“
Frrm’s Strategic ~ .. » ‘f « ..
Partners I
Finn’s Major
CompetitorS Strategic Business SBU-Level

Unit ‘ . Performance

 

 

 

 

 

 

 

Essay 2

Attending to the needs of multiple stakeholders is becoming an important element
in strategic marketing. Even though several scholars have called for research that
examines the implications of efforts directed at a broader set of stakeholders beyond the
customer (e. g., Maignan and Ferrell 2004), for the most part, marketing researchers have
failed to examine the ﬁrm’s focus on the simultaneous interests of multiple stakeholders
and the effects of such a focus, as was previously discussed. Essay 2 addresses this gap in
the literatm'e by examining the antecedents and consequences of a focal ﬁrm’s
stakeholder focus. A ﬁrm’s stakeholder focus broadly refers to the amount of attention,
resources, and time the ﬁrm devotes to addressing the interests of multiple stakeholder
groups (e. g., customers, employees, suppliers, shareholders, communities, and

regulators). Importantly, ﬁrms respond to the claims of their stakeholders within an

14

environment that consists of other ﬁrms that are also responding to their stakeholders’
demands. Given that ﬁrms do not operate in a vacuum, an important objective of this
study is to examine whether the supply chain participants’ (business-to—business
customers, primary suppliers, and strategic partners) stakeholder focus along with that of
a ﬁrm’s major competitors in the marketplace shape the focal ﬁrm’s stakeholder focus.
Customer satisfaction is the key intermediate marketing outcome in this study. A
ﬁrm’s stakeholder focus is related to customer satisfaction in that information about
satisfaction projects what the ﬁrm has done to its customers within the dynamics of the
overall marketplace and the ﬁrm’s multiple stakeholders (F omell 2007, p. 8). As such,
another objective is to investigate the impact of the focal ﬁrm’s stakeholder focus on
customer satisfaction. Additionally, it is of importance to investigate the impact of
customer satisfaction on ﬁrm performance, given that previous research has produced
conﬂicting evidence. While some studies have found that customer satisfaction translates
into economic beneﬁts for ﬁrms (e. g., Anderson, Fomell, and Lehmann 1994), others
have concluded that by driving up costs, it can negatively affect the ﬁrm’s bottom-line
performance (e. g., Anderson and Mittal 2000). Hence, this study reexarnines the nature of
the customer satisfaction—performance relationship with a particular focus on a potential
non-linear relationship between the variables. To accomplish the aforementioned
objectives, this paper draws on an integration of theories and literature bases to develop a
strategic marketing model centered on stakeholders which involves ﬁve separate types of
directly connected ﬁrms (see Figure 3). This model is tested using data compiled from
four databases spanning the years of 2004 to 2007 (i.e., American Customer Satisfaction

Index, Mergent Horizon, Kinder Lydenburg Domini Statistical Tool for Analyzing

15

Trends in Social and Environmental Performance, and Standard & Poor’s Compustat
North America database).
FIGURE 3

A Horizontal Focus on Stakeholders

 

Industry

 

 

 

 

Strategic Group

 

 

 

 

 

 

 

 

 

 

 

Strategic SBU-Level
Business Unit ‘ . Performance

 

 

 

 

 

 

 

Essay 3

Success in the global marketplace is increasingly determined by the information
and know-how organizations possess (e.g., Kogut and Zander 1993). The signiﬁcance of
knowledge as a strategic weapon for organizations to excel in the competitive global
marketplace has prompted a number of marketing scholars to examine how organizations
learn about the market (e.g., Hurley and Hult 1998; Sinkula 1994; Slater and Narver
1995). Studies have concluded that through organizational learning, organizations
develop new knowledge that facilitates behavior change (e.g., Slater and Narver 1995).
Interestingly, while valuable contributions have been made in this stream, research has

almost exclusively focused on how organizations learn about customers (e. g., Sinkula,

16

Baker, and Noordewier 1997; Slater and Narver 1995), thereby disregarding how such
organizations learn about their other primary stakeholders (i.e., suppliers, employees,
shareholders, community, and regulators). This is unfortunate given the increasing
importance organizations place on meeting their stakeholders’ demands, as was
previously discussed. In addition, the extant marketing literature on organizational
learning, for the most part, does not account for the alternative mechanisms that
organizations use to acquire information about the market. For example, organizations
can differ in their reliance on obtaining information ﬁom their own experience
(experiential learning), from the observed behavior of other organizations with whom
they have no direct links (vicarious learning), or from direct communication with their
peers (contact learning) (e.g., Lieberman and Asaba 2006; Ordanini, Rubera, and
DeFillippi 2008). These knowledge acquisition mechanisms may affect the way
organizations respond to their stakeholders and whether their stakeholder practices are
innovative or imitative.

Accordingly, this study aims to answer the following research questions: (1) does
stakeholder-focused organizational learning inﬂuence stakeholder-focused
responsiveness?; (2) is the organization’s degree of innovative and imitative stakeholder
practices affected by the mechanism(s) it relies on to acquire stakeholder-related
knowledge?; and (3) are there interaction effects between these knowledge acquisition
mechanisms? To delve deeper into the complexities inherent in learning about and
responding to stakeholders, the unit of analysis is the strategic business unit (see Figure
4). The hypotheses are tested with data obtained from 349 marketing and supply chain

executives representing the SBUs of 285 ﬁrms across all economic sectors.

17

FIGURE 4

An SBU Focus on Stakeholders

 

Industry

Strategic Group

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

ORGANIZATION OF THE DISSERTATION

This dissertation is organized as follows. First, Essay 1 investigates whether
developing ﬁrm strategies based on a stakeholder-focused approach versus a market-
driven approach to doing business inﬂuences performance. This is followed by Essay 2,
which examines how a ﬁrm’s stakeholder focus is shaped by that of entities in its supply
chain environment and marketplace, and at the same time, links a ﬁrm’s stakeholder
focus to customer satisfaction and performance. Then, Essay 3 investigates whether
stakeholder-focused organizational learning inﬂuences how an organization responds to
its stakeholders. The last section consists of the overall conclusions and the major

contributions of this dissertation.

18

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REFERENCES

Anderson, Eugene W. and Vikas Mittal (2000), “Strengthening the Satisfaction-Proﬁt
Chain,” Journal of Service Research, 3 (2), 107-120.

Anderson, Eugene W., Claes F omell, and Donald R. Lehmann (1994), “Customer
Satisfaction, Market Share, and Proﬁtability: Findings from Sweden,” Journal of
Marketing, 58(3), 53-66.

Banerjee, Subhabrata Bobby, Easwar S. lyer, and Rajiv K Kashyap (2003), “Corporate
Environmentalism: Antecedents and Inﬂuence of Industry Type,” Journal of Marketing,
67(2), 106-122.

Barone, Michael J ., Anthony D. Miyazaki and Kimberly A. Taylor (2000), “The
Inﬂuence of Cause-Related Marketing on Consumer Choice: Does One Good Turn
Deserve Another?” Journal of the Academy of Marketing Science, 28 (2), 248-262.

Berman, Shawn L., Andrew C. Wicks, Suresh Kotha, and Thomas M. Jones (1999),
“Does Stakeholder Orientation Matter? The Relationship between Stakeholder

Management Models and Firm Financial Performance,” Academy of Management
Journal, 42 (5), 488-506.

Bloch, Peter H. (1995), “Seeking the Ideal Form: Product Design and Consumer
Response,” Journal of Marketing, 59(3), 16-29.

Brown, Steven P. and Son K. Lam (2008), “A Meta-Analysis of Relationships Linking
Employee Satisfaction to Customer Responses,” Journal of Retailing, 84 (3), 243-255.

Buchanan, Lauranne (1992), “Vertical Trade Relationships: The Role of Dependence and
Symmetry in Attaining Organizational Goals,” Journal of Marketing Research, 29 (1),
65-75.

Cannon, Joseph P. and Christian Homburg (2001), “Buyers-Supplier Relationships and
Customer Firm Costs,” Journal of Marketing, 65 (1), 29-43.

Christen, Markus, Ganesh lyer, and David Soberman (2006), “Job Satisfaction, Job
Performance, and Effort: A Reexamination Using Agency Theory,” Journal of
Marketing, 70(1), 137-150.

Clarkson, Max (1995), “A Stakeholder Framework for Analyzing and Evaluating
Corporate Social Performance,” Academy of Management Review, 20 (1), 92-117.

Darke, Peter R., Laurence Ashworth, and Robin J .B. Ritchie (2008), “Damage from
Corrective Advertising: Causes and Cures,” Journal of Marketing, 72 (6), 81-97.

19

Day, George S. (1994), “The Capabilities of Market-Driven Organizations,” Journal of
Marketing, 58 (4), 37-52.

Day, George S. and Liam Fahey (1988), “Valuing Market Strategies,” Journal of
Marketing, 52 (3), 45-57.

Deshpande, Rohit, John U. Farley, and Frederick E. Webster, Jr. (1993), “Corporate
Culture, Customer Orientation, and Innovativeness in Japanese Firms: A Quadrad
Analysis,” Journal of Marketing, 57(1), 23-37.

Drumwright, Minette E. (1996), “Company Advertising With a Social Dimension: The
Role of Noneconomic Criteria,” Journal of Marketing, 60 (4), 71-87.

Fang, Eric, Robert W. Palmatier and Kenneth R. Evans (2008), “Inﬂuence of Customer
Participation on Creating and Sharing of New Product Value,” Journal of the Academy of
Marketing Science, 36(3), 322-336

Ferrell, O.C., Tracy L. Gonzalez-Padron, G. Tomas M. Hult, and Isabelle Maignan
(2010), “From Market Orientation to Stakeholder Orientation,” Journal of Public Policy
and Marketing, 29(1), 93-96.

Fomell, Claes (2007), The Satisﬁed Customer: Winners and Losers in the Battle for
Buyer Preference, New York, NY: Pal grave MacMillan.

Freeman, R. Edward (1984), Strategic Management: A Stakeholder Approach.
Marshﬁeld, MA: Pitrnan Publishing Inc.

Garrett, Dennis E. (1987), “The Effectiveness of Marketing Policy Boycotts:
Environmental Opposition to Marketing,” Journal of Marketing, 51 (2), 46-57.

Greenley, Gordon B. and Gordon R. Foxall (1998), “External Moderation of Associations
among Stakeholder Orientations and Company Performance,” International Journal of
Research in Marketing, 15 (l ), 51-69.

Handelrnan, Jay M. and Stephen J. Arnold (1999), “The Role of Marketing Actions with
a Social Dimension: Appeals to the Institutional Environment,” Journal of Marketing, 63
(3), 33—48.

Hartline, Michael D. and QC. Ferrell (1996), “The Management of Customer-Contact
Service Employees: An Empirical Investigation,” Journal of Marketing, 60(4), 52-70.

Homburg, Christian, Jan Wieseke, and Torsten Bomemann (2009), “Implementing the

Marketing Concept at the Employee-Customer Interface: The Role of Customer Need
Knowledge,” Journal of Marketing, 73 (4), 64-81 .

20

Homburg, Christian, Jan Wieseke, and Wayne D. Hoyer (2009), “Social Identity and the
Service-Proﬁt Chain,” Journal of Marketing, 73 (2), 38-54.

Hurley, Robert F. and G. Tomas M. Hult (1998), “Innovation, Market Orientation, and
Organizational Learning: An Integration and Empirical Examination,” Journal of
Marketing, 62 (3), 42-54.

Hutt, Michael D., Michael P. Mokwa, and Stanley J. Shapiro (1986), “The Politics of
Marketing: Analyzing the Parallel Political Marketplace,” Journal of Marketing, 50(1),
40-51.

Jap, Sandy D. (1999), “Pie-Expansion Efforts: Collaboration Processes In Buyer-Supplier
Relationships,” Journal of Marketing Research, 36 (4), 461-475.

Jaworski, Bernard J. and Ajay K. Kohli (1991), “Supervisory Feedback: Alternative
Types and Their Impact on Salespeople’s Performance and Satisfaction,” Journal of
Marketing Research, 28 (2), 190-201.

Jaworski, Bernard J. and Ajay K. Kohli (1993), “Market Orientation: Antecedents and
Consequences,” Journal of Marketing, 57(3), 53-70.

Jones, Thomas M. (1995), “Instrumental Stakeholder Theory: A Synthesis of Ethics and
Economics,” Academy of Management Review, 20 (2), 404-43 7.

Jones, Tim, Shirley F. Taylor and Harvir S. Bansal (2008), “Commitment to a Friend, a
Service Provider, or a Service Company — Are they Distinctions worth Making?” Journal
of the Academy of Marketing Science, 36 (4), 473 -487

Joshi, Ashwin W. (2009), “Continuous Supplier Performance Improvement: Effects of
Collaborative Communication and Control,” Journal of Marketing, 73 (1), 133—150

Kerin, Roger A. and Raj Sethurarnan (1998), “Exploring the Brand Value-Shareholder
Value Nexus for Consumer Goods Companies,” Journal of the Academy of Marketing
Science, 26(4), 260-273.

Kogut, Bruce and Udo Zander (1993), “Knowledge of the Firm and the Evolutionary
Theory of the Multinational Corporation,” Journal of International Business Studies, 24
(4), 625-645.

Kohli, Ajay K. (1985), “Some Unexplored Supervisory Behaviors and Their Inﬂuence on
Salespeople’s Role Clarity, Speciﬁc Self-Esteem, Job Satisfaction, and Motivation,”
Journal of Marketing Research, 22 (4), 424-433

Kohli, Ajay K. and Bernard J. Jaworski (1990), “Market Orientation: The Construct,
Research Propositions, and Managerial Implications,” Journal of Marketing, 54 (2), 1-18.

21

Kumar, V., Rajkumar Venkatesan, and Werner Reinartz (2008), “Performance
Implications of Adopting a Customer-Focused Sales Campaign,” Journal of Marketing,
72(5), 50-68.

Lieberman, Marvin B. and Shigeru Asaba (2006), “Why Do Firms Irnitate Each Other?”
Academy of Management Review, 31 (2), 366-3 85.

Lockheed Martin (2009), “”Customers,” (accessed September 1, 2009), available at
http://www.lockheedmartin.com/customers.

Luo, Xueming and C. B. Bhattacharya (2006), “Corporate Social Responsibility,
Customer Satisfaction, and Market Value,” Journal of Marketing, 70 (4), 1-18.

Luo, Xueming and Naveen Donthu (2006), “Marketing's Credibility: A Longitudinal
Investigation of Marketing Communication Productivity and Shareholder Value,”
Journal of Marketing, 70(4), 70-91.

Madden, Thomas J ., Frank Fehle, Susan Fournier (2006), “Brands Matter: An Empirical
Demonstration of the Creation of Shareholder Value Through Branding,” Journal of the
Academy of Marketing Science, 34 (2), 224-235.

Maignan, Isabelle and O. C. Ferrell (2004), “Corporate Social Responsibility and
Marketing: An Integrative Framework,” Journal of the Academy of Marketing Science,
32 (1), 3-19.

Maxham, James G. 111, Richard G. Netemeyer, Donald R. Lichtenstein (2008), “The
Retail Value Chain: Linking Employee Perceptions to Employee Performance, Customer
Evaluations, and Store Performance,” Marketing Science, 27 (2), 147-167.

Menon, Ajay and Anil Menon (1997), “Enviropreneurial Marketing Strategy: The
Emergence of Corporate Environmentalism as Market Strategy,” Journal of Marketing,
61 (I), 51-67.

Mithas, Sunil, M.S. Krishnan, and Claes Fomell (2005), “Why Do Customer
Relationship Management Applications Affect Customer Satisfaction?” Journal of
Marketing, 69 (4), 201-209.

Narver, John C. and Stanley F. Slater (1990), “The Effect of a Market Orientation on
Business Proﬁtability,” Journal of Marketing, 54 (4), 20-35.

Ordanini, Andrea, Gaia Rubera, and Robert DeFillippi (2008), “The Many Moods of

Inter-organizational Imitation: A Critical Review,” International Journal of Management
Reviews, 10(4), 375-398.

22

Qu, Riliang and Christine T. Ennew (2005), “Developing a Market Orientation in a
Transitional Economy: The Role of Government Regulation and Ownership Structure,”
Journal of Public Policy & Marketing, 24 (1), 82-89.

Ramaswami, Sridhar N. and J agdip Singh (2003), “Antecedents and Consequences of
Merit Pay Fairness for Industrial Salespeople,” Journal of Marketing, 67 (4), 46-66.

Rao, Raghunath Singh, Rajesh K. Chandy, and Jaideep C. Prabhu (2008), “The Fruits of
Legitimacy: Why Some New Ventures Gain More ﬁom Innovation Than Others,”
Journal of Marketing, 72(4), 58—75.

Roy, Subroto, K. Sivakurnar, and Ian F. Wilkinson (2004), “Innovation Generation in
Supply Chain Relationships: A Conceptual Model and Research Propositions,” Journal
of the Academy of Marketing Science, 32 (1 ), 61-79.

Ruekert, Robert W. (1992), “Developing a Market Orientation: An Organizational
Strategy Perspective,” International Journal of Research in Marketing, 9(3), 225-245.

Sawhney, Mohanbir and Jeff Zabin (2002), “Managing and Measuring Relational Equity
in the Network Economy,” Journal of the Academy of Marketing Science, 30 (4), 313-
332.

Selnes, Fred and James Sallis (2003), “Promoting Relationship Learning,” Journal of
Marketing, 67(3), 80-95.

Sen, Sankar, C.B. Bhattacharya, and Daniel Korschun (2006), “The Role of Corporate
Social Responsibility in Strengthening Multiple Stakeholder Relationships: A Field
Experiment,” Journal of the Academy of Marketing Science, 34 (2), 158-166.

Sinkula, James M. (1994), “Market Information Processing and Organizational
Learning,” Journal of Marketing, 58 (1), 35-45.

Sinkula, James M., William E. Baker, and Thomas Noordewier (1997), “A Framework
for Market-Based Organizational Learning: Linking Values, Knowledge, and Behavior,”
Journal of the Academy of Marketing Science, 25 (4), 305-318.

Skinner, Steven J ., Jule B. Gassenheirner, and Scott W. Kelley (1992), “Cooperation in
Supplier-Dealer Relations,” Journal of Retailing, 68 (2), 174-193.

Slater, Stanley F. and John C. Narver (1995), “Market Orientation and the Learning
Organization,” Journal of Marketing, 59(3), 63-74.

Sorescu, Alina, Venkatesh Shankar, and Tarun Kushwaha (2007), “New Product

Preannouncements and Shareholder Value: Don’t Make Promises You Can’t Keep,”
Journal of Marketing Research, 44 (3), 468-489.

23

Srinivasan, Shuba, Koen Pauwels, Jorge Silva-Risso, and Dominique M. Hanssens
(2009), “Product Innovations, Advertising, and Stock Returns,” Journal of Marketing, 73
(1), 24—43.

Srivastava, Rajendra K., Tasadduq A. Shervani, and Liam Fahey (1998), “Market-Based
Assets and Shareholder Value: A Framework for Analysis,” Journal of Marketing, 62 (1),
2-18.

Varadarajan, P. Rajan and Anil Menon (1988), “Cause-Related Marketing: A
Coalignrnent of Marketing Strategy and Corporate Philanthropy,” Journal of Marketing,
52(3), 58-74.

Walker, Steven F. and Jeffrey W. Marr (2001 ), Stakeholder Power: A Winning Plan for
Building Stakeholder Commitment and Driving Corporate Growth. Cambridge, MA:
Perseus Publishing.

Webster, Frederick E., Jr. (1992), “The Changing Role of Marketing in the Corporation,”
Journal of Marketing, 56(4), 1-17.

Wieseke, Jan, Michael Ahearne, Son K. Lam, and Rolf van Dick (2009), “The Role of
Leaders in Internal Marketing,” Journal of Marketing, 73 (2), 123-145.

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Essay 1
MARKETING STRATEGY AND PERFORMANCE:
COMPARING MARKET AND STAKEHOLDER APPROACHES

Strategic marketing researchers have long been interested in uncovering why
some organizations outperform others. Research emerging from the classical view of
marketing strategy - based primarily on the market orientation literature — has
consistently found that those ﬁrms that acquire information about the needs and wants of
their customers and that respond to this information by developing and implementing
strategies that target those needs and wants outperform ﬁrms that do otherwise (e.g.,
Jawoski and Kohli 1993; Kirca, Jayachandran, and Bearden 2005; Ruekert 1992).

Changes in the business environment have caused numerous ﬁrms to shift from
the more traditional market-driven approach (e. g., Day 1994) to embrace a newer
approach to marketing strategy development — the stakeholder-focused approach.
Drawing on stakeholder theory, this approach consists of developing mutually trusting
and cooperative relationships with multiple stakeholder groups (Jones 1995). This
requires ﬁrms to be cognizant of the interests of the different stakeholders and to develop
strategies that are focused on addressing those interests. A stakeholder refers to “any
group or individual who can affect or is affected by the achievement of the organization’s
objectives” (Freeman 1984, p. 46), and includes customers, employees, suppliers,
shareholders, communities, and regulators (Clarkson 1995). According to advocates of
the stakeholder-focused approach, ﬁrms that develop and implement strategies that are
centered on meeting a broad set of stakeholder demands reduce contracting costs, achieve

a good reputation, obtain a competitive advantage (e. g., Jones 1995), and are rewarded

25

with superior ﬁnancial performance (Berman et al. 1999) and improved shareholder value
(Hillman and Keirn 2001).

A company that has adopted a stakeholder approach is Cisco Systems. By
gathering information about its stakeholders’ demands, by monitoring how well the
company’s actions meet these demands, and by seeking to improve stakeholder
satisfaction, Cisco is highly committed to its stakeholder relationships (Cisco 2009). For
example, the company delivers high-quality products that satisfy its customers; helps
employees achieve work-life balance by offering ﬂexible work schedules; develops long-
terrn relationships with its suppliers by meeting regularly with them, discussing
environmental threats, and proposing potential solutions; establishes trust among
shareholders through its commitment to transparency in ﬁnancial reporting; and is
actively involved in local communities through volunteering efforts and charitable
contributions (Cisco 2008). This has helped the company become the “world’s most
valuable enterprise” providing computer networking solutions (Walker and Marr 2001).

Interestingly, even though these different approaches to developing and
implementing marketing strategies have been examined independently (e.g., Greenley
and Foxall 1998; Jaworski and Kohli 1993), there are no studies in the marketing
literature that assess and compare the relative effects of these approaches on ﬁrm
performance. This is unfortunate given the increasing interest by marketing executives in
gaining a better understanding of the development of effective marketing strategies, an
area which represents a top research priority for the Marketing Science Institute
(Marketing Science Institute 2008). Unless the market-driven approach and the

stakeholder-focused approach are considered simultaneously, researchers and executives

26

alike are left unsure about their relative merit in explaining performance. Against this
backdrop, the objective of this essay is to assess which marketing strategy development
approach is relatively more important to achieve superior market performance, while
accounting for ﬁrm and industry effects.

To accomplish this objective, this paper draws on level-speciﬁc theories and
literature bases (e. g., resource-based view, industrial organization economics, the
strategic group literature) to develop a multilevel model that examines the relative
inﬂuences of the ﬁrm, strategic group, and industry effects on ﬁrm performance. The
multilevel modeling approach serves to tease out the ﬁrm- and industry-level effects, and
concentrate on the strategic-group level, which captures the two different marketing
strategy development approaches (i.e., stakeholder-focused approach and market-driven
approach).

The unique contribution of this paper is a focus on two sets of strategic groups
derived from long-covered topics in marketing strategy, such as market orientation and
marketing capabilities (market-driven group), along with more recently tackled marketing
strategy areas such as marketing exchanges with multiple stakeholders (stakeholder-
focused group). Speciﬁcally, this paper develops hypotheses related to the performance
implications of developing a ﬁrm’s strategy based on a stakeholder-focused and market-
driven approach to doing business. It uses systems theory — which stresses the
interdependence of the ﬁrm and its environment (e.g., Scott 1981) -— to theoretically
integrate the three hierarchical levels of analysis. Then, it tests the hypotheses using a
sample of ﬁrms from manufacturing and services industries (n=1,716) with data from a

four-year period (2004-2007) taken prior to the worldwide economic downturn in 2008.

27

As such, this paper simultaneously considers the three levels of analysis in an open
interconnected system to better understand the value of the two different forms of
marketing strategy development.
SYSTEMS THEORY AND MULTILEVEL INFLUENCES

Systems theory holds that every system, regardless of its nature (e. g., mechanical,
biological, social) is composed of multiple elements that are interconnected (Kast and
Rosenzweig 1972). Although some systems are closed (i.e., self-contained), ﬁrms are
most appropriately viewed as operating within an open system. The open systems
perspective stresses the interdependence of the ﬁrm and its environment (Scott 1981). In
particular, it emphasizes the reciprocal ties that bind the ﬁrm with those elements and
ﬂows that surround it (Scott and Davis 2007). Given that the environment is an important
source of information, energy, and materials, interaction with the environment is essential
for the survival of the ﬁrm (e.g., Kast and Rosenzweig 1972; Scott and Davis 2007).

Another key feature of open systems is the hierarchical relationships between
subsystems (Simon 1962). Speciﬁcally, “all systems are made up of subsystems and are
themselves subsumed in larger systems -— an arrangement that creates linkages across
systems and confounds the attempt to erect clear boundaries around them” (Scott and
Davis 2007, p. 96). Hence, open systems theory provides a useful foundation to integrate
the three hierarchical levels of analysis in this study — the ﬁrm, the strategic group, and
the industry — to examine the relative inﬂuence of each level on ﬁrm performance. As
depicted in Figure 5, these three levels are integrated with one another such that both the
strategic group and the industry to which a ﬁrm is associated shape the ﬁrm, which in

turn, has an effect on the ﬁrm’s performance (e.g., Short et al. 2007). In the following

28

sections, a series of level-speciﬁc theories and literature bases build on complex open
systems theory to delineate why ﬁrms, strategic groups, and industries are important
sources of ﬁrm performance.

FIGURE 5

Hierarchical Linear Model of the Effects of the Firm, Strategic Group,
and Industry Levels on Market Performance (Tobin’s Q)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Level 3:
Industry H4
Product-based
' versus Service-
based Firms
Level 2:
Strategic Group
Market
H2 Performance
Market-Driven Groups H3 (Tobin’s Q)
Stakeholder Groups
Low/Stable
versus High
Technology
Firms
Level 1:
Firm H1
Time Period: 2004-2005 Time Period: 2006-2007

 

Firm-Level Effects on Market Performance
A number of theoretical perspectives focus on ﬁrm-level effects to explain
variation in performance outcomes across ﬁrms. The resource-based view of the ﬁrm

(RBV) identiﬁes the ﬁrm’s idiosyncratic resources as the primary determinant of
29

competitive advantage and ﬁrm performance (Barney 1991). The RBV portrays resources
as those tangible and intangible assets and capabilities possessed by a ﬁrm that enable the
ﬁrm to implement valuable strategies that improve its efﬁciency and effectiveness
(Barney 1991; Wemerfelt 1984). Firm resources include brand names, patents, corporate
culture, trade contacts, knowledge, management skills, and efficient procedures (Barney
1986; Barney 1991; Grant 1991; Wemerfelt 1984). In order for resources to be a source
of sustained competitive advantage, they must be valuable, rare, imperfectly irnitable, and
non-substitutable (Barney 1991).

An important contribution of the RBV is that it explains long-lived variation in
the proﬁtability of ﬁrms within the same industry (Peteraf 1993). Due to the
accumulation of unique resources, organizations evolve differently, thus exhibiting
distinct organizational structures and implementing different marketing strategies
(Hawawini, Subramanian, and Verdin 2003). By having unique strategic marketing
resources, ﬁrms erect barriers to imitation with the objective of generating above-normal
returns and protecting their competitive advantage in the marketplace (W ernerfelt 2005).
Rumelt (1984, 1987) argues that through isolating mechanisms — such as response lags,
producer learning, and reputation — ﬁrms prevent imitative competition.

The ﬁrm’s portfolio of unique strategic resources also inﬂuences the strategies
that the ﬁrm adopts (Peteraf 1993; Wemerfelt 2005). According to the RBV, these
resources enable the ﬁrm to either achieve superior performance through differentiation
of its products and services or to produce more efﬁciently to achieve a low-cost position
(Conner 1991; Hunt and Morgan 1995; Porter 1980). At a given point in time, customers

have divergent preferences, but due to constraints in inputs, the ﬁrm is unable to satisfy

30

all of the customers’ wants and needs (cf. Peteraf 1993). Instead, the ﬁrm will seek a
balance between the external market demands and the internal resources and capabilities
to satisfy a segment of the market (Grant 1991). Different ﬁrms possess different
resources and hence will target different segments, giving rise to intra-industry
heterogeneity and proﬁt level variation (Hunt and Morgan 1995). As such, idiosyncratic
ﬁrm characteristics (e. g., marketing resources and capabilities) explain different
performance outcomes across ﬁrms within an industry (Barney 1991).

From an open systems theory view, the ﬁrm level is an essential component of a
larger system that inﬂuences the performance of ﬁrms (Ashmos and Huber 1987). While
the RBV holds that the ﬁrm’s portfolio of resources shapes its performance (Barney
1991), external factors “may change the signiﬁcance of resources to the ﬁrm” (Penrose
1959, p. 79). Speciﬁcally, the value of marketing and other resources depends on the
context in which the ﬁrm operates (Priem and Butler 2001 ). As such, it is critical to
evaluate the inﬂuence of the ﬁrm’s internal environment while simultaneously
accounting for the strategic group and industry effects (Rouse and Daellenbach 1999). An
important tenet of open systems theory is that the interdependencies within a subsystem
tend to be more signiﬁcant than those between subsystems (Scott and Davis 2007). As a
result, the ﬁrm level has substantial explanatory power in terms of performance (cf.
Rumelt 1991). After isolating the effects of the strategic group- and industry-levels, the
ﬁrm effects will be an important source of performance. Therefore:

H1: Firms’ market performance within strategic groups and industries varies
based on differences in ﬁrm-level characteristics.

31

Strategic Group-Level Effects on Market Performance

The notion that a ﬁrm’s performance is inﬂuenced by the strategic group in which
it resides has long been tackled in the strategic marketing and management literatures
(e.g., Barney and Hoskisson 1990; Caves and Porter 1977; Fiegenbaum and Thomas
1990; Lewis and Thomas 1990; Nair and Kotha 2001; Porter 1979; Vorhies and Morgan
2003). Supporters contend that strategic group membership along with its related
collective behavior is a main driver of durable performance differences among ﬁrms
within an industry (e.g., Mehra 1996). A strategic group is deﬁned as a cluster of ﬁrms
competing in the same industry that implement similar strategies (Porter 1980). Firms
within a strategic group closely resemble each other, but they differ ﬁ'om ﬁrms outside
the group on key strategic dimensions, such as marketing approaches, innovation, and
scales of activity (Porter 1979). Due to their similarity in structure, group members are
likely to recognize their mutual dependence, respond in the same manner to competitive
changes, and accurately anticipate each other’s reactions (Caves and Porter 1977). This
behavioral congruence suggests that group members act as a reference group
(F iegenbaum and Thomas 1995). Firms benchmark group members and adjust their
marketing strategies toward the group target.

The extant literature on strategic groups has identiﬁed two main reasons that
explain variation in performance outcomes across groups within an industry: the presence
of mobility barriers (Caves and Porter 1977) and the existing conditions of rivalry (Cool

and Dierickx 1993). Mobility barriers are factors that impede ﬁrms from moving from
one strategic position to another (Porter 1980). These mobility barriers represent an

in vestment in a collective capital asset whose beneﬁts are enjoyed among the ﬁrms

32

within the group (McGee and Thomas 1986). This translates into a relative cost
advantage for such ﬁrms over competitors in other groups which would have to engage in
costly investments to overcome the barriers and enter the group. Given that these
investments are generally risky and the beneﬁts gained from them may not compensate
for the costs incurred, they are expected to deter a ﬁrm’s efforts to change groups (Porter
1979). As a result, ﬁrms within a group have low costs associated with replicating the
actions of their group members while ﬁrms outside the group may have a substantial cost
disadvantage, which impedes marketing imitation (Hatten and Hatten 1987). In short,
mobility barriers prevent ﬁrms in low-perforrning groups from shifting into high-
performing groups, thereby explaining sustainable differences in performance (Nair and
Kotha 2001; Porter 1980).

Intergroup rivalry in an industry is a frmction of market interdependence among
groups (i.e., the degree to which different groups are targeting the same customers), the
strategic distance between groups (i.e., the degree to which strategies differ), and the
number and size of the groups (Porter 1979). According to Porter (1980), a particular
group will be most exposed to intergroup rivalry when it faces a larger number of groups
that are relatively equal in size, targets the same market segments, and implements
different strategies. Excessive intergroup rivalry can reduce a ﬁrm’s proﬁt (Nair and
Kotha 2001). Intragroup rivalry can also have important implications for the performance
of the ﬁrms (cf. Cool and Dierickx 1993). Speciﬁcally, ﬁrms within a strategic group
may generate above-normal returns to the extent that the group structure hinders the

emergence of perfect competition within it (Nair and Kotha 2001).

33

From the perspective of open systems theory, the strategic group level represents
a system that is composed of multiple subsystems of a lower order (i.e., ﬁrms) and is in
turn contained within a suprasystem (i.e., industry). As such, strategic groups represent a
useful intermediate level of analysis that serves to explain variation in performance
within an industry (Thomas and Venkatraman 1988). The importance of the strategic
group level has been demonstrated in several studies (e. g., Leask and Parker 2007; Nair
and Kotha 2001). However, since the ﬁrm, strategic group, and industry effects on
performance are intertwined, it is important to examine the direct effects of group
membership while isolating the effects of the ﬁrm and industry levels (Nair and Kotha
2001). As an initial generic hypothesis addressing the overall inﬂuence of strategic
groups, the following hypothesis predicts that after isolating the ﬁrm and industry effects,
strategic groups inﬂuence ﬁrm performance (Short et a1. 2007). Stated formally:

H2: Firms’ market performance within industries varies across strategic
groups.

However, the inﬂuence of group membership on ﬁrm performance is a function of
the strategically relevant characteristics that deﬁne group membership, such as
simultaneously focusing on multiple stakeholders’ interests (stakeholder-focused
strategic group) or targeting the marketplace via market orientation efforts (market-driven
strategic group). Each group’s focus resonates among ﬁrms in today’s marketplace and
offers unique marketing strategy-making and implementation guidelines. Speciﬁcally,
each group requires a different set of market strategies, and so, any variation in
performance can be explained by the effectiveness of the strategic marketing action

signiﬁed by each approach (Hatten and Hatten 1987).

34

Market-driven strategic groups. Rooted in marketing strategy and based mainly
on the market orientation literature (e.g., Day 1994; Kohli and J aworski 1990; Matsuno
and Mentzer 2000; Narver and Slater 1990), the market-driven strategic group is
composed of ﬁrms that emphasize customers, employees, and supplier relationships when
developing and implementing strategies and place relatively less emphasis on other
stakeholders (e.g., shareholders, regulators, and communities). Firms within the market-
driven strategic group possess superior outside-in capabilities, speciﬁcally market sensing
and customer linking capabilities, which allow them to anticipate and respond to
changing market conditions ahead of competitors (Day 1994). These capabilities function
as mobility barriers that insulate the ﬁrms from entry by members of other groups (Porter
1979, 1980).

As has been discussed extensively in the market orientation literature, customers
are central to market-driven ﬁrms. For instance, Narver and Slater (1990) identify
customer orientation as an essential behavioral component of a market orientation, while
Deshpande, Farley, and Webster (1993) argue that a customer orientation is synonymous
with a market orientation. Further, Webster (1992) views customer relationships as the
fnm’s key strategic resource. As such, ﬁrms that put their customers’ interests ﬁrst
achieve superior performance (Deshpande, Farley, and Webster 1993). This entails a
sufﬁcient understanding of the ﬁrm’s current and potential customers (N arver and Slater
1990), which can be obtained through the generation and dissemination of market
intelligence in the marketplace and across the different units within the ﬁrm (Kohli and

Jaworski 1990). Those ﬁrms that invest substantial resources to understanding their

35

customers and that coordinate the actions of all the functions of the ﬁrm attain a
competitive advantage (Slater and Narver 1994).

The second type of relationship that the market-driven strategic group focuses on
is that with employees. The importance of employee relationships is captured within the
interfunctional coordination component of a market orientation (N arver and Slater 1990).
Speciﬁcally, ﬁrms must draw upon and effectively integrate their human resources to be
able to create superior value for their customers. This requires the marketing function to
be sensitive and responsive to the needs of the other units and ﬁmctions within the ﬁrm.
Another reason ﬁrms nurture employee relationships is because of the direct impact
satisﬁed employees can have on performance outcomes, such as on the level of customer
satisfaction (Homburg and Stock 2004).

Market-driven strategic groups also concentrate on supplier relationships. The
inclusion of a supplier focus into the market-driven strategic group is justiﬁed by Day
(1994), who argues that market-driven ﬁrms seek “closer, more collaborative
relationships with suppliers based on a high level of coordination, participation in joint
programs, and close communication links” (Day 1994, p. 44-45). Matsuno and Mentzer
(2000) and Matsuno, Mentzer, and Rentz (2000) provide further rationale that the market-
driven strategic group consists of ﬁrms that are cognizant of their suppliers’ needs.
Speciﬁcally, Matsuno, Mentzer, and Rentz (2000) expand Kohli, Jaworski, and Kumar’s
(1993) market orientation scale (MARKOR) to incorporate suppliers into assessments of
intelligence generation (e.g., whether the ﬁrm spends time with its suppliers to learn
about their business), intelligence dissemination (e.g., whether the ﬁrm has cross-

functional meetings regularly to discuss market developments regarding suppliers), and

36

responsiveness (e.g., whether the ﬁrm is slow to start new business with new suppliers).
Firms within this group recognize that supplier relationships can be instrumental to the
fum’s ability to achieve its objectives (Buchanan 1992). For instance, suppliers can help
drive down a ﬁrm’s cost structure (Cannon and Homburg 2001) and interactions between
the ﬁrm and its suppliers can lead to both incremental and radical innovations (Roy,
Sivakurnar, and Wilkinson 2004).

Stakeholder-focused strategic groups consist of ﬁrms that, from a strategy
development standpoint, are highly focused on addressing the interests of multiple
stakeholders beyond those addressed by market-driven strategic groups. A stakeholder
refers to “any group or individual who can affect or is affected by the achievement of the
organization’s objectives” (Freeman 1984, p. 46). Stakeholder theory holds that a
particular group can be identiﬁed as a stakeholder that merits managerial attention if it
possesses at least one of the following attributes: power, legitimacy, and urgency
(Mitchell, Agle, and Wood 1997). This study concentrated the strategic group’s focus on
primary stakeholders, who are those that are crucial for the ﬁrm’s survival and continued
market success (Clarkson 1995). Primary stakeholders center on six segments in the
marketplace and include customers, employees, suppliers, shareholders, regulators, and
communities. Drawing on resource dependence theory, the importance of the six
stakeholders lies in their capacity to furnish resources that are critical to the ﬁrm’s
ongoing operations (Pfeffer and Salancik 1978). It is the dependence of the ﬁrm on such
actors for resources that provides them with power over the ﬁrm (F rooman 1999). In turn,
the possession of power classiﬁes these actors as stakeholders worthy of managerial

attention (Mitchell, Agle, and Wood 1997).

