DOING GOOD, DOING BAD, AND DOING WELL: INVESTIGATING …

The Pennsylvania State University

The Graduate School

The Mary Jean and Frank P. Smeal College of Business

DOING GOOD, DOING BAD, AND DOING WELL: INVESTIGATING THE DYNAMIC

EFFECTIVENESS OF SUSTAINABILITY STRATEGY

A Dissertation in

Business Administration

by

Charles Alfred Kang

© 2014 Charles Alfred Kang

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

August 2014

ii

This dissertation of Charles Alfred Kang was reviewed and approved* by the following:

Rajdeep Grewal

Irving & Irene Bard Professor of Marketing

Dissertation Advisor

Chair of Committee

Duncan K. H. Fong

Professor of Marketing and Professor of Statistics

Shrihari (Hari) Sridhar

Assistant Professor of Marketing

Saurabh Bansal

Assistant Professor of Supply Chain Management

Brent W. Ambrose

Smeal Professor of Risk Management

Director of Ph.D. Program at the Smeal College of Business

* Signatures are on file in the Graduate School

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ABSTRACT

In my dissertation, I investigate the dynamic effectiveness in sustainability strategy. In

essay 1, I examine the dynamic relationship among corporate social responsibility (CSR),

corporate social irresponsibility (CSI), and firm performance. Specifically, I address the

questions of whether and how CSR relates to firm value, and, in so doing identify four

mechanisms pertaining to this relationship that have been proposed in the literature: (1) slack

resources lead to CSR, (2) CSR improves performance, (3) CSR makes amends for past CSI, and

(4) CSR insures against subsequent CSI. I propose an economic theory model to demonstrate the

complex interplay among CSR, CSI, and firm value, and empirically test aforementioned four

mechanisms by using a structural panel vector autoregression specification. The results suggest

that firms benefit financially from CSR and that CSI antecedes CSR. In essay 2, I study the

effective sustainability practice management in the lens of portfolio theory. In particular, I seek

to address what types of sustainability portfolios strategy promise the greatest return in terms of

its breadth, depth, and the ratio of philanthropic vs. business related practices. The findings

suggest that the effect of a firm’s CSI breadth is negative. Yet, this effect is mitigated by

philanthropic/business ratio of CSI. Further, the findings suggest that this mitigation effect of

philanthropic / business ratio on the breadth of CSI – firm performance link become less strong

when the firm’s sustainability portfolio depth increases. In addition, the results show that the

effect of a firm’s CSR breadth on firm performance is negatively moderated by the depth of CSI

practices. Both essays contribute to the extant CSR / Sustainability literature and the marketing-

finance interface literature by investigating the roles of CSR, CSI on firm performance and

suggest guidelines to develop effective sustainability strategy to the practitioners.

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TABLE OF CONTENTS

LIST OF FIGURES ........................................................................................................................ v

LIST OF TABLES ......................................................................................................................... vi

ACKNOWLEDGMENTS ............................................................................................................ vii

CHAPTER 1: INTRODUCTION ................................................................................................... 1

1.1 Corporate Social Responsibility and Irresponsibility............................................................ 1

1.2 Triple Bottom Line (TBL)..................................................................................................... 2

1.3 Research Directions............................................................................................................... 4

CHAPTER 2: PERFORMANCE IMPLICATIONS OF CORPORATE SOCIAL

RESPONSIBILITY AND IRRESPONSIBILTY ........................................................................... 5

2.1 Introduction ........................................................................................................................... 6

2.2 