37

Firms in the stakeholder-focused strategic group adopt a broader perspective than
those within the market-driven group. Similar to ﬁrms in the market-driven strategic
group, they attend to the interests of customers, employees, and suppliers, which can
lower their cost structure, create superior value for their customers, and offer innovative
products. However, by also focusing on shareholders, regulators, and communities, ﬁrms
in the stakeholder-focused strategic group stand to gain additional beneﬁts. For example,
ﬁrms that pay attention to the enhancement of shareholder value implement strategies
that bring ﬁnancial beneﬁts that exceed the costs incurred (e.g., Day and F ahey 1988;
Srivastava, Shervani, and Fahey 1998). As a result, by meeting shareholder demands,
ﬁnancial performance is maximized.

Firms within the stakeholder-focused strategic group also attend to regulators.
Regulators are “important stakeholders that exert external political and economic forces
on the ﬁrm” (Banerjee, Iyer, and Kashyap 2003, p. 109). There is empirical evidence that
regulatory forces have an impact on the ﬁrm’s environmental corporate strategy
(Banerjee, lyer, and Kashyap 2003). Firms within the stakeholder-focused group not only
respond to these forces by complying with regulators’ demands but are also active
participants in the regulatory process by exerting some inﬂuence over legislation (e. g.,
Henriques and Sadorsky 1999). Previous research has concluded that focus on regulators
and regulations can help ﬁrms introduce better practices and enhances the
competitiveness of ﬁrms (e. g., Buysse and Verbeke 2003; Porter and van der Linde
1995). For example, environmental regulations foster greater innovation, product costs

reduction, and continuous improvement (e.g., Porter and van der Linde 1995).

38

In addition to paying attention to customers, employees, suppliers, shareholders,
and regulators, the stakeholder-focused strategic group also equally attends to the
interests of the community stakeholders. Community stakeholders include
nongovernmental organizations, geographic communities, and special interest groups
organized around a political or social cause (e.g., Kassinis and Vafeas 2006). These
groups are particularly important given that they can inﬂuence public opinion in favor of
or against a ﬁrm (e.g., Henriques and Sadorsky 1999). In this context, improved social
performance results from allocating company resources into social arenas, such as
support to local schools and housing initiatives for the disadvantaged (e. g., Waddock and
Graves 1997). Social performance, in turn, may lead to a competitive advantage and
superior performance (e.g., Brarnmer and Millington 2008; Waddock and Graves 1997).

According to the instrumental approach to stakeholder theory, ﬁrms that
simultaneously attend to the needs of these six stakeholders achieve superior market
performance (Donaldson and Preston 1995). Speciﬁcally, relational interactions with the
primary stakeholders can develop intangible, socially complex resources, such as
reputation and brand equity, which can in turn create value (Hilhnan and Keim 2001).
Because of the tacit nature of these strategic resources as well as the long-term
investment they require, they are difficult to replicate. In the context of strategic groups,
these investments represent mobility barriers that impede ﬁrms outside the stakeholder-
focused strategic group from imitating strategic decisions without considerable costs and
signiﬁcant elapsed time (McGee and Thomas 1986). Further, these barriers enable the
ﬁrms within the group to sustain their advantages over those in other groups (Porter

1979)

39

Drawing on stakeholder theory and the literature on market orientation, it is
predicted that the stakeholder-focused strategic group is relatively more important for
ﬁrm performance than the market-driven strategic group. The stakeholder-focused
strategic group seeks to satisfy the demands of all the primary stakeholders, often without
favoring one stakeholder at the expense of others (Clarkson 1995). Hence, this view of
strategic groups is more comprehensive in nature than the market-driven group. On the
other hand, the market-driven group emphasizes some stakeholders, while downplaying
others. Paying simultaneous attention to the legitimate interests of all the primary
stakeholders has important implications for ﬁrm performance (Donaldson and Preston
1995). In particular, ﬁrms that develop mutually trusting, cooperative relationships with
all stakeholders attain a competitive advantage over ﬁrms that do not act as
comprehensively in attending to the needs of all primary stakeholders (Jones 1995). In
addition, the intangible, socially complex resources that these relationships can create
(Hillman and Keim 2001) constitute mobility barriers that are difﬁcult to overcome
(McGee and Thomas 1986). In turn, these barriers can serve to explain why ﬁrms within
the stakeholder-focused strategic group are persistently closer aligned with superior
market performance than are ﬁrms deﬁned by the market-driven group (Porter 1980). As
such, deﬁning strategic groups based on stakeholders should result in a greater effect on
performance than groups deﬁned solely by market-driven. Thus:

H3: Firms’ market performance within an industry varies across types of

strategic groups, with the stakeholder-focused strategic group effect being
greater than the market-driven group effect.

40

Industry-Level Effects on Market Performance

Researchers in varied ﬁelds — such as marketing (Banerjee, Iyer, and Kashyap
2003), economics (Schmalensee 1985), organizational ecology (Harman and Freeman
1977), and strategic management (Rumelt 1991) — have shown that the industry in which
a ﬁrm operates shapes the performance of the ﬁrm. Industrial organization economics
(10) is perhaps the most dominant view used to explain this stream of research (Bain
1956; Mason 193 9). The structure-conduct-performance (SCP) paradigm within IO
proposes that the structural elements of an industry inﬂuence the strategies (conduct)
ﬁrms can pursue, which in turn determine their performance (Roquebert, Phillips, and
Westfall 1996). As such, the industry is the main unit of analysis, and the industry
structure in which the ﬁrm operates is seen as the primary determinant of ﬁrm
performance (Hawawini, Subramanian, and Verdin 2003).

Research adopting the 10 perspective has made two basic assumptions. First,
ﬁrms within an industry are identical regarding the resources they control. Second,
should resource heterogeneity develop, it will be temporary given that resources are
highly mobile. As such, homogeneity of strategies among ﬁrms competing in the same
industry exists since, for example, marketing actions taken by a ﬁrm are easily
observable and duplicated by other ﬁrms. For instance, Mauri and Michaels (1998) found
that ﬁrms within an industry are likely to develop uniform strategies for investing in
technology and advertising. Consequently, common industry characteristics explain the
similarity in strategies and performance among intra-industry ﬁrms.

Drawing on the hierarchical nature of complex open systems, the industry level

contains both the strategic group- and the ﬁrm-levels (cf. Kast and Rosenzweig 1972).

41

According to this perspective, the industry in which the ﬁrm operates shapes the ﬁrm
(Scott and Davis 2007), which, in turn, has an impact on its market performance. Support
for this notion can be found in a number of studies. Speciﬁcally, previous research has
examined the relative inﬂuence of ﬁrm and industry characteristics on ﬁrm performance
by using various variance components models (e.g., McGahan and Porter 1997;
Roquebert, Phillips, Westfall 1996; Rumelt 1991; Schmalensee 1985). Although there is
some discrepancy in the results with regards to the magnitude of the effects, these studies
provide evidence that industry conditions inﬂuence ﬁrm proﬁtability. Therefore, to more
holistically understand the effects of strategic groups and ﬁrms, the following hypothesis
about industries is set forth:

H4: Firms’ market performance varies based on industry differences.

METHOD

Data Collection

The sample was developed by combining data ﬁom the Kinder Lydenburg
Domini Statistical Tool for Analyzing Trends in Social and Environmental Performance
(i.e., KLD STATS) and Standard & Poor’s Compustat database for a span of four years
(2004 to 2007). Firms were studied using data taken prior to the most recent drastic
worldwide economic downturn, which is commonly viewed to have started in the spring
of 2008. Following Jap (1999), a one-year time lag was used in the analysis to be able to
make causal inferences. Strategic group traits used data from 2004 to 2005 while market
performance was based on data ﬁom 2006 to 2007. The ﬁrm and industry levels were
included as dummy coded variables (Short et al. 2007). Data from each two-year period

were averaged to provide more stable measures than single year data (cf. Bahadir,

42

Bharadwaj, and Srivastava 2008). All ﬁrms with data ﬁom 2004 to 2007 across KLD and
Compustat were included. Complete data from KLD and Compustat for all measures and
years were obtained for n=1,7l6 ﬁrms. Table 2 reports the sample sizes for the various
segments and different levels (ﬁrm, strategic group, and industry levels).

TABLE 2

Sample Size: of Firms, Strategic Groups, and Industries

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Focus of the Strategic Group
Level of Stakeholder- .
Sample Analysis Focused Market-Dnven
A roach Approach
All Industry 9 9
Firms Strategic Group 17 23
Firm 1716 1716
Industry 4 4
”33:: Strategic Group 6 10
Firm 825 825
. Industry 5 5
33:}: Strategic Group 11 13
Firm 891 891
. Industry 3 3
mghFEﬁfls‘ Strategic Group 7 9
Firm 3 17 3 17
Industry 9 9
$213211: Strategic Group 17 23
Firm 1399 1399

 

 

KLD is a dataset of ﬁrms rated by KLD Research and Analytics, Inc., a social
investment ﬁrm, since 1991. It started in 1991 with 650 ﬁrms covered in the S&P 500
Index and the Domini 400 Social Index and has expanded to approximately 3,100 ﬁrms
included in the Russell 1000 Index, the Large Cap Social Index, the Russell 2000 Index,

and the Broad Market Social Index. Through their commercial database of corporate

43

ratings, SOCRATES, KLD Research Analytics, Inc. provides ratings on over 90
indicators in seven major areas including community, corporate governance, diversity,
employee relations, environment, human rights, and products. The indicators include both
positive and negative ratings (strengths and concerns). The ratings are based on an
integration of ﬁve sources: (1) direct communication with ﬁrms, (2) global research
ﬁrms, (3) media, (4) public documents, and (5) government and NGO information. KLD
has been used in a variety of studies on stakeholders in marketing (e.g., Sen and
Bhattacharya 2001) and management (e.g., Berman et al. 1999; Hillman and Keim 2001;
Waddock and Graves 1997).

Compustat is a widely used database that includes ﬁmdarnental ﬁnancial and
market information data on about 24,000 active and 10,000 inactive publicly held ﬁrms
in the US. and Canada. The database provides thousands of income statement, balance
sheet, statement of cash ﬂows, and supplemental data items. Compustat has been used in
numerous marketing studies to measure performance-related variables. For example, a
recent collection of marketing studies used Compustat variables in conjunction with
studying brands in mergers and acquisitions (Bahadir, Bharadwaj, and Srivastava 2008),
customer satisfaction (Luo and Homburg 2008), brand portfolio strategy (Morgan and
Rego 2009), and innovation related to consumer packaged goods (Sorescu and Spanjol
2008)

The demographics of the overall sample for 2007 are provided in Table 3. Table 4

reports how the ﬁrms were distributed among the different industries.

44

TABLE 3

Demographics of the Firms in the Overall Sample for 2007

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mean Std. Dev.

Total assets $14,705 million $92,988

Total liabilities $12,026 million $86,338

Net income $ 327 million $ 1,930

Revenue $ 5,179 million $18,255

TABLE 4
Sample Size by Industry
Industry NAICS Codes 11
Agriculture, forestry and
ﬁsheries 111-115 4
Minerals 21 1-213 77
Construction 233-235 28
Manufacturing 31 1-339; 51 1 834
Transportation,
communications and utilities 221; 481—493; 513 197
Wholesale trade 421-422 38
Retail trade 441-454 167
Finance, insurance and real
estate 521-533 490
Service industries 512; 514; 541-814 309
Public administration 921-928 0
Deﬁning Strategic Groups

As theoretically justiﬁed earlier in the paper, the formation of strategic groups
was based on two approaches: stakeholder-focused approach (and involved customers,
suppliers, employees, regulators, shareholders, and the community) and market-driven
approach (customers, suppliers, and employees). The measures to assess these
dimensions were obtained from KLD STATS. A battery of formative measures used in
several previous studies (e.g., Berman et al. 1999; Sen and Bhattacharya 2001; Waddock

and Graves 1997) were included: 6 items for customers, 20 items for employees, 3 items

45

for suppliers, 9 items for shareholders, 7 items for regulators, and 20 items for
community based on theoretically deﬁned properties (see Appendix A). These items
centered on issues such as “the company's products have notable social beneﬁts that are
highly unusual or unique for its industry” (customers); “the company has outstanding
employee beneﬁts or other programs addressing work/life concerns, e. g., childcare, elder
care, or ﬂextime” (employees); “the company does at least 5% of its subcontracting, or
otherwise has a demonstrably strong record on purchasing or contracting, with women-
and/or minority-owned businesses” (suppliers); “the company owns between 20% and
50% of another company KLD has cited as having an area of social strength, or is more
than 20% owned by a ﬁrm that KLD has rated as having social strengths” (shareholders);
“the company has shown markedly responsible leadership on public policy issues and/or
has an exceptional record of transparency and accountability concerning its political
involvement in state or federal-level US. politics, or in non-US. politics” (regulators);
and “the company has consistently given over 1.5% of trailing three-year net earnings
before taxes to charity, or has otherwise been notably generous in its giving”
(community).

The scores for each dimension were adjusted based on the number of items to
standardize the effects (cf. Graves and Waddock 1994). For each dimension, a total score
was calculated by adding KLD items that were labeled as strengths and subtracting those
labeled concerns. The average scores ranged from -.86 to .92 for community (if = -.00), -
2.00 to 1.00 for suppliers (1? = -.07), -1.00 to 1.00 for employees (f = -.10), -1.60 to .00
for regulators (f = -.08), -1.33 to .50 for shareholders (f = -.13), and -2.00 to 1.00 for

customers ( f = -.12).

46

A two-stage clustering procedure was used to group ﬁrms into strategic groups
within each industry. A two-stage procedure is valuable because it increases the validity
of the ﬁnal cluster solutions obtained for each of the approaches, i.e., stakeholder-focused
and market-driven approaches (e. g., Ketchen and Shook 1996). Speciﬁcally, hierarchical
clustering (i.e., Ward’s method) was used to determine the number of appropriate groups
in each broad industry category as well as their cluster centroids. Following standard
practice, the largest percentage change in the agglomeration coefﬁcient was used to
suggest the optimal number of strategic groups in each broad industry. The cluster
centroids were then used as the starting point for a non-hierarchical clustering procedure
(i.e., K-means). Criterion validity was assessed through MAN OVA signiﬁcance tests
following the procedures recommended by Ketchen and Shook (1996). As expected, the
F -tests ﬁom Wilks’s lambda, provided by the MANOVA, indicated signiﬁcant
differences in market performance based on strategic group membership for all industries
in the overall sample (p<.01).

Inclusion of Segmentation Variables

Figure 5 portrays two segmentation (moderator) variables in the multilevel
framework. These include product-focused vs. service-focused ﬁrms and low/ stable vs.
high technology ﬁrms. Theory does not allow to robustly specify predictions as to the
nature of the potential variations in the multilevel effects across sample segments.
However, previous research has shown that product and service ﬁrms have uniquely
different characteristics that affect strategy (Berry 1999), as do low/stable and high tech
ﬁrms (Slater, Hult, and Olson 2007). As such, segmentation analysis is incorporated for

product ﬁrms, service ﬁrms, low/stable tech ﬁrms, and high tech ﬁrms into the analyses.

47

Table 5 reports how the sample was distributed among product and service ﬁrms. The
product-focused ﬁrms (n=825) are those that are mainly focused on delivering products
based on their industry classiﬁcation, while the service-focused ﬁrms are mainly focused
on delivering services (n=891).

TABLE 5

Sample Size by Product vs. Service Firm Classiﬁcation

 

 

 

 

Product-Focused Firms Service-Focused Firms
Industry NAICS Codes n Industry NAICS Codes n
Agriculture, Transportation, . .

forestry and 1 1 1-1 15 4 communications 221’ 481.493’ 197
. . . . 513
ﬁsheries and utrlrtres
Minerals 211-213 77 Wholesale trade 421-422 38
Construction 233-235 28 Retail trade 441-454 167
Manufacturrng 31 1-339; 51 l 834 Finance, Insurance 521-533 490
and real estate
Service industries 33’ 514’ 54] - 309
Total 825 891

 

 

The identiﬁcation of the low/stable vs. high technology ﬁrms was based on the
American Electronics Association’s (2003) classiﬁcation of high tech industries. Founded
in 1943, AeA is the largest high-tech trade association in the US. Forty-nine industries at
the six digit level of NAICS adhere to AeA’s core deﬁnition of what constitutes a high
tech industry: “an industry had to be a maker/creator of technology, whether it be in the
form of products, communications, or services” (AeA 2003, p. 4). These 49 industries fall
into 16 industry categories: computer and peripheral equipment, communications
equipment, consumer electronics, electronic components, semiconductors, defense
electronics, measuring and control instruments, electromedical equipment, photonics,
communications services, soﬂware publishers, computer systems design and related

services, internet services, engineering services, R&D testing labs, and computer training.

48

A total of n=317 ﬁrms in the overall sample of n=1,716 ﬁrms were classiﬁed as high
technology ﬁrms. The ﬁrms in the remaining NAICS classiﬁcations are considered either
low or stable technology ﬁrms (n=1,399).

Market Performance

This study focuses on Tobin’s Q as the market performance measure. Of the
myriad performance measures used previously in multilevel studies, Tobin’s Q has been
the most common in a variety of marketing studies (e.g., Anderson, F ornell, and
Mazvancheryl 2004; Lee and Grewal 2004; Luo and Bhattacharya 2006; Luo and Donthu
2006; Rao, Agarwal, and Dahlhoff 2004; Sorescu and Spanj012008). More speciﬁcally,
Tobin’s Q centers on “market performance” and, as such, aligns more closely with
marketing strategy making and marketing implementation than return measures which are
often used in multilevel studies (e.g., ROA). Tobin’s Q was developed by James Tobin
(1978), a Nobel laureate in economics, based on the concept that the collective market
value of all ﬁrms on the stock market should be equal to their replacement costs.

The Q ratio allows marketing strategists to predict ﬁrms’ behavioral actions. As
such, Tobin’s Q is a long-term, future-focused ﬁnancial metric that is based on the
market’s assessment of the ﬁrm’s present and future cash ﬂows (Luo and Donthu 2006;
Rao, Agarwal, and Dahlhoff 2004; Sorescu and Spanjol 2008). The formula for Tobin’s
Q includes “the sum of the market value of equity, the book value of debt, and deferred
taxes divided by the book value of total assets minus intangible assets” (Thomas and
Waring 1999, p. 739). A low Q value (between zero and one) means that the replacement
costs of the ﬁrm’s assets is greater than the stock value (i.e., the stock is undervalued in

the market). A high Q value (greater than one), on the other hand, indicates that a ﬁrm’s

49

stock is more expensive than the cost of replacing its assets (i.e., the stock is overvalued).
The average Tobin’s Q for 2006 and 2007 was 1.40 (with a range of .60 to 12.78 and std
dev = .77).

Hierarchical Linear Modeling

Testing of the multilevel effects on Tobin’s Q in the overall and segmented
samples (i.e., product vs. service ﬁrms; low/stable vs. high tech ﬁrms) was done via
hierarchical linear modeling (Raudenbush, Bryk, Cheong, and Congdon 2004). HLM is
particularly appropriate for this study because of the hierarchical nature of the data (i.e.,
ﬁrms are nested in strategic groups which are nested in industries), and because it
provides for simultaneous partitioning of the variance-covariance components (Bryk and
Raudenbush 1992).

A three-level model was used to test the effects of ﬁrms (level-1) nested within
effects of strategic groups (level-2) nested within the effect of industries (level-3). The
level-1 model corresponds to the Tobin’s Q performance of each ﬁrm as a ﬁrnction of a
strategic group mean and random error. Thus, performanceijk = nojk + eijk, where
performanceijk is the average performance for Tobin’s Q of ﬁrm i in strategic group j and
industry k. nojk is the mean performance of strategic group j in industry k. eiJ-k is a random
ﬁrm effect (the deviation of ﬁrm ijk’s score from the strategic group mean). The effects
are assumed to be normally distributed with a zero mean and variance oz. The subscripts
i, j, and k designate ﬁrms, strategic groups, and industries with i = 1,2,.. ., ”jk ﬁrms within
strategic group j in industry k; j = 1,2,. . ., Jk strategic groups within industry k, and k =

1,2,..., K industries.

50

The level-2 (strategic group) and level-3 (industry) models follow a similar format
to that of the level-l model. As such, the level-2 model examines each the strategic group
mean, nojk, as an outcome that varies randomly around the industry mean: nojk = 13001: + rOjk,
where 13001: is the strategic group Tobin’s Q mean in industry k. ﬁn} is a random strategic
group effect (the deviation of strategic group jk’s mean from the industry mean). Similar
to the level-1 assumptions, these effects are assumed to be normally distributed with a
zero mean and variance In. The level-3 model corresponds to the variability among
industries, with the industry mean (13001;) varying randomly around a grand mean: [300k =
7000 + umk, where 7000 is the grand mean. uook is the random industry effect (the deviation
of industry k’s mean ﬁom the grand mean). These effects are also assumed to be
normally distributed and have a zero mean and variance 15.

As in many HLM studies, the objective of this study in analyzing the three-level
model is to partition the total variance in performanceijk (i.e., Tobin’s Q) into its
components for the overall sample and the four segmented samples: among ﬁrms within
strategic groups (0'2), among strategic groups within industries (In), and among industries
(13). This variance component partitioning allows for the estimation of the variance that
can be attributed to ﬁrms within strategic groups [62/ (02 + t, + 13)], to strategic groups
within industries [1,; / (0'2 + In + T19], and among industries [1:3 / (02 + 1:, + rB)]. However,
the main focus is placed on the strategic group level given the focus of the study, the
corporate ﬁrm effects modeled, and the coarse-grained industry categorization employed.
The hierarchical linear and nonlinear modeling soﬂware HLM 6.07 was used to conduct

the analyses (Raudenbush, Bryk, Cheong, and Congdon 2004).

51

RESULTS

The clustering procedures resulted in an average of 1.89 groups per industry using
the stakeholder-focused approach (range: 1-3 groups) and 2.56 groups using the market-
driven approach (range: 1-5 groups). As indicated earlier, Table 2 reports on the number
of ﬁrms, strategic groups, and industries covered in each analysis. Table 6 summarizes
variances components and percent of total variance that is explained by the ﬁrm, strategic
group, and industry levels in the ten HLM models for the overall, segmented samples
(product vs. service ﬁrms and low/stable tech vs. high tech ﬁrms), and type of strategic

group used (stakeholder-focused or market-driven group).

52

TABLE 6

The Effects of the Firm, Strategic Group, and Industry Levels
on Market Performance (Tobin’s Q)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Focus of the Strategic Group
Stakeholder- Market-Driven
Focused A‘ proach Approach
Sample Level of Variance Percent Variance Percent
Analysis Component of Total Component of Total
Industry 0.0185 0.0308 0.03 77 0.0626
All Strategic Group 0.0335 0.0558 0.0106 0.0177
Firms Firm 0.5487 0.9134 0.5528 0.9197
Total 0.6008 1 .0000 0.601 1 1.0000
Indus 0.0241 0.0942 0.0202 0.0800
Product Strategic Group 0.0020 0.0078 0.0050 0.0197
Firms Firm 0.2295 0.8980 0.2275 0.9003
T Total 0.2555 1.0000 .2 0.2527 1.0000
Industry 0.0158 0.0174 0.043 1 0.0472
Service Strategic Group 0.0447 0.0494 0.0154 0.0169
Firms Firm 0.8456 0.9332 0.8546 0.9360
W Total 0.9061 m 1.0000 0.9130 1.0000
Industry 0.0028 0.0072 0.0166 0.0427
High Tech Strategic Group 0.0119 0.0307 0.0000 0.0001
Firms Firm 0.3710 0.9621 0.3719 0.9572
Total 0.3856 1.0000 0.3886 1.0000
Industry 0.0167 0.0260 0.0367 0.0571
Low/Stable Strategic Group 0.0401 0.0622 0.0132 0.0206
Tech Firms Firm 0.5871 0.9118 0.5927 0.9223
Total 0.6439 1 .0000 0.6426 1 .0000

 

 

 

 

 

The examination of Hypothesis 1 revealed that the variance accounted for by the

Firm Effects on Market Performance

53

ﬁrm level on the ﬁrm’s market performance ranged from 89.80 to 96.21 percent across
sample types (p<.05). The variances accounted for in the overall analyses were all at the
low end of the 90 percent range: 91.34 percent for stakeholder-focused and 91.97 percent
for market-driven. Regarding the product vs. service segments, the ﬁrm level explained a

greater amount of the variance in Tobin’s Q in the two models for the service ﬁrms

 

(ranging ﬁ'om 93.32 to 93.60 percent) than in the product ﬁrms (ranging ﬁom 89.80 to
90.03 percent). Similarly, in the low/stable tech vs. high tech analyses, it was found that
the ﬁrm level explained a greater amount of the variance in each of the high tech models
(ranging ﬁom 95.72 to 96.21 percent) compared with the low/stable tech ﬁrms (ranging
from 91.18 to 92.23 percent). The variances that are attributed to the ﬁrm level compare
to other multilevel studies involving the ﬁrm level (e. g., McGahan and Porter 1997;
Rumelt 1991), and are consistent with the high ﬁrm-level variances using Tobin’s Q as
the performance variable (Short et al. 2007). Overall, Hypothesis 1 is supported.
Strategic Group Effects on Market Performance

A number of interesting results were identiﬁed for the strategic group level — i.e.,
the tests of Hypotheses 2 and 3. The variances accounted for by the strategic group level
ranged from .01 to 6.22 percent across all segments. The group level was signiﬁcant for
both the stakeholder-focused and market-driven models in the overall analyses. The
stakeholder-focused approach to strategic groups achieved a variance of 5.58 percent
compared with 1.77 percent for market-driven groups (p<.05). Thus, both Hypotheses 2
and 3 are supported in the analyses.

Delving more deeply into the complexities of the analyses and results, it was
found that the results and signiﬁcance levels varied in the segmented samples. For the
models involving the stakeholder-focused strategic groups, the group level was
signiﬁcant at the p<.05 level in three of the models, at the p<.10 in one model (product
ﬁrms), and insigniﬁcant among high tech ﬁrms. For the market-driven strategic group
analyses, the group level was signiﬁcant at the p<.05 level in three of the models, at the

p<.10 level in one model (service ﬁrms), and insigniﬁcant among high tech ﬁrms.

54

The variance accounted for by the strategic group level, which was deﬁned based
on a stakeholder-focused approach, ranged ﬁom .78 percent among product ﬁrms to 6.22
percent for low/stable tech ﬁrms. In the case of market-driven strategic groups, the range
started at .01 percent in the high tech sample and peaked at 2.06 percent in the low/stable
tech sample. An interesting ﬁnding was that the strategic group level explained less
variance than the ﬁrm and industry levels in six of the ten models. However, the four
models where the group level outperformed the industry level occurred for the
stakeholder-focused strategic groups. In addition, the stakeholder strategic groups
outperformed groups deﬁned by the market-driven approach in four out of ﬁve cases (the
exception was among product ﬁrms), lending additional credence to ﬁrms developing
their strategy and actions based on a stakeholder-focused approach.

In analyzing the segmented samples, it was found that the stakeholder-focused
strategic group has a larger effect among service ﬁrms (4.94 percent) than it does among
product ﬁrms (.78 percent). In addition, the stakeholder group effect is larger among
stable/low tech ﬁrms (6.22 percent) than it is for high tech ﬁrms (3.07 percent). These
effects in the service and low/stable tech samples also drive the results in the overall
sample. The stakeholder-focused strategic group effect in the overall sample (5.58
percent) outperformed the industry-level effect (3.08 percent).

Industry Effects on Market Performance

In examining Hypothesis 4, it was found that the variance accounted for by the
industry level of analysis varied from .72 to 9.42 percent across all sample types. Seven
of the ten samples had the industry level signiﬁcant at p<.05, one was signiﬁcant at p<.10

(low/stable tech ﬁrms using stakeholder-focused strategic groups), and two samples had

55

non-signiﬁcant industry levels (service ﬁrms using stakeholder-focused groups and high
tech ﬁrms using stakeholder-focused groups). Regarding the product vs. service
segments, the industry level explained a greater amount of the variance in market
performance in the two models for the product ﬁrms (ranging from 8.00 to 9.42 percent)
compared with the service ﬁrms (ranging from 1.74 to 4.72 percent). Similarly, in the
low/stable tech vs. high tech analyses, it was found that the industry level explained a
greater amount of the variance in each of the low/stable tech models (ranging from 2.60
to 5.71 percent) compared with the high tech ﬁrms (ranging ﬁ'om .72 to 4.27 percent). In
sum, Hypothesis 4 was supported in the overall analysis and among all product ﬁrm
analyses and among all low/stable tech ﬁrm analyses. Partial support was found among
service ﬁrms and high tech ﬁrms.
DISCUSSION

A key aspect of this study was to examine the value of marketing-strategy-driven
strategic groups and their relative effects on ﬁrms’ market performance. The classical
view of marketing strategy, especially as rooted within a market orientation view of the
ﬁrm, was captured within the strategic group labeled “market-driven” approach. The
broader and more recent marketing strategy foundation stemming ﬁ'om a stakeholder-
focused approach was also included. In this context, the market performance implications
of these two types of strategic groups (stakeholder-focused and market-driven) were
studied alongside the inﬂuences of ﬁrms and industry characteristics.

Overall, the results reveal that placing more emphasis on a broad set of
stakeholders (i.e., customers, employees, suppliers, shareholders, communities, and

regulators) by devoting attention and resources to addressing their simultaneous interests

56

when developing marketing strategy is relatively more important for market performance
than engaging in more limited market-driven efforts — which focus on customers,
employees, and suppliers, while place relatively less emphasis on shareholders,
communities, and regulators. This ﬁnding holds across service and low/stable technology
ﬁrms. In addition, the results indicate that ﬁrm-level characteristics account for the
majority of the variance associated with performance, regardless of the sample type. The
industry structure also shapes performance, especially in product and low/ stable
technology ﬁrms. This section discusses the implications of the study for research and
managerial practice.
Research Implications

In a general sense, this study answers calls for research that incorporates
marketing strategy insights from an expanded view of marketing beyond its traditionally
heavy emphasis on customers (Maignan and Ferrell 2004). Firms today develop and
implement marketing strategies in accordance with multiple stakeholders (cf. Handehnan
and Arnold 1999). By delineating two types of strategic groups, this study tested the
effectiveness of adopting a stakeholder-focused and a market-driven approach, accounted
for ﬁrm and industry effects, and addressed the relative market performance implications
of each. As such, an important contribution of this study is that it is the ﬁrst to
demonstrate that, in general, developing marketing strategy based on a stakeholder-
focused approach (i.e., taking into consideration the needs and interests of the six primary
stakeholders) is more effective than strategy based on a market-driven approach (i.e.,
where emphasis is placed on customers, employees, and suppliers, and relatively less

attention is paid to shareholders, communities, and regulators).

57

For example, in the overall results, the stakeholder-focused approach to deriving
strategic groups accounted for approximately 6 percent of the variance compared with
about 2 percent for the market-driven approach. Brush and Bromiley (1997) pointed out
that when interpreting variance components, their relative importance can be examined
via the square root of the variance at each level of analysis. In terms of market
performance (Tobin’s Q), the relative importance of the market-driven approach (9.90
percent) to deriving strategic groups is roughly half as important as the industry (18.65
percent for the industry in the market-driven sample) in which the ﬁrm operates in the
sample of all ﬁrms (see Table 7). However, using the stakeholder-focused approach to
obtain strategic groups, the relative importance of the group level is about 1.5 times that
of the industry (17.28 percent for the group level vs. 12.83 percent for the industry level).
This relative importance holds consistent when analyzing product ﬁrms, service ﬁrms,

low/stable tech ﬁrms, and high tech ﬁrms.

58

TABLE 7

The Relative Impgrtance of Firm, Strategic Group, and Industry Levels

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Focus of the Strategic Group
Stakeholder- Market-Driven
Focused Approach Approach
Sample Level of V3366 Relative ngigce Relative
AnalySlS Explained Importance Explained Importance
Industry 0.0308 0.1283 0.0626 0. 1865
All Strategic Group 0.0558 0.1728 0.0177 0.0990
F inns Firm 0.9134 0.6989 0.9197 0.7145
. Total 1.0000 1 .0000 1.0000 1 .0000
I Industry 0.0942 0.2286 0.0800 0.2062
Product Strategic Group 0.007 8 0.0657 0.0197 0.1022
Firms Firm 0.8980 0.7057 0.9003 0.6916
, , Total 1.0000 1.0000 1.0000 1.0000
Industry 0.0174 0.1000 0.0472 0.1652
Service Strategic Group 0.0494 0.1683 0.0169 0.0988
Firms Firm 0.9332 0.7317 0.93 60 0.7360
f Total 1.0000 1.0000 1.0000 1.0000
Industry 0.0072 0.0682 0.0427 0.1731
High Tech Strategic Group 0.0307 0.1413 0.0001 0.0074
Firms Firm 0.9621 0.7905 0.95 72 0.8196
._. _ ,.... W Total 1.0000 14.0000 . . 1.0000 _ _1 .0000‘
Industry 070260 0.1181 “ 0.0571‘ 0T1 779
Low/Stable Stratggic Grog) 0.0622 0.1827 0.0206 0.1069
Tech Firms Firm 0.9118 0.6993 0.9223 0.7152
Total 1 .0000 1.0000 1 .0000 1.0000

 

 

These ﬁndings are consistent with stakeholder theory, which predicts that those
ﬁrms that develop stakeholder relationships on the basis of mutual trust and cooperation
obtain a competitive advantage over ﬁrms that do otherwise (Jones 1995). Hence, given
that additional stakeholders beyond those emphasized by the classical marketing strategy
literature (e.g., customers) can cooperate, threaten, or otherwise affect the ﬁrm and its
marketing activities (Polonsky, Schuppisser, and Beldona 2002), ﬁrrther research is

needed to investigate how a ﬁrm’s relationships with its primary stakeholders shape

59

intermediate marketing outcomes such as customer satisfaction and brand equity.
Theoretically, these primary stakeholders include customers, employees, suppliers,
shareholders, communities, and regulators. Another important avenue for future research
centers on the inﬂuence of secondary stakeholders, such as the media, that are not vital
for the ﬁrm’s survival but can still mobilize public opinion in favor of or against a ﬁrm
(Clarkson 1995). By engaging in actions such as civil suits and protests (Eesley and
Lenox 2006), these stakeholders can affect the customers’ perceptions of the ﬁrm.

In addition, this study contributes to the long-standing debate surrounding the
determinants of ﬁrm performance by examining the relative importance of three levels of
analysis across different samples (product versus service ﬁrms; low/stable technology
versus high technology ﬁrms). Consistent with Short et al. (2007), the results provide
strong support for the notion that the ﬁrm’s unique characteristics are the driving forces
behind its performance, while the industry structure matters considerably less. However,
the analyses of the segmented samples offer additional insights. While the ﬁrm effects are
stronger in service ﬁrms relative to product ﬁrms, the industry effects are greater in
product ﬁrms relative to service ﬁrms. One possible explanation for this difference is that
by taking actions to learn about competitors’ products —— such as engaging in reverse
engineering initiatives — product ﬁrms may be able to imitate their rivals’ offerings more
easily than can service ﬁrms, which have to deal with the intangibility and variability that
characterize services (Zeitharnl, Parasurarnan, and Berry 1985). The difﬁculty service
ﬁrms face in copying their rivals (i.e., irrimitability) gives successful service ﬁrms a

greater source of sustained competitive advantage than product ﬁrms can achieve.

60

In a similar vein, the ﬁrm level explains a greater amount of variance in the
market performance of high tech ﬁrms compared with low/stable tech ﬁrms, while the
industry level has a larger effect on low/stable tech ﬁrms in relation to high tech ﬁrms.
This may be because ﬁrms competing in low/stable tech industries do not experience as
much change in their business environment as those ﬁrms operating in high tech
industries. Hence, it is possible for low/stable tech ﬁrms to have time to learn about the
actions of their rivals and imitate them more easily than high tech ﬁrms - given that high
tech ﬁrms operate in a rapidly changing environment (Teece, Pisano, and Shuen 1997).
This greater susceptibility to imitation of low/stable tech ﬁrms may be one reason the
industry plays a more signiﬁcant role than it does in high tech ﬁrms. While possible
explanations for the differences between the samples have been advanced, research
exploring the causes of such differences is needed. Future research could draw on
institutional theory (e.g., DiMaggio and Powell 1983) to examine whether imitation (i.e.,
isomorphism) is more prevalent in product and Iow/ stable tech ﬁrms, where the industry
is a more signiﬁcant driver of performance than in service and high tech ﬁrms.
Managerial Implications

The results of the study offer a number of important implications for managerial
practice. First, the results show that, in general, attending to the demands of a broad set of
stakeholders beyond those of the customers, employees, and suppliers when developing
and implementing marketing strategies pays off. This basic ﬁnding suggests that
managers should invest resources to gather information about the interests and
expectations of all primary stakeholders; to regularly monitor how the ﬁrm is meeting

these expectations; and to modify the ﬁrm’s practices to better satisfy the relatively more

61

important stakeholders. A critical implication is that ﬁrms should establish, based on their
contingencies, which set of primary stakeholders is critical to ﬁrm success in their
industry. The broad implication is that all six primary stakeholders (i.e., customers,
employees, suppliers, shareholders, communities, and regulators) should be attended to in
developing and implementing marketing strategy. However, attending to each primary
stakeholder at an equal level — as theory suggests — is likely to be a lost opportunity cost.
Instead, emphasizing the needs and expectations of a broad set of stakeholders but not
necessarily at the same relative level can be managerially ﬁ'uitful and signiﬁcant.

At the broad level, attending to all primary stakeholders appears to be particularly
important for ﬁrms that compete in the services and low/stable technology industries, as
the results indicate that adopting a stakeholder approach to marketing strategy
development in these sectors is relatively more important for market performance than
adopting a more limited market-driven approach, which places comparatively less
emphasis on shareholders, regulators, and communities. This result partially explains
why ﬁrms such as United Parcel Service, Inc. and Gap, Inc. —- which are consistently
ranked in Business Ethics’ “100 Best Corporate Citizens,” Forbes’ “America’s Most
Reputable Companies,” and Fortune magazine’s “America’s Most Admired Companies”
for their effective interaction with different stakeholder groups — perform better than
other ﬁrms in their industry that are not as comprehensive in meeting stakeholders’
demands. Therefore, managers should not only value their customer, employee, and
supplier relationships as has been the traditional marketing strategy focus, but they
should also consider the needs and interests of other primary stakeholders, such as

shareholders, regulators, and communities.

62

Moreover, the study provides further evidence that the ﬁrm’s idiosyncratic
characteristics represent the greatest source of market performance. This suggests that
ﬁrms should make it a priority to cultivate strategic marketing resources, such as brand
names, managerial expertise, and technological knowledge, to maximize market
performance. This is especially important for high technology ﬁrms (e.g., consumer
electronics manufacturers and communication services providers), given that for these
ﬁrms, market performance is mainly determined by internal organizational factors, while
the industry inﬂuence is negligible. Hence, managers operating in high technology ﬁrms
should also invest in dynamic capabilities, such as product development routines, which
enable them to continually develop innovative products and services, while promptly
responding to the rapidly changing market environment (Teece, Pisano, and Shuen 1997).

This, in turn, helps ﬁrms outperform their rivals (e.g., Eisenhardt and Martin 2000).

63

Label in
the KLD
Database

PRO-str-A

PRO-str-B

PRO-str-C

PRO-str-X

PRO-con-A

PRO-con-D

DIV-str-A

DIV-str-B

DIV-str-C

DIV-str-D

APPENDIX A

Formative Measures of the Stakeholder Dimensions

(from the KLD STATS Ratings)

Stakeholder Dimension
and Item Description

Customers

Quality. The company has a long-term, well-developed, company-

wide quality program, or it has a quality program recognized as
exceptional in US. industry.

R&D/Innovation. The company is a leader in its industry for
research and development (R&D), particularly by bringing notably
innovative products to market.

Beneﬁts to Economically Disadvantaged. The company has as part
of its basic mission the provision of products or services for the
economically disadvantaged.

Other Product Strength. The company's products have notable
social beneﬁts that are highly unusual or unique for its industry.

Product Safety. The company has recently paid substantial ﬁnes or
civil penalties, or is involved in major recent controversies or
regulatory actions, relating to the safety of its products and services.

Marketing/Contracting Concern. The company has recently been
involved in major marketing or contracting controversies, or has
paid substantial ﬁnes or civil penalties relating to advertising
practices, consumer fraud, or government contracting.

Employees

CEO. The company's chief executive officer is a woman or a
member of a minority group.

Promotion. The company has made notable progress in the
promotion of women and minorities, particularly to line positions
with proﬁt-and-loss responsibilities in the corporation.

Board of Directors. Women, minorities, and/or the disabled hold
four seats or more (with no double counting) on the board of
directors, or one-third or more of the board seats if the board
numbers less than 12.

Work/Life Beneﬁts. The company has outstanding employee

beneﬁts or other programs addressing work/ life concerns, e.g.,
childcare, elder care, or ﬂextirne.

64

Identiﬁed as a
Strength /
Concern in the
KLD Database

Strength

Strength

Strength

Strength

Concern

Concern

Strength

Strength

Strength

Strength

Label in
the KLD
Database

DIV-str-F

DIV-str-G

DlV-str-X

DIV-con-B

DIV-con-X

EMP-str-A

EMP-str-C

EMP-str-D

EMP-str-F

EMP-str-G

EMF-str-X

Identiﬁed as a
Strength /
Concern in the
KLD Database

Item Description

Employees Continued

Employment of the Disabled. The company has implemented Strength
innovative hiring programs; other innovative human resource

programs for the disabled, or otherwise has a superior reputation as

an employer of the disabled.

Gay & Lesbian Policies. The company has implemented notably Strength
progressive policies toward its gay and lesbian employees. In

particular, it provides beneﬁts to the domestic partners of its

employees.

Other Diversity Strength. The company has made a notable Strength
commitment to diversity that is not covered by other KLD ratings.

Non-Representation. The company has no women on its board of Concern
directors or among its senior line managers.

Other Diversity Concern. The company is involved in diversity Concern
controversies not covered by other KLD ratings.

Union Relations. The company has taken exceptional steps to treat Strength
its unionized workforce fairly. KLD renamed this strength from
Strong Union Relations.

Cash Proﬁt Sharing. The company has a cash proﬁt-sharing Strength
program through which it has recently made distributions to a
majority of its workforce.

Employee Involvement. The company strongly encourages worker Strength
involvement and/or ownership through stock options available to a

majority of its employees; gain sharing, stock ownership, sharing of

ﬁnancial information, or participation in management decision-

making.

Retirement Beneﬁts Strength. The company has a notably strong Strength
retirement beneﬁts program. KLD renamed this strength from
Strong Retirement Beneﬁts.

Health and Safety Strength. The company has strong health and Strength
safety programs.

Other Employee Relations Strength. The company has strong Strength
employee relations initiatives not covered by other KLD ratings.

65

Label in
the KLD
Database

EMP-con-A

EMP—con-B

EMP—con-C

EMP-con-D

EMP-con-X

DlV-str-E

HU M-str-G

HUM-con-F

Identiﬁed as a
Strength /
Concern in the
KLD Database

Item Description

Employees Continued

Union Relations. The company has a history of notably poor union Concern
relations. KLD renamed this concern from Poor Union Relations.

Health and Safety Concern. The company recently has either paid Concern
substantial ﬁnes or civil penalties for willful violations of employee

health and safety standards, or has been otherwise involved in major

health and safety controversies.

Workforce Reductions. The company has made signiﬁcant Concern
reductions in its workforce in recent years.

Retirement Beneﬁts Concern. The company has either a Concern
substantially under-ﬁmded deﬁned beneﬁt pension plan, or an
inadequate retirement beneﬁts program.

Other Employee Relations Concern. The company is involved in Concern
an employee relations controversy that is not covered by other KLD
ratings.

am

Women & Minority Contracting. The company does at least 5% Strength
of its subcontracting, or otherwise has a demonstrably strong record

on purchasing or contracting, with women-and/or minority-owned

businesses.

Labor Rights Strength. The company has outstanding transparency Strength
on overseas sourcing disclosure and monitoring, or has particularly

good union relations outside the US, or has undertaken labor

rights-related initiatives that KLD considers outstanding or

innovative.

Labor Rights Concern. The company's operations have had major Concern

recent controversies primarily related to labor standards in its supply
chain.

66

Label in
the KLD
Database

CGOV-str-A

CGOV-str-C

CGOV-str-D

CGOV-str-X

CGOV-con-

CGOV-con-F

CGOV-con-
G

CGOV-con-
H

CGOV-con-

Item Description

Shareholders

Limited Compensation. The company has recently awarded
notably low levels of compensation to its top management or its
board members. The limit for a rating is total compensation of less
than $500,000 per year for a CEO or $30,000 per year for outside
directors.

Ownership Strength. The company owns between 20% and 50%
of another company KLD has cited as having an area of social
strength, or is more than 20% owned by a ﬁrm that KLD has rated
as having social strengths. When a company owns more than 50%
of another ﬁrm, it has a controlling interest, and KLD treats the
second ﬁrm as if it is a division of the ﬁrst.

Transparency Strength. The company is particularly effective in
reporting on a wide range of social and environmental performance
measures, or is exceptional in reporting on one particular measure.

Other Corporate Governance Strength. The company has a
unique and positive corporate culture, or has undertaken a
noteworthy initiative not covered by KLD’s other corporate
governance ratings.

High Compensation. The company has recently awarded notably
high levels of compensation to its top management or its board
members. The limit for a rating is total compensation of more than
$10 million per year for a CEO or $100,000 per year for outside
directors.

Ownership Concern. The company owns between 20% and 50% of

a company KLD has cited as having an area of social concern, or is
more than 20% owned by a ﬁrm KLD has rated as having areas of
concern. When a company owns more than 50% of another ﬁrm, it
has a controlling interest, and KLD treats the second ﬁrm as if it is a
division of the ﬁrst.

Accounting Concern. The company is involved in signiﬁcant
accounting-related controversies.

Transparency Concern. The company is distinctly weak in
reporting on a wide range of social and environmental performance
measures.

Other Corporate Governance Concern. The company is involved

with a controversy not covered by KLD’s other corporate
governance ratings.

67

Identiﬁed as a

Strength /

Concern in the
KLD Database

Strength

Strength

Strength

Strength

Concern

Concern

Concern

Concern

Concern

Label in
the KLD
Database

CGOV-str-E

COM-con-D

CGOV-con-I

DlV-con-A

ENV-con-A

ENV-con-B

PRO-con-E

Identiﬁed as a
Strength /
Concern in the
KLD Database

Item Description

Rggulators

Political Accountability Strength. The company has shown Strength
markedly responsible leadership on public policy issues and/or has

an exceptional record of transparency and accountability concerning

its political involvement in state or federal-level US. politics, or in

non-US. politics.

Tax Disputes. The company has recently been involved in major Concern
tax disputes involving Federal, state, local or non-US. government

authorities, or is involved in controversies over its tax obligations to

the community. .

Political Accountability. The company has been involved in Concern
noteworthy controversies on public policy issues and/or has a very

poor record of transparency and accountability concerning its

political involvement in state or federal-level US. politics, or in

non-U.S. politics.

Controversies. The company has either paid substantial ﬁnes or Concern
civil penalties as a result of afﬁrmative action controversies, or has

otherwise been involved in major controversies related to

affirmative action issues.

Hazardous Waste. The company's liabilities for hazardous waste Concern
sites exceed $50 million, or the company has recently paid
substantial ﬁnes or civil penalties for waste management violations.

Regulatory Problems. The company has recently paid substantial Concern
ﬁnes or civil penalties for violations of air, water, or other

environmental regulations, or it has a pattern of regulatory

controversies under the Clean Air Act, Clean Water Act or other

major environmental regulations.

Antitrust. The company has recently paid substantial ﬁnes or civil Concern
penalties for antitrust violations such as price ﬁxing, collusion, or

predatory pricing, or is involved in recent major controversies or

regulatory actions relating to antitrust allegations.

68

Identiﬁed as a
Strength /
Concern in the
KLD Database

Label in
the KLD Item Description
Database

Community

COM-str-A Charitable Giving. The company has consistently given over 1.5% Strength
of trailing three-year net earnings before taxes (NEBT’) to charity, or
has otherwise been notably generous in its giving.

COM-str-B Innovative Giving. The company has a notably innovative giving Strength
program that supports nonproﬁt organizations, particularly those
promoting self-sufﬁciency among the economically disadvantaged.
Companies that permit nontraditional federated charitable giving
drives in the workplace are often noted in this section as well.

COM-str-C Support for Housing. The company is a prominent participant in Strength
public/private partnerships that support housing initiatives for the
economically disadvantaged, e.g., the National Equity Fund or the
Enterprise Foundation.

COM-str-D Support for Education. The company has either been notably Strength
innovative in its support for primary or secondary school education,
particularly for those programs that beneﬁt the economically
disadvantaged, or the company has prominently supported job-
training programs for youth.

COM-str-F Non-US Charitable Giving. The company has made a substantial Strength
effort to make charitable contributions abroad, as well as in the US.
To qualify, a company must make at least 20% of its giving, or have
taken notably innovative initiatives in its giving program, outside
the US.

COM-str-G Volunteer Programs. The company has an exceptionally strong Strength
volunteer program.

COM-str-X Other Community Strength. The company either has an Strength
exceptionally strong in-kind giving program or engages in other
notably positive community activities.

COM-con-A Investment Controversies. The company is a ﬁnancial institution Concern
whose lending or investment practices have led to controversies,
particularly ones related to the Community Reinvestment Act.

COM-con-B Negative Economic Impact. The company’s actions have resulted Concern
in major controversies concerning its economic impact on the
community. These controversies can include issues related to
environmental contamination, water rights disputes, plant closings,
"put-or-pay" contracts with trash incinerators, or other company
actions that adversely affect the quality of life, tax base, or property
values in the community.

69

Identiﬁed as a
Strength /
Concern in the
KLD Database

Label in
the KLD Item Description
Database

Communigy Continued

COM-con-X Other Community Concern. The company is involved with a Concern
controversy that has mobilized community opposition, or is engaged
in other noteworthy community controversies.

ENV-str-A Beneﬁcial Products and Services. The company derives substantial Strength
revenues from innovative remediation products, environmental
services, or products that promote the efﬁcient use of energy, or it
has developed innovative products with environmental beneﬁts.
(The term “environmental service” does not include services with
questionable environmental effects, such as landﬁlls, incinerators,
waste-to-energy plants, and deep injection wells.)

ENV-str-B Pollution Prevention. The company has notably strong pollution Strength
prevention programs including both emissions reductions and toxic-
use reduction programs.

ENV-str-C Recycling. The company either is a substantial user of recycled Strength
materials as raw materials in its manufacturing processes, or a major
factor in the recycling industry.

ENV-str-D Clean Energy. The company has taken signiﬁcant measures to Strength
reduce its impact on climate change and air pollution through use of
renewable energy and clean fuels or through energy efﬁciency. The
company has demonstrated a commitment to promoting clirnate-
friendly policies and practices outside its own operations.

ENV-str-X Other Environment Strength. The company has demonstrated 3 Strength
superior commitment to management systems, voluntary programs,
or other environmentally proactive activities.

EN V-con-C Ozone Depleting Chemicals. The company is among the top Concern
manufactmers of ozone depleting chemicals such as HCF Cs, methyl
chloroform, methylene chloride, or bromines.

ENV-con-D Substantial Emissions. The company's legal emissions of toxic Concern
chemicals (as deﬁned by and reported to the EPA) from individual
plants into the air and water are among the highest of the companies
followed by KLD.

ENV-con-E Agricultural Chemicals. The company is a substantial producer of Concern
agricultrual chemicals, i.e., pesticides or chemical fertilizers.

70

Label in
the KLD
Database

HUM-str-D

HUM-con-G

Communigy Continued

Indigenous Peoples Relations Strength. The company has
established relations with indigenous peoples near its proposed or
current operations (either in or outside the US.) that respect the
sovereignty, land, culture, human rights, and intellectual property of

indigenous peoples.

Indigenous Peoples Relations Concern. The company has been
involved in serious controversies with indigenous peoples (either in
or outside the US.) that indicate the company has not respected the
sovereignty, land, culture, human rights, and intellectual property of

indigenous peoples.

Item Description

71

Identiﬁed as a
Strength /

Concern in the
KLD Database

Strength

Concern

REFERENCES

American Electronics Association (2003), Deﬁning the High- Tech Industry, Washington,
DC: American Electronics Association.

Anderson, Eugene W., Claes Fomell, and Sanal K. Mazvancheryl (2004), “Customer
Satisfaction and Shareholder Value,” Journal of Marketing, 68 (October), 172-185.

Ashmos, Donde P. and George P. Huber (1987), “The Systems Paradigm in Organization
Theory: Correcting the Record and Suggesting the Future,” Academy of Management
Review, 12 (4), 607-621.

Bahadir, S. Cem, Sundar G. Bharadwaj, and Rajendra K. Srivastava (2008), “Financial
Value of Brands in Mergers and Acquisitions: Is Value in the Eye of the Beholder?”
Journal of Marketing, 72 (November), 49-64.

Bain, J .S. (1956), Barriers to New Competition: Their Character and Consequences in
Manufacturing Industries. Cambridge, MA: Harvard University Press.

Banerjee, Subhabrata Bobby, Easwar S. lyer, and Rajiv K. Kashyap (2003), “Corporate
Environmentalism: Antecedents and Inﬂuence of Industry Type,” Journal of Marketing
67 (April), 106-122.

Barney, Jay B. (1986), “Strategic Factor Markets: Expectations, Luck, and Business
Strategy,” Management Science, 32 (10), 1231-1241.

Barney, Jay B. (1991), “Firm Resources and Sustained Competitive Advantage,” Journal
of Management, 17 (1), 99-120.

Barney, Jay B. and Robert E. Hoskisson (1990), “Strategic groups: Untested Assertions
and Research Proposals,” Managerial and Decision Economics, 11 (3), 187-198.

Berman, Shawn L., Andrew C. Wicks, Suresh Kotha, and Thomas M. Jones (1999),
“Does Stakeholder Orientation Matter? The Relationship between Stakeholder

Management Models and Firm Financial Performance,” Academy of Management
Journal, 42 (5), 488-506.

Berry, Leonard L. (1999), Discovering the Soul of Service: The Nine Drivers of
Sustainable Business Success, New York, NY: The Free Press.

Brarnmer, Stephen and Andrew Millington (2008), “Does it Pay to be Different? An
Analysis of the Relationship between Corporate Social and Financial Performance,”
Strategic Management Journal, 29 (12), 1325-1343.

72

Brush, Thomas H. and Philip Bromiley (1997), “What Does a Small Corporate Effect
Mean? A Variance Components Simulation of Corporate and Business Effects,” Strategic
Management Journal, 18 (10), 825-835.

Bryk, Anthony S. and Stephen W. Raudenbush (1992), Hierarchical Linear Models:
Applications and Data Analysis Methods. Newbury Park, CA: Sage Publications.

Buchanan, Lauranne (1992), “Vertical Trade Relationships: The Role of Dependence and
Symmetry in Attaining Organizational Goals,” Journal of Marketing Research, 29 (1),
65-75.

Buysse, Kristel and Alain Verbeke (2003), “Proactive Environmental Strategies: A
Stakeholder Management Perspective,” Strategic Management Journal, 24 (5), 453-470.

Cannon, Joseph P. and Christian Homburg (2001), “Buyers-Supplier Relationships and
Customer Firm Costs,” Journal of Marketing, 65 (1), 29-43.

Caves, RE. and ME. Porter (1977), “From Entry Barriers to Mobility Barriers:
Conjectural Decisions and Contrived Deterrence to New Competition,” Quarterly
Journal of Economics, 91 (2), 241-262.

Cisco (2008), “Corporate Citizenship and CSR Reports: CSR Report 2008,” (accessed
November 14, 2009), available at
http://www.cisco.com/web/about/ac227/csr2008/index.htrnl.

Cisco (2009), “Citizenship Governance: Listening to Stakeholders,” (accessed November
13, 2009), available at
http://www.cisco.com/web/about/ac227/ac222/citizenship/citizenship_
governance/listening_to_stakeholders.html.

Clarkson, Max (1995), “A Stakeholder Framework for Analyzing and Evaluating
Corporate Social Performance,” Academy of Management Review, 20 (1), 92-117.

Conner, Kathleen R. (1991), “A Historical Comparison of Resource-Based Theory and
Five Schools of Thought within Industrial Organization Economics: Do We Have a New
Theory of the Firm?” Journal of Management, 17 (l ), 121-154.

Cool, Karel and Ingemar Dierickx (1993), “Rivalry, Strategic Groups and Firm
Proﬁtability,” Strategic Management Journal, 14 (1), 47-59.

Day, George S. (1994), “The Capabilities of Market-Driven Organizations,” Journal of
Marketing, 58 (4), 37-52.

Day, George and Liam Fahey (1988), “Valuing Market Strategies,” Journal of
Marketing, 52 (3), 45-57.

73

Deshpande, Rohit, John U. F arley, and Frederick E. Webster, Jr. (1993), “Corporate
Culture, Customer Orientation, and Innovativeness in Japanese Firms: A Quadrad
Analysis,” Journal of Marketing, 57 (I), 23-37.

DiMaggio, Paul J. and Walter W. Powell (1983), “The Iron Cage Revisited: Institutional
Isomorphism and Collective Rationality in Organizational Fields,” American
Sociological Review, 48 (April), 147—160.

Donaldson, Thomas and Lee E. Preston (1995), “The Stakeholder Theory of the
Corporation: Concepts, Evidence, and Implications,” Academy of Management Review,
20(1), 65-91.

Eesley, Charles and Michael J. Lenox (2006), “Firm Responses to Secondary Stakeholder
Action,” Strategic Management Journal, 27 (8), 765-781.

Eisenhardt, Kathleen M. and Jeffrey A. Martin (2000), “Dynamic Capabilities: What are
They?” Strategic Management Journal, 21 (10/1 1), 1105-1 121.

Fiegenbaum, Avi and Howard Thomas (1990), “Strategic Groups and Performance: The
US. Insurance Industry, 1970-84,” Strategic Management Journal, 11 (3), 197-215.

Fiegenbaum, Avi and Howard Thomas (1995), “Strategic Groups as Reference Groups:
Theory, Modeling and Empirical Examination of Industry and Competitive Strategy,”
Strategic Management Journal, 16 (6), 461 -476.

Freeman, R. Edward (1984), Strategic Management: A Stakeholder Approach.
Marshﬁeld, MA: Pitrnan Publishing Inc.

Frooman, Jeff (1999), “Stakeholder Inﬂuence Strategies,” Academy of Management
Review, 24 (2), 191-205.

Grant, Robert M. (1991), “The Resource-Based Theory of Competitive Advantage:
Implications for Strategy Formulation,” California Management Review, 33 (3), 114-135.

Graves, Samuel B. and Sandra A. Waddock (1994), "Institutional Owners and Corporate
Social Performance," Academy of Management Journal, 37 (4), 1034-1046.

Greenley, Gordon E. and Gordon R. Foxall (1998), “External Moderation of Associations
among Stakeholder Orientations and Company Performance,” International Journal of
Research in Marketing, 15 (1), 51-69.

Handelman, Jay M. and Stephen J. Arnold (1999), “The Role of Marketing Actions with

a Social Dimension: Appeals to the Institutional Environment,” Journal of Marketing, 63
(3), 33-48.

74

Harman, Michael T. and John Freeman (1977), “The Population Ecology of
Organizations,” American Journal of Sociology, 82 (5), 929-964.

Hatten, Kenneth J. and Mary Louise Hatten (1987), “Strategic Groups, Asymmetrical
Mobility Barriers and Contestability,” Strategic Management Journal, 8 (4), 329-342.

Hawawini, Gabriel, Venkat Subramanian, and Paul Verdin (2003 ), “Is Performance
Driven By Industry- or Firm-Speciﬁc Factors? A New Look at the Evidence,” Strategic
Management Journal, 24(1), 1-16.

Henriques, Irene and Perry Sadorsky (1999), “The Relationship between Environmental
Commitment and Managerial Perceptions of Stakeholder Importance,” Academy of
Management Journal, 42 (1), 87-99.

Hillman, Amy J. and Gerald D. Keim (2001), “Shareholder Value, Stakeholder
Management, and Social Issues: What's the Bottom Line?” Strategic Management
Journal, 22 (2), 125-139.

Homburg, Christian and Ruth M. Stock (2004), “The Link between Salespeople’s Job
Satisfaction and Customer Satisfaction in a Business-to-Business Context: A Dyadic
Analysis,” Journal of Marketing, 32 (2), 144-158.

Hunt, Shelby D. and Robert M. Morgan (1995), “The Comparative Advantage of
Competition,” Journal of Marketing, 59 (2), 1-15.

Jap, Sandy D. (1999), “Pie Expansion Efforts: Collaboration Processes in Buyer-Supplier
Relationships,” Journal of Marketing Research, 36 (4), 461-475.

Jaworski, Bernard J. and Ajay K. Kohli (1993), “Market Orientation: Antecedents and
Consequences,” Journal of Marketing, 57(3), 53-70.

Jones, Thomas M. (1995), “Instrumental Stakeholder Theory: A Synthesis of Ethics and
Economics,” Academy of Management Review, 20 (2), 404-43 7.

Kassinis, George and Nikos Vafeas (2006), “Stakeholder Pressures and Environmental
Performance,” Academy of Management Journal, 49 (l ), 145-159.

Kast, Fremont E. and James E. Rosenzweig (1972), “General Systems Theory:

Applications for Organization and Management,” Academy of Management Journal, 15
(4), 447- 465.

Ketchen, David J. and Christopher L. Shook (1996), “The Application of Cluster

Analysis in Strategic Management Research: An Analysis and Critique,” Strategic
Management Journal, 17 (6), 441-458.

75

Kirca, Ahmet H., Satish Jayachandran, and William O. Bearden (2005), “Market
Orientation: A Meta-Analytic Review and Assessment of Its Antecedents and Impact on
Performance,” Journal of Marketing, 69 (2), 24-41.

Kohli, Ajay K. and Bernard J. Jaworski (1990), “Market Orientation: The Construct,
Research Propositions, and Managerial Implications,” Journal of Marketing, 54(2), 1-18.

Kohli, Ajay K., Bernard J. Jaworski and Ajith Kumar (1993), “MARKOR: A Measure of
Market Orientation,” Journal of Marketing Research, 30 (4), 467-477.

Leask, Graham and David Parker (2007), “Strategic Groups, Competitive Groups and
Performance within the UK. Pharmaceutical Industry: Improving our Understanding of
the Competitive Process,” 28 (7), 723-745.

Lee, Ruby and Rajdeep Grewal (2004), “Strategic Responses to New Technologies and
Their Impact on Firm Performance,” Journal of Marketing, 68 (October), 1578-171.

Lewis, Pam and Howard Thomas (1990), “The Linkage between Strategy, Strategic
Groups, and Performance in the UK. Retail Grocery Industry,” Strategic Management
Journal, 11 (5), 385-397.

Luo, Xueming and C. B. Bhattacharya (2006), “Corporate Social Responsibility,
Customer Satisfaction, and Market Value,” Journal of Marketing, 70 (4), 1-18.

Luo, Xueming and Christian Homburg (2008), “Satisfaction, Complaint, and the Stock
Value Gap,” Journal of Marketing, 72 (July), 29-43.

Luo, Xueming and Naveen Donthu (2006), Marketing’s Credibility: A Longitudinal
Investigation of Marketing Communication Productivity and Shareholder Value,”
Journal of Marketing, 70 (October), 70-91.

Maignan, Isabelle and O. C. Ferrell (2004), “Corporate Social Responsibility and
Marketing: An Integrative Framework,” Journal of the Academy of Marketing Science,
32 (1), 3-19.

Marketing Science Institute (2008), Research Priorities: 2008-2010 Guide to MS]
Research Programs and Procedures. Cambridge, MA: Marketing Science Institute.

Mason, Edward S. (1939), “Price and Production Policies of Large-Scale Enterprises,”
American Economic Review, 29 (1), 61-74.

Matsuno, Ken and John T. Mentzer (2000), “The Effects of Strategy Type on the Market
Orientation — Performance Relationship,” Journal of Marketing, October), 1-16.

76

Matsuno, Ken, John T. Mentzer, and Joseph 0. Rentz (2000), “A Reﬁnement and
Validation of the MARKOR Scale,” Journal of the Academy of Marketing Science, 28
(4), 527-539.

Mauri, Alfredo J. and Max P. Michaels (1998), “F irm and Industry Effects within
Strategic Management: An Empirical Examination,” Strategic Management Journal, 19
(3), 211-219.

McGahan, Anita A. and Michael E. Porter (1997), “How Much Does Industry Matter,
Really?” Strategic Management Journal, 18 (SI), 15-30.

McGee, John and Howard Thomas (1986), “Strategic Groups: Theory, Research, and
Taxonomy,” Strategic Management Journal, 7 (2), 141-160.

Mehra, Ajay (1996), “Resource and Market Based Determinants of Performance in the
US. Banking Industry,” Strategic Management Journal, 17 (4), 307-322.

Mitchell, Ronald K., Bradley R. Agle, and Donna J. Wood (1997), “Toward a Theory of
Stakeholder Identiﬁcation and Salience: Deﬁning the Principle of Who and What Really
Counts,” Academy of Management Review, 22 (4), 853-886.

Morgan, Neil A. and Lopo L. Rego (2009), “Brand Portfolio Strategy and Firm
Performance,” Journal of Marketing, 73 (January), 59-74.

Nair, Anil and Suresh Kotha (2001), “Does Group Membership Matter? Evidence from
the Japanese Steel Industry,” Strategic Management Journal, 22 (3), 221-235.

Narver, John C. and Stanley F. Slater (1990), “The Effect of a Market Orientation on
Business Proﬁtability,” Journal of Marketing, 54 (4), 20-35.

Penrose, Edith T. (1959), The Theory of Growth of the Firm. New York: Oxford
University Press Inc.

Peteraf, Margaret A. (1993), “The Comerstones of Competitive Advantage: A Resource-
Based View,” Strategic Management Journal, 14 (3), 179-191.

Pfeffer, Jeffrey and Gerald R. Salancik (1978), The External Control of Organizations: A
Resource Dependence Perspective. New York: Harper & Row.

Polonsky, Michael Jay, Des. Stefan W. Schuppisser, and Srikanth Beldona (2002), “A
Stakeholder Perspective for Analyzing Marketing Relationships,” Journal of Market-
Focused Management, 5 (2), 109-126.

Porter, Michael E. (1979), “The Structure within Industries and Companies’
Performance,” The Review of Economics and Statistics, 61 (2), 214-227.

77

Porter, Michael E. (1980), Competitive Strategy: Techniques for Analyzing Industries and
Competitors. New York: The Free Press.

Porter, Michael R. and Claas van der Linde (1995), “Toward a New Conception of the
Environment-Competitiveness Relationship,” Journal of Economic Perspectives, 9 (4),
97-1 18.

Priem, Richard L. and John E. Butler (2001), “Is the Resource-Based ‘View’ 3 Useful
Perspective for Strategic Management Research?” Academy of Management Review, 26
(1), 22-40.

Rao, Vithala, Manoj K. Agarwal, and Denise Dahlhoff (2004), “Howls Manifest
Branding Strategy Related to the Intangible Value of a Corporation?” Journal of
Marketing, 68 (4), 126-141.

Raudenbush, Stephen, Anthony Bryk, Yuk Fai Cheong, and Richard Congdon (2004),
HLM 6: Hierarchical Linear & Nonlinear Modeling. Lincolnwood, IL: SSI Scientiﬁc
Software International.

Roquebert, Jaime A., Robert L. Phillips, and Peter A. Westfall (1996), “Markets vs.
Management: What ‘Drives’ Proﬁtability?” Strategic Management Journal, 17(8), 653-
664.

Rouse, Michael J. and Urs S. Daellenbach (1999), “Rethinking Research Methods for the
Resource-Based Perspective: Isolating Sources of Sustainable Competitive Advantage,”
Strategic Management Journal, 20 (5), 487-494.

Roy, Subroto, K. Sivakurnar, and Ian F. Wilkinson (2004), “Innovation Generation in
Supply Chain Relationships: A Conceptual Model and Research Propositions,” Journal
of the Academy of Marketing Science, 32 (1), 61-79.

Ruekert, Robert W. (1992), “Developing a Market Orientation: An Organizational
Strategy Perspective,” International Journal of Research in Marketing, 9 (3), 225-245.

Rumelt, Richard P. (1984), “Toward a Strategic Theory of the Firm”, R. Lamb (ed.),
Competitive Strategic Management. Englewood Cliffs: Prentice-Hall.

Rumelt, Richard P. (1987), “Theory, Strategy, and Entrepreneurship”, D. Teece (ed.), The
Competitive Challenge. Cambridge: Ballinger.

Rumelt, Richard P. (1991), “How Much Does Industry Matter?” Strategic Management
Journal, 12(3), 167-185.

Schmalensee, Richard (1985), “Do Markets Differ Much?” American Economic Review,
75 (June), 341-351.

78

Scott, W. Richard (1981), “Developments in Organization Theory, 1960-1980,”
American Behavioral Scientist, 24 (3), 407-422.

Scott, W. Richard and Gerald F. Davis (2007), Organizations and Organizing: Rational,
Natural, and Open System Perspectives, Upper Saddle River, NJ: Pearson Prentice-Hall.

Sen, Sankar and CB. Bhattacharya (2001), “Does Doing Good Always Lead to Doing
Better? Consumer Reactions to Corporate Social Responsibility,” Journal of Marketing
Research, 38 (May), 225-243.

Short, Jeremy C., David J. Ketchen, Timothy B. Palmer, and G. Tomas M. Hult (2007),
“Firm, Strategic Group, and Industry Inﬂuences on Performance,” Strategic Management
Journal, 28 (2), 147-167.

Simon, Herbert A. (1962), “The Architecture of Complexity,” Proceedings of the
American Philosophical Society, 106, 467-482.

Slater, Stanley R, G. Tomas M. Hult, and Eric M. Olson (2007), “On the
Importance of Matching Strategic Behavior and Target Market Selection to

Business Strategy in High-Tech Markets,” Journal of the Academy of Marketing
Science, 35 (1), 5-17.

Slater, Stanley F. and John C. Narver (1994), “Does Competitive Environment Moderate
the Market Orientation-Performance Relationship?,” Journal of Marketing, 58 (1), 46-55.

Sorescu, Alina B. and Jelena Spanjol (2008), “Innovation’s Effect on Firm Value and
Risk: Insights from Consumer Packaged Goods,” Journal of Marketing, 72 (March), 114-
132.

Srivastava, Rajendra K., Tasadduq A. Shervani, and Liam Fahey (1998), “Market-Based
Assets and Shareholder Value: A Framework for Analysis,” Journal of Marketing, 62 (1),
2-18.

Teece, David J ., Gary Pisano, and Amy Shuen (1997), “Dynamic Capabilities and
Strategic Management,” Strategic Management Journal, 18 (7), 509-533.

Thomas, Howard and N. Venkatraman (1988), “Research on Strategic Groups: Progress
and Prognosis,” Journal of Management Studies, 25 (6), 537-555.

Thomas, LG, 111 and Geoffrey Waring (1999), “Competing Capitalisms: Capital
Investment in America, German, and Japanese Firms,” Strategic Management Journal,
20 (8), 729-748.

Tobin, James (1978), “Monetary Policies and the Economy: The Transmission
Mechanism,” Southern Economic Journal, 44 (January), 421-431.

79

Vorhies, Douglas W. and Neil A. Morgan (2003), “Trading Off Between Value Creation
and Value Appropriation: The Financial Implications of Shifts in Strategic Emphasis,”
Journal of Marketing, 67 (January), 100-115.

Waddock, Sandra and Samuel B. Graves (1997), "The Corporate Social Perfonnance-
Financial Performance Link," Strategic Management Journal, 18 (4), 303-319.

Walker, Steven F. and Jefﬁey W. Marr (2001), Stakeholder Power: A Winning Plan for
Building Stakeholder Commitment and Driving Corporate Growth Cambridge, MA:
Perseus Publishing.

Webster, Frederick E., Jr. (1992), “The Changing Role of Marketing in the Corporation,”
Journal of Marketing, 56 (4), 1-17.

Wemerfelt, Birger (1984), “A Resource-Based View of the Firm,” Strategic Management
Journal, 5 (2), 171-180.

Wemerfelt, Birger (2005), “Product Development Resources and the Scope of the Firm,”
Journal of Marketing, 69 (2), 15-23.

Zeithaml, Valarie A., A. Parasurarnan, and Leonard L. Berry (1985), “Problems and
Strategies in Services Marketing,” Journal of Marketing, 49(2), 33-46.

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Essay 2

STAKEHOLDERS, CUSTOMER SATISFACTION, AND PERFORMANCE

Firms that respond to the interests of their stakeholders do not operate in a
vacuum. Instead, they operate within an environment that consists of other ﬁrms
responding to their stakeholders as well (e.g., DiMaggio and Powell 1983). Institutional
theory holds that to attain legitimacy, a ﬁrm tends to be isomorphic to other ﬁrms in its
environment, with ﬁrms resembling each other and behaving similarly over time (e.g.,
Dacin 1997). This suggests that the way a particular ﬁrm treats its stakeholders inﬂuences
the manner in which other ﬁrms facing similar conditions deal with their stakeholders. As
such, this paper examines whether the supply chain participants’ stakeholder focus along
with that of a focal ﬁrm’s major competitors in the marketplace shape the ﬁrm’s
stakeholder focus. Speciﬁcally, a ﬁrm’s stakeholder focus is deﬁned as the amount of
attention, resources, and time the ﬁrm devotes to addressing the interests of multiple
stakeholder groups (cf. Mitchell, Agle, and Wood 1997).

Supply chain entities such as business-to-business customers, primary suppliers,
and strategic partners (e.g., Simchi-Levi, Kaminsky, and Sirnchi-Levi 2003), along with
major competitors which represent a dynamic element of the marketplace (e. g., Porter
1996), affect the focal ﬁrm’s strategic mindset, tactical activities, and even sometimes
cultural makeup (e.g., Mintzberg, Quinn, and Ghosal 1998). Further, according to
institutional theory, different ﬁrms’ strategies can converge via three mechanisms —
coercive, mimetic, and normative (DiMaggio and Powell 1983). Coercive isomorphism is
driven by the problem of legitimacy in exchange relationships where ﬁrms imitate other

ﬁrms that they are dependent on in order to attain legitimacy. Mirnetic isomorphism

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arises under conditions of environmental uncertainty, whereby ﬁrms mimic other entities,
especially those they regard as more successful or those with whom they have boundary-
spanning ties (e.g., Galaskiewicz and Wasserman 1989; McFarland, Bloodgood, and
Payan 2008). Normative isomorphism stems from the propagation of norms through
social networks, where members of a ﬁrm learn what practices are considered appropriate
within the ﬁeld (e.g., Guler, Guillen, and Macpherson 2002).

Customer satisfaction — a measure of a marketing outcome which is characterized
as an overall assessment of a ﬁrm’s offerings (e.g., Anderson, Fomell, and Rust 1997) —
is the key intermediate marketing outcome in this study. A ﬁrm’s stakeholder focus is
related to customer satisfaction in that information about satisfaction projects what the
ﬁrm has done to its customers within the dynamics of the overall marketplace and the
ﬁrm’s multiple stakeholders (Fomell 2007, p. 8). As such, this study investigates the
impact of the ﬁrm’s stakeholder focus on customer satisfaction. Additionally, previous
research provides ample evidence that customer satisfaction leads to superior
performance (e. g., Anderson, Fomell, and Lehmann 1994), but at the same time, there is
evidence suggesting that an excessive focus on customer satisfaction can be
counterproductive as it can hurt the ﬁrm’s bottom-line performance (e.g., Anderson,
Fomell, and Rust 1997). Hence, this paper reexarnines the nature of the customer
satisfaction—performance relationship with a particular focus on a potential non-linear
relationship between the variables.

The paper is structured as follows. The next section reviews the literature that
underscores the importance of attending to the interests of multiple stakeholder groups.

Then, based on an integration of institutional theory, stakeholder theory, and the

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literatures on market orientation and customer satisfaction, the paper develops a number
of hypotheses delineating a select set of antecedents and consequences of a focal ﬁrm’s
stakeholder focus and the moderating role of R&D. Regarding the latter, it delineates
theoretical logic that the impact of the supply chain participants’ and a competitor’s
stakeholder focus on the focal ﬁrm’s stakeholder focus is moderated by the ﬁrm’s R&D
intensity. Through R&D, a ﬁrm obtains information by its own means, which both
lessens dependence on other ﬁrms to acquire this resource and reduces the degree of
uncertainty faced by a ﬁrm. Institutional theory suggests that lower levels of
interorganizational dependence and uncertainty decrease the ﬁrrn’s degree of imitation of
supply chain entities and competitors (e.g., DiMaggio and Powell 1983). Then, the
following section describes the data collection, measures, and analyses involving a
lagged structure of secondary data from four sources over a four-year period (2004-
2007). The ﬁnal section presents the results and discusses their implications for research
and managerial practice.
CONCEPTUAL FRAMEWORK AND HYPOTHESES

The stakeholder approach seeks to broaden management’s vision of its
responsibilities beyond proﬁt maximization to incorporate the claims of non-stockholding
groups (e.g., Freeman 1984; Mitchell, Agle, and Wood 1997). In particular, stakeholder
theory views the ﬁrm as “an organizational entity through which numerous and diverse
participants accomplish multiple, and not always entirely congruent, purposes”
(Donaldson and Preston 1995, p. 70). Given the disparate interests of these various
stakeholders, ﬁrms are unlikely to fulﬁll all the demands of each stakeholder group

(J awahar and McLaughlin 2001). Toward this end, stakeholder theory is intended to

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address the key question, “which groups are stakeholders deserving or requiring
management attention, and which are not?” (Mitchell, Agle, and Wood 1997, p. 855).

Stakeholder theory has developed along different research traditions. According
to Donaldson and Preston (1995), there are three approaches to stakeholder theory —
descriptive/empirical, normative, and instrumental — which are distinct, yet mutually
supportive. The descriptive/empirical approach focuses on the actual behaviors of ﬁrms.
It seeks to describe how ﬁrms actually interact with stakeholders. The normative
approach is prescriptive as it identiﬁes moral guidelines that dictate how ﬁrms should
treat stakeholders. One of the central tenets of this approach is that ﬁrms should attend to
the claims of all of their stakeholders, not only to those of their shareholders (e. g. Jones
and Wicks 1999). The instrumental approach to stakeholder theory is intended to describe
what will happen if ﬁrms behave in a particular way (Jones 1995). It is used to identify
the linkage between stakeholder management and corporate objectives such as
proﬁtability and growth (Donaldson and Preston 1995).

In this study, the instrumental approach to stakeholder theory is adopted given
that it provides a ﬁarnework for examining the relationship between stakeholder
management and performance (Donaldson and Preston 1995). The instrumental
stakeholder theory proposes that ﬁrms that develop mutually trusting and cooperative
relationships with their stakeholders will have a competitive advantage over those ﬁrms
that do not (Jones 1995). Clarkson (1995) argues that a ﬁrm’s survival and performance
is a function of the ability of its managers to create sufﬁcient wealth, value, or

satisfaction for all its primary stakeholder groups, without favoring one group at the

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expense of others. In this sense, the claims of all legitimate stakeholders are of intrinsic
value, and no set of claims is assumed to dominate the rest (e.g. Jones and Wicks 1999).

Over the years, several authors have provided different deﬁnitions of what
constitutes a stakeholder, yet Freeman’s original deﬁnition remains the most widely used.
He deﬁnes stakeholder as “any group or individual who can affect or is affected by the
achievement of the organization’s objectives” (Freeman 1984, p. 46). This deﬁnition
reﬂects a broad view of stakeholders, which captures the empirical reality that ﬁrms can
be affected by, or they can affect, virtually anyone (Mitchell, Agle, and Wood 1997). On
the other hand, narrow views of stakeholders accommodate the practical reality that
resources, attention, and time to deal with external constraints are limited.

According to Mitchell, Agle, and Wood (1997), stakeholders can be identiﬁed by
their possession of at least one of three key attributes: power, legitimacy, and urgency.
Power is the extent to which an entity can impose its will in the relationship through
coercive, utilitarian, or normative means. Legitimacy is deﬁned as “a generalized
perception or assumption that the actions of an entity are desirable, proper, or appropriate
within some socially constructed system of norms, values, beliefs, and deﬁnitions”
(Suchman 1995, p. 574). Urgency refers to the degree to which stakeholder demands
require immediate attention. It is based on both time sensitivity (i.e., the extent to which
managerial delay is unacceptable to the stakeholder) and criticality (i.e., the importance
of the demands to the stakeholder). The amount of attention management should pay to a
particular stakeholder is a function of the combination of power, legitimacy, and urgency.

Managers will give low priority to the claims of a stakeholder who possesses only one

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attribute, moderate priority if two attributes are present and high priority if all three are
held.

Based on their boundaries (e.g., Mitchell, Agle, and Wood 1997), stakeholders
can be classiﬁed into two main groups: primary and secondary (e.g., Clarkson 1995).
Primary stakeholder groups are those without whose continued participation the ﬁrm
cannot survive. They include shareholders, employees, customers, and suppliers, along
with what is known as the public stakeholder groups, which are comprised of
governments and communities that impact the operations of the ﬁrm by providing
inﬁastructure, regulating its activities, and requiring tax payments (Clarkson 1995). The
logic behind designating these six groups as important stakeholders that can inﬂuence the
ﬁrm can be explained by resource dependence theory (e.g., Pfeffer and Salancik 1978).
According to this theory, a ﬁrm is dependent on environmental actors who control
resources that are critical for the continued survival of the ﬁrm. Speciﬁcally, a ﬁrm
depends on customers for revenues, employees for labor, suppliers for inputs (e.g., Porter
2008), shareholders for capital (e.g., Day and Fahey 1988), communities for natural
resources (e. g., Porter and Kramer 2006), and regulators for funds and access to markets
(e. g., Birnbaum 1985). It is this dependence of ﬁrms on environmental actors for
resources that confers those actors’ power over a ﬁrm (e.g., Frooman 1999). The
possession of power to inﬂuence the ﬁrm’s actions automatically denominates the entity
as a stakeholder (Mitchell, Agle, and Wood 1997).

In contrast, secondary stakeholder groups, such as the media and special interest
groups, do not have a contractual obligation with the ﬁrm nor exercise any legal authority

over the ﬁrm (Eesley and Lenox 2006). These groups inﬂuence or are inﬂuenced by the

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ﬁrm but are not vital for the ﬁrm’s survival (Clarkson 1995). As such, this study focuses
on the six primary stakeholder groups: customers, employees, suppliers, shareholders,
regulators, and the community. The importance of these six stakeholder groups in
marketing is explained next.

Customers. The signiﬁcance for ﬁrms to focus on customers has been discussed
extensively in the marketing literature. For example, Webster (1992) identiﬁes customer
relationships as the most important business asset. He maintains that it is critical for ﬁrms
to make long-term commitments to nurturing customer relationships with quality, service,
and innovation. Similarly, Deshpande, Farley, and Webster (1993, p. 27) deﬁne a
customer orientation as “the set of beliefs that puts the customer’s interests ﬁrst.” A
customer orientation requires the ﬁrm to understand its target customers in order to
continuously deliver superior value for them (Narver and Slater 1990). This involves
taking actions on the basis of market intelligence pertaining to current and future
customer needs (Kohli and Jaworski 1990). Those businesses that devote signiﬁcant
resources to understanding their customers and competitors and coordinate the activities
of the different functions of the business for an integrated value-creation effort are
rewarded with superior proﬁtability, sales growth, and new product success relative to
other ﬁrms (Slater and Narver 1994).

Employees. Employees are “the source of a company’s success” (Henriques and
Sadorsky 1999, p. 89). Workers who are highly satisﬁed with their jobs are perceived by
customers as more balanced and pleased with their environment. Such workers have a
positive inﬂuence on customer satisfaction (e.g., Homburg and Stock 2004). This is

particularly important in service ﬁrms, where employees typically have direct contact

87

with customers (e.g., Heskett et al. 1994). In addition, salespeople’s job satisfaction has
an impact on the quality of customer interaction (e.g., Homburg and Stock 2004). For
these reasons, it is important for the ﬁrm to attend to the interests of its employees and
keep them satisﬁed with their jobs.

Suppliers. Relationships with suppliers can be instrumental to the ﬁrm’s ability to
improve its performance (e.g., Buchanan 1992). For example, the process of
collaboration between a ﬁrm and its supplier has been identiﬁed as a system resource of
the ﬁrm that enhances performance and competitive advantages through coordination
efforts and idiosyncratic investments (Jap 1999). By cultivating a collaborative culture,
establishing objectives for joint learning, and developing relational trust, management
can promote relationship learning (Selnes and Sallis 2003). This type of learning can
improve performance, by enabling customers and suppliers to identify means through
which to enhance quality and increase ﬂexibility. As an extension, this “knowledge
interface” can also be managed to produce both incremental and radical innovations
(Roy, Sivakumar, and Wilkinson 2004). In addition, a supplier can help the ﬁrm achieve
a competitive advantage by driving down a ﬁrm’s total costs (Cannon and Homburg
2001). On the other hand, failure to comply with a supplier’s demands can negatively
affect a ﬁrm, as suppliers may stop their delivery of a key input (Henriques and Sadorsky
1999).

Shareholders. Firms have an important obligation to shareholders — to maximize
their wealth (e.g., Day and Fahey 1988; Rao and Bharadwaj 2008). As such, top
management increasingly requires that marketing move away from exclusively focusing

on measures such as market share and sales growth to incorporating shareholder value

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creation as a criterion for the evaluation of strategic initiatives (e.g., Day and F ahey 1988;
Srivastava, Shervani, and Fahey 1998). In this regard, marketing accountability is
achieved only when a marketing action that leads to intermediate outcomes such as
customer satisfaction, loyalty, and market share also contributes to the enhancement of
shareholder wealth (Rao and Bharadwaj 2008).

Regulators. Regulators are “important stakeholders that exert external political
and economic forces on the ﬁrm” (Banerjee, Iyer and Kashyap 2003, p. 109). Constraints
imposed by regulators have an impact on a variety of marketing activities including, for
example, the design of products (Bloch 1995), advertising (Pechmann 1996), and
packaging (Morgan 1988). For instance, federal regulations require over-the-counter
pharmaceuticals to be packaged in tamper-resistant containers (Morgan 1988).
Compliance with these and other regulations impose additional costs, but as opposed to
the demands of other stakeholder groups, compromise usually does not occur in this area;
ﬁrms must comply with regulator demands (e.g., Bloch 1995).

Community. Community stakeholders include nongovernmental organizations,
communities formed because of their geography (i.e., location), and other community
groups organized around a political or social cause (Kassinis and Vafeas 2006). These
stakeholders are inﬂuential because of their ability to mobilize public opinion in favor of
or against a ﬁrm’s actions (Henriques and Sadorsky 1999). As such, marketing actions
with a social dimension, such as contributing to local charities or sponsoring little league
sports teams, typically increase consumer support for the organization (Handelrnan and
Arnold 1999). On the other hand, disregarding community interests may cause a loss of

consumer support in the form of boycotts.

89

Given the important role the six primary stakeholder groups play in marketing, it
is imperative to investigate the factors that drive a ﬁrm to attend to its primary
stakeholders and the implications of addressing the stakeholders’ interests. Against this
backdrop, the development of the model depicted in Figure 6 is rooted in institutional
theory, stakeholder theory, and the literatures on market orientation and customer
satisfaction to identify critically important antecedents and select consequences of a
ﬁrm’s stakeholder focus. As such, the model in Figure 6 is based on an integration of
theories and literature bases, captured within the deﬁnitional boundaries of a ﬁrm’s
stakeholder focus (i.e., the amount of attention, resources, and time the ﬁrm devotes to
addressing the interests of multiple stakeholder groups — Mitchell, Agle, and Wood
1997), and set within the ﬁamework of six primary stakeholder groups (i.e., customers,
employees, suppliers, shareholders, regulators, and the community). Previous research
provides evidence that these six stakeholder groups are crucial for the realization of the
ﬁrm’s objectives (Clarkson 1995) and the ﬁrm’s achievement of superior performance
(Fomell et a1. 2006). The remaining portion of this section provides the rationale to

support the links depicted in Figure 6.

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91

Supply Chain Members’ Stakeholder Focus and the Effects on the Focal Firm

The link between the supply chain’s stakeholder focus (i.e., involving the three
entities of a ﬁrm’s business-to-business customers, primary suppliers, and strategic
partners) and the ﬁrm’s stakeholder focus can be explained by institutional theory (e.g.,
DiMaggio and Powell 1983; Scott 1987). Institutional theory holds that to attain
legitimacy, a ﬁrm tends to be isomorphic to other ﬁrms in its environment and, as a
result, over time ﬁrms within the same population will resemble each other (e. g., Dacin
1997). DiMaggio and Powell (1983) identify three mechanisms — coercive, mimetic, and
normative pressures — through which such resemblance of ﬁrms occurs.

Coercive isomorphism is driven by two different forces -— political inﬂuence and
the problem of legitimacy (e.g., DiMaggio and Powell 1983). The latter is particularly
relevant within the context of this study. Speciﬁcally, to gain legitimacy in exchange
relationships, ﬁrms imitate other ﬁrms that they are dependent on (cf. McFarland,
Bloodgood, and Payan 2008). As such, interorganizational dependence is a key factor in
isomorphic change. Since a ﬁrm depends on its business-to-business customers, primary
suppliers, and strategic partners to achieve ﬁrm goals, the ﬁrm will mimic the structure,
climate, and behavioral focus of these other ﬁrms (DiMaggio and Powell 1983).

Mimetic isomorphism arises under conditions of environmental uncertainty (e.g.,
DiMaggio and Powell 1983; McFarland, Bloodgood, and Payan 2008). When faced with
ambiguous situations, ﬁrms model themselves after other ﬁrms in their ﬁeld, especially
after those they regard as more legitimate or successful (e.g., Lieberman and Asaba
2006). Firms are also likely to mimic other ﬁrms in their environment with whom they

have boundary-spanning ties (Galaskiewicz and Wasserman 1989). Managers who have

92

ties to peers in other ﬁrms are able to see how such ﬁrms deal with environmental
constraints and “learn what is and what is not acceptable to various stakeholders”
(Galaskiewicz and Wasserman 1989, p. 456). This learning, in turn, inﬂuences the way
they will behave themselves in the marketplace.

Normative isomorphism results from the diffusion of ideas and normative rules
via social networks (e.g., Mizruchi and F ein 1999). Normative rules dictate the
organizational and professional behavior that is considered appropriate within the ﬁeld
(e.g., Guler, Guillen, and Macpherson 2002). In particular, professional and trade
associations often serve as vehicles for the establishment and dissemination of such
normative rules (DiMaggio and Powell 1983). Through these professional networks,
members of one ﬁrm inﬂuence those of another (e.g., Mizruchi and F ein 1999).

Support for the notion that ﬁrms imitate other ﬁrms in their environment can be
found in several studies. For example, McFarland, Bloodgood, and Payan (2008)
examine how ﬁrm behaviors become imitated among supply chain members and ﬁnd that
institutional pressures inﬂuence the propagation of such behaviors. Speciﬁcally, these
authors ﬁnd that imitation between ﬁrms in the supply chain occurs under conditions of
environmental uncertainty, and that the degree of imitation depends on the perceived
similarity and ﬁ'equency of contact between boundary-sparming personnel. In addition,
Siguaw, Simpson, and Baker (1998) study the inﬂuence of a supplier’s market orientation
on the ﬁrm’s market orientation. They ﬁnd that the market orientation of the supplier has
a direct effect on the market-oriented behaviors exhibited by the focal ﬁrm. This
discussion suggests that a ﬁrm’s interactions with its business-to-business customers,

primary suppliers, and strategic partners will induce the ﬁrm to be inﬂuenced by and

93

imitate these supply chain members’ behaviors (cf. McFarland, Bloodgood, and Payan
2008). Therefore:

H1: The ﬁrm’s stakeholder focus is positively affected by the stakeholder focus
of the ﬁrm’s (a) business-to-business customers, (b) primary suppliers,
and (c) strategic partners.

Competitors’ Stakeholder Focus and the Effect on the Focal Firm

It is argued that the major competitors’ stakeholder focus will inﬂuence the
stakeholder focus of the ﬁrm given the importance of such competitors in the
marketplace in which the ﬁrm operates. Support for this relationship can be established
using institutional theory and the literature on market orientation. As is suggested by
institutional theory, mimetic and normative pressures lead ﬁrms within the same
population to become increasingly similar to one another (DiMaggio and Powell 1983).
Speciﬁcally, mimetic processes cause ﬁrms to imitate other ﬁrms in their ﬁeld that they
perceive to be more successful or legitimate (e.g., DiMaggio and Powell 1983; Greve
2000; Haveman 1993). As Grewal and Dharwadkar (2002) point out, the notion of
mimetic pressures is consistent with the competitive strategy literature, which suggests
that to increase its competitiveness, a ﬁrm mimics the planning procedures and decision
making routines of successful companies (e.g., Dickson 1992). In mimetic isomorphism,
the diffusion of processes and behaviors may occur unintentionally, through employee
transfer, or explicitly by seeking advice from consulting ﬁrms (DiMaggio and Powell
1983). Similarly, normative pressures compel organizations to adopt certain practices that
are held sacred in their ﬁeld (e.g., Galaskiewicz and Wasserman 1989).

A number of studies empirically demonstrate that institutional pressures lead

ﬁrms to imitate their competitors. In a study about diversiﬁcation into new markets,

94

Haveman (1993) ﬁnds that ﬁrms mimic the behavior of successful competitors by
following them into new markets. Similarly, Greve (2000) studies the factors that affect
market niche entry decisions and ﬁnds a link between mimetic isomorphism and niche
choices. Speciﬁcally, Greve (2000) concludes that small organizations observe the
behaviors of large organizations and then imitate those behaviors to reap advantages in
the marketplace. In addition, Beliveau, Cottrill, and O’Neill (1994) examine the factors
that predict corporate social responsibility (CSR) and ﬁnd that a ﬁrm is more likely to
engage in CSR if other ﬁrms in its industry have done so.

The second relevant research stream addressing the relationship between
competitors’ stakeholder focus and the ﬁrm’s stakeholder focus is the literature on market
orientation (e.g., Kohli and J aworski 1990; Narver and Slater 1990). Market orientation
emphasizes the ability of the ﬁrm to primarily learn about its current and potential
customers and competitors to be able to continuously sense and respond to trends in the
marketplace (Day 1994). Narver and Slater (1990) identify competitor orientation (i.e.,
the ﬁrm’s understanding of the short-term strengths and weaknesses and long-term
strategies of its competitors) as a key component of a market Orientation. This involves
generating and disseminating intelligence about competitors and taking action in response
to the gathered intelligence (Kohli and Jaworski 1990). For example, Jaworski and Kohli
(1993) incorporate competitors into assessments of intelligence generation (e.g., whether
intelligence on a ﬁrm’s competitors is generated independently by several departments),
intelligence dissemination (e.g., the time it takes for one department to alert others about
important competitor information), and responsiveness (e.g., the ﬁrm’s response to

competitors’ price changes). From this perspective, if a ﬁrm that constantly monitors

95

competitors’ actions learns that its rivals are effectively catering to multiple constituents,
it will be prompted to respond to market trends by attending to the interests of different
stakeholders.

Building on institutional theory and the market orientation literature, the
theoretical rationale is compelling that if the ﬁrm’s competitors are focused on the
interests of multiple stakeholders and, for example, offer warranties for their products,
provide ample beneﬁts to their employees, have long-term commitments with their
suppliers, and are actively engaged in community activities (e.g., sponsoring little league
sports teams), the ﬁrm will respond by engaging in similar behaviors. As such, the
stakeholder focus of the ﬁrm’s major competitors should shape the focal ﬁrm’s
stakeholder focus.

H2: The ﬁrm’s stakeholder focus is positively affected by the stakeholder focus
of the ﬁrm’s major competitors.

The Moderating Effect of R&D on the Stakeholder Relationships

In this study, the Financial Accounting Standards Board’s (1974) deﬁnition of
R&D is adopted as a comprehensive way to deﬁne boundaries around the construct (and
to align it with the data obtained from COMPUSTAT). In this context, “research is
planned search or critical investigation aimed at discovery of new knowledge with the
hope that such knowledge will be useful in developing a new product or service or new
process or technique or in bringing about a signiﬁcant improvement to an existing
product or process” while “development is the translation of research ﬁndings or other
knowledge into a plan or design for a new product or process or for a signiﬁcant

improvement to an existing product or process whether intended for sale or use.”

96

Within these deﬁnitional boundaries, research and development incorporate key
properties that are also at the core of the concepts of market orientation (J aworski and
Kohli 1993), market-based learning (Sinkula 1994), and innovation (Han, Kim, and
Srivastava 1998; Hurley and Hult 1998) that have become staples in the strategic
marketing literature (e.g., Day 1994). For example, “discovery of new knowledge with
the hope that such knowledge will be useful in developing a new product or service” in
the deﬁnition of “research” parallels market orientation’s focus on “generation of market
intelligence”, as does “translation of. . .knowledge into a. . .new product” (responsiveness
in market orientation terms) in the deﬁnition of “development” (J aworski and Kohli
1993, p. 54).

The considerable overlap that exists between R&D and marketing (e.g., Griffm
and Hauser 1996) has led to the examination of R&D in a number of marketing studies
(e.g., Dutta, Narasimhan, and Rajiv 1999; Li and Calantone 1998; Luo and Donthu 2006).
Dutta, Narasimhan, and Rajiv (1999) ﬁnd that the interaction of R&D and marketing
capabilities is an important determinant of ﬁrm performance. In a study of the impact of
market knowledge competence on new product advantage, Li and Calantone (1998) ﬁnd
that a ﬁrm’s R&D strength has a positive effect on new product advantage. In addition,
they ﬁnd that top management’s perceived importance of market knowledge positively
inﬂuences R&D strength. Li and Calantone (1998) maintain that R&D strength is crucial
to convert market knowledge into a tangible product offering. In the context of our study,
the importance of R&D lies in using the knowledge the ﬁrm has generated about its

stakeholders to develop new products, services, processes, or techniques that satisfy their

97

claims. For instance, if regulators require that automakers meet a fuel efficiency standard
for their models, ﬁrms can engage in R&D to comply with this demand.

As previously discussed, one central idea underlying institutional theory is that
interorganizational dependence and uncertainty lead ﬁrms to imitate others in their ﬁeld
(e.g., DiMaggio and Powell 1983). Theoretically, as developed in H1 and H2, this means
that the stakeholder focus of a ﬁrm’s business-to-business customers, primary suppliers,
strategic partners, and major competitors will shape the stakeholder focus of a ﬁrm.
However, it is argued that this relationship is contingent on the focal ﬁrm’s intensity of
R&D for a number of reasons. According to Cohen and Levinthal (1989), the objective of
R&D is to “generate new information [and to] ...enhance the ﬁrm’s ability to assimilate
and exploit existing information” (p. 569). Information is a strategic resource (e. g.,
Barney 1991) that can supply knowledge to the ﬁrm which can then be used to attend to
the interests of different stakeholders through the development of new and innovative
products, services, processes, or techniques.

As a ﬁrm proactively acquires such information by its own means via R&D, it
will become less dependent on its business-to-business customers, primary suppliers, and
strategic partners to obtain this important resource (e.g., Pfeffer and Salancik 1978). On
the basis of institutional theory, less dependence of a ﬁrm on other ﬁrms will lower its
level of imitation (e.g., DiMaggio and Powell 1983). This suggests that as a ﬁrm engages
in R&D, its stakeholder focus will be less affected by the stakeholder focus of other
entities in the marketplace. Similarly, information obtained through R&D lessens the
degree of environmental uncertainty faced by a ﬁrm, which will ultimately decrease the

ﬁrm’s imitation of supply chain entities and competitors. Therefore, the more a ﬁrm

98

invests in R&D, the less it will be inﬂuenced by the stakeholder focus of its business-to-
business customers, primary suppliers, strategic partners, and major competitors. Stated
formally:
H3: The ﬁrm’s research and development intensity moderates the relationship
negatively between the ﬁrm’s stakeholder focus and the stakeholder focus
of the ﬁrm’s (a) business-to-business customers, (b) primary suppliers, (c)
strategic partners, and (d) major competitors.
The Relationship between Stakeholder Focus and Customer Satisfaction
According to the instrumental approach to stakeholder theory, ﬁrms that
effectively manage stakeholder relationships will enjoy advantages over those that do not
(e.g., Jones 1995). Support for this aspect of stakeholder theory can be found in several
studies. For example, Berman et al. (1999) ﬁnd that managerial attention to employees
and customers enhances ﬁrm outcomes. Similarly, Hilhnan and Keim (2001) provide
empirical evidence that stakeholder management leads to shareholder wealth creation.
However, one of the most intriguing effects of a ﬁrm adopting a stakeholder focus is that
on customer satisfaction. Customer satisfaction signiﬁes a critically important marketing
outcome that links strategy and tactics (e. g., stakeholder focus) with performance
implications for ﬁrms (Fomell et al. 2006). In the context of viewing the “customer as an
asset,” a ﬁrm’s stakeholder focus is then theoretically connected to customer satisfaction
in that information about customer satisfaction tells us what the company has done to its
customers within the dynamics of the overall marketplace and the ﬁrm’s multiple
stakeholders (F omell 2007, p. 8). In addition, given that customer satisfaction is an
important predictor of ﬁrm performance (e. g., Anderson, Fomell, and Lehmann 1994), it

follows that a ﬁrm’s stakeholder focus as a part of its strategy and tactics will inﬂuence

customer satisfaction (of. Berman et a1 1999; Hilhnan and Keim 2001). However, efforts

99

directed at multiple stakeholders are subject to diminishing, and ultimately, decreasing
returns.

At low levels of stakeholder focus, ﬁrms are not attending to the interests of their
stakeholders, which would result in low customer satisfaction. For example, research has
found that engaging in behavior deemed unethical by customers negatively affects these
customers’ judgments of perceived fairness, which in turn, lowers the level of customer
satisfaction (e. g., Ingram, Skinner, and Taylor 2005). For instance, a ﬁrm that exploits its
employees in foreign plants (e.g., sweatshop labor) or harms the community where it has
operations will likely generate disapproval among its customers, thereby reducing the
level of customer satisfaction. In less extreme cases, a ﬁrm that does not pay attention to
the claims of its stakeholders may have employees who are not satisﬁed with their jobs,
and as a result, treat the customers poorly (cf. Homburg and Stock 2004); alternatively,
the ﬁrm may offer low quality products that easily break down and are not covered by
warranty. On the basis of the expectations-disconﬁrmation paradigm (e.g., Oliver 1980),
in these examples, actual outcomes fall short of expectations (i.e., negative
disconﬁrmation), which leads customers to be dissatisﬁed with the consumption
experience (e.g., Szyrnanski and Henard 2001).

As the ﬁrm’s level of stakeholder focus starts increasing, customer satisfaction
will improve for a number of reasons. For example, at higher levels of stakeholder focus,
the ﬁrm acts in accordance with the community’s social and cultural norms. It may
engage in institutional actions such as becoming involved with the community, which
increases customers’ support for the ﬁrm (Handelrnan and Arnold 1999). Similarly, a

ﬁrm with high levels of stakeholder focus is likely to respond to its stakeholder

100

obligations with corporate social responsibility initiatives, which have been found to
positively inﬂuence customer satisfaction (Luo and Bhattacharya 2006). Further, a

' stakeholder-focused ﬁrm treats its employees with respect, compensates them fairly, and
provides high quality products at competitive prices. These factors enhance the
consumption experience, and customers will be satisﬁed as the actual outcomes likely
meet or exceed prior expectations (e.g., Szymanski and Henard 2001).

However, devoting an excessive amount of resources, attention, and time to
stakeholder relationships is not optimal. The ongoing development and maintenance of
such relationships is costly for the ﬁrm. As a ﬁrm’s stakeholder focus reaches a high
level, the ﬁrm will inevitably be incurring direct costs in a number of initiatives the
customer may not be aware of such as contributing to charities in remote locations, or
indirect costs because of failing to devote attention to critical areas of concern to make
the ﬁrm prosperous. Such overemphasis in certain areas can lead to, for example, an
increase in the prices customers have to pay (e.g., Monroe 1973). From the customer’s
viewpoint, if the offering remains the same, while the price increases, satisfaction will
decrease. This follows from the literature dealing with the antecedents of customer
satisfaction, which models value (i.e., the extent to which an offering can provide
customers what they want relative to the total price they pay) as a predictor of satisfaction
(e.g., Churchill and Surprenant 1982; Szymanski and Henard 2001).

Taken together, increases in the level of stakeholder focus by a ﬁrm will enhance
customer satisfaction up to an optimal point. Beyond this point, the costs of satisfying the
many demands of the multiple stakeholders will translate to an increase in the prices that

the customers have to pay for the ﬁrm’s products and services. This, in turn, will lower a

101

customer’s satisfaction with the product or service offering. Consistent with this
conceptual logic and the instrumental approach to stakeholder theory, the link between a
ﬁnn’s stakeholder focus and customer satisfaction includes an inﬂection point where
achieving a greater degree of stakeholder focus adversely affects customer satisfaction.
Therefore:

H4: The stakeholder focus of the ﬁrm has an inverted U-shaped relationship
with the satisfaction of the ﬁrm’s end customers (i.e., there is an inﬂection
point where achieving a greater degree of stakeholder focus adversely
affects customer satisfaction as perceived by the ﬁrm’s end customers).

The Relationship between Customer Satisfaction and Firm Performance .

Prior research in marketing provides ample evidence that highly satisﬁed
customers bring in economic beneﬁts for ﬁrms (e.g., Anderson, F omell, and Lehmann
1994). These customers are more likely to engage in favorable word-of-mouth
communication (e.g., Fomell 1992; Van Dolen, Dabholkar, and de Ruyter 2007),
repurchase the ﬁrm’s products and services (e. g., Heitrnann, Lehmann, and Herrmann
2007; Maxham and Netemeyer 2002; Szymanski and Henard 2001), be willing to pay
more for them (e. g., Homburg, Koschate, and Hoyer 2005), buy more frequently and in
greater quantity, and they are more tolerant to price increases (Anderson, Fomell, and
Lehmann 1994). In addition, a high level of customer satisfaction often protect the ﬁrm’s
market share from competitors, reduce transaction costs and the costs of attracting new
customers, lower failure costs, and improve the overall reputation of the ﬁrm (Anderson,
Fomell, and Lehmann 1994). As a result, several studies have found customer
satisfaction to be positively associated with ﬁrm performance (e. g., Anderson, Fomell,

and Mazvancheryl 2004; F omell et al. 2006; Gruca and Rego 2005; Luo and

Bhattacharya 2006).

102

On the other hand, there is also evidence suggesting that an excessive focus on
customer satisfaction can adversely affect the ﬁrm’s bottom-line performance (e.g.,
Anderson, Fomell, and Rust 1997; Fomell et al. 2006). For example, in services
industries where there are tradeoffs between customer satisfaction and productivity,
providing a high level of customization to tailor to individual needs and preferences,
ﬁrms may drive up costs, leave customers unattended, harm sales per employee, and
ultimately, affect the proﬁtability of the ﬁrm. Consistent with this logic, Anderson and
Mittal (2000) conclude that the impact of customer satisfaction on proﬁtability is
nonlinear and more complex than previously studied (cf. Zeitharnl 2000). They maintain
that efforts aimed at increasing customer satisfaction, such as offering products and
services with more and better features as well as providing more attention to customers
by employees consume a ﬁrm’s resources and may be subject to diminishing returns.
Gomez, McLaughlin, and Wittink (2004) provide empirical evidence of nonlinearities in
the link between customer satisfaction and sales performance. In particular, they ﬁnd that
for positive changes in satisfaction, the satisfaction-performance function is positive at a
decreasing rate, while for negative changes the function decreases at a decreasing rate.

Based on this review, research on the link between customer satisfaction and
performance has provided inconclusive ﬁndings except that a relationship exists. In
particular, research suggests that increases in customer satisfaction will lead to superior
performance up to a certain point. After this point, the rising costs of improving
satisfaction will outweigh the beneﬁts (e.g., Anderson and Mittal 2000). In addition, an
excessive focus on satisfaction may detract the ﬁrm from other important factors, such as

productivity, which are also critical for ﬁrm performance. Consistent with this logic, the

103

relationship between customer satisfaction and performance is likely to include an
inﬂection point where achieving a greater degree of customer satisfaction adversely
affects the ﬁrm’s bottom-line performance. Stated formally:
H5: Satisfaction of the fnm’s end customers has an inverted U-shaped
relationship with the ﬁrm’s (a) retum-on-sales, (b) retum-on-assets, and
(c) market performance (i.e., there is an inﬂection point where achieving a
greater degree of end customer satisfaction adversely affects the ﬁrrn’s
performance).
METHOD
Data Collection
To address the linkages involved in the ﬁve hypotheses, four separate but
complementary databases were used to create the sample: (1) The American Customer
Satisfaction Index (ACSI); (2) Mergent Horizon; (3) Kinder Lydenburg Domini
Statistical Tool for Analyzing Trends in Social and Environmental Performance (KLD);
and (4) Standard & Poor’s Compustat North America database (Compustat). Firms were
studied using four years of data taken prior to the drastic economic downturn that started
in the spring of 2008. Four years of data were selected to employ a lagged structure for
the empirical analysis (i.e., one year for each set of variables) to allow for causal
inferences (Palmer and Wiseman 1999). Complete data from the four databases,
spanning the years ﬁ'om 2004 to 2007, were obtained for n=138 ﬁrms. Directly tied to
these 138 ﬁrms, stakeholder data were obtained from the entities of these focal ﬁrms’
supply chains along with data from their major competitors. For each of the focal ﬁrms

(n=138), an average of 8.18 business-to—business customers (total n=1,129, range: 1-107),

27.86 primary suppliers (total n=3,844, range: 1-225), 23.71 strategic partners (total

104

0b

=3,272, range: 1-352), and 20.12 major competitors (total n=2,777, range: 1-202) were
obtained (see Figure 7).
FIGURE 7

Sample Size of Focal Firms and their Connections to Entities
in the Supply Chain Environment and Marketplace

 

Primary
Suppliers
n=3,844
mean=27.86
range= l -225

 

 

 

 

 

 

 

Strategic Major

Partners Focal Competitors

n=3,272 Firm n=2,777
mean=23.7l n=138 mean=20.12
range=l-352 range=1-202

 

 

 

 

 

 

 

 

 

 

 

 

 

BZB
Customers

n=1,129
mean=8.l8
range=1-107

 

 

 

ACSI was used for customer satisfaction as a part of the relationships depicted in
Figure 6. The ACSI database included the fewest ﬁrms of the four databases and,
consequently, placed the greatest constraints on the resulting sample size that could be
obtained across the databases. Out of the 194 ﬁrms that are currently assessed annually as
a part of the ACSI, a sample size of n=l 38 ﬁrms was obtained that had complete data
across the four databases. ACSI served as the database to collect customer satisfaction

data for the focal ﬁrms in this study (e. g., Anderson, F omell, and Mazvancheryl 2004;

105

Luo and Hombm'g 2007). As a background, the ACSI metric was created in 1994, is
reported on a 0 to 100 level, and is regularly used in the marketing literature as a robust
indicator of customer satisfaction at the ﬁrm level (e.g., F omell et al. 2006).

Mergent Horizon is a database that provides information on ﬁrms and industries
along with the capabilities for ﬁnding, ﬁltering, and organizing information on a ﬁrm’s
business-to-business customers, primary suppliers, strategic partners, and major
competitors. Mergent Horizon was used to identify the focal ﬁrm’s business-to-business
customers, primary suppliers, strategic partners, and major competitors. Data pertaining
to the stakeholder focus of these entities were then collected from KLD. In the Mergent
Horizon database, comprehensive coverage is available for more than 6,200 ﬁrms that are
actively traded in the US. Mergent Horizon was developed by Mergent, Inc. — a leading
provider of business and ﬁnancial data on publicly listed global ﬁrms that was founded in
1900.

KLD is a dataset of ﬁrms rated by KLD Research and Analytics, Inc., a social
investment ﬁrm, which began the ratings in 1991 (initially including 650 ﬁrms and now
assessing about 3,100 ﬁrms). Through their commercially available database of corporate
ratings, SOCRATES, KLD Research Analytics, Inc. provides stakeholder-related ratings
on over 90 indicators in seven major areas: community, corporate governance, diversity,
employee relations, environment, human rights, and products. The indicators include both
positive and negative ratings (strengths and concerns). Their ratings are based on ﬁve
sources: (1) direct communication with ﬁrms, (2) global research ﬁrms, (3) media, (4)

public documents, and (5) government and NGO information. KLD is used widely in

106

studies on stakeholders (e.g., Berman et al. 1999; Hillman and Keim 2001; Sen and
Bhattacharya 2001; Waddock and Graves 1997).

Compustat is a widely used database which includes ﬁnancial and market
information data on some 24,000 active and 10,000 inactive publicly traded ﬁrms in the
US. and Canada. The database provides thousands of income statement, balance sheet,
statement of cash ﬂows, and supplemental data variables. Compustat was used to collect
data on R&D, goodwill, ﬁrm size, ﬁrm age, retum-on-sales, return-on-assets, and Tobin’s
Q. Compustat has been used in a large number of marketing studies to measure
performance variables. For example, recent marketing studies used Compustat data when
researching brands in mergers and acquisitions (Bahadir, Bharadwaj, and Srivastava
2008), customer satisfaction (Luo and Homburg 2008), brand portfolio strategy (Morgan
and Rego 2009), and innovation in consumer packaged goods (Sorescu and Spanjol
2008)

Study Measures

For theory testing purposes, a stakeholder focus involves an equally weighted
integration of attributes associated with customers, suppliers, employees, regulators,
shareholders, and the community. The measures to assess these dimensions were
obtained ﬁom KLD STATS. A battery of formative measures used in several previous
studies (e.g., Berman et al. 1999; Hillman and Keim 2001; Sen and Bhattacharya 2001;
Waddock and Graves 1997) was included: 6 items for customers, 20 items for employees,
3 items for suppliers, 9 items for shareholders, 7 items for regulators, and 20 items for
community based on theoretically deﬁned properties (see Appendix B). These items

centered on issues such as “the company’s products have notable social beneﬁts that are

107

highly unusual or unique for its industry” (customers); “the company has outstanding
employee beneﬁts or other programs addressing work/life concerns, e.g., childcare, elder
care, or ﬂextime” (employees); “the company does at least 5% of its subcontracting, or
otherwise has a demonstrably strong record on purchasing or contracting, with women-
and/or minority-owned businesses” (suppliers); “the company owns between 20% and
50% of another company KLD has cited as having an area of social strength, or is more
than 20% owned by a ﬁrm that KLD has rated as having social strengths” (shareholders);
“the company has shown markedly responsible leadership on public policy issues and/or
has an exceptional record of transparency and accountability concerning its political
involvement in state or federal-level US. politics, or in non-US. politics” (regulators);
and “the company has consistently given over 1.5% of trailing three-year net earnings
before taxes to charity, or has otherwise been notably generous in its giving”
(community).

The scores for each dimension were adjusted based on the number of items to
standardize the effects (cf. Graves and Waddock 1994). For each stakeholder dimension,
a total score was calculated by adding KLD items that were labeled as strengths and
subtracting those labeled concerns. The average stakeholder scores for the focal ﬁrms
(n=138) ranged ﬁom -.43 to .38 for community (J? = .01), -l.00 to .50 for suppliers (3? = -
.06), -.35 to .61 for employees ()7 = .07), -.67 to .00 for regulators (f = -.11), -.40 to .25
for shareholders (J? = -.15), and -1.00 to .25 for customers (a? = -.22). Overall stakeholder
scores were obtained for the antecedents (business-to-business customers, primary
suppliers, strategic partners, and major competitors) in 2004 and for the focal ﬁrms in

2005. For these years, the overall stakeholder averages were 55 = -.06 for the focal ﬁrms,

108

f = -.09 for the business-to-business customers, a? = -.04 for the primary suppliers, 1? = -
.07 for the strategic partners, and f = -.07 for the major competitors.

The moderator variable of R&D was obtained ﬁ'om the Compustat dataset based
on 2004 data. In accordance with its inclusion in Compustat, R&D is deﬁned directly on
the Financial Accounting Standards Board (1974) deﬁnition: “Research is planned search
or critical investigation aimed at discovery of new knowledge with the hope that such
knowledge will be useful in developing a new product or service or new process or
technique or in bringing about a signiﬁcant improvement to an existing product or
process. . .development is the translation of research ﬁndings or other knowledge into a
plan or design for a new product or process or for a signiﬁcant improvement to an
existing product or process whether intended for sale or use.” The average R&D for the
focal ﬁrms (n=138) was 623.77 million with a range from .00 to 7,779.00 and a standard
deviation of 1,684.73 in 2004.

Customer satisfaction scores for the n=138 focal ﬁrms were drawn from the ACSI
database using 2006 scores (Anderson, Fomell, and Mazvancheryl 2004; Luo and
Homburg 2007). This satisfaction metric is based on over 65,000 yearly interviews and
has three components: perceived quality, customer expectations, and perceived value
among customers (Fomell et al. 1996). With the data based on more than 65,000
customer interviews (approximately 335 customer responses per ﬁrm) and being reported
on a scale ﬁ‘om 0 to 100, the focal ﬁrms’ (n=138) ACSI scores for 2006 averaged 75.36
with a range from 55 to 87 and a standard deviation of 5.96.

Data to calculate the three performance variables (i.e., retum-on-sales, return-on-

assets, and Tobin’s Q) were obtained ﬁom Compustat based on 2007 ﬁgures. ROS has

109

been shown to be a good ﬁnancial performance variable to benchmark against
competitors, because it provides insights into the ﬁrms’ pricing and cost structure (Day
and Wensley 1988). The ROS ratio is calculated as the net income divided by total sales
(revenue). A low ROS ratio indicates that low earnings are generated from revenues to
pay for ﬁxed expenses and achieve proﬁts. A low ROS is also a potential signal that a
ﬁrm is unable to control its production and marketing expenses. The focal ﬁrms’ average
ROS in 2007 was .07 with a range from -.74 to .43 and a standard deviation of .10.

ROA is an appropriate performance measure to assess what the ﬁrm can do with
the assets that it possesses (Short et al. 2007). In essence, ROA projects the amount of
proﬁt a ﬁrm can achieve with the amount of assets it controls. Inna-industry comparisons
are usually effective while inter-industry comparisons are often too different and
inconsistent to be valuable. The ROA ratio is calculated as the net income divided by
total assets. A high ROA is attributable to a high proﬁt margin, a rapid turnover of assets,
or a combination of both. A low ROA ratio, compared with industry averages, indicates
an inefﬁcient use of business assets. The focal ﬁrms’ average ROA in 2007 was .05 with
a range of -.46 to .34 and a standard deviation of .08.

Tobin’s Q was used to measure a ﬁrm’s market performance (i.e., overall market
effectiveness). The Q ratio was developed by James Tobin, a Nobel laureate in
economics, based on the notion that the collective market value of all publicly traded
ﬁrms should be equal to their replacement costs (e.g., Tobin 1978). In that sense, Tobin’s
Q is a measure of a ﬁrm’s “market value” (Short et al. 2007). The formula for Tobin’s Q
includes “the sum of the market value of equity, the book value of debt, and deferred

taxes divided by the book value of total assets minus intangible assets” (Thomas and

110

Waring 1999, p. 739). A low Q value (between 0 and 1) means that the replacement costs
of the ﬁrm’s assets is greater than its market value (the stock is undervalued). A high Q
value (greater than 1) indicates that a ﬁrm’s market value is more expensive than the cost
of replacing its assets (the stock is overvalued). The focal ﬁrms’ average Q ratio in 2007
was 1.48 with a range from 1.00 to 6.51 and a standard deviation of .76.

The ﬁnal set of measures used in the model testing included a series of control
variables (i.e., the focal ﬁrm’s goodwill in 2004, size in 2005 and 2006, and age in 2005
and 2006). The goodwill (value of a ﬁrm above its net asset value), size (natural log of
the focal ﬁrms’ total number of employees), and age variable (natural log of the focal
ﬁrms’ years in existence) measures were obtained from Compustat. Table 8 reports the
average, range, and standard deviation for each of these variables. The focal fnm’s
goodwill values pertain to 2004, while the ﬁrm size and age contain the 2005 values. As
logically expected, the average age for the focal ﬁrms increased by one year in 2006 from

that in 2005, as did the range of years, while the standard deviation stayed the same.

 

 

 

TABLE 8
Average, Range, and Standard Deviation

of the Control Variables

Average Std. Dev. Range
Goodwill“ (in thousands) 5,038.46 10,897 .00 - 71,191.00
Fm” 3'26 (Pum'f' 0f b 96.72 185.16 70 — 1,900
employees in thousands)
F'm' i‘g" (“m“b“ 0f 41.98 33.95 .00 — 153
WIN)
a 2004 values

b 2005 values

111

Hierarchical Regression and Polynomial Regression

The relationships detailed in Hypotheses 1 through 5 and depicted in Figure 6
were tested via hierarchical regression. H1 through H3 were tested simultaneously in one
model, with the control variables (focal ﬁrm’s stakeholder focus and goodwill) and
potential direct effect of the moderator variable (R&D) entered in step I, followed by the
direct effects of the stakeholder focus of the focal ﬁrm’s business-to-business customers,
primary suppliers, strategic partners, and major competitors entered in step 2, and the
four moderators involving R&D entered in step 3. The stakeholder scores for the focal
ﬁrm’s business-to-business customers, primary suppliers, strategic partners, and major
competitors were averaged.

The relationships involved in H4 and H5 were tested via hierarchical polynomial
regression. For H4, the control variables (prior year’s satisfaction score for the focal ﬁrm
and natural log of the ﬁrm’s age and size) were entered in step 1, with the focal ﬁrm’s
positive and negative stakeholder scores entered in step 2, and the curvilinear variable for
each of the positive and negative stakeholder scores entered in step 3. As such, the
stakeholder score for the focal ﬁrm was disaggregated into the KLD items that are
viewed as positive and those that are viewed as negative inﬂuences to tease out potential
differences in how ﬁrms tackle each type. For H5, the control variables (prior year’s
performance score for the focal ﬁrm and natural log of the ﬁrm’s age and size) were
entered in step 1, with the focal ﬁrm’s satisfaction score entered in step 2, and the

curvilinear variable for satisfaction entered in step 3.

112

RESULTS

The dataset involving a sample size of n=138 focal ﬁrms and their, on average,
8.18 business-to-business customers (total n=1,129, range: 1-107), 27.86 primary
suppliers (total n=3,844, range: 1-225), 23.71 strategic partners (total n=3,272, range: 1-
352), and 20.12 major competitors (total n=2,777, range: 1-202) was used to empirically
assess Hypotheses 1 to 5 using a lagged structure involving data from 2004 to 2007. The
correlation matrix is not included because of its complexity. It is available upon request.
Antecedents, Moderators, and Stakeholder Focus of the Firm

The examination of Hypotheses l to 3 was conducted in one simultaneous
hierarchical regression involving the direct effects of the stakeholder focus of the focal
ﬁrm’s business-to-business customers, primary suppliers, strategic partners, and major
competitors on the frrm’s stakeholder focus (111 to H2) along with the moderating effects
of R&D (H3). The antecedents and moderator variables were taken in 2004 and the
stakeholder focus of the focal ﬁrm was taken in 2005 to allow for causality to be inferred
ﬁom the analysis. The ﬁrm’s stakeholder focus in 2004, goodwill in 2004, and R&D in
2004 were included as control variables.

As can be seen in Table 9, a number of signiﬁcant relationships were detected at an
effect size of B>.99 (a=.01). Based on the step 3 results, the control variables of the
ﬁrm’s stakeholder focus in 2004 (|3=.49, p<.01) and goodwill in 2004 (B=.-.34, p<.01)
were signiﬁcant, but the ﬁrm’s R&D investment in 2004 was not. The direct effects of
the B2B customers’ stakeholder focus in 2004 (13:66, p=.03) and the competitors’
stakeholder focus in 2004 (0:.16, p=.08) signiﬁcantly affected the focal ﬁrm’s

stakeholder focus in 2005. The effects between the primary suppliers’ and strategic

113

partners’ stakeholder focus on one hand and the focal ﬁrm’s stakeholder focus on the
other were insigniﬁcant. R&D moderated two of the relationships: the B2B customers’
stakeholder focus (B=.8l, p<.01) and primary suppliers’ stakeholder focus (B=.-.35,
p<.01) on the focal ﬁrm’s stakeholder focus. The adjusted R2=.72 for the model (F-
value=7.79, p<.01). The AR2=.11 (p=.05) between step 2 (direct effects) and step 3

(moderators). These results indicate that Hypotheses la, 2, and 3a were supported.

114

Hierarchical Regression Results with the Firm’s

TABLE 9

Stakeholder Focus in 2005 as the Criterion Variable

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Standardized . . Signiﬁcance
Beta (P) t-Statlstlc (p)

Step 1: Controls
Firm’s Stakeholder Focus 2004 .67 5.54 <.01
F irm’s Goodwill 2004 -.28 -2.32 .13
Firm’s R&D Investment 2004 .19 1.57 .03
R-square=.65
Adjusted R-square=.6l
F-value=16.38 (p<.01)
Step 2: Direct Effects
Firm’s Stakeholder Focus 2004 .55 4.06 <.01
Firm’s Goodwill 2004 -.31 -2.62 <.05
F irm’s R&D Investment 2004 .20 1.64 .12
Customers’ Stakeholder Focus 2004 .01 .05 .48
Suppliers’ Stakeholder Focus 2004 .00 .01 .49
Partners’ Stakeholder Focus 2004 .17 1.08 .14
Competitors’ Stakeholder Focus 2004 .24 1.97 <.05
R-square=.72
Adjusted R-square =.63
AR-square =.07 (p=.30)
F-value=8.07 (p<.01)
Step 3: Moderators
Firm’s Stakeholder Focus 2004 .49 3.81 <.01
Firm’s Goodwill 2004 -.34 -3.25 <.01
Firm’s R&D lnvestrnent 2004 -. 12 -.65 .52
Customers’ Stakeholder Focus 2004 .66 1.94 .03
Suppliers’ Stakeholder Focus 2004 -.11 -.81 .21
Partners’ Stakeholder Focus 2004 .09 .59 .28
Competitors’ Stakeholder Focus 2004 .16 1.43 .08
R&D "‘ Customers SF 2004 .81 1.95 .06
R&D * Suppliers SF 2004 -.35 2.56 .02
R&D * Partners SF 2004 .00 .02 .98
R&D * Competitors SF 2004 -.18 -1 .29 .22
R-square=.83
Adjusted R-square =.72
AR-square =.11 (p=.05)
F-value=7.79 (p<.01)
Effect Size: §>.99 (a=.01)

 

 

 

 

 

One-tailed tests were used for directional relationships (business-to-business customers, primary
suppliers, strategic partners, and major competitors) and two-tailed tests were used for all other
relationships (control variables and the four R&D moderator variables).

115

Stakeholder Focus of the Firm and Satisfaction

The examination of Hypothesis 4 with the focal ﬁrm’s customer satisfaction
score in 2006 was conducted via a hierarchical polynomial regression. This involved the
ﬁrm’s size (log) in 2005, age (log) in 2005, and customer satisfaction score in 2005 as
controls. The stakeholder focus of the ﬁrm in 2005 was disaggregated into a “positive”
stakeholder focus (i.e., KLD items labeled strengths) and a “negative” stakeholder focus
(i.e., KLD items labeled concerns) to examine the complexities involving attending to
and disregarding stakeholders. As such, the focal ﬁrm’s positive and negative stakeholder
scores from 2005 were also included as direct controls on the ﬁrm’s BZC customer
satisfaction in 2006 (see Table 10). The hypothesized focus was placed on the curvilinear
effects involving the polynomials of the ﬁrm’s positive stakeholder focus and negative
stakeholder focus on satisfaction. Collectively, these variables resulted in an equation
with an effect size of B>.99 (o.=.01), an adjusted R2 score of .86, and a signiﬁcant AR2
between step 2 (controls and direct effects) and step 3 (positive and negative
polynomials). The ﬁrm’s customer satisfaction score in 2005 had a positive effect on
satisfaction in 2006 (B=.93, p<.01), as did the direct effect of a ﬁrm’s negative
stakeholder focus in 2005 (B=.l l, p=.05). The polynomial involving negatively phrased
KLD items (i.e., concerns) had a signiﬁcant curvilinear effect on the ﬁrm’s B2C
satisfaction in 2006 (B=-. 13, p=.01) while the polynomial involving the positively worded
KLD items (i.e., strengths) was insigniﬁcant. As such, Hypothesis 4 is partially

supported.

116

TABLE 10

Hierarchical Regression Results with Satisfaction of the
Firm’s B2C Customers in 2006 as the Criterion Variable

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Stagedtzrzlprged t-S ta tis tic Srgnr(l;;ance
Step 1: Controls
F irrn’s Customer Satisfaction 2005 .92 24.53 <.01
Firm’s Size 2005 (Log People) .09 2.28 .03
Firm’s Age 2005 (LogYears) .01 .24 .82
R-square =.86
Adjusted R-square =.85
F-value=209.50 (p<.01)
Step 2: Direct Effects
F irm’s Customer Satisfaction 2005 .92 24.25 <.01
Finn’s Size in 2005 (Lﬂople) .07 1.50 .14
Firm’s Age in 2005 (Loglears) .01 .18 .86
Positive Stakeholder Focus 2005 .02 .37 .71
Negative Stakeholder Focus 2005 .02 .41 .68
R-squarc =.86
Adjusted R-square =.85
AR-square =.00 (p=.84)
F-value=123.80 (p<.01)
Step 3: Curvilinear Effects
Firm’s Customer Satisfaction 2005 .93 24.76 <.Ol
Firm’s Size 2005 (liogPeople) .07 1.59 .11
F irm’s Age 2005 (Log Years) -,00 -,09 _93
Positive Stakeholder Focus 2005 .07 1.25 .21
Negative Stakeholder Focus 2005 .11 1.97 .05
(Positive Stakeholder Focus 2005)"2 -.07 -l .28 .20
(Negative Stakeholder Focus 2005)"2 -.13 -2.71 .01

 

R-square =.87

 

Adjusted R-square =.86

 

AR-square =.01 (p=.02)

 

F-value d95.41 (p<.01)

 

 

Effect Size: B>.99 (a=.01)

 

 

 

 

117

 

Satisfaction and Performance

The examination of Hypothesis 5 was conducted in three separate hierarchical
polynomial regressions. The equations involved direct effects of four control variables on
retum-on-sales in 2007, retum-on-assets in 2007, and Tobin’s Q in 2007: the focal ﬁrm’s
performance (return-on-sales, retum-on-assets, and Tobin’s Q respectively) in 2006, size
(log) in 2006, age (log) in 2006, and customer satisfaction score in 2006. The
hypothesized focus was placed on the curvilinear effects of the ﬁrm’s customer
satisfaction in 2006 on retum-on-sales in 2007, retum-on-assets in 2007, and Tobin’s Q
in 2007. Involving these variables resulted in all three equations with effect sizes of
B>.99 (a=.01), adjusted R2 scores ranging from .33 to .90, and signiﬁcant AR2 for all
three models between step 2 (controls) and step 3 (customer satisfaction squared). As can
be seen in Tables 11, 12, and 13, the three models also resulted in parallel results in that
each of the performance control variables in 2006 (i.e., retum-on-sales, retum-on-assets,
and Tobin’s Q), respectively, affected their corresponding performance variable in 2007
(the B ranged ﬁ'om .48 to .96, p<.01). The polynomials (satisfaction squared) in each of
the performance equations were signiﬁcant and negative (i.e., inverted-U shape): B=-.22
(p=.01) in the retum-on-sales model, B=-.l8 (p=.02) in the retum-on-assets model, and

B=-.06 (p=.08 in the Tobin’s Q model. These results support Hypotheses 5a, 5b, and 5c.

118

TABLE 11

Hierarchical Regression Results with Return-on-Sales in 2007

as the Criterion Variable

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Standardized . . Signiﬁcance
Beta (15) t-Statrstrc (p)

Step 1: Controls
Firm’s Return-on-Sales 2006 .58 7.37 <.Ol
Firm’s Size 2006 (Log People) -.12 -l.53 .13
Firm’s 5372006 (Mars) -.02 -.25 .80
R-square =.34
Adjusted R-square =.32
F-value=l 8.74 (p<.01)
Step 2: Direct Effect
F irm’s Return-on-Sales 2006 .54 6.73 <.01
Finn’s Size 2006 (Log People) -.12 -1.53 .13
Finn’s Age 2006 Mars) -.05 -.63 .53
Firm’s Customer Satisfaction 2006 .14 1.64 .10
R-square =.36
Adjusted R-square =.34
AR-square =.02 (p=.10)
F-value=14.94 (p<.01)
Step 3: Curvilinear Effect
Firm’s Return-on-Sales 2006 .48 5.85 <.01
Firm’s Size 2006 (Log People) -.12 -l .61 .11
Firm’s Age 2006 (Log Years) -.06 -.72 .47
Firm’s Customer Satisfaction 2006 .13 1.57 .12
(Firm’s Customer Satisfaction 2006)"2 -.22 -2.75 .01
R-square =.40
Adjusted R-square =.38
AR-square =.04 (p<.01)
F-value=14.20 (p<.01)
Effect Size: B>.99 (a=.01)

 

 

119

 

TABLE 12

Hierarchical Regression Results with Return-on-Assets in 2007
as the Criterion Variable

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Standardized . . Signiﬁcance
Beta (13) t-Statrstrc (p)

Step 1: Controls
Finn’s Return-on-Assets 2006 .58 7.13 <.01
Firm’s Size 2006 (Log People) -.12 -1.45 .15
Firm’s Agg 2006 (Log Years) -.05 -.57 .57
R-square =.32
Adjusted R-square =.30
F-value=l7.05 (p<.01)
Step 2: Direct Effect
Firm’s Retum-on-Assets 2006 .55 6.34 <.01
Firm’s Size 2006 (Log People) -.11 -1.37 .17
F irm’s Age 2006 (LogYears) -.07 -.80 .43
Firm’s Customer Satisfaction 2006 .09 1.07 .29
R-square =.33
Adjusted R-sqw =.30
AR-square =.01 (p=.29)
F -value=1 3.09 (p<.01)
Step 3: Curvilinear Effect
Firm’s Return—on-Assets 2006 .54 6.39 <.01
Firm’s Size 2006 (weople) -.12 -1.46 .15
F irm’s Age 2006 (Log Years) -.07 -.80 .43
Firm’s Customer Satisfaction 2006 .07 .84 .41
(Firm’s Customer Satisfaction 2006)"2 -.18 -2.34 .02
R-square =.36
Adjusted R-square =.33
AR-square =.03 (p=.02)
F-value=12.00 (p<.01)
Effect Size: 13>.99 (a=.01L

 

120

 

TABLE 13

Hierarchical Regression Results with Tobin’s Q in 2007 as the Criterion Variable

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Standardized . . Signiﬁcance
Beta (13) t Statistic (p)

Step 1: Controls
Firm’s Tobin’s Q 2006 .95 27.27 <.01
F irm’s Size 2006 (LogPeople) .01 .26 .80
Firm’s Age_2006 (LoLYears) .04 1.00 .32
R-square =.89
Adjusted R-square =.89
F-value=247.87 (p<.01)
Step 2: Direct Effect
Firm’s Tobin’s Q 2006 .94 27.14 <.01
F irm’s Size 2006 (Log People) .01 .22 .83
Firm’s Age 2006 (Logiears) .03 .74 .46
F irm’s Customer Satisfaction 2006 .04 1.08 .28
R-square =.89
Adjusted R-square =.89
AR-square =.00 (p=.28)
F-value=186.53 (p<.01)
Step 3: Curvilinear Effect
F irm’s Tobin’s Q 2006 .96 27.35 <.01
Firm’s Size 2006 (Log People) .01 .26 .80
Firm’s Age 2006 (L3 Years) .03 .87 .39
Firm’s Customer Satisfaction 2006 .04 1.19 .24
(F irm’s Customer Satisfaction 2006)"2 -.06 -1.77 .08
R-square =.90
Adjusted R-square =.90
AR-square =.011p=.08)
F-value=153.50 (p<.01)
Effect Size: B>.99 (a=.01)

 

121

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DISCUSSION AND IMPLICATIONS

This research studied the inﬂuence of the stakeholder focus of various entities
within a focal ﬁrm’s supply chains and marketplace on the stakeholder focus of the focal
ﬁrm. In addition, it investigated the impact of a ﬁrm’s stakeholder focus on customer
satisfaction and reexamined the customer satisfaction—performance relationship. The
conceptual model was tested with secondary data obtained from four different databases.
The results indicate that the stakeholder focus of the focal ﬁrm’s business-to-business
customers, primary suppliers, and major competitors has a direct or moderated effect on
the focal ﬁrm’s stakeholder focus. The analysis also reveals that while a negative
stakeholder focus has an inverted U-shaped relationship with the satisfaction of the ﬁrm’s
end customers, a positive stakeholder focus has no effect on customer satisfaction. In
turn, the relationship between customer satisfaction and ﬁrm performance is in the form
of an inverted-U, as there is an inﬂection point where achieving a greater degree of
customer satisfaction, adversely affects the ﬁrm’s performance.

By extension, the empirical ﬁndings of U-shaped relationships indicate that ﬁrms
possibly have multi-curve relationships (e.g., S-shaped) involving their stakeholder focus
and customer satisfaction, and customer satisfaction and performance, respectively, if
they stay in business. That is, ﬁrms that stay in business are likely to experience their
downward trend (negative portion of the inverted U-shaped curve) to ﬂatten and/or turn
upwards. On the other hand, if the curve continues a downward pattern, the ﬁrm is likely
to go bankrupt at some point. Thus, the inverted U-shaped relationships are both
indicative of “more is not always better” and of a deeper level of complexity involving a

ﬁrm’s stakeholder focus, customer satisfaction, and performance than previous linear

122

relationships. These ﬁndings involving satisfaction, along with those of the institutional
theory-based relationships addressing a ﬁrm’s stakeholder focus and R&D, have
important implications for both research and managerial practice.

From an institutional theory perspective, this study sheds new light on the fnm’s
imitation of stakeholder practices of different types of organizations within its supply
chain environment and marketplace. Moreover, it explores the role of R&D. It was
demonstrated that imitation between ﬁrms in the supply chain can occur moving
upstream (cf. McFarland, Bloodgood, and Payan 2008), as the business-to-business
customers’ stakeholder focus affects the focal ﬁrm’s stakeholder focus. The business-to-
business customers represent an important source of revenues for the ﬁrm and as such,
the focal ﬁrm is dependent on them. This, in turn, leads the ﬁrm to copy the behaviors
exhibited by its business-to-business customers. In addition, the results indicate that the
more the ﬁrm invests in R&D, the more its stakeholder focus will resemble that of its
B2B customers. One possible explanation is that the ﬁrm invests in R&D in part to
provide innovative solutions to its B2B customers. These investments create greater
dependence on the BZB customers, which increases the level of imitation (e.g., DiMaggio
and Powell 1983). In contrast, the analysis reveals that the opposite occurs when it comes
to the focal ﬁrm’s primary suppliers. In particular, the more a ﬁrm invests in R&D, the
less it will be inﬂuenced by the stakeholder focus of its suppliers. One reason for this is
that as the focal ﬁrm invests in R&D, it becomes less dependent on its suppliers to obtain
strategic resources such as information. This, in turn, lowers the level of imitation, and
hence, the focal ﬁrm’s stakeholder focus is less affected by its suppliers’ stakeholder

focus.

123

Moreover, the results indicate that the major competitors’ stakeholder focus is
positively related to the stakeholder focus of the focal ﬁrm. This suggests that ﬁrms not
only pay close attention to the stakeholder practices of their competitors (e.g., Narver and
Slater 1990), but consistent with the tenets of institutional theory (e. g., DiMaggio and
Powell 1983) and the literature on competitive strategy (e.g., Dickson 1992), they also
respond by mimicking their competitors’ actions. In this context, future research could
examine whether ﬁrms respond in the same manner to the positive and negative actions
of other entities. It is possible that ﬁrms are more likely to imitate positive actions and
less likely to imitate the negative actions of other organizations in their environment.

In addition, by developing the stakeholder focus concept, this study makes a
Signi ﬁcant step toward broadening the scope of marketing to emphasize additional
stakeholders beyond customers (e.g., Maignan and Ferrell 2004). At the same time, this
study contributes to stakeholder theory (e. g., Jones 1995) by incorporating customer
satisfaction as an intermediate marketing outcome that links stakeholder management and
performance. In this context, the ﬁndings suggest that the relationship between a ﬁrm’s
Stakeholder focus and customer satisfaction is more complex than was originally
Pmdicted. Speciﬁcally, while the results provide support for an inverted U-shaped
relationship between a negative stakeholder focus and customer satisfaction, no such
Effect emerged for a positive stakeholder focus.

This suggests that stakeholder management is a zero sum game. Stakeholders
have competing interests, and as such, there are tradeoffs among the various stakeholders.
For eXarnple, while customers want lower prices, employees want salary increases,

communities want charitable contributions, and shareholders want an increase in their

124

wealth. Due to the conﬂicting nature of these demands and the ﬁrm’s ﬁnite resources, it
is highly unlikely that a ﬁrm is able to satisfy them all. Hence, it seems like one
stakeholder group gains at the expense of the others. This would explain why investing
signiﬁcant resources, attention, and time to address the claims of the six primary
stakeholder groups (i.e., customers, employees, suppliers, shareholders, regulators, and
the community) by engaging in positive stakeholder practices does not lead to an
improvement in the level of customer satisfaction. A stakeholder-focused ﬁrm that seeks
to address the claims of the six primary stakeholders has to balance their competing
claims. As a result, it will effectively meet some of the demands of the six stakeholders,
but will also leave other demands unmet. From the customers’ viewpoint, since the ﬁrm
has met some of their needs and wants, but has failed to deliver on several aspects, they
are neither satisﬁed nor dissatisﬁed with the ﬁrm.

The opposite seems to occur when it comes to a negative stakeholder focus, since
disregarding some stakeholder demands increases the satisfaction of the end customers
up to a point. One explanation for this is that ﬁrms give priority to the interests of the
customers and fail to attend to the claims of other stakeholder groups to be able to
allocate more resources to achieving customer satisfaction. Since the ﬁrm is meeting the
customers’ needs and wants, the result is an increase in customer satisfaction. In this
case, the customers win, at the expense of the other stakeholders. However, after a certain
point, engaging in more negative stakeholder practices harms customer satisfaction. At
these higher levels of a negative stakeholder focus, the ﬁrm is ignoring the interests of its
stakeholders, including those of its customers. Therefore, these ﬁndings are consistent

with the tenets of the instrumental approach to stakeholder theory (e. g., Donaldson and

125

Preston 1995) in that managing stakeholder relationships is essential for the ﬁrm, because
not doing so can be detrimental for the achievement of corporate objectives (e. g.,
customer satisfaction). However, contrary to the predictions of such theory, attending to
the interests of the six primary stakeholders does not necessarily result in a competitive
advantage (cf. Jones 1995).

While possible explanations for the ﬁndings on the stakeholder focus—customer
satisfaction link have been advanced, research exploring the causes of these ﬁndings is
needed. Future studies can disaggregate the stakeholder focus construct into the six
dimensions (i.e., customers, employees, suppliers, shareholders, regulators, and the
community) to examine whether addressing the interests of select stakeholder groups
matters more for the satisfaction of the end customers than other groups. It is possible
that a ﬁrm that attends more to its customers, employees, and suppliers has a different
level of customer satisfaction than one that attends to its customers, regulators, and
shareholders. Also, by disaggregating the stakeholder focus into its different dimensions,
future research can examine if paying attention to a particular stakeholder group results in
a decrease in customer satisfaction. Furthermore, these effects may vary by economic
sector. In addition, by conducting research at the customer level, future studies can
determine if customers are aware of the different positive and negative stakeholder
practices of ﬁrms and whether this has an impact on their level of satisfaction with the
ﬁrms or with their brands.

Another contribution this study makes is that it is among the ﬁrst to empirically
demonstrate an inverted U-shaped relationship between customer satisfaction and

performance. This suggests that customer satisfaction leads to superior performance up to

126

an optimal point, after which attaining a greater degree of customer satisfaction
negatively affects performance. While this ﬁnding provides support for Anderson and
Mittal’s (2000) arguments for the nonlinear nature of this link - i.e., customer satisfaction
does not always lead to superior performance given that efforts directed at increasing the
level of satisfaction consume ﬁrm resources — it contradicts the direct positive linear
relationship that has been obtained in previous research (e. g., Anderson, F omell, and

Mazvancheryl 2004; Gruca and Rego 2005; Luo and Bhattacharya 2006).

127

Label in
the KLD
Database

PRO—str-A

PRO-str-B

PRO-str-C

PRO-str-X

PRO-con-A

PRO-con-D

DIV-str-A

DlV-str—B

DlV-str-C

DlV-str-D

APPENDIX B

Formative Measures of the Stakeholder Dimensions

(from the KLD STATS Ratings)

Stakeholder Dimension
and Item Description

Einstein—erg

Quality. The company has a long-term, well-developed, company-
wide quality program, or it has a quality program recognized as
exceptional in US. industry.

R&D/Innovation. The company is a leader in its industry for
research and development (R&D), particularly by bringing notably
innovative products to market.

Benefits to Economically Disadvantaged. The company has as part
of its basic mission the provision of products or services for the
economically disadvantaged.

Other Product Strength. The company's products have notable
social beneﬁts that are highly unusual or unique for its industry.

Product Safety. The company has recently paid substantial ﬁnes or
civil penalties, or is involved in major recent controversies or
regulatory actions, relating to the safety of its products and services.

Marketing/Contracting Concern. The company has recently been
involved in major marketing or contracting controversies, or has
paid substantial ﬁnes or civil penalties relating to advertising
practices, consumer fraud, or government contracting.

Employees

CEO. The company's chief executive officer is a woman or a
member of a minority group.

Promotion. The company has made notable progress in the
promotion of women and minorities, particularly to line positions
with proﬁt-and-loss responsibilities in the corporation.

Board of Directors. Women, minorities, and/or the disabled hold
four seats or more (with no double counting) on the board of
directors, or one-third or more of the board seats if the board
numbers less than 12.

Work/Life Benefits. The company has outstanding employee

beneﬁts or other programs addressing work/life concerns, e.g.,
childcare, elder care, or ﬂextime.

128

Identiﬁed as a
Strength /
Concern in the
KLD Database

Strength

Strength

Strength

Strength

Concern

Concern

Strength

Strength

Strength

Strength

Label in
the KLD
Database

DIV-str-F

DIV-Str-G

DlV-str-X

DlV-con-B

DlV—con-X

EMP-str-A

EMP-str-C

EMP—str—D

EMP—str-F

EMP-str-G

EMP-str-X

Identified as a
Strength /
Concern in the
KLD Database

Item Description

Employees Continued

Employment of the Disabled. The company has implemented Strength
innovative hiring programs; other innovative human resource

programs for the disabled, or otherwise has a superior reputation as

an employer of the disabled.

Gay & Lesbian Policies. The company has implemented notably Strength
progressive policies toward its gay and lesbian employees. In

particular, it provides beneﬁts to the domestic partners of its

employees.

Other Diversity Strength. The company has made a notable Strength
commitment to diversity that is not covered by other KLD ratings.

Non-Representation. The company has no women on its board of Concern
directors or among its senior line managers.

Other Diversity Concern. The company is involved in diversity Concern
controversies not covered by other KLD ratings.

Union Relations. The company has taken exceptional steps to treat Strength
its unionized workforce fairly. KLD renamed this strength from
Strong Union Relations.

Cash Proﬁt Sharing. The company has a cash proﬁt-sharing Strength
program through which it has recently made distributions to a
majority of its workforce.

Employee Involvement. The company strongly encourages worker Strength
involvement and/or ownership through stock options available to a

majority of its employees; gain sharing, stock ownership, sharing of

ﬁnancial information, or participation in management decision-

making.

Retirement Beneﬁts Strength. The company has a notably strong Strength
retirement beneﬁts program. KLD renamed this strength from
Strong Retirement Beneﬁts.

Health and Safety Strength. The company has strong health and Strength
safety programs.

Other Employee Relations Strength. The company has strong Strength
employee relations initiatives not covered by other KLD ratings.

129

Label in
the KLD
Database

EMP—con-A

EMP-con-B

EMP-con-C

EMP-con-D

EMP-con-X

DlV-str-E

HUM-str-G

HUM-con-F

Identified as a
Strength /
Concern in the
KLD Database

Item Description

Employees Continued

Union Relations. The company has a history of notably poor union Concern
relations. KLD renamed this concern from Poor Union Relations.

Health and Safety Concern. The company recently has either paid Concern
substantial ﬁnes or civil penalties for willful violations of employee

health and safety standards, or has been otherwise involved in major

health and safety controversies.

Workforce Reductions. The company has made signiﬁcant Concern
reductions in its workforce in recent years.

Retirement Benefits Concern. The company has either a Concern
substantially under-funded deﬁned beneﬁt pension plan, or an
inadequate retirement beneﬁts program.

Other Employee Relations Concern. The company is involved in Concern
an employee relations controversy that is not covered by other KLD
ratings.

Simian

Women & Minority Contracting. The company does at least 5% Strength
of its subcontracting, or otherwise has a demonstrably strong record

on purchasing or contracting, with women-and/or minority-owned

businesses.

Labor Rights Strength. The company has outstanding transparency Strength
on overseas sourcing disclosure and monitoring, or has particularly

good union relations outside the US, or has undertaken labor

rights-related initiatives that KLD considers outstanding or

innovative.

Labor Rights Concern. The company's operations have had major Concern

recent controversies primarily related to labor standards in its supply
chain.

130

Label in
the KLD
Database

CGOV-str-A

CGOV-str-C

CGOV-str-D

CGOV-str-X

CGOV-con-
B

CGOV-con-F

CGOV-con-
G

CGOV—con-
H

CGOV-con-
X

Item Description

Shareholders

Limited Compensation. The company has recently awarded
notably low levels of compensation to its top management or its
board members. The limit for a rating is total compensation of less
than $500,000 per year for a CEO or $30,000 per year for outside
directors.

Ownership Strength. The company owns between 20% and 50%
of another company KLD has cited as having an area of social
strength, or is more than 20% owned by a ﬁrm that KLD has rated
as having social strengths. When a company owns more than 50%
of another ﬁrm, it has a controlling interest, and KLD treats the
second ﬁrm as if it is a division of the ﬁrst.

Transparency Strength. The company is particularly effective in
reporting on a wide range of social and environmental performance
measures, or is exceptional in reporting on one particular measure.

Other Corporate Governance Strength. The company has a
unique and positive corporate culture, or has undertaken a
noteworthy initiative not covered by KLD’s other corporate
governance ratings.

High Compensation. The company has recently awarded notably
high levels of compensation to its top management or its board
members. The limit for a rating is total compensation of more than
$10 million per year for a CEO or $100,000 per year for outside
directors.

Ownership Concern. The company owns between 20% and 50% of

a company KLD has cited as having an area of social concern, or is
more than 20% owned by a ﬁrm KLD has rated as having areas of
concern. When a company owns more than 50% of another ﬁrm, it
has a controlling interest, and KLD treats the second ﬁrm as if it is a
division of the ﬁrst.

Accounting Concern. The company is involved in signiﬁcant
accormting—related controversies.

Transparency Concern. The company is distinctly weak in
reporting on a wide range of social and environmental performance
measures.

Other Corporate Governance Concern. The company is involved

with a controversy not covered by KLD’s other corporate
governance ratings.

131

Identiﬁed as a

Strength /

Concern in the
KLD Database

Strength

Strength

Strength

Strength

Concern

Concern

Concern

Concern

Concern

Label in
the KLD
Database

CGOV-str-E

COM-con-D

CGOV-con-I

DlV-con-A

ENV-con-A

ENV-con-B

PRO-con-E

Identified as a
Strength /
Concern in the
KLD Database

Item Description

Regulators

Political Accountability Strength. The company has shown Strength
markedly responsible leadership on public policy issues and/or has

an exceptional record of transparency and accountability concerning

its political involvement in state or federal-level US. politics, or in

non-US. politics.

Tax Disputes. The company has recemly been involved in major Concern
tax disputes involving Federal, state, local or non-U.S. government

authorities, or is involved in controversies over its tax obligations to

the community.

Political Accountability. The company has been involved in Concern
noteworthy controversies on public policy issues and/or has a very

poor record of transparency and accountability concerning its

political involvement in state or federal-level US. politics, or in

non-U.S. politics.

Controversies. The company has either paid substantial ﬁnes or Concern
civil penalties as a result of afﬁrmative action controversies, or has

otherwise been involved in major controversies related to

afﬁnnative action issues.

Hazardous Waste. The company's liabilities for hazardous waste Concern
sites exceed $50 million, or the company has recently paid
substantial ﬁnes or civil penalties for waste management violations.

Regulatory Problems. The company has recently paid substantial Concern
ﬁnes or civil penalties for violations of air, water, or other

environmental regulations, or it has a pattern of regulatory

controversies under the Clean Air Act, Clean Water Act or other

major environmental regulations.

Antitrust. The company has recently paid substantial ﬁnes or civil Concern
penalties for antitrust violations such as price ﬁxing, collusion, or

predatory pricing, or is involved in recent major controversies or

regulatory actions relating to antitrust allegations.

132

Identified as a
Strength /
Concern in the
KLD Database

Label in
the KLD Item Description
Database

Community

COM-str-A Charitable Giving. The company has consistently given over 1.5% Strength
of trailing three-year net earnings before taxes (NEBT') to charity, or
has otherwise been notably generous in its giving.

COM-str—B Innovative Giving. The company has a notably innovative giving Strength
program that supports nonproﬁt organizations, particularly those
promoting self-sufficiency among the economically disadvantaged.
Companies that permit nontraditional federated charitable giving
drives in the workplace are often noted in this section as well.

COM-str-C Support for Housing. The company is a prominent participant in Strength
public/private partnerships that support housing initiatives for the
economically disadvantaged, e.g., the National Equity Fund or the
Enterprise Foundation.

COM-str-D Support for Education. The company has either been notably Strength
innovative in its support for primary or secondary school education,
particularly for those programs that beneﬁt the economically
disadvantaged, or the company has prominently supported job-
training programs for youth.

COM-str-F Non-US Charitable Giving. The company has made a substantial Strength
effort to make charitable contributions abroad, as well as in the U.S.
To qualify, a company must make at least 20% of its giving, or have
taken notably innovative initiatives in its giving program, outside
the U.S.

COM-str-G Volunteer Programs. The company has an exceptionally strong Strength
volunteer program.

COM-str-X Other Community Strength. The company either has an Strength
exceptionally strong in-kind giving program or engages in other
notably positive community activities.

COM-con-A Investment Controversies. The company is a ﬁnancial institution Concern
whose lending or investment practices have led to controversies,
particularly ones related to the Community Reinvestment Act.

COM-con-B Negative Economic Impact. The company’s actions have resulted Concern
in major controversies concerning its economic impact on the
community. These controversies can include issues related to
environmental contamination, water rights disputes, plant closings,
"put-or-pay" contracts with trash incinerators, or other company
actions that adversely affect the quality of life, tax base, or property
values in the community.

133

Label in
the KLD
Database

COM-con-X

ENV-Str-A

ENV-str-B

ENV-str-C

ENV-str—D

ENV-Str-X

ENV—con-C

ENV—con-D

ENV—con-E

Identified as a
Strength /
Concern in the
KLD Database

Item Description

Community Continued

Other Community Concern. The company is involved with a Concern
controversy that has mobilized community opposition, or is engaged
in other noteworthy community controversies.

Beneﬁcial Products and Services. The company derives substantial Strength
revenues from innovative remediation products, environmental

services, or products that promote the efﬁcient use of energy, or it

has developed innovative products with environmental beneﬁts.

(The term “environmental service” does not include services with

questionable environmental effects, such as landﬁlls, incinerators,

waste-to-energy plants, and deep injection wells.)

Pollution Prevention. The company has notably strong pollution Strength
prevention programs including both emissions reductions and toxic-
use reduction programs.

Recycling. The company either is a substantial user of recycled Strength
materials as raw materials in its manufacturing processes, or a major
factor in the recycling industry.

Clean Energy. The company has taken signiﬁcant measures to Strength
reduce its impact on climate change and air pollution through use of

renewable energy and clean fuels or through energy efﬁciency. The

company has demonstrated a commitment to promoting climate-

friendly policies and practices outside its own operations.

Other Environment Strength. The company has demonstrated a Strength
superior commitment to management systems, voluntary programs,
or other environmentally proactive activities.

Ozone Depleting Chemicals. The company is among the top Concern
manufacturers of ozone depleting chemicals such as HCF Cs, methyl
chloroform, methylene chloride, or bromines.

Substantial Emissions. The company's legal emissions of toxic Concern
chemicals (as deﬁned by and reported to the EPA) from individual

plants into the air and water are among the highest of the companies

followed by KLD.

Agricultural Chemicals. The company is a substantial producer of Concern
agricultural chemicals, i.e., pesticides or chemical fertilizers.

134

Label in
the KLD
Database

HUM-str-D

HUM-con-G

Community Continued

Indigenous Peoples Relations Strength. The company has
established relations with indigenous peoples near its proposed or
current operations (either in or outside the U.S.) that respect the
sovereignty, land, culture, human rights, and intellectual property of

indigenous peoples.

Indigenous Peoples Relations Concern. The company has been
involved in serious controversies with indigenous peoples (either in
or outside the U.S.) that indicate the company has not respected the
sovereignty, land, culture, human rights, and intellectual property of

indigenous peoples.

Item Description

135

Identified as a
Strength /
Concern in the
KLD Database

Strength

Concern

REFERENCES

Anderson, Eugene W. and Vikas Mittal (2000), “Strengthening the Satisfaction-Proﬁt
Chain,” Journal of Service Research, 3 (2), 107-120.

Anderson, Eugene W., Claes F omell, and Donald R. Lehmann (1994), “Customer
Satisfaction, Market Share, and Proﬁtability: Findings ﬁ'orn Sweden,” Journal of
Marketing, 58(3), 53-66.

Anderson, Eugene W., Claes Fomell, and Roland T. Rust (1997), “Customer Satisfaction,
Productivity, and Proﬁtability: Differences between Goods and Services,” Marketing
Science, 16(2), 129-145.

Anderson, Eugene W., Claes Fomell, and Sanal K. Mazvancheryl (2004), “Customer
Satisfaction and Shareholder Value,” Journal of Marketing, 68(4), 172-185.

Bahadir, S. Cem, Sundar G. Bharadwaj, and Rajendra K. Srivastava (2008), “Financial
Value of Brands in Mergers and Acquisitions: Is Value in the Eye of the Beholder?”
Journal of Marketing, 72 (November), 49-64.

Banerjee, Subhabrata Bobby, Easwar S. lyer, and Raj iv K Kashyap (2003), “Corporate

Environmentalism: Antecedents and Inﬂuence of Indusz Type,” Journal of Marketing, 67
(2), 10cm.

Barney, Jay (1991), “F irm Resources and Sustained Competitive Advantage,” Journal of
Management, 17 (1), 99-120.

Beliveau, Barbara, Melville Cottrill, and Hugh M. O’Neill (1994), “Predicting Corporate
Social Responsiveness: A Model Drawn from Three Perspectives,” Journal of Business
Ethics, 13 (9), 731-738.

Berman, Shawn L., Andrew C. Wicks, Suresh Kotha, and Thomas M. Jones (1999),
“Does Stakeholder Orientation Matter? The Relationship between Stakeholder

Management Models and Firm Financial Performance,” Academy of Management
Journal, 42 (5), 488-506.

Bimbaum, Philip H. (1985) “Political Strategies of Regulated Organizations as Functions
of Context and Fear,” Strategic Management Journal, 6 (2), 135-150.

Bloch, Peter H. (1995), “Seeking the Ideal Form: Product Design and Consumer
Response,”
Journal of Marketing, 59(3), 16-29.

Buchanan, Lauranne (1992), “Vertical Trade Relationships: The Role of Dependence and
Symmetry in Attaining Organizational Goals,” Journal of Marketing Research, 29 (1),
65-75.

136

Cannon, Joseph P. and Christian Homburg (2001), “Buyers-Supplier Relationships and
Customer Firm Costs,” Journal of Marketing, 65 (1), 29-43.

Churchill, Gilbert A., Jr., and Carol Surprenant (1982), “An Investigation into the
Determinants of Customer Satisfaction,” Journal of Marketing Research, 19 (4), 491 -
504.

Clarkson, Max (1995), “A Stakeholder Framework for Analyzing and Evaluating
Corporate Social Performance,” Academy of Management Review, 20 (1), 92-117.

Cohen, Wesley M. and Daniel A. Levinthal (1989), “Innovation and Learning: The Two
Faces of R&D,” The Economic Journal, 99 (September), 569-596.

Dacin, M. Tina (1997), “Isomorphism in Context: The Power and Prescription of
Institutional Norms,” Academy of Management Journal, 40 (1), 46-81.

Day, George S. (1994), “The Capabilities of Market-Driven Organizations,” Journal of
Marketing, 58 (4), 37-52.

Day, George S. and Liam Fahey (1988), “Valuing Market Strategies,” Journal of
Marketing, 52 (3), 45-57.

Day, George S. and Robin Wensley (1988), “Assessing Advantage: A Framework for
Diagnosing Competitive Superiority,” Journal of Marketing, 52 (April), 1-20.

Deshpande, Rohit, John U. Farley, and Frederick E. Webster, Jr. (1993), “Corporate
Culture, Customer Orientation, and Innovativeness in Japanese Firms: A Quadrad
Analysis,” Journal of Marketing, 57 (1), 23-37.

Dickson, Peter Reid (1992), “Toward a General Theory of Competitive Rationality,”
Journal of Marketing, 56(1), 69-83.

DiMaggio, Paul J. and Walter W. Powell (1983), “The Iron Cage Revisited: Institutional
Isomorphism and Collective Rationality in Organizational Fields,” American
Sociological Review, 48 (April), 147-160.

Donaldson, Thomas and Lee E. Preston (1995), “The Stakeholder Theory of the
Corporation: Concepts, Evidence, and Implications,” Academy of Management Review,
20(1), 65-91.

Dutta, Shantanu, Om Narasimhan, and Surendra Rajiv (1999), “Success in High-
Technology Markets: ls Marketing Capability Critical?” Marketing Science, 18 (4), 547-
568.

Eesley, Charles and Michael J. Lenox (2006), “Firm Responses to Secondary Stakeholder
Action,” Strategic Management Journal, 27 (8), 765-781.

137

Financial Accounting Standards Board (1974), Statement of Financial Accounting
Standards No. 2: Accounting for Research and Development Costs, Norwalk, CT:
Financial Accounting Foundation.

Fomell, Claes (1992), “A National Customer Satisfaction Barometer: The Swedish
Experience,” Journal of Marketing, 56 (1), 6-21.

Fomell, Claes (2007), The Satisﬁed Customer: Winners and Losers in the Battle for
Buyer Preference, New York, NY: Palgrave MacMillan.

Fornell, Claes, Michael D. Johnson, Eugene W. Anderson, Jaesung Cha, and Barbara
Everitt Bryant (1996), "The American Customer Satisfaction Index: Nature, Purpose, and
Findings," Journal of Marketing, 60 (4), 7-18.

Fornell, Claes, Sunil Mithas, Forrest V. Morgeson III, and MS. Krishnan (2006),
“Customer Satisfaction and Stock Prices: High Returns, Low Risk,” Journal of
Marketing, 70(1), 3-14.

Freeman, R. Edward (1984), Strategic Management: A Stakeholder Approach.
Marshﬁeld, MA: Pitrnan Publishing Inc.

Frooman, Jeff (1999), “Stakeholder Inﬂuence Strategies,” Academy of Management
Review, 24(2), 191-205.

Galaskiewicz, Joseph, and Stanley Wasserman (1989), “Mimetic Processes within an
Interorganizational Field: An Empirical Test,” Administrative Science Quarterly, 34 (3),
454-479.

Gomez, Miguel 1., Edward W. McLaughlin, and Dick R. Wittink (2004), “Customer
Satisfaction and Retail Sales Performance: An Empirical Investigation,” Journal of
Retailing, 80(4), 265-278.

Graves, Samuel B. and Sandra A. Waddock (1994), "Institutional Owners and Corporate
Social Performance," Academy of Management Journal, 37 (4), 1034-1046.

Greve, Henrich R. (2000), “Market Niche Entry Decisions: Competition, Learning, and
Strategy in Tokyo Banking, 1894-1936,” Academy of Management Journal, 43 (5), 816-
836.

Grewal, Rajdeep and Ravi Dharwadkar (2002), “The Role of the Institutional
Environment in Marketing Channels,” Journal of Marketing, 66 (3), 82-97

Griffin, Abbie and John R. Hauser (1996), “Integrating R&D and Marketing: A Review

and Analysis of the Literature,” Journal of Product Innovation Management, 13 (3), 191-
21 5 .

138

Gruca, Thomas S., Lopo L. Rego (2005), “Customer Satisfaction, Cash Flow, and
Shareholder Value,” Journal of Marketing, 69 (3), 115-130.

Guler, Isin, Mauro F. Guillen, and John Muir Macpherson (2002), “Global Competition,
Institutions, and the Diffusion of Organizational Practices: The International Spread of
ISO 9000 Quality Certiﬁcates,” Administrative Science Quarterly, 47 (2), 207-232.

Han, Jin K., Namwoon Kim, and Raj endra K. Srivastava (1998), “Market Orientation and
Organizational Performance: Is Innovation a Missing Link?” Journal of Marketing, 62
(4), 30-45.

Handelrnan, Jay M. and Stephen J. Arnold (1999), “The Role of Marketing Actions with
a Social Dimension: Appeals to the Institutional Environment,” Journal of Marketing, 63
(3), 33-48. '

Haveman, Heather A. (1993), “Follow the Leader: Mimetic Isomorphism and Entry into
New Markets,” Administrative Science Quarterly, 38 (4), 593-627.

Heide, Jan B. and George John (1988), “The Role of Dependence Balancing in
Safeguarding Transaction-Speciﬁc Assets in Conventional Channels,” Journal of
Marketing, 52(1), 20—35.

Heitrnann, Mark., Donald R. Lehmann, Andreas Herrmann (2007), “Choice Goal
Attainment and Decision and Consumption Satisfaction,” Journal of Marketing
Research, 44 (2), 234-250.

Henriques, Irene and Perry Sadorsky (1999), “The Relationship between Environmental
Commitment and Managerial Perceptions of Stakeholder Importance,” Academy of
Management Journal, 42(1), 87-99.

Heskett, James L., Thomas 0. Jones, Gary W. Loveman, W. Earl Sasser, Jr., and Leonard
A. Schlesinger (1994), “Putting the Service-Proﬁt Chain to Work,” Harvard Business
Review, 72(2), 164-174.

Hillman, Amy J. and Gerald D. Keim (2001), “Shareholder Value, Stakeholder
Management, and Social Issues: What’s the Bottom Line?” Strategic Management
Journal, 22 (2), 125-139.

Homburg, Christian, Nicole Koschate, and Wayne D. Hoyer (2005), “Do Satisﬁed
Customers Really Pay More? A Study of the Relationship Between Customer
Satisfaction and Willingness to Pay,” Journal of Marketing, 69 (2), 84-96.

Homburg, Christian and Ruth M. Stock (2004), “The Link between Salespeople’s Job

Satisfaction and Customer Satisfaction in a Business-to-Business Context: A Dyadic
Analysis,” Journal of Marketing, 32 (2), 144-158.

139

Hurley, Robert F. and G. Tomas M. Hult (1998), “Innovation, Market Orientation, and
Organizational Learning: An Integration and Empirical Examination,” Journal of
Marketing, 62 (3), 42-54.

Ingram, Rhea, Steven J. Skinner, and Valerie A. Taylor (2005), “Consumers’ Evaluation
of Unethical Marketing Behaviors: The Role of Customer Commitment,” Journal of
Business Ethics, 62 (3), 237-252.

Jap, Sandy D. (1999), “Pie-Expansion Efforts: Collaboration Processes In Buyer-Supplier
Relationships,” Journal of Marketing Research, 36 (4), 461-475.

Jawahar, I.M. and Gary L. McLaughlin (2001), “Toward a Descriptive Stakeholder
Theory: An Organizational Life Cycle Approach,” Academy of Management Review, 26
(3), 397-414.

Jaworski, Bernard J. and Ajay K. Kohli (1993), “Market Orientation: Antecedents and
Consequences,” Journal of Marketing, 57(3), 53-70.

Jones, Thomas M. (1995), “Instrumental Stakeholder Theory: A Synthesis of Ethics and
Economics,” Academy of Management Review, 20 (2), 404-43 7.

Jones, Thomas M. and Andrew C. Wicks (1999), “Convergent Stakeholder Theory,”
Academy of Management Review, 24 (2), 206-221.

Kassinis, George and Nikos Vafeas (2006), “Stakeholder Pressures and Environmental
Performance,” Academy of Management Journal, 49 (l ), 145-159.

Kohli, Ajay K. and Bernard J. Jaworski (1990), “Market Orientation: The Construct,
Research Propositions, and Managerial Implications,” Journal of Marketing, 54 (2), 1-18.

Li, Tiger and Roger J. Calantone (1998), “The Impact of Market Knowledge Competence
on New Product Advantage: Conceptualization and Empirical Examination,” Journal of
Marketing, 62(4), 13-29.

Lieberman, Marvin B. and Shigeru Asaba (2006), “Why Do Firms Imitate Each Other?”
Academy of Management Review, 31 (2), 366-385.

Luo, Xueming and C. B. Bhattacharya (2006), “Corporate Social Responsibility,
Customer Satisfaction, and Market Value,” Journal of Marketing, 70 (4), 1-18.

Luo, Xueming and C. B. Bhattacharya (2009), “The Debate over Doing Good: Corporate

Social Performance, Strategic Marketing Levers, and F irm-Idiosyncratic Risk,” Journal
of Marketing, 73 (6), 198-213.

140

Luo, Xueming and Naveen Donthu (2006), “Marketing's Credibility: A Longitudinal
Investigation of Marketing Communication Productivity and Shareholder Value,”
Journal of Marketing, 70(4), 70-91.

Luo, Xueming and Christian Homburg (2008), “Satisfaction, Complaint, and the Stock
Value Gap,” Journal of Marketing, 72 (July), 29-43.

Luo, Xueming and Christian Homburg (2007), "Neglected Outcomes of Customer
Satisfaction," Journal of Marketing, 71 (2), 133-49.

Maignan, Isabelle and O. C. Ferrell (2004), “Corporate Social Responsibility and
Marketing: An Integrative Framework,” Journal of the Academy of Marketing Science,
32(1), 3-19.

Maxham 111, James G., Richard G. Netemeyer (2002), “Modeling Customer Perceptions
of Complaint Handling Over Time: The Effects of Perceived Justice on Satisfaction and
Intent,” Journal of Retailing, 78 (4), 239-252.

McFarland, Richard G., James M. Bloodgood, and Janice M Payan (2008), “Supply
Chain Contagion,” Journal of Marketing, 72 (2), 63-79.

Mintzberg, Henry, James Brian Quinn, and Surnantra Ghosal (1998), The Strategy
Process, Hertfordshire, United Kingdom: Simon and Schuster International Group.

Mitchell, Ronald K., Bradley R. Agle, and Donna J. Wood (1997), “Toward a Theory of
Stakeholder Identiﬁcation and Salience: Deﬁning the Principle of Who and What Really
Counts,” Academy of Management Review, 22 (4), 853-886.

Mizruchi, Mark S. and Lisa C. Fein (1999), “The Social Construction of Organizational
Knowledge: A Study of the Uses of Coercive, Mimetic, and Normative Isomorphism,”
Administrative Science Quarterly, 44 (4), 653-683.

Monroe, Kent B. (1973), “Buyers' Subjective Perceptions of Price,” Journal of Marketing
Research, 10(1), 70-80.

Morgan, Fred W. (1988), “Tarnpered Goods: Legal Developments and Marketing
Guidelines,” Journal of Marketing, 52 (2), 86-96.

Morgan, Neil A. and Lopo L. Rego (2009), “Brand Portfolio Strategy and Firm
Performance,” Journal of Marketing, 73 (January), 59-74.

Narver, John C. and Stanley F. Slater (1990), “The Effect of a Market Orientation on
Business Proﬁtability,” Journal of Marketing, 54 (4), 20-35.

Oliver, Richard L. (1980), “A Cognitive Model of the Antecedents and Consequences of
Satisfaction Decisions,” Journal of Marketing Research, 17 (4), 460-469.

141

Palmer, Timothy B. and Robert M. Wiseman (1999), “Decoupling Risk Taking from
Income Stream Uncertainty: A Holistic Model of Risk,” Strategic Management Journal,
20 (11), 1037-1062.

Pechmann, Cornelia (1996), “Do Consumers Overgeneralize One-Sided Comparative
Price Claims, and Are More Stringent Regulations Needed?,” Journal of Marketing
Research, 33 (2), 150-162.

Pfeffer, Jeffrey and Gerald R. Salancik (1978), The External Control of Organizations: A
Resource Dependence Perspective. New York: Harper & Row.

Porter, Michael E. (2008), “The Five Competitive Forces that Shape Strategy,” Harvard
Business Review, 86(1), 78-97.

Porter, Michael E. (1996), On Competition, Boston, MA: Harvard Business School Press.

Porter, Michael E. and Mark R. Kramer (2006), “Strategy & Society: The Link between
Competitive Advantage and Corporate Social Responsibility,” Harvard Business Review,
84 (12), 78-93.

Rao, Rarnesh KS. and Neeraj Bharadwaj (2008), “Marketing Initiatives, Expected Cash
Flows, and Shareholders' Wealth,” Journal of Marketing, 72 (1), 16-26.

Roy, Subroto, K. Sivakurnar, and Ian F. Wilkinson (2004), “Innovation Generation in
Supply Chain Relationships: A Conceptual Model and Research Propositions,” Journal
of the Academy of Marketing Science, 32 (1), 61-79.

Scott, W. Richard (1987), “The Adolescence of Institutional Theory,” Administrative
Science Quarterly, 32 (4), 493-511.

Selnes, Fred and James Sallis (2003), “Promoting Relationship Learning,” Journal of
Marketing, 67(3), 80-95.

Sen, Sankar and CB. Bhattacharya (2001), “Does Doing Good Always Lead to Doing
Better? Consumer Reactions to Corporate Social Responsibility,” Journal of Marketing
Research, 38 (2), 225-243.

Short, Jeremy C., David J. Ketchen, Jr., Timothy B. Palmer, and G. Tomas M. Hult
(2007), “Firm, Strategic Group, and Industry Inﬂuences on Performance,” Strategic
Management Journal, 28 (2), 147-167. A

Siguaw, Judy A., Penny M. Simpson, and Thomas L. Baker (1998), “Effects of Supplier
Market Orientation on Distributor Market Orientation and the Channel Relationship: The
Distributor Perspective,” Journal of Marketing, 62 (3), 99-111.

Sirnchi-Levi, David, Philip Kaminsky, and Edith Sirnchi-Levi (2003), Managing the
Supply Chain, New York, NY: The McGraw-Hill Companies.

142

Sinkula, James M. (1994), “Market Information Processing and Organizational
Learning,” Journal of Marketing, 58 (1), 35-45.

Slater, Stanley F. and John C. Narver (1994), “Does Competitive Environment Moderate
the Market Orientation-Performance Relationship?,” Journal of Marketing, 58 (1), 46-55.

Sorescu, Alina B. and Jelena Spanjol (2008), “Innovation’s Effect on Firm Value and
Risk: Insights from Consumer Packaged Goods,” Journal of Marketing, 72 (March), 114-
1 32.

Srivastava, Rajendra K., Tasadduq A. Shervani, and Liam Fahey (1998), “Market-Based
Assets and Shareholder Value: A Framework for Analysis,” Journal of Marketing, 62 (1),
2-1 8.

Suchman, Mark C. (1995), “Managing Legitimacy: Strategic and Institutional
Approaches,” Academy of Management Review, 20 (3 ), 571-610.

Szymanski, David M., and David H. Henard (2001), “Customer Satisfaction: A Meta-
Analysis of the Empirical Evidence,” Journal of the Academy of Marketing Science, 29
(1 ), 1 6-3 5.

Thomas, L.G., III and Geofﬁey Waring (1999), “Competing Capitalisms: Capital
Investment in America, German, and Japanese Firms,” Strategic Management Journal,
20(8), 729-748.

Tobin, James (1978), “Monetary Policies and the Economy: The Transmission
Mechanism,” Southern Economic Journal, 44 (January), 421-431.

Van Dolen, Willemijn M., Pratibha A. Dabholkar, and Ko de Ruyter (2007), “Satisfaction
with Online Commercial Group Chat: The Inﬂuence of Perceived Technology Attributes,
Chat Group Characteristics, and Advisor Communication Style,” Journal of Retailing, 83
(3), 339-358.

Waddock, Sandra A. and Samuel B. Graves (1997), “The Corporate Social Performance-
Financial Performance Link,” Strategic Management Journal, 18 (4), 303-319.

Webster, Frederick E., Jr. (1992), “The Changing Role of Marketing in the Corporation,”
Journal of Marketing, 56 (4), 1-17.

Zeitharnl, Valarie A. (2000), “Service Quality, Proﬁtability, and the Economic Worth of

Customers: What We Know and What We Need to Learn,” Journal of the Academy of
Marketing Science, 28 (1), 67-85.

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Essay 3
STAKEHOLDER-FOCUSED ORGANIZATIONAL LEARNING,
RESPONSIVENESS, AND INNOVATION / IMITATION

In the global marketplace, organizations increasingly “compete on the basis of the
superiority of their information and know-how” (Kogut and Zander 1993, p. 640). The
need to be well-equipped with information led U.S. organizations to spend an estimated
$73 billion on knowledge management software in 2007 (McGreevy 2007). If applied
effectively, high quality and easy-to-access information can bring beneﬁts to
organizations. For example, IBM employs a knowledge management system that is used
by employees to capture, share, and reuse intellectual capital (e. g., information, know-
how, experiences, ideas, and models). This tool has helped IBM deliver superior value to
its customers and shareholders (Huang 1998). Similarly, British Petroleum (BP) actively
supports knowledge management by using various tools such as a voluntary corporate
intranet that helps employees ﬁnd expert help when needed and an “after-action review”
that facilitates learning by asking a few questions that center on whether there were any
differences between what was supposed to happen and the actual outcome of an action as
well as the reasons for those differences (Stewart 1999). These methods resulted in
savings for BP that amounted to $260 million in just one year (Stewart 1999).

The importance of knowledge as a strategic weapon for organizations to excel in
the competitive global marketplace has prompted a number of marketing scholars to
examine how organizations learn about the market (e.g., Hurley and Hult 1998; Sinkula
1994; Slater and Narver 1995). Through organizational learning, organizations develop

new knowledge that facilitates behavior change (e.g., Slater and Narver 1995). The

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emphasis in this study is on stakeholder-focused organizational learning. This type of
learning centers on gaining new stakeholder-related knowledge which has the potential to
inﬂuence an organization’s behaviors toward its stakeholders (cf. Slater and Narver
1995). Based on the extant literature on organizational learning (e.g., Huber 1991;
Sinkula 1994), four learning processes associated with stakeholder-focused
organizational learning are considered in this research to affect stakeholder-focused
responsiveness (i.e., knowledge acquisition, information distribution, information
interpretation, and organizational memory). Responsiveness is deﬁned as the
organization’s propensity to take action in response to the stakeholder-related knowledge
that is acquired, distributed, interpreted, and stored (cf. Kohli and J aworski 1990).

At the same time, organizations learn in an environment that consists largely of
other organizations that are also learning (Levitt and March 1988). Realistic models of
organizational learning must account for the alternative mechanisms that organizations
use to acquire information about the market (e.g., Lieberman and Asaba 2006). As such,
organizations can vary in their reliance on obtaining information from their own
experience (experiential learning), from the observed behavior of other organizations
with whom they have no direct links (vicarious learning), or from direct communication
with their peers (contact learning) (e.g., Lieberman and Asaba 2006; Ordanini, Rubera,
and DeFillippi 2008). Such differences in the way organizations acquire information can
lead them to respond differently to stakeholders, varying in the degree of innovative and
imitative stakeholder practices.

Accordingly, the research questions in this study are: (1) does stakeholder-

focused organizational learning inﬂuence stakeholder-focused responsiveness?; (2) is the

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organization’s degree of innovative and imitative stakeholder practices affected by the
mechanism(s) it relies on to acquire stakeholder-related knowledge?; and (3) are there
interaction effects between these knowledge acquisition mechanisms? While addressing
these questions, the study makes the following contributions. First, it expands the almost
exclusive focus on customers of market-based organizational learning (e. g., Sinkula,
Baker, and Noordewier 1997) to include organizational learning about additional primary
stakeholders (i.e., suppliers, employees, shareholders, community, and regulators) who
are also essential for the continued success of the organization (e. g., Clarkson 1995 ).
Second, the study integrates institutional theory, social network theory, and the literature
on organizational learning to examine alternative modes of knowledge acquisition and
the impact of these varying modes on an organization’s propensity to innovate and
imitate.
ORGANIZATIONAL LEARNING

Organizational learning is “the development of new knowledge or insights that
have the potential to inﬂuence behavior” (Slater and Narver 1995, p. 63). It can be of two
types — lower-level or higher-level learning (e. g., Fiol and Lyles 1985). Lower-level
learning (also known as single-loop or adaptive learning) is the most common and basic
type of learning (e.g., Baker and Sinkula 1999; Slater and Narver 1995). It takes place
within a given set of rules that reﬂect the organization’s long-standing assumptions about
its external environment and its strategy (e. g., Fiol and Lyles 1985; Slater and Narver
1995). This form of learning focuses on the immediate impact on a speciﬁc
organizational activity. As such, it results in learning that is sequential and incremental.

In contrast to lower-level learning, higher-order learning (also known as double-loop or

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generative learning) is not centered on a particular activity, but rather on changing overall
rules (e.g., Fiol and Lyles 1985). Speciﬁcally, it leads to the development of new
cognitive frameworks, which have a lasting effect on the organization. This, in turn, can
create a competitive advantage for the organization (e. g., Baker and Sinkula 1999; Slater
and Narver 1995).

Previous research has identiﬁed four processes that are associated with
organizational learning (e.g., Huber 1991; Sinkula 1994). These are: knowledge
acquisition, information distribution, information interpretation, and organizational
memory. The remaining portion of this section discusses these four learning processes.
Knowledge Acquisition

Knowledge acquisition is the process by which organizations obtain knowledge
(Huber 1991). It involves collecting information about the external environment and then
bringing that information into the boundaries of the organization (Moorrnan 1995).
Particularly, organizations acquire information about customer needs, market
segmentation, competitor practices, and the changing role of channel partners (Day
1994). This process is essential, because without it, the organization would not be able to
keep up with changes in its market environment (Sinkula, Baker, and Noordewier 1997).
As such, it is undertaken collectively by different individuals and departments throughout
the organization, and not just by the marketing department (e. g., Kohli and J aworski
l 990).

Different mechanisms exist through which organizations acquire information.

First, organizations can acquire information independently, from their own experience

(experiential learning - e.g., Day 1994; Huber 1991; Levitt and March 1988). As pointed

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out by Slater and Narver (1995), this experience can be intemally-focused (i.e.,
exploitation) or extemally-focused (i.e., exploration — March 1991). Information acquired
from intemally-focused experience consists of relying on information that is currently
available to the organization in order to achieve its objectives. This is evident from the
experience-based learning curve, where for example, a manufacturing organization gains
experience producing a new product, and then uses the knowledge obtained ﬁom that
experience to improve productivity (e. g., Huber 1991; Levinthal and March 1993). On
the other hand, information acquired from extemally-focused experience consists of
gaining new information through searching (March 1991). This entails investing
signiﬁcant resources and time in scanning the organization’s external environment for
information about changes and in formally and periodically evaluating how well the
organization is meeting its own standards, as well as the expectations of its stakeholders
(Huber 1991). Some examples include the routine analysis of customer databases and
formal market research, such as focus groups, customer attitude surveys, and assessment
of sales response in test markets (e.g., Kohli and Jaworski 1990).

Organizations can also acquire second-hand experience (Huber 1991) by drawing
inferences from the observed behavior of other organizations with whom they have no
direct links (vicarious learning) or from direct communication with others (contact
learning — e.g., Lieberman and Asaba 2006; Ordanini, Rubera, and DeFillippi 2008).
Previous research has identiﬁed three different modes of vicarious learning — frequency-
based, trait-based, and outcome-based learning (e.g., Haunschild and Miner 1997;
Srinivasan, Haunschild, and Grewal 2007). F requency—based learning consists of learning

from those practices that have been executed by large numbers of other organizations in

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the ﬁeld. Trait-based learning involves learning from the practices that have been adopted
by organizations with particular traits, like large size and success. Lastly, with outcome-
based learning, organizations learn ﬁom the outcomes experienced by other organizations
after adopting certain practices. As such, organizations learn what actions tend to produce
positive outcomes, as well as those that tend to produce negative outcomes and so should
be avoided. To acquire information about the practices implemented by others,
organizations rely on benchmarking, the press, conferences, statistics, and books
(Ordanini, Rubera, and DeFillippi 2008).

Social network theory points to the importance of social relationships as an
instrument for knowledge acquisition (e. g., Tichy, Tushman, and Fombrun 1979).
Consistent with this perspective, contact learning refers to learning from the experiences
of others “by the means of personal and formal relationships between organizations and
their members” (Ordanini, Rubera, and DeFillippi 2008, p. 385). These ties enable
organizations to readily observe and communicate with peers in other organizations,
which in turn, facilitates learning about new practices in the ﬁeld (e.g., Kraatz 1998).
Examples of knowledge acquisition from contact learning include discussions with
suppliers and working with lead business-to-business customers (e. g., Kohli and J aworski
1990; Slater and Narver 1995). In sum, both vicarious and contact leaming involve
learning from other organizations. The main difference lies in the source of information
used. While vicarious learning uses impersonal sources such as newspapers and reports,
contact learning relies on personal sources, whereby individuals have direct contact with

each other (cf. Daft and Weick 1984).

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Information Distribution

For an organization to understand and effectively respond to market needs, newly
acquired market information must be communicated to relevant departments and
individuals throughout the organization (e.g., Kohli and J aworski 1990; Sinkula, Baker,
and Noordewier 1997). Information distribution is the process by which organizations
share information from different sources (Huber 1991). Depending on who acquires such
information, it can ﬂow from the marketing department to other departments, as well as
in the opposite direction (Kohli and Jaworski 1990). As organizations eliminate the
ﬁrnctional barriers that obstruct the ﬂow of information between departments, they
enhance their ability to make and implement prompt decisions (Slater and Narver 1995).
Further, effective information distribution serves to coordinate the actions of different
departments, which in turn, facilitates the achievement of organizational objectives
(Kohli and Jaworski 1990).

In a study about market information distribution across functional baniers, Maltz
and Kohli (1996) identify two aspects of this process — distribution formality (i.e.,
number of formal information distribution events relative to the total number of
distribution events during a particular time period) and frequency (i.e., number of
distribution events between a sender and a recipient during a particular time period).
Formal information distribution is structured and includes meetings, written memos, and
cross-functional teams, whereas informal distribution takes place during interpersonal
interactions, such as hall talks (Moorrnan 1995). Maltz and Kohli (1996) ﬁnd that a
balance between informal and formal distribution mechanisms is ideal for maximizing

the quality of the information that is shared throughout the organization. Informal

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communications offer greater opportunities for recipients to be more open and ask for
more clariﬁcation, while formal communications tend to be more credible and veriﬁable,
which in turn, motivates the recipients of the information to use it. In addition, their
ﬁndings suggest that frequent information distribution improves the quality of
information as perceived by the recipient. At the same time, too frequent communications
can be counterproductive since they can cause information overload, where the recipient
does not fully process the information.
Information Interpretation

Before an organization can act on new information, it must ﬁrst interpret it (e.g.,
Day 1994; Sinkula, Baker, and Noordewier 1997). Information interpretation is the
process by which an organization gives distributed information one or more commonly
understood meanings (Huber 1991). This may entail formal techniques such as devil’s
advocacy or more informal ones such as team meetings where individuals share their
interpretations of market information (e. g., Moorman 1995). In this context, disagreement
among participants leads to a closer examination of the validity of different alternatives
(Slater and Narver 1995). Particularly, a high level of equivocality in the market
information (e. g., Sinkula 1994) generates multiple and conﬂicting views about the
organizational situation and the course of action the organization should follow (Daft and
Lengel 1986). In this case, the information may need to be cycled among members a
number of times before the conﬂicts are resolved and a common interpretation is reached
(Daft and Weick 1984).

In addition, the interpretation that is given to new information is inﬂuenced by the

mental models of managers (e.g., Day and Nedungadi 1994; Huber 1991). These mental

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models consist of decision rules for sorting incoming market information and of
heuristics that are useful for determining how to act on such information (e.g., Day 1994).
This enables managers to simplify and impose order on the ambiguous competitive
environment, which in turn shapes their interpretation of the market situation (Day and
Nedungadi 1994). In general, managers adopt distinct mental models of competitive
advantage that differ in their relative emphasis on customer and competitor dimensions.
If managers have adequate mental models that reﬂect reality, then their interpretation of
the information is likely to lead to effective organizational actions (e. g., Sinkula, Baker,
and Noordewier 1997). On the other hand, distorted mental models are conducive to
misinterpretation and ﬂawed responses. These negative consequences can be avoided by
using scenarios and other devices that compel managers to articulate, inspect, and
eventually modify their mental models (Day 1994).
Organizational Memory

It is essential for knowledge to be retained within the organization in spite of
personnel turnover and the passage of time (e. g., Levitt and March 1988; Slater and
Narver 1995). Organizational memory is the process by which organizations store
knowledge for future use (Huber 1991). It may be manifested as shared beliefs (e. g.,
frames of reference, models, values, norms, and organizational stories), formal and
informal routines (e. g., operating procedures and scripts), and physical artifacts (e.g.,
organizational structure and features of products) (Moorman and Miner 1997). The
degree to which these are utilized determines how long organizational memory is

preserved (Slater and Narver 1995).

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According to Moorman and Miner (1997), organizational memory can be
characterized as having four distinct dimensions: level, dispersion, accessibility, and
content. The level of organizational memory is a function of the amount of stored
information and experience the organization has about a given phenomenon (Moorman
and Miner 1997). Organizations with high levels of organizational memory become more
competent at ﬁltering information, thereby separating relevant from irrelevant
information (Sinkula 1994). As a result, these organizations distribute, interpret, and store
less of the information they acquire. Although this ﬁltering of information can be seen as
an advantage, it can also bring negative outcomes. For instance, it can lead the
organization to attend only to that information that aligns with historical actions and
procedures, even when new procedures that may be more effective than old ones exist
(Sinkula 1994; Slater and Narver 1995). The second dimension concerns the extent to
which organizational memory is dispersed throughout the organization (Moorman and
Miner 1997). Speciﬁcally, within organizations, memory can be stored in different
retention bins such as in individuals, the organizational culture, and the workplace
ecology (Walsh and Ungson 1991).

The third dimension, accessibility, refers to the degree to which organizational
memory can be retrieved when necessary (Moorman and Miner 1997). Accessibility
depends on the frequency in which a routine is used, the recency in which it was last
used, and its organizational proximity (Levitt and March 1988). In particular, recently
used and frequently used routines are more accessible, and hence, more easily retrieved
than those that are seldom used. The last dimension, memory content, refers to the

meaning of the information that is stored (Moorman and Miner 1997). It can be

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procedural (i.e., knowledge associated with skills needed to perform a particular task) or
declarative (i.e., knowledge about certain concepts, facts, or events).

In addition, organizational memory plays a number of important roles within
organizations. First, it serves as an information ﬁlter, inﬂuencing the manner in which
information and experiences are sorted (e. g., Moorman and Miner 1997; Sinkula 1994).
Second, organizational memory enhances decision making and facilitates decision
implementation (e.g., Walsh and Ungson 1991). Third, it ﬁrnctions as a source of answers
to inquiries that the organization has encountered in the past (Day 1994). Particularly,
memory inﬂuences an organization’s ability to preserve long-term learning that builds
from its history (Sinkula, Baker, and Noordewier 1997). Those organizations that fail to
store knowledge and experiences about what has and has not worked in the past “will
have to repeat their failures and rediscover their success formulas over and over again”
(Day 1994, p. 44).

HYPOTHESES DEVELOPMENT

Based on the extant literature on organizational learning (e. g., Huber 1991;
Sinkula 1994; Slater and Narver 1995), stakeholder-focused organizational learning is
deﬁned as the development of new stakeholder-related knowledge that facilitates changes
in behaviors toward the stakeholders. A stakeholder is “any group or individual who can
affect or is affected by the achievement of the organization’s objectives” (Freeman 1984,
p. 46). For a particular group or individual to ﬁrlﬁll these criteria, it must possess at least
one of the following key attributes: power, legitimacy, or urgency (Mitchell, Agle, and
Wood 1997). In this study, the focus is on the organization’s knowledge about its primary

stakeholders, who are those that are essential for the continued success of the

154

organization (Clarkson 1995). Primary stakeholder groups normally consist of customers,
employees, suppliers, shareholders, regulators, and the community. On the basis of
resource dependence theory, an organization depends on these six groups since they
control important resources that are necessary for the survival of the organization (Pfeffer
and Salancik 1978). The organization’s dependence on these groups for resources gives
them power over the organization (Frooman 1999). In turn, the possession of power — one
of the key attributes of stakeholders — designates the group as a stakeholder that merits
managerial attention (Mitchell, Agle, and Wood 1997).

In addition, the focus of the study is on the marketing outcomes of this
organizational learning about primary stakeholders. Two of the most important outcomes
derived mainly from the market orientation and organizational learning literatures are
responsiveness (e.g., Hult, Ketchen, and Slater 2005; Ketchen, Hult, and Slater 2007) and
innovation (e.g., Han, Kim, and Srivastava 1998; Hurley and Hult 1998). Consistent with
previous studies, the examination is centered on the impact of stakeholder-focused
organizational learning on stakeholder-focused responsiveness. Stakeholder-focused
responsiveness is deﬁned as the organization’s propensity to take action in response to
the stakeholder-related knowledge that is acquired, distributed, interpreted, and stored
(e.g., Kohli and J aworski 1990). Recent research has found that the information an
organization learns through market research may affect its decision to pursue either a
strategy of innovation or imitation (e.g., Ofek and Turut 2008). This suggests that the
way the organization responds to stakeholder-focused organizational learning varies in
the degree its stakeholder practices are characterized as innovative or imitative. Hence,

both innovation and imitation are treated as forms of responsiveness.

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Stakeholder-Focused Organizational Learning and Responsiveness

The ﬁrst set of hypotheses links the four processes of stakeholder-focused
organizational learning with responsiveness. The resource-based view (RBV) of the ﬁrm
(e.g., Wemerfelt 1984) provides the rationale for the expectation that each learning
process inﬂuences the organization’s response toward the stakeholders. The RBV holds
that the resources possessed by a ﬁrm enable the frrrn to conceive of value-creating
strategies that improve its efficiency and effectiveness (Barney 1991). This creates a
competitive advantage, which in turn, results in superior performance. As such, a central
premise of this theoretical perspective is that resources “allow the ﬁrm to do a better job
of taking strategic actions” (Ketchen, Hult, and Slater 2007, p. 962). Drawing on the
RBV, the contention is not that the four learning processes (knowledge acquisition,
information distribution, information interpretation, and organizational memory)
represent unique resources independently, but that the conﬂuence of these processes can
create a unique learning resource (cf. Hult, Ketchen, and Slater 2005). This resource —
stakeholder-focused organizational learning - enables the organization to respond to
stakeholders by taking better actions (cf. Day 1994; Slater and Narver 1995).

Stakeholder-focused organizational learning contains several key attributes that
classify this type of learning as a strategic resource. First, it is valuable, given that it
allows the organization to respond to environmental opportunities and threats (i.e.,
stakeholder demands) by implementing strategies that could enhance its efﬁciency or
effectiveness (e.g., Barney 1991; Newbert 2008). Second, stakeholder-focused
organizational learning is rare, because it is not widely held. It is likely that most

organizations concentrate on only a few stakeholders and not on all primary stakeholders.

156

Third, it is non-substitutable and inimitable (Barney 1991). In particular, stakeholder-
focused organizational learning is path-dependent, which deters imitation given the
difﬁculty of repeating the developmental process and the signiﬁcant time lag involved
(e.g., Peteraf 1993). For this resource, history matters. For instance, organizational
memory, a process related to organizational learning, houses information about past
experiences with stakeholders, which could then be used for future interactions (e. g.,
Huber 1991). This type of learning is also a very complex social phenomenon. As such,
even if the organization’s competitors understand that it is a source of competitive
advantage, they may not engage in systematic efforts to replicate it due to the difﬁculty
this entails (Barney 1991).

Knowledge acquisition and responsiveness. The ﬁrst prediction links knowledge
acquisition and stakeholder-focused responsiveness. According to the information
processing literature, data collection (i.e., knowledge acquisition) is an antecedent to
action (e. g., Daft and Weick 1984). Those organizations that actively engage in
stakeholder-focused knowledge acquisition activities recognize stakeholder needs before
their rivals and are motivated to ﬁnd solutions to those needs (e.g., Slater and Narver
1995). In addition, organizations that regularly obtain information about procedures and
practices that do and do not work respond to this information by modifying their
procedures with the objective of improving productivity and customer satisfaction (e. g.,
Day 1994). This suggests that by continuously monitoring the outcomes of their
stakeholder practices, organizations take actions aimed at enhancing such practices and

ultimately at improving stakeholder satisfaction. Hence, the hypothesis is that the more

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the organization emphasizes stakeholder-focused knowledge acquisition activities, the
more responsive it will be to stakeholder needs in its most important country market.

Information distribution and responsiveness. The organizational learning
literature suggests that as stakeholder-related information is distributed throughout the
organization, individuals and units gain new understanding about stakeholders and their
needs (e.g., Huber 1991). As a result, the organization as a whole becomes more educated
and aware of stakeholders’ demands, which improves the organization’s ability “to make
rapid decisions and execute them effectively” (Slater and Narver 1995, p. 65). As such,
the process of information distribution shapes the organizational direction of the
organization (e.g., Kohli and Jaworski 1990). Consistent with this contention, Sinkula,
Baker, and Noordewier (1997) empirically found that market information distribution
directly affects the extent to which an organization changes its marketing strategies.
Accordingly, the expectation is that an organization is more responsive to stakeholders’
demands if stakeholder-related information is widely distributed throughout the
organization.

Information interpretation and responsiveness. A central tenet of the
information processing literature is that an organization’s interpretation of information
about its external environment inﬂuences the actions (e. g., strategies) it subsequently
takes (e.g., Daft and Weick 1984). Information interpretation leads to a shared
understanding of opportunities and problems that exist in the organization’s environment,
which in turn provides a concerted direction for individuals throughout the organization
(e.g., Daft and Lengel 1986). Empirical research also provides support for this premise

(e.g., Thomas, Clark, and Gioia 1993). Building on these notions, it is expected that an

158

organization that actively interprets information concerning stakeholders will be more
responsive to their needs than organizations that do otherwise.

Organizational memory and responsiveness. Organizational memory is also
expected to be positively associated with stakeholder-focused responsiveness. A powerful
feature of organizational memory is its role in guiding actions (e. g., Moorman and Miner
1997). For instance, an organization’s memory may contain policies and procedures for
dealing with particular stakeholders (e. g., Day 1994). This, in turn, dictates individual
and group actions toward the stakeholders (Moorman and Miner 1997). In addition, the
stakeholder information that is housed in organizational memory can contribute to
efficient and effective decision making (e. g., Walsh and Ungson 1991). By reviewing its
past decisions, an organization can determine which actions are likely to satisfy
stakeholder demands, as well as which are likely to produce negative outcomes, such as
the withdrawal of economic or social participation in the form of boycotts. This decision
response information that resides in the memory shapes the way the organization
responds to a current decision situation. As such, the expectation is that an organization
with a high level of organizational memory that is rich in knowledge about stakeholder
claims and about standard practices for treating stakeholders will be more responsive to
stakeholders than other organizations with lower levels of organizational memory (cf.
Moorman and Miner 1998a).

Consistent with theory, conceptual logic, and the supporting empirical literature
regarding knowledge acquisition, information distribution, information interpretation, and

organizational memory, the following hypothesis states:

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H1: Stakeholder-focused responsiveness is positively inﬂuenced by (a)
stakeholder-focused knowledge acquisition, (b) stakeholder-related
information distribution, (c) stakeholder-related information interpretation,
and (d) stakeholder-focused organizational memory.

Knowledge Acquisition Mechanisms, Innovation, and Imitation

The manner in which the organization responds to the knowledge it acquires about
stakeholders and their needs is a ﬁmction of the mechanism it relies on to collect such
information. In particular, the contention is that to the extent an organization acquires
stakeholder-related information from direct experience (i.e., experiential learning), it will
respond with more innovative stakeholder practices. Innovative stakeholder practices are
deﬁned as those practices that are targeted toward the organization’s stakeholders, which
are new to the market (e. g., Garcia and Calantone 2002). Alternatively, an organization
that relies more on vicarious learning will be more inclined to respond to stakeholders’
demands by engaging in imitative stakeholder practices. These are practices that are not
new to the market, as they have already been executed by the organization’s rivals,
suppliers, clients, partners, or by other organizations, not necessarily within the same
ﬁeld as the organization. An organization that undertakes stakeholder-related knowledge
acquisition activities through contact learning can respond to stakeholders either by
engaging in innovative or imitative stakeholder practices.

The literature on organizational learning suggests that organizations that acquire
information about stakeholders from extemally-focused experience or exploration (i.e.,
experiential learning) are likely to discover and implement innovative stakeholder
practices (e.g., Levinthal and March 1993; McGrath 2001). Speciﬁcally, exploration is

related to experimentation with new alternatives (e.g., March 1991). Potential

consequences of this include the development of new technologies, processes, products,

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or modes of management (e.g., Levinthal and March 1993; McGrath 2001). Similarly, it
has been argued that exploration facilitates generative learning (e.g., Slater and Narver
1995), which in turn, leads to radical innovation (e.g., Baker and Sinkula 2002). He and
Wong (2004) provide empirical support for the positive association between exploration
and product innovation intensity. As such, the organization’s degree of innovative
stakeholder practices is expected to be strongly related to stakeholder-focused
experiential knowledge acquisition.

Institutional theory and the literatures on competitive strategy and organizational
learning provide the basis for the expectation that an organization’s degree of imitative
stakeholder practices is closely related to stakeholder-focused vicarious learning.
According to institutional theory (speciﬁcally to the idea of mimetic isomorphism), under
conditions of environmental uncertainty, organizations observe the actions of others in
their ﬁeld (i.e., vicarious learning), especially of those that they perceive to be more
legitimate or successful, and then imitate those actions in an attempt to gain legitimacy
(e. g., DiMaggio and Powell 1983). Within the context of an organization’s interactions
with stakeholders, attaining legitimacy is pivotal. This is evident by the attention given to
the proliferation of rankings, such as Fortune’s “Most Admired Companies,” which
periodically evaluates organizations’ performance with regard to how they treat
stakeholders (e.g., Waddock, Bodwell, and Graves 2002). Hence, organizations that are
unclear about how to deal with their stakeholders are likely to engage in vicarious
learning and to replicate the stakeholder practices of other organizations.

Consistent with the notion of mimetic isomorphism, the competitive strategy

literature asserts that organizations observe and copy the behaviors of their successful

16]

rivals (e.g., Grewal and Dharwadkar 2002). This suggests that organizations that study
how admired organizations respond to stakeholders will imitate the stakeholder practices
of those organizations (cf. Dickson 1992). The third research stream that allows for the
connection between imitative stakeholder practices and vicarious learning is
organizational learning. According to this research stream, organizations do not simply
mimic common practices and routines as stipulated by institutional theory, but instead
engage in inferential learning (e. g., Miner and Haunschild 1995). As such, they observe
the beneﬁts and drawbacks that other organizations obtain ﬁom their strategic decisions
and only imitate those decisions that seem to produce positive outcomes (e. g., Ordanini,
Rubera, and DeFillippi 2008).

Several studies empirically demonstrate that organizations engage in vicarious
learning and imitate others based on outcomes, traits, and frequency in a variety of
contexts including investment banking decisions (Haunschild and Miner 1997), market
choices (Korn and Baum 1999), and new product introductions (Srinivasan, Haunschild,
and Grewal 2007). Based on these ﬁndings and building on institutional theory and the
literatures on competitive strategy and organizational learning, it is likely that those
organizations that acquire stakeholder-related information by observing the stakeholder
practices of other organizations will respond to stakeholders by employing similar
practices.

Knowledge acquisition through contact learning is similar to that of vicarious
learning in that it can result in imitative practices. Social network theory suggests that
those organizations that are connected to greater network ties are likely to obtain more

information about their contacts, which in turn facilitates imitation (e. g., Lieberman and

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Asaba 2006). In a study that builds on institutional theory, Galaskiewicz and Wasserman
(1989) ﬁnd that when organizations are faced with ambiguous situations, they are likely
to imitate those organizations with which they have network ties. This occurs because
network ties enable managers to observe how other organizations deal with
environmental conditions comparable to their own and to learn about strategies that they
themselves might subsequently adopt. Similarly, the relationship between contact
learning and imitative practices can be explained from an organizational learning
perspective. Kraatz (1998) ﬁnds that organizations engage in an inferential learning
process, where they imitate the actions of network contacts whose actions have been
more successful.

Conversely, information acquired from an organization’s contacts can be a source
of innovation development (e.g., Irnai, Nonaka, and Takeuchi 1985). Powell, Koput, and
Smith-Doerr (1996, p. 142) conclude that “the locus of innovation is found within the
networks of interorganizational relationships.” According to these authors, an
organization’s network ties facilitate innovation given that they both provide timely
access to information that is otherwise unavailable and enable the organization to expand
its own learning capabilities. Therefore, those organizations that acquire stakeholder-
related information through contact learning should respond to stakeholders with
innovative practices of a lesser degree than those organizations that rely on experiential
learning. These organizations should also respond with imitative practices of a lesser
degree than those that acquire vicarious knowledge. The preceding discussion leads to the

following hypotheses:

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H2a: The organization’s degree of innovative stakeholder practices is associated
with the degree of stakeholder-focused experiential knowledge acquisition,
the degree of stakeholder-focused contact knowledge acquisition, and the
degree of stakeholder-focused vicarious knowledge acquisition in
descending order of importance.

H2b: The organization’s degree of imitative stakeholder practices is associated
with the degree of stakeholder-focused vicarious knowledge acquisition,
the degree of stakeholder-focused contact knowledge acquisition, and the
degree of stakeholder-focused experiential knowledge acquisition in
descending order of importance.

Combinative Effects between Knowledge Acquisition Mechanisms

As discussed above, various theoretical bases (e.g., institutional theory, social
network theory) and literature streams (e.g., organizational learning, competitive
strategy) point to several different mechanisms of knowledge acquisition. Typically,
organizations do not restrict themselves to a sole method, but instead draw on some
combination of these methods to acquire information (e.g., Lieberman and Asaba 2006).
This occurs because: “Knowledge facilitates the use of other knowledge” (Powell, Koput,
and Smith-Doerr 1996, p. 120). For instance, an organization that collects stakeholder-
related information from disparate sources is likely to be more cognizant of the needs of
its stakeholder and of stakeholder practices executed by other organizations. This
complementary information equips the organization to respond more effectively to its
stakeholders. Similarly, the concept of “combinative capabilities” suggests that
organizations synthesize and apply stakeholder-related information acquired from
different sources to respond to market opportunities (Kogut and Zander 1992).
Accordingly, the prediction is that organizations that rely on a combination of

stakeholder-focused knowledge acquisition mechanisms use this complementary

information to respond to stakeholders. Stated formally:

164

H3: The combinative effects between each pair of stakeholder-focused
knowledge acquisition types (a: experiential; b: vicarious, and c: contact)
have a positive association with stakeholder-focused responsiveness.

METHOD
Data Collection

An online survey was constructed, using Qualtrics, to assess stakeholder practices
and stakeholder-focused knowledge (e. g., knowledge acquisition, information
distribution, information interpretation, organizational memory, responsiveness,
innovativeness, and imitativeness). The mailing list was purchased ﬁom Dun and
Bradstreet Information Services. Given the paper’s focus on stakeholder issues related to
the marketplace of a ﬁrm, both marketing and supply chain executives were targeted as
potential respondents.

Through a qualifying email invitation sent to marketing and supply chain
executives at 1,072 ﬁrms, a total of 598 executives were identiﬁed as willing to respond
to the survey on stakeholder practices. The executives in the sampling ﬁame had
managerial positions with titles such as Brand Manager, Director of Strategic Marketing,
Vice President of Marketing, Project Manager, Director of Supply Chain Operations, and
Vice President of Operations. The ﬁrms represented a broad cross-section of industries
(e. g., computers, express delivery, food, retail, automotive, and defense).

Prior to collecting the data, the scale items were pre-tested with 10 experts in
stakeholder, marketing, supply chain, and social science research practices. The pretest
resulted in some changes being made, mainly to the instructions to the respondents.

Huber and Power’s (1985) guidelines were followed on how to get quality data from key

informants. The survey was developed using the method established by Dillman, Smyth,

165

and Christian (2009). A cover letter was sent via email, which included a web link to the
survey which was constructed using Qualtrics.

The surveys were made available online via Qualtrics to the 598 identiﬁed
managers between March 15 and March 26 of 2010. Three waves of survey mailings
were performed, with a total of 349 executives responding for an effective response rate
of 58.36 percent. These individuals represented SBUs of 285 different ﬁrms (or 26.58
percent of the 1,072 ﬁrms). Each wave of surveys (ﬁrst, second, third) was sent out on a
different weekday (with 2 to 7 days in between each mailing) and/or at a different time to
maximize the likelihood of obtaining responses. The ﬁrst wave (March 15, 2010) resulted
in 146 responses, the second wave (March 22, 2010) resulted in 102 responses, and the
third wave (March 24, 2010) resulted in 101 responses. The data collection concluded at
the end of the business day on March 26, 2010.

The 349 respondents included 53.9 percent marketing executives and 46.1 percent
supply chain executives. Table 14 reports the demographics of the ﬁrms in the sample. In
terms of the respondents acting as key informants, they had an average score of 5.04
(standard deviation = 1.33) on the question “I have great knowledge of stakeholders
pertaining to my industry” and an average of score of 5.09 (standard deviation = 1.24) on
the question “I have great knowledge of stakeholder practices in my organization.” Both
questions used a seven-point Likert-type scale ranging from “strongly disagree” to
“strongly agree.” In addition, on a scale from 1 = crucial to 7 = negligible, the
respondents rated the overall importance of each of the six stakeholders as: 1.31 (standard
deviation = .70) for customers, 1.62 (standard deviation = .90) for employees, 2.25

(standard deviation = 1.07) for shareholders, 2.40 (standard deviation = 1.03) for

166

suppliers, 2.62 (standard deviation = 1.09) for regulators, and 2.77 (standard deviation =
1.22) for local communities.

TABLE 14

Demographics of the Firms in the Sample (n=349)

 

 

Mean Std. Dev.
Annual sales $13.29 billion $3.49 billion
Age 43.08 years 32.18 years
R&D Investments $360.13 million $1.10 billion
Advertising Expenditures $311.22 million $713.31 million
Intangible assets $2.98 billion $9.31 billion

 

Utilizing the procedures recommended by Armstrong and Overton (1977 ), no
evidence of non-response bias was found when comparing the ﬁrst and last quartiles of
the respondents on the study measures. Additionally, no statistical difference was found
between the ﬁrms in the sample and those not in the sample in annual sales for 2009
($13.29 billion for the ﬁrms in the sample and $13.13 billion for the ﬁrms in the sampling
ﬁame that did not respond). The sampling method also follows stakeholder investigations
of similar phenomena and the response rate compares favorably with other strategically
oriented studies (e.g., Hult, Ketchen, and Arrfelt 2007).

Study Measures

Appendix C lists the measurement items used in the study. Where possible,
established scales were adapted based on the context of this study. Speciﬁcally, the focus
of the survey questions is on stakeholder-focused organizational learning (knowledge
acquisition, information distribution, information interpretation, and organizational
memory — Huber 1991) and two types of outcomes (responsiveness and

innovation/imitation).

167

Knowledge Acquisition. Three types of knowledge acquisition were used in this
study (i.e., experiential, vicarious, and contact), based on work in market orientation
(e. g., Kohli, Jaworski, and Kumar 1993), organizational learning (e.g., Miner and
Haunschild 1995), and institutional theory (e.g., Galaskiewicz and Wasserman 1989).
Each of the scales is made up of four items. The experimental knowledge acquisition
scale is adapted from Kohli, Jaworski, and Kumar’s (1993) intelligence generation scale.
This scale has been shown to have adequate measurement properties. For example,
studies using this scale report reliabilities ranging from .66 to .80 (e.g., Hult, Ketchen,
and Arrfelt 2007; Hult, Ketchen, and Slater 2005; Jaworski and Kohli 1993; Matsuno and
Mentzer 2000). The experimental knowledge acquisition scale captures an organization’s
gathering of stakeholder-related information through searching (March 1991).

New scales were developed for the other two types of knowledge acquisition
based on the general structure of the intelligence generation items in the MARKOR scale
(Kohli, Jaworski, and Kumar 1993). The vicarious knowledge acquisition scale reﬂects
an organization’s acquisition of stakeholder-related information by observing the
behavior toward stakeholders of other organizations with whom it has no direct links with
(i.e., competitors) (e.g., Ordanini, Rubera, and DeFillippi 2008). The contact knowledge
acquisition scale captures the acquisition of stakeholder-related information by observing
the stakeholder practices of others with whom the organization has a relationship (e. g.,
Ordanini, Rubera, and DeFillippi 2008).

Information distribution. To measure information distribution, items from Kohli,
Jaworski, and Kumar’s (1993) intelligence dissemination were adapted to the study

context. Previous studies have reported good reliabilities for this scale, ranging ﬁ'om .78

168

to .82 (e.g., Hult, Ketchen, and Arrfelt 2007; Jaworski and Kohli 1993; Matsuno and
Mentzer 2000). The scale consists of ﬁve items that capture the distribution of
stakeholder information across the organization.

Information interpretation. The information interpretation measure was
motivated by Hult, Ketchen, and Slater’s (2004) two-item scale of shared meaning, which
was subsequently expanded to a four-item scale by Hult, Ketchen, and Arrfelt (2007).
This scale is based on Huber (1991) and has been shown to have good reliability, as it has
ranged from .92 to .94 (e.g., Hult, Ketchen, and Arrfelt 2007; Hult, Ketchen, and Slater
2004). The scale captures an organization’s interpretation of stakeholder information.

Organizational memory. Moorman and Miner’s (1997) organizational memory
scale was adapted for this study. In direct and adapted versions of this scale, reliabilities
have ranged ﬁom .79 to .96 (e.g., Moorman and Miner 1997, 1998b; Hanvanich,
Sivakurnar, Hult 2006). As adapted, the scale consists of four items that capture the
amount of knowledge, experience, and familiarity an organization has about its
stakeholders relative to its major competitors.

Responsiveness. Responsiveness is measured based on a scale adapted from
Kohli, Jaworski, and Kumar’s (1993) study of market orientation (MARKOR). Previous
studies provide evidence of the adequacy of this measure in terms of reliability.
Speciﬁcally, reliabilities have ranged from .74 to .88 (e.g., Hult, Ketchen, Cavusgil, and
Calantone 2006; Hult, Ketchen, and Slater 2005; Jaworski and Kohli 1993; Matsuno and
Mentzer 2000). The scale consists of ﬁve items and captures the extent to which an
organization takes action to stakeholder-related information (e.g., Kohli and J aworski

1990).

169

Innovativeness. The innovativeness measure was adapted from the scale
developed by Hurley and Hult (1998), which has been proven to have good measurement
properties. For example, the reliabilities of this scale have ranged from .77 to .90 (e.g.,
Hurley and Hult 2008; Luo, Sivakurnar, and Liu 2005; Menguc and Auh 2006; Hult,
Ketchen, and Arrfelt 2007). As adapted, the scale consists of four items.

Imitativeness. The imitativeness scale was motivated by Hurley and Hult’s (1998)
innovativeness scale, which, as was previously discussed, has been shown to have good
measurement properties. The scale was modiﬁed accordingly to reﬂect the imitative
natm'e of this measure as opposed to the innovative nature of Hurley and Hult’s (1998)
scale. The imitativeness scale consists of four items.

Conﬁrmatory Factor Analysis and Hierarchical Regression

The psychometric properties were evaluated simultaneously in one conﬁrmatory
factor analysis (CF A) using LISREL 8.80 (Joreskog, Siirbom, Du Toit and Du Toit,
2000). This included nine latent constructs which were all stakeholder focused
(experiential knowledge acquisition, vicarious knowledge acquisition, contact knowledge
acquisition, information distribution, information interpretation, organizational memory,
responsiveness, innovativeness, and imitativeness) and their total set of 40 reﬂective
measures.

The relationships detailed in Hypotheses 1, 2a, 2b, and 3 were tested via
hierarchical regression (H1, H2a, and H2b) and interaction-based hierarchical regression
(H3). Four regression models were speciﬁed to examine the hypotheses. All equations
included three control variables which were entered in step 1 in each case. The control

variables entered in step 1 of each of the four regression models were the same and

170

included a dummy variable for the industry (6-digit NAICS code), size (natural log of
total sales), and age (natural log of age).

To examine H1, a hierarchical regression model was conducted with controls
entered in step 1 and experiential knowledge acquisition, vicarious knowledge
acquisition, contact knowledge acquisition, information distribution, information
interpretation, and organizational memory entered in step 2. To examine H2a and H2b, a
hierarchical regression was conducted with controls entered in step 1 and experiential
knowledge acquisition, vicarious knowledge acquisition, and contact knowledge
acquisition entered in step 2. H2a used innovativeness as the criterion variable while H2b
used imitativeness as the criterion variable.

To examine H3, an interaction-based hierarchical regression was conducted with
controls entered in step 1; experiential knowledge acquisition, vicarious knowledge
acquisition, and contact knowledge acquisition entered in step 2; and the three
interactions entered in step 3 (i.e., experiential knowledge acquisition * vicarious
knowledge acquisition; experiential knowledge acquisition * contact knowledge
acquisition; and vicarious knowledge acquisition * contact knowledge acquisition).

RESULTS
Measurement Results

Table 15 reports the means, standard deviations, correlation matrix, and shared
variances for the nine study constructs. Table 16 summarizes the measurement analysis
(i.e., composite reliabilities, average variances extracted, factor loadings, and ﬁt

statistics). All measures were subjected to reliability and validity assessments.

171

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TABLE 16

Composite Reliabilities, Average Variances Extracted,
Factor Loadings, and Fit Statistics (n=349)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Composite Average Factor
Construct Reliability Variance Loadrngs
Extracted Range
Experimental Knowledge Acquisition (EKA) .86 .62 .70 - .86
Vicarious Knowledge Acquisition (VKA) .93 .76 .85 - .88
Contact Knowledge Acquisition (CKA) .87 .70 .77 - .87
lnfonnation Distribution (IDL .88 .61 .71 - .85
lnfonnation Interpretation (19 .92 .75 .83 - .91
Organizational Memory (OM) .95 .86 .83 - .98
Responsiveness (RE) .82 .61 .73 - .87
Innovativeness (IN) .94 .84 .87 - .95
Imitativeness (1M) .93 .81 .87 - .95
Fit Statistics:
x2 = 2187.22
Degrees of Freedom = 428
NFI = .95
DELTA2 = .95
CFI = .95
RMSR = .06

Fit statistics. The CF A model ﬁt was evaluated using a series of ﬁt indices
recommended by Gerbing and Anderson (1992) and Hu and Bentler (1999) — the normed
ﬁt index (N F I), DELTA2, comparative ﬁt index (CFI), and root mean square residual
(RMSR) — along with the reporting of chi-square (x2) and degrees of freedom (d.f.). After
removing eight items with loadings below .70 (cf. Fomell and Larcker 1981), an
excellent ﬁt to the data was achieved in the conﬁrmatory factor analysis. The NF I = .95,
DELTA2 = .95, CF 1 = .95, and RMSR = .06 (x2 = 2187.22, d.f. = 428).

Composite Reliability. The nine latent factors’ reliabilities were assessed by
calculating their composite reliability (Fomell and Larcker 1981). The factor loadings
and their t-values were also examined (Anderson and Gerbing 1988). The scales’

reliabilities ranged ﬁ'om .82 to .95, all of which are above the recommended threshold for

173

CF A-based composite reliabilities (Peter 1979). The factor loadings ranged ﬁ'om .70 to
.98 (p<.01). Table 12 reports the complete CFA results.

Discriminant Validity. The scales were also found to have good discriminant
validity via two analyses. First, by comparing the average variances extracted (AVE) for
each scale with the pairwise shared variances of all possible combinations among the
nine scales, the AVEs ranged ﬁ'om .61 to .86 while the shared variances ranged from .03
to .58. Second, the technique suggested by Bagozzi and Phillips (1982) was used to again
examine discriminant validity via a different type of analysis. This entails examining all
possible pairs of the nine constructs in a series of two-factor CF A models using LISREL
8.80 (Joreskog et al 2000). Speciﬁcally, each pairwise CFA model was run twice. In the
ﬁrst analysis, the a coefﬁcient was constrained to unity. In the second analysis, the a
coefficient was allowed to vary ﬁ'eely. Based on the results of a x2 difference test
between pairs of constructs, the unconstrained models were better than the associated
constrained models (i.e., x2“) > 3.84 was exceeded in all cases, with the AXZU) ranging
from 179.92 to 923.82 for all possible pairwise combinations).

Common Method Variance. Following Olson, Slater, and Hult (2005), the one-
factor test was employed to examine if common method variance was inherent in the
dataset (McFarlin and Sweeney 1992; Podsakoff and Organ 1986; Sanchez and Brock
1996). The logic behind this test is that if common method variance poses a serious
threat, a single latent factor would account for all manifest variables. The one-factor

model resulted in a 12 = 6066.38 with d.f. = 464 versus x2 = 2187.22 with d.f. = 428 for

the theoretically deﬁned measurement model. Thus, the A1206) = 3879.16 and no

174

empirical evidence exists that common method variance is a serious inhibiting element in
testing the hypotheses.
Hypothesis Results

Four regression models were conducted to assess H1, H2a, H2b, and H3. Cases
with missing data were excluded listwise (i.e., any case with missing data was excluded),
and the enter method was used to include variables in the equations at each step of the
hierarchical regression analyses (cf. Cohen, Cohen, West, and Aiken 2003). All predictor
variables and the control variables of age and size were standardized by mean centering.
Industry was included as a dummy variable using the 6-digit NAICS code. The Variance
Inﬂation Factors ranged from 1.00 to 3.72 for the predictor variables in the four
equations, indicating that multicollinearity did not signiﬁcantly affect the analysis (Cohen
et al. 2003). The effect sizes - probability of ﬁnding the R25 achieved — was at least
B>.90 at or=.05 using the method suggested by Cohen (1988). Tables 13 (HI), l4 (H2a),
15 (HZb), and 16 (H3) summarize the results.

Organizational Learning and Responsiveness (H1 ). As can be seen in Table 17,
a number of signiﬁcant relationships were detected at an effect size of B>.99 (o.=.01).
Based on the step 2 results, the control variables of size (B=-.17, p<.01) was signiﬁcant
but age and industry were not. The direct effects of experiential knowledge acquisition
(B=.23, p<.01), contact knowledge acquisition (B=.l l, p<.10), information distribution
(13:27, p<.01), information interpretation (B=.26, p<.01), and organizational memory
(B=.13, p<.10) signiﬁcantly affected stakeholder-focused responsiveness but vicarious
knowledge acquisition did not. The AR2=.63 (p=.01) between step 1 (controls) and step 2

(direct effects), and the equation had R2=.67 and Adjusted R2=.64. The Variance Inﬂation

175

Factors ranged from 1.06 to 3.72 for the nine variables in the model. These results
indicate that Hla, ch, Hld, Hle, and Hlfwere supported but not Hlb.

TABLE 17

Hierarchical Regression Results with
Stakeholder-Focused Responsiveness as the Criterion Variable (H1)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Standardized . . Significance
Beta (13) t-Statrstrc (I!)

Stg 1: Controls
Industry .08 .80 .42
Firm Size -.17 -1.84 .07
Firm Ag .05 .51 .61
R-square =.04
Adjusted R-square =.01
F-value 1.40 (p=.25L
Step 2: Direct Effects
Industry -.06 -1.04 .30
Firm Size -.17 -2.86 .01
Firm Age -.08 -1.38 .17
Experiential Knowledge Acquisition .23 2.66 <.01
Vicarious Knowledge Acquisition -.02 -.I9 .85
Contact Knowledge Acquisition .11 1.35 .09
Information Distribution .27 2.47 <.01
lnfonnation Interpretation .26 2.66 <.01
Organizational MemoD' .13 l .59 .06
R-square =.67
Adjusted R-square =.64
AR-square =.63 (p<.01)
F -value=23.46 (p<.01)
Effect Size: ﬁ>.99 (a=.01)

 

One-tailed tests were used for the direct (hypothesized) relationships and two-tailed tests were
used for the control variables.

Knowledge Acquisition and Innovativeness (HZa). As can be seen in Table 18,
the model testing supported the basic premise of H2a that experiential knowledge
acquisition is the primary driver of innovativeness. At an effect size of B>.99 (a=.01),
based on the step 2 results, experiential knowledge acquisition was found to affect
innovativeness (B=.45, p<.01) but neither of the vicarious knowledge acquisition, contact
knowledge acquisition, or controls was signiﬁcant in the model. The AR2=.25 (p=.01)

between step 1 (controls) and step 2 (direct effects), and the equation had R2=.28 and

176

Adjusted R2=.24. The Variance Inﬂation Factors ranged from 1.01 to 2.55 for the six
variables in the model. These results indicate that H2a was partially supported, with the
most critical aspect of the hypothesis being supported (i.e., that experiential knowledge
acquisition is the key knowledge acquisition driver of innovativeness).

TABLE 18

Hierarchical Regression Results with
Innovativeness as the Criterion Variable (HZa)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Standardized . . Significance
Beta (P) t-Statlstlc (p)

Step 1: Controls
Industry .07 .78 .44
Firm Size -.06 -.59 .56
Firm Age .16 1.73 .09
R-square =.03
Adjusted R-square =.01
F-value 1.26 (p=.29
Step 2: Direct Effects
Industry -.00 -.02 .98
Firm Size -.02 -.20 .85
Firm Age .1 l 1.26 .21
Experiential Knowledge Acquisition .45 4.28 <.01
Vicarious Knowledge Acquisition -.02 -.16 .44
Contact Knowledge Acquisition .12 1.02 .15
R-square =.28
Adjusted R-square =.24
AR-square =.25 (p<.01)
F-value=6.68 (p<.01)
Effect Size: B>.99 (a=.01)

 

 

 

 

 

 

One-tailed tests were used for the direct (hypothesized) relationships and two-tai led tests were
used for the control variables.

Knowledge Acquisition and Imitativeness (1121)). As can be seen in Table 19, the
model testing supported the basic premise of H2b that vicarious knowledge acquisition is
the primary driver of imitativeness. At an effect size of B>.90 (a=.05), based on the step 2
results, vicarious knowledge acquisition was found to affect imitativeness (B=.21, p<. 10),
as was the control of industry (B=.-.16, p<. 10), but neither of the experiential knowledge

acquisition, contact knowledge acquisition, or controls of age and size was signiﬁcant in

177

the model. Given that they key variable — vicarious knowledge acquisition - was only
signiﬁcant at the p<. 10 level, two additional regression analyses were conducted to
examine the relationships in a fme-grained manner. Speciﬁcally, the model was run with
both pairwise exclusion of data and mean substitution of data with missing values, in
addition to the listwise deletion of data used for all regression models, and both
additional models rendered vicarious knowledge acquisition signiﬁcant at the p<.05 level.
The AR2=.10 (p=.01) between step 1 (controls) and step 2 (direct effects), and the
equation had R2=.12 and Adjusted R2=.07. The Variance Inﬂation Factors ranged from
1.01 to 2.59 for the six variables in the model. These results indicate that H2b was
partially supported, with the most critical aspect of the hypothesis being supported (i.e.,
that vicarious knowledge acquisition is the key knowledge acquisition driver of

imitativeness).

178

TABLE 19

Hierarchical Regression Results with Imitativeness as the Criterion Variable (HZb)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Standardized . . Significance
Beta (13) t-Statrstrc (p)

Step 1: Controls
Industry -.10 -1.02 .3 1
Firm Size .01 .08 .94
Firm Age .09 .92 .36
R-square =.02
Adj‘prsted R-square =.01
F-value .68 (p=.57)
Step2: Direct Effects
Industry -.16 -1.64 .10
Firm Size .03 .37 .71
Firm Age .05 .50 .62
Experiential Knowledge Acquisition .07 .63 .26
Vicarious Knowledge Acquisition .21 1.45 .08
Contact Knowledge Acquisition .08 .64 .26
R-square =. 12
Adjusted R-square =.07
AR-srLuare =.10 (p<.01)
F-value=2.41 (p<.05)
Effect Size: B>.90 (u=.05)

 

One-tailed tests were used for the direct (hypothesized) relationships and two-tailed tests were
used for the control variables.

Combinative Knowledge Acquisition and Responsiveness (H3). As can be seen
in Table 20, a number of signiﬁcant relationships were detected at an effect size of [>99
(a=.01). Based on the step 3 results, the control variable of size (B=-.13, p<.10) was
signiﬁcant but age and industry were not. The direct effects of experiential knowledge
acquisition (B=.62, p<.01) and contact knowledge acquisition (B=.l9, p<.05) signiﬁcantly
affected stakeholder-speciﬁc responsiveness but vicarious knowledge acquisition did not.
The combinative effects of experiential knowledge acquisition * contact knowledge
acquisition (13:35, p<.01) and vicarious knowledge acquisition * contact knowledge
acquisition (B=.20, p<.05) affected responsiveness. The AR2=.46 (p=.01) between step 1

(controls) and step 2 (direct effects), and the AR2=.04 (p=.05) between step 2 and step 3

179

(combinative effects). The equation had an overall R2=.54 and Adjusted R2=.49. The
Variance Inﬂation Factors ranged from 1.06 to 3.60 for the nine variables in the model.
These results indicate that H3c was supported but not H3a and H3b.

TABLE 20

Interaction-Based Hierarchical Regression Results with
Responsiveness as the Criterion Variable (H3)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Standardized . . Significance
Beta (l3) t-Statrstrc (p)

SteJr 1: Controls
Industry .08 .80 .42
Firm Size -.17 -1.84 .07
Firm Age .05 .51 .61
R-square =.04
Adjusted R-square =.01
F-value 1.40 (p=.25)
Stg 2: Direct Effects
Industry -.03 -.38 .71
Firm Size -.14 -1.97 .05
F inn Age -.03 -.43 .67
Experiential Knowledge Acquisition .53 6.19 <.01
Vicarious Knowledge Acquisition .08 .74 .23
Contact Knowledge Acquisition .18 1.81 <.05
R-square =.50
Adjusted R-square =.47
AR-square =46 (p=.07)
F-value=17.75 (p<.01)
Step 3: Combinative Effects
Industry -.04 -.53 .60
Firm Size -.13 -1.82 .07
Firm Age -.04 -.65 .52
Experiential Knowledge Acquisition .62 6.82 <.01
Vicarious Knowledge Acquisition .03 .25 .40
Contact Knowledge Acquisition .19 1.91 <.05
EKA "' VKA Combinative Effect .17 1.50 .14
EKA “ CKA Combinative Effect -.35 -2.76 <.01
VKA "' CKA Combinative Effect .20 2.06 <.05
R-square =.54
Adjusted R-square =.49
AR-square =.04 (p<.05)
F-value =13.37 (p<.01)
Effect Size: ﬂ>.99 (a=.01)

 

 

One-tailed tests were used for the direct (hypothesized) relationships and two-tailed tests were
used for the control variables.

180

DISCUSSION AND IMPLICATIONS

A key objective of this study was to examine whether stakeholder-focused
organizational learning inﬂuences the organization’s propensity to take action in response
to its stakeholders’ needs. Drawing upon insights from institutional theory and the
literatures on competitive strategy and organizational learning, it also investigated if the
knowledge acquisition mechanisms the organization relies on to acquire stakeholder-
related knowledge determine how innovative or imitative its stakeholder practices are. In
addition, it examined the interaction effects that exist between the knowledge acquisition
mechanisms. The empirical analysis based on the responses of 349 marketing and supply
chain management executives to an online survey reveal that the four organizational
learning processes have a direct effect on stakeholder-focused responsiveness. The results
also indicate that while experiential knowledge acquisition is related to innovative
stakeholder practices, vicarious knowledge acquisition is related to imitative ones.
Furthermore, with one exception, combinative effects were found between the knowledge
acquisition mechanisms. These ﬁndings have several important implications for research
and practice.

First, this study addresses calls for research that broadens the scope of traditional
marketing concepts to include additional stakeholders beyond the widely-studied
customers, and to a lesser extent, suppliers (e.g., Maignan and Ferrell 2004). It achieves
this by developing the construct of stakeholder-focused organizational learning which is
more encompassing than market-based organizational learning (e. g., Sinkula, Baker, and
Noordewier 1997) — and involves learning about the organization’s primary stakeholders

(i.e., customers, employees, suppliers, shareholders, regulators, and the community)

181

given that these groups are essential for the continued success of the organization (e. g.,
Clarkson 1995). Also, by studying stakeholder-focused organizational learning as an
antecedent to stakeholder-focused responsiveness, this paper contributes to stakeholder
theory, which holds that organizations that respond to their stakeholders have a
competitive advantage over other organizations (e.g., Jones 1995). Research in this area
has mostly focused on tying stakeholder practices and performance (e.g., Berman et al.
1999; Greenley and Foxall 1998; Hillman and Keim 2001). This is the ﬁrst empirical
study that shows that for organizations to be responsive to their stakeholders, they need to
effectively acquire, distribute, interpret, and store stakeholder-related knowledge. In this
context, further research is needed to explore other potential factors such as
organizational culture that prompt ﬁrms to address their stakeholders’ demands.

F urtherrnore, by identifying stakeholder-focused organizational learning as a
strategic resource that enables the organization to respond to stakeholders by taking better
actions, this study also contributes to the RBV (e.g., Barney 1991). Advocates of this
theoretical perspective contend that the value of the RBV does not lie in predicting a
simple resources-performance relationship as is often done in the literature, but in
incorporating an “action” element into the ﬁamework to discover what organizations do
with their resources that lead them to have a competitive advantage and superior
performance (Ketchen, Hult, and Slater 2007). This study concentrated on the strategic
resources-strategic action link of the RBV framework to exarrrine if the possession of
stakeholder-focused organizational learning results in responsiveness toward the
organization’s stakeholders. To fully capture the different components of the RBV

framework, future research should investigate whether stakeholder-focused

182

responsiveness leads to a competitive advantage and better performance, and hence, if it
ﬁmctions as a mediator in the stakeholder-focused organizational learning—ﬁrrn
performance relationship. Even though both RBV and stakeholder theory predict that
better performance should follow stakeholder-focused responsiveness, it is possible that
too much stakeholder-focused organizational learning and responsiveness are detrimental
to ﬁrm performance. Excessive attention to learning about and responding to stakeholders
shifts the organization’s focus away ﬁ'om other important issues such as productivity,
which in turn would hamper performance.

The ﬁnding that an organization’s degree of experiential learning is positively
related to the degree of innovative stakeholder practices is consistent with the
organizational learning literature. This literature suggests that investing signiﬁcant
resources to gain ﬁrst-hand information about stakeholders through searching results in
the experimentation of new stakeholder practices (e. g., March 1991), and ultimately, in
the development of new technologies, processes, products, or modes of management
(e.g., Levinthal and March 1993). On the other hand, it was found that contact knowledge
acquisition and vicarious knowledge acquisition do not lead to innovative stakeholder
practices. It seems that since these mechanisms consist of observing the stakeholder
practices implemented by others, they do not prompt the organization to seek new
solutions to fulﬁll stakeholder demands given that such action has already been taken by
those organizations they observe. In a broader context, these ﬁndings suggest that the
world’s most innovative companies, such as Apple and Nintendo, which consistently
offer inventive products that satisfy their stakeholders’ demands and outperform their

rivals in their business models and processes (BusinessWeek 2009), rely on experiential

183

knowledge acquisition as their primary mechanism to learn about their stakeholders’
needs and wants.

Moreover, the results indicate that the degree of vicarious knowledge acquisition
has a positive inﬂuence on the degree of imitative stakeholder practices, while
experiential and contact knowledge acquisition have no effect. This is consistent with
institutional theory, particularly with the notion of mimetic isomorphism, and with the
literatures on competitive strategy and organization learning. Building on these streams,
it seems that because organizations are unclear about how they should interact with their
stakeholders, they (I) gather information about the stakeholder-focused actions taken by
their competitors, (2) evaluate the outcomes these actions produce for their rivals, and (3)
mimic those actions that produced positive outcomes. Hence, the vicarious knowledge
acquisition-imitation link helps explain why in any particular industry, organizations
engage in similar activities. One example is the automotive industry and the production
of hybrid cars. While the hybrid car — which strongly caters to customers’, regulators’,
and the community’s demands — was originally developed by Toyota, it is now produced
by a number of car manufacturers. Organizations such as General Motors and Ford that
operate in the uncertain automotive industry observe the stakeholder practices of other
successful automakers (e. g., Toyota and its production of the Prius hybrid car) and often
imitate their competitors’ practices (e. g., developing their own versions of the hybrid car)
in an attempt to gain legitimacy.

Another contribution this study makes is that it is among the ﬁrst to empirically
examine the interactions between the different forms of knowledge acquisition. By doing

so, it adopts a realistic model of learning (e. g., Lieberman and Asaba 2006) given that

184

ﬁrms often acquire information from different sources. .The ﬁndings indicate that the
combinative effects of vicarious knowledge acquisition. and contact knowledge
acquisition are positively related to stakeholder-focused responsiveness. These two
knowledge acquisition mechanisms are similar in that they both consist of acquiring
second-hand experience. As such, an organization that relies on them, in essence, requires
the same set of skills - drawing inferences from the observed behavior of other
organizations (e. g., Lieberman and Asaba 2006; Ordanini, Rubera, and DeFillippi 2008).
By continuously engaging in both knowledge acquisition mechanisms to obtain
stakeholder-related information, an organization can master this skill over time. This, in
turn, allows the organization to efficiently synthesize the complementary information
acquired, prompting it to respond more effectively to its stakeholders.

On the other hand, contrary to expectations, the combination of experiential
knowledge acquisition and contact knowledge acquisition has negative effects on
stakeholder-focused responsiveness. One possible explanation for this result is that these
mechanisms require different sets of skills. As previously discussed, contact knowledge
acquisition consists of observing the behavior of other organizations, speciﬁcally of those
with whom the organization has ties (e.g., Ordanini, Rubera, and DeFillippi 2008).,
Alternatively, experiential knowledge acquisition requires the organization to be more
proactive in scanning its external environment for ﬁrst-hand information about
stakeholders and their interests (e. g., Huber 1991 ). Hence, while experiential knowledge
acquisition by itself prompts an organization to develop innovative solutions that respond
to stakeholder demands, when combined with contact knowledge acquisition, the effect is

negative due in part to the potentially conﬂicting stakeholder-related information the

185

 

organization gathers from its network ties and to the challenge the organization faces in
synthesizing information acquired ﬁom such different sources. However, if the different
skill sets these two mechanisms require is the reason for the ﬁndings, then it is still
unclear why the combination of experiential knowledge acquisition and vicarious
knowledge acquisition has no effects on stakeholder-focused responsiveness, given that
these two also consist of different skill sets. Future research should investigate this
further. In addition, further research is needed to explore the relationship between the
combination of the knowledge acquisition modes and the level of innovativeness and
imitativeness of the organization’s stakeholder practices.

For managers, this study underscores the importance of cultivating stakeholder-
focused organizational learning. Senior managers must make sure that mechanisms to
facilitate stakeholder-focused knowledge acquisition, information distribution,
interpretation, and storage are in place throughout the organization. These four processes
are important given that they enable stakeholder-focused organizations to be cognizant of
their stakeholders’ needs, respond to these ever-changing needs in a timely manner, and
hence nurture their stakeholder relationships over the long run. This, in turn, should
translate into superior performance (Jones 1995). In addition, organizations that have as a
top priority to be at the forefront of their industry in stakeholder practices must actively
engage in experiential knowledge acquisition. While acquiring stakeholder-related
information ﬁ'om second-hand experience keeps organizations informed about new
stakeholder practices in the ﬁeld (e.g., Huber 1991) and prompts them to respond, it is the
knowledge that organizations acquire from the stakeholders themselves and from their

experiences with these groups that leads them to be innovative.

186

APPENDIX C

Measures

Experiential Knowledge Acqu'uition (adapted from Kohli, Jaworski, and Kumar 1993, JMR)
[7-point Likert-type scale ranging from “strongly disagree” to “strongly agree”]

We meet with our stakeholders often to ﬁnd out what they will need in the future.

We do a lot of in-house market research regarding our stakeholders.

We are fast to detect changes in our stakeholders’ preferences.

We often review the likely effect of changes in the business environment on our stakeholders.

swwr

Vicarious Knowledge Acquisition (new scale based on Kohli, Jaworski, and Kumar 1993, JMR)
[17-pointLikert-type scale ranging from “strongly disagree” to‘ ‘strongly agree”]

We continually monitor the stakeholder-related actions of our main competitors.

We do a lot of benchmarking on our stakeholders and their relationship with our main competitors.
We are quick to detect changes in our main competitors’ stakeholder practices.

We pay close attention to the outcomes experienced by our main competitors as a result of their
stakeholder-related actions.

PPN?‘

Contact Knowledge Acquisition (new scale based on Kohli, Jaworski, and Kumar 1993, JMR)

[7-point Likert-type scale ranging ﬁom “strongly disagree” to “strongly agree”]. “Other organizations” in
the items below refer to primary suppliers, strategic partners, and business-to-business customers of your
organization.

1. We observe closely the stakeholder practices of other organizations with which we have a relationship.
2. We do a lot of networking with other organizations to obtain information about our stakeholders.

3. We are able to learn about our stakeholders by working with other organizations.

4 Our relationships with other organizations provide timely access to information about our stakeholders.

Information Distribution (adapted from Kohli, Jaworski, and Kumar 1993, JMR)

[l7-point Likert-type scale ranging ﬁ'om “strongly disagree” to “strongly agree”]
We have inter-unit meetings often to discuss stakeholder trends and developments.

2. Personnel rn our unit often spend time discussing stakeholders’ future needs with other units.

3. When something important happens to a major stakeholder, our unit knows about it within a short
period.

4. Data on stakeholder satisfaction are disseminated at all levels in our unit on a regular basis.

5. When one unit ﬁnds out something important about our stakeholders, it is fast to alert relevant other
units.

Information Interpretation (Motivated by Hult, Ketchen, and Slater 2004, AMJ; Hult, Ketchen, and
Arrfelt 2007, SMJ)

[7-point Likert-type scale ranging fi’om “strongly disagree” to “strongly agree”]

We develop a shared understanding of stakeholder-related information between units.

We develop a shared understanding of stakeholder-related information within our unit.

We develop a shared understanding of available stakeholder-related information in our organization.
We develop a shared understanding of the implications of a stakeholder activity.

PPNT‘

Organizational Memory (adapted from Moorman and Miner 1997, JMR)
[7-point Likert-type scale ranging from “strongly disagree” to “strongly agree”]
Compared with major competitors in the industry, our organization has:

1. A great deal of knowledge about our stakeholders.

2 A great deal of experience with our stakeholders.

3. A great deal of familiarity with our stakeholders.

4 Invested a great deal of R&D to understand our stakeholders.

187

Responsiveness (Kohli, Jaworski, and Kumar 1993, JMR)
[l7-point Likert-type scale ranging from‘ ‘strongly disagree” to “strongly agree”]

2.

3.
4.
5

For one reason or another we never ignore changes in our stakeholders’ needs.

Several units get together periodically to plan a response to changes taking place regarding our
stakeholders.

The stakeholder-focused activities of different departments in our business unit are well coordinated.
Stakeholder complaints never fall on deaf ears in our business unit.

When our stakeholders like us to modify our practices, we make a concerted effort to do so.

Innovativeness (adapted from Hurley and Hult 1998, JM)
[7-point Likert-type scale ranging ﬁ'om “strongly disagree” to “strongly agree”]

VPPNT‘

Innovative stakeholder practices are readily used in our organization.

Management actively implements innovative strategies pertaining to our stakeholders.
Innovation is readily implemented in program/project management of our stakeholders.

People are never penalized for new stakeholder-related innovations they tried that do not work.
Implementing innovations targeted to our stakeholders’ needs is never perceived as too risky.

Imitativeness (new scale — adapted ﬁ'om innovativeness scale - Hurley and Hult 1998, JM)
[7-point Likert-type scale ranging from “strongly disagree” to “strongly agree”]

MPPPP

lmitative stakeholder practices are readily used in our organization.

Management actively implements imitative strategies pertaining to our stakeholders.
Imitation is readily implemented in program/project management of our stakeholders.
People are never penalized for stakeholder-related imitations they tried that do not work.
Implementing imitations targeted to our stakeholders’ needs is never perceived as too risky.

188

REFERENCES

Anderson, James C. and David W. Gerbing (1988), “Some Methods for Respecifying
Measurement Models to Obtain Unidimensional Construct Measurement,” Journal of
Marketing Research, 19 (November), 453-460.

Armstrong, J. Scott and Terry S. Overton (1977), “Estimating Nonresponse Bias in Mail
Surveys,” Journal of Marketing Research, 14 (August), 396-402.

Bagozzi, Richard P. and Lynn W. Phillips (1982), “Estimating and Testing
Organizational Theories: A Holistic Construal,” Administrative Science Quarterly, 27
(September), 459-489.

Baker, William E. and James M. Sinkula (1999), “The Synergistic Effect of Market
Orientation and Learning Orientation on Organizational Performance,” Journal of the
Academy of Marketing Science, 27 (4), 411-427.

Baker, William E. and James M. Sinkula (2002), “Market Orientation, Learning
Orientation and Product Innovation: Delving into the Organization’s Black Box,” Journal
of Market-F ocused Management, 5 (1), 5-23.

Barney, Jay B. (1991), “F irm Resources and Sustained Competitive Advantage,” Journal
of Management, 17 (1 ), 99-120.

Berman, Shawn L., Andrew C. Wicks, Suresh Kotha, and Thomas M. Jones (1999),
“Does Stakeholder Orientation Matter? The Relationship between Stakeholder

Management Models and Firm Financial Performance,” Academy of Management
Journal, 42 (5), 488-506.

BusinessWeek (2009), “The 50 Most Innovative Companies,” (accessed April 1, 2010),
available at http://bwnt.businessweek.com/interactive_reports/innovative__5 0__2009/#

Clarkson, Max (1995), “A Stakeholder Framework for Analyzing and Evaluating
Corporate Social Performance,” Academy of Management Review, 20 (1), 92-117.

Cohen, Jacob (1988), Statistical Power for the Behavioral Sciences, Hillsdale, NJ:
Lawrence Erlbaum Associates Publishers.

Cohen, Jacob, Patricia Cohen, Stephen G. West, and Leona S. Aiken (2003), Applied
Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd Edition),
Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

Daft, Richard L. and Karl E. Weick (1984), “Toward a Model of Organizations as
Interpretation Systems,” Academy of Management Review, 9 (2), 284-295

189

Daft, Richard L. and Robert H. Lengel (1986), “Organizational lnfonnation
Requirements, Media Richness and Structural Design,” Management Science, 32 (5),
554-57 1 .

Day, George S. (1994), “The Capabilities of Market-Driven Organizations,” Journal of
Marketing, 58 (1), 37-52.

Day, George S. and Prakash Nedungadi (1994), “Managerial Representations of
Competitive Advantage,” Journal of Marketing, 58 (2), 31-44.

Dickson, Peter Reid (1992), “Toward a General Theory of Competitive Rationality,”
Journal of Marketing, 56(1), 69-83.

Dillman, Don A., Jolene D. Smyth, and Leah Melani Christian (2009), Internet, Mail,
and Mixed-ode Surveys: The Tailored Design Method (3rd Edition), Hoboken, NJ: John
Wiley & Sons, Inc.

DiMaggio, Paul J. and Walter W. Powell (1983), “The Iron Cage Revisited: Institutional
Isomorphism and Collective Rationality in Organizational Fields,” American
Sociological Review, 48 (April), 147-160.

F iol, C. Marlene and Marjorie A. Lyles (1985), “Organizational Learning,” Academy of
Management Review, 10 (4), 803-813.

F omell, Claes and David F. Larcker (1981), “Evaluating Structural Equation Models with
Unobservable Variables and Measurement Error,” Journal of Marketing Research, 18

(February), 39-50.

Freeman, R. Edward (1984), Strategic Management: A Stakeholder Approach.
Marshﬁeld, MA: Pitrnan Publishing Inc.

Frooman, Jeff (1999), “Stakeholder Inﬂuence Strategies,” Academy of Management
Review, 24 (2), 191-205.

Galaskiewicz, Joseph, and Stanley Wasserman (1989), “Mimetic Processes within an
Interorganizational Field: An Empirical Test,” Administrative Science Quarterly, 34(3),
454-479.

Garcia, Rosanna and Roger Calantone (2002), “A Critical Look at Technological
Innovation Typology and Innovativeness Terminology: A Literature Review,” Journal of
Product Innovation Management, 19 (2), 110-132.

Gerbing, David W. and James C. Anderson (1992), “Monte Carlo Evaluations of

Goodness of Fit Indices for Structural Equation Models,” Sociological Methods and
Research, 21 (2), 132-160.

190

Greenley, Gordon B. and Gordon R. F oxall (1998), “External Moderation of Associations
among Stakeholder Orientations and Company Performance,” International Journal of
Research in Marketing, 15 (1), 51-69.

Grewal, Rajdeep and Ravi Dharwadkar (2002), “The Role of the Institutional
Environment in Marketing Channels,” Journal of Marketing, 66 (3), 82-97

Han, J in K., Namwoon Kim, and Rajendra K. Srivastava (1998), “Market Orientation and
Organizational Performance: Is Innovation a Missing Link?” Journal of Marketing, 62
(4), 30-45.

Hanvanich, Sangphet, K. Sivakumar, and G. Tomas M. Hult (2006), “The Relationship of
Learning and Memory with Organizational Performance: The Moderating Role of
Turbulence,” Journal of the Academy of Marketing Science, 34 (4), 600-612.

Haunschild, Pamela R. and Anne S. Miner (1997), “Modes of Interorganizational
Imitation: The Effects of Outcome Salience and Uncertainty,” Administrative Science
Quarterly, 42 (3), 472-500.

He, Zi-Lin and Poh-Kam Wong (2004), “Exploration vs. Exploitation: An Empirical Test
of the Ambidexterity Hypothesis,” Organization Science, 15 (4), 481-494.

Hillman, Amy J. and Gerald D. Keim (2001), “Shareholder Value, Stakeholder
Management, and Social Issues: What’s the Bottom Line?” Strategic Management
Journal, 22(2), 125-139.

Hu, Li-Tze and Peter M. Bentler (1999), “Cutoff Criteria for Fit Indexes in Covariance
Structure Analysis: Conventional Criteria versus New Alternatives,” Structural Equation
Modeling, 6(1), 1-55.

Huber George P. and Daniel J. Power (1985), “Retrospective Reports of Strategic-Level
Managers: Guidelines for Increasing Accuracy,” Strategic Management Journal, 6 (2),
171—1 80.

Huang, K.T. (1998), “Capitalizing on Intellectual Assets,” IBM Systems Journal, 37 (4),
[available at http://www.research.ibm.com/joumal/sj/374/huang.html].

Huber, George P. (1991), “Organizational Learning: The Contributing Processes and the
Literatures,” Organization Science, 2 (1), 88-115.

Hult, G. Tomas M., David J. Ketchen, Jr., and Mathias Arrfelt (2007), “Strategic Supply

Chain Management: Improving Performance through a Culture of Competitiveness and
Knowledge Development,” Strategic Management Journal, 28 (10), 1035-1052.

191

Hult, G. Tomas M., David J. Ketchen, Jr., and Stanley F. Slater (2004), “Information
Processing, Knowledge Development, and Strategic Supply Chain Performance,”
Academy of Management Journal, 47 (2), 241-253.

Hult, G. Tomas M., David J. Ketchen, Jr., and Stanley F. Slater (2005), "Market
Orientation and Performance: An Integration of Disparate Approaches," Strategic
Management Journal, 26 (12), 1173-1 181.

Hult, G. Tomas M., David J. Ketchen, Jr., S. Tamer Cavusgil, and Roger J. Calantone
(2006), “Knowledge as a Strategic Resource in Supply Chains,” Journal of Operations
Management, 24 (5), 458—475

Hurley, Robert F. and G. Tomas M. Hult (1998), “Innovation, Market Orientation, and
Organizational Learning: An Integration and Empirical Examination,” Journal of
Marketing, 62 (3), 42-54.

Irnai, Ken-ichi, Ikujiro Nonaka, and Hirotaka Takeuchi (1985), “Managing the New
Product Development Process: How Japanese Companies Learn and Unleam,” in The
Uneasy Alliance: Managing the Productivity-Technology Dilemma, Kim B. Clark, Robert
H. Hayes, and Christopher Lorenz, eds. Boston: Harvard Business School Press, 337-3 75.

Jaworski, Bernard J. and Ajay K. Kohli (1993), “Market Orientation: Antecedents and
Consequences,” Journal of Marketing, 57 (July), 53-70.

Jones, Thomas M. (1995), “Instrumental Stakeholder Theory: A Synthesis of Ethics and
Economics,” Academy of Management Review, 20 (2), 404-437.

Jones, Thomas M. and Andrew C. Wicks (1999), “Convergent Stakeholder Theory,”
Academy of Management Review, 24 (2), 206-221.

Joreskog, Karl G., Dag Sorbom, Stephen Du Toit, and Mathilda Du Toit (2000), LISREL
8: New Statistical Features (2“d Edition), Chicago, IL: Scientiﬁc Software International,
Inc.

Ketchen, David J ., Jr., G. Tomas M. Hult, and Stanley F. Slater (2007), "Toward Greater
Understanding of Market Orientation and the Resource-Based View, " Strategic
Management Journal, 28 (9), 961-964.

Kogut, Bruce and Udo Zander (1992), “Knowledge of the Firm, Combinative
Capabilities, and the Replication of Technology,” Organization Science, 3 (3), 383-397.

Kogut, Bruce and Udo Zander (1993), “Knowledge of the Firm and the Evolutionary

Theory of the Multinational Corporation,” Journal of International Business Studies, 24
(4), 625-645.

192

Kohli, Ajay K. and Bernard J. J aworski (1990), “Market Orientation: The Construct,
Research Propositions, and Managerial Implications,” Journal of Marketing, 54 (2), 1-18.

Kohli, Ajay K., Bernard J. Jaworski and Ajith Kumar (1993), “MARKOR: A Measure of
Market Orientation,” Journal of Marketing Research, 30 (4), 467-477.

Korn, Helaine J. and Joel A. C. Baum (1999), “Chance, Irnitative, and Strategic
Antecedents to Multirnarket Contact,” Academy of Management Journal, 42 (2), 171-
193.

Kraatz, Matthew S. (1998), “Learning by Association? Interorganizational Networks and
Adaptation to Environmental Change,” Academy of Management Journal, 41 (6), 621-
643.

Levinthal, Daniel A. and James G. March (1993), “The Myopia of Learning,” Strategic
Management Journal, 14 (Winter), 95-112.

Levitt, Barbara and James G. March (1988), “Organizational Learning,” Annual Review
of Sociology, 14, 319-340.

Lieberman, Marvin B. and Shigeru Asaba (2006), “Why Do Firms Imitate Each Other?”
Academy of Management Review, 31 (2), 366-385.

Luo, Xueming, K. Sivakurnar, and Sandra S. Liu (2005) “Globalization, Marketing
Resources, and Performance: Evidence ﬁom China,” Journal of the Academy of
Marketing Science, 33 (1), 50-65.

Maignan, Isabelle and O. C. Ferrell (2004), “Corporate Social Responsibility and
Marketing: An Integrative Framework,” Journal of the Academy of Marketing Science,
32 (1), 3-19.

Maltz, Elliot and Ajay K. Kohli (1996), “Market Intelligence Dissemination Across
Functional Boundaries,” Journal of Marketing Research, 33 (1), 47-61.

March, James G. (1991), “Exploration and Exploitation in Organizational Learning,”
Organization Science, 2 (1), 71-87.

Matsuno, Ken and John T. Mentzer (2000), “The Effects of Strategy Type on the Market
Orientation — Performance Relationship,” Journal of Marketing, 64 (4), 1-16.

McFarlin, Dean B. and Paul D. Sweeney (1992), “Distributive and Procedural Justice as
Predictors of Satisfaction with Personal and Organizational Outcomes,” Academy of
Management Journal, 35 (3), 626—37.

McGrath, Rita Gunther (2001), “Exploratory Learning, Innovative Capacity, and
Managerial Oversight,” Academy of Management Journal, 44 (1), 1 18-131.

193

McGreevy, Maura (2007), “AMR Research Finds Spending on Knowledge Management
Will Hit $73B in 2007,” (accessed April 20, 2009), [available at
http://www.amrresearch.com/Content/View.aspx?compURI=tcm%3 a7-
33845&title=AMR+Research+Finds+Spending+on+Knowledge+Management+Will+Hit
+%247BB+in+2007].

Menguc, Bulent and Seigyoung Auh (2006) “Creating a Firm-Level Dynamic Capability
through Capitalizing on Market Orientation and Innovativeness,” Journal of the Academy
of Marketing Science, 34 (1), 63-73.

Miner, Anne S. and Pamela R. Haunschild (1995), “Population Level Learning,” in
Research in Organizational Behavior, L.L. Cummings and Barry M. Staw, eds.
Greenwich, CT: JAI Press Inc., 115-166.

Mitchell, Ronald K., Bradley R. Agle, and Donna J. Wood (1997), “Toward a Theory of
Stakeholder Identiﬁcation and Salience: Deﬁning the Principle of Who and What Really
Counts,” Academy of Management Review, 22 (4), 853-886.

Moorman, Christine (1995), “Organizational Market Information Processes: Cultural
Antecedents and New Product Outcomes,” Journal of Marketing Research, 32 (3), 318-
335.

Moorman, Christine and Anne S. Miner (1997), “The Impact of Organizational Memory
on New Product Performance and Creativity,” Journal of Marketing Research, 34 (1),
91-106.

Moorman, Christine and Anne S. Miner (1998a), “Organizational Improvisation and
Organizational Memory,” Academy of Management Review, 23 (4), 698-723.

Moorman, Christine and Anne S. Miner (1998b), “The Convergence of Planning and
Execution: Improvisation in New Product Development,” Journal of Marketing, 62 (3),
1-20.

Newbert, Scott L. (2008), “Value, Rareness, Competitive Advantage, and Performance:
A Conceptual-Level Empirical Investigation of the Resource-Based View of the Firm,”
Strategic Management Journal, 29 (7), 745-768.

Ofek, Elie and Ozge Turut (2008), “To Innovate or Imitate? Entry Strategy and the Role
of Market Research,” Journal of Marketing Research, 45 (5), 575-592.

Olson, Eric M., Stanley F. Slater, and G. Tomas M. Hult (2005), "The Performance

Implications of Fit among Business Strategy, Marketing Organization Structure, and
Strategic Behavior," Journal of Marketing, 69(3), 49-65.

194

 

Ordanini, Andrea, Gaia Rubera, and Robert DeFillippi (2008), “The Many Moods of
Inter-organizational lrnitation: A Critical Review,” International Journal of Management
Reviews, 10 (4), 375-398.

Peter, J. Paul (1979), Reliability: A Review of Psychometric Basics and Recent
Marketing Practices,” Journal of Marketing Research, 16 (February), 6-17.

Peteraf, Margaret A. (1993), “The Comerstones of Competitive Advantage: A Resource-
Based View,” Strategic Management Journal, 14 (3), 179-191.

Pfeffer, Jeffrey and Gerald R. Salancik (1978), The External Control of Organizations: A
Resource Dependence Perspective. New York: Harper & Row.

Podsakoff, Philip M. and Dennis Organ (1986), “Self-Reports in Organizational
Research: Problems and Prospects,” Journal of Management, 12 (Winter), 531—43.

Powell, Walter W., Kenneth W. Koput, and Laurel Smith-Doerr (1996),
“Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in
Biotechnology,” Administrative Science Quarterly, 41 (1), 116-145.

Sanchez, Juan 1. and Petra Brock (1996), “Outcomes of Perceived Discrimination Among
Hispanic Employees: Is Diversity Management a Luxury or a Necessity?” Academy of
Management Journal, 39 (3), 704—719.

Sinkula, James M. (1994), “Market Information Processing and Organizational
Learning,” Journal of Marketing, 58 (1), 35-45.

Sinkula, James M., William E. Baker, and Thomas Noordewier (1997), “A Framework
for Market-Based Organizational Learning: Linking Values, Knowledge, and Behavior,”
Journal of the Academy of Marketing Science, 25 (4), 305-318.

Slater, Stanley F. and John C. Narver (1995), “Market Orientation and the Learning
Organization,” Journal of Marketing, 59(3), 63-74.

Srinivasan, Raji, Pamela Haunschild, and Rajdeep Grewal (2007), “Vicarious Learning in
New Product Introductions in Early Years of a Converging Market,” Management
Science, 53 (1), 16-28.

Stewart, Thomas A. (1999), “Telling Tales at BP Amoco Knowledge Management at
Work,” Fortune, June (7), [available at
http://money.cnn.com/magazines/fortune/fortune_archive/ l 999/ 06/ 07/26 1 05 0/ indexhtm]

Tichy, Noel M., Michael L. Tushman, and Charles F ombrun (1979), “Social Network
Analysis for Organizations,” Academy of Management Review, 4 (4), 507-519.

195

Thomas, James B., Shawn M. Clark, and Dennis A. Gioia (1993), “Strategic
Sensemaking and Organizational Performance: Linkages among Scanning, Interpretation,

Action, and Outcomes,” Academy of Management Journal, 36 (2), 239-270.

Waddock, Sandra A., Charles Bodwell, and Samuel B. Graves (2002), “Responsibility:
The New Business Imperative,” Academy of Management Review, 16 (2), 132-147.

Walsh, James P. and Gerardo Rivera Ungson (1991), “Organizational Memory,”
Academy of Management Review, 16 (1), 57-91.

Wemerfelt Birger (1984), “A Resource-Based View of the Firm,” Strategic Management
Journal, 5 (2), 171-180.

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CONCLUSION

Traditionally, marketing studies have heavily focused on customers as the only
stakeholder group that inﬂuences and is inﬂuenced by marketing phenomena. While
notable contributions have been made in the literature, such a narrow view is no longer
realistic. Today, multiple stakeholder groups, beyond the customers, impact the activities
of the ﬁrm, including those of the marketing ﬁmction. Given the increasing inﬂuence of
stakeholders, this three-essay dissertation addressed the existing gap in the marketing
literature by studying the importance of paying attention and responding to stakeholders
across different levels of analysis.

Speciﬁcally, Essay 1 investigated whether developing marketing strategies based
on a stakeholder-focused approach (i.e., one that emphasizes a broad set of stakeholders
by devoting attention and resources to addressing their simultaneous interests) is more
effective than developing strategies based on a market-driven approach (i.e., one that
focuses on customers, employees, and suppliers, while placing relatively less emphasis
on shareholders, regulators, and communities) in achieving market performance. Essay 2
took into account the reality that ﬁrms do not operate in a vacuum to examine how the
stakeholder focus (i.e., amount of attention, resources, and time the ﬁrm devotes to
addressing the interests of multiple stakeholder groups) of entities within a focal ﬁrm’s
supply chain environment and marketplace affect the stakeholder focus of the focal ﬁrm.
In addition, it studied the link between a stakeholder focus and customer satisfaction, and
reexamined the customer satisfaction—performance relationship. Essay 3 concentrated on
how organizations learn about their stakeholders’ interests to investigate whether

stakeholder-focused organizational learning inﬂuences stakeholder-focused

197

responsiveness. It also examined whether the degree of innovative and imitative
stakeholder practices is affected by the mechanisms the organization relies on to acquire
stakeholder-related knowledge. Table 21 summarizes the main ﬁndings and the

implications of the dissertation.

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199

While achieving the aforementioned objectives, this dissertation makes several
contributions. First, it underscores the importance of attending to the interests of the
primary stakeholders. Essay 1 showed that, in general, developing marketing strategies
based on a broad stakeholder focus — taking into account the demands of the six primary
stakeholders — is more effective for market performance than developing marketing
strategies based on a narrower, market-driven approach — which concentrates on
customers, employees, and suppliers, while paying relatively less attention to
shareholders, communities, and regulators. This ﬁnding is consistent with the tenets of
stakeholder theory, which predict that those ﬁrms that develop and nurture multiple
stakeholder relationships obtain a competitive advantage over ﬁrms that do otherwise
(Jones 1995). However, while Essay 1 prescribes that ﬁrms should devote attention and
resources to addressing the needs of the primary stakeholders and demonstrates that
stakeholder theory applies when it comes to market performance, Essay 2 suggests that it
is not so straightforward. In particular, Essay 2 ﬁnds that attending to the claims of the
six primary stakeholders by engaging in positive stakeholder practices does not lead to an
improvement in the level of customer satisfaction. Conversely, disregarding some
stakeholder demands (i.e., negative stakeholder focus) increases customer satisfaction up
to a point.

One possible explanation is that stakeholder management is a zero sum game.
Stakeholders have conﬂicting demands, and at the same time, ﬁrms have ﬁnite resources,
making it highly unlikely for ﬁrms to satisfy all of their stakeholders’ interests. As a
result, one stakeholder group gains at the expense of the others. A ﬁrm that tries to

equally address the claims of all the stakeholders will successfully meet some of its

200

stakeholders’ demands, but will inevitably leave other demands unmet. From the
customers’ perspective, since the ﬁrm has met some of their needs and wants, but has
failed to meet others, they will be neither satisﬁed nor dissatisﬁed. As such, it seems that
a ﬁrm which has customer satisfaction as a key objective is better off giving priority to
the interests of the customers by allocating more resources to ensure their satisfaction.
This, in turn, will unavoidably take resources away from addressing the other
stakeholders’ interests.

Taken together, this discussion suggests that ﬁrms should actively monitor both
the internal and the external business environment to be cognizant of the demands of their
stakeholders and of how these demands change over time. They must then fulﬁll those
interests that are common across the six groups. However, since a number of the
stakeholder claims will be conﬂicting, it is imperative that ﬁrms prioritize among the six
groups based on their objectives. By doing so, ﬁrms can more easily determine what
action to take in those cases where a conﬂict exists.

To better understand how ﬁrms can effectively prioritize among the six primary
stakeholder groups, future research should break down the stakeholder focus construct
into six dimensions — customer focus, supplier focus, employee focus, regulator focus,
shareholder focus, and community focus — to investigate the relative importance of each
stakeholder group for performance. It is likely that the resources a ﬁrm should devote to a
particular group depends on the industry where the ﬁrm competes, on the country where
it operates (e.g., developed versus developing markets), and on where the ﬁrm’s products
are in their life cycle (cf. Jawahar and McLaughlin 2001). Marketing researchers should

examine the moderating effects of these context variables on the prioritization of the

201

different stakeholders. In addition, ﬁirther research should study the different paths
through which a stakeholder focus leads to performance (e.g., corporate reputation, brand
equity, innovation). For example, it is possible that one reason why attending to the six
stakeholders when developing marketing strategies positively inﬂuences market
performance (i.e., Essay 1) is that by seeking solutions to address the competing demands
of the different stakeholders, a stakeholder-focused ﬁrm becomes more innovative than
those competitors that attend to a more limited set of stakeholders, and hence do not need
to be as creative in their strategies. In turn, the greater innovation of the stakeholder-
focused ﬁrm leads to superior performance.

In terms of the factors that lead ﬁrms to attend to and take actions in response to
their stakeholders and their needs, Essays 2 and 3 shed new light. Essay 2 showed that the
business-to-business customers’ stakeholder focus as well as the major competitors’
stakeholder focus inﬂuence that of a focal ﬁrm. As such, ﬁrms not only pay close
attention to the stakeholder practices of other entities in their environment, but consistent
with institutional theory (e.g., DiMaggio and Powell 1983), they respond by copying
those actions. Furthermore, Essay 3 revealed that for ﬁrms to be responsive to their
stakeholders, they need to effectively acquire, distribute, interpret, and store stakeholder-
related knowledge.

In addition, the way ﬁrms respond to their stakeholders depends in part on the
mechanism they rely on to acquire stakeholder-related information. Speciﬁcally, ﬁrms
that engage in imitative stakeholder practices rely on stakeholder-focused vicarious
knowledge acquisition (e.g., drawing inferences from the observed behavior of other

organizations with whom they have no direct links with — e. g., Lieberman and Asaba

202

2006). It seems that because these ﬁrms are unclear about how they should interact with
their stakeholders, they (I) gather information about the stakeholder-focused actions
taken by their competitors, (2) evaluate the outcomes these actions produce for their
rivals, and (3) mimic those actions that produced positive outcomes. On the other hand,
those ﬁrms that are innovative in their stakeholder practices rely on stakeholder-focused
experiential knowledge acquisition as their primary mechanism to learn about their
stakeholders’ needs and wants. These ﬁrms invest substantial resources to gain ﬁrst-hand
information about stakeholders through searching (e. g., March 1991).

This dissertation is also subject to certain limitations. First, while secondary
stakeholder groups such as the media and special interests groups have an inﬂuence on
the ﬁrm (Clarkson 1995), the focus in the three studies was exclusively placed on the six
primary stakeholder groups. It is possible that in some ﬁrms, these secondary
stakeholders are considered as important as some of the primary stakeholders. Inclusion
of secondary stakeholders into the studies may have offered additional insights about the
interplay between primary and secondary stakeholders on one hand and the ﬁrm on the
other. Second, for theory testing purposes, it was assumed in the three essays that the six
primary stakeholders are equally important to ﬁrms. For example, in Essay 2, a
stakeholder focus was operationalized as an equally weighted integration of attributes
associated with customers, suppliers, employees, regulators, shareholders, and the
community. A drawback of this assumption is that different ﬁrms having the same
stakeholder focus score may differ in their focus toward particular stakeholders.

Disaggregating the stakeholder focus into the different dimensions (i.e., customer focus,

203

supplier focus, employee focus, regulator focus, shareholder focus, and community
focus) would shed additional light about ﬁrms’ interactions with stakeholders.

Third, Essays 1 and 2 relied on secondary data compiled from multiple databases
spanning a four-year period. While this minimizes the threat of common method bias, the
data sources do not take into account how a ﬁrm’s composition changes over the years.
For example, if a ﬁrm acquires another ﬁrm, it is not reﬂected in the data. However, such
acquisition may have an impact on the results. Lastly, Essay 3 uses cross-sectional survey
data, which restricts the establishment of causality. Longitudinal data would help
determine whether there is a lag between the attainment of stakeholder-focused
organizational learning on one hand and responsiveness, innovative and imitative

stakeholder practices on the other.

204

 

REFERENCES

Clarkson, Max (1995), “A Stakeholder Framework for Analyzing and Evaluating
Corporate Social Performance,” Academy of Management Review, 20 (1), 92-117.

DiMaggio, Paul J. and Walter W. Powell (1983), “The Iron Cage Revisited: Institutional
Isomorphism and Collective Rationality in Organizational Fields,” American
Sociological Review, 48 (April), 147-160.

Jawahar, I. M. and Gary L. McLaughlin (2001), “Toward A Descriptive Stakeholder
Theory: An Organizational Life Cycle Approach,” Academy of Management Review, 26
(3), 397-414.

Jones, Thomas M. (1995), “Instrumental Stakeholder Theory: A Synthesis of Ethics and
Economics,” Academy of Management Review, 20 (2), 404-437.

Lieberman, Marvin B. and Shigeru Asaba (2006), “Why Do Firms Imitate Each Other?”
Academy of Management Review, 31 (2), 366-385.

March, James G. (1991), “Exploration and Exploitation in Organizational Learning,”
Organization Science, 2 (1), 71-87.

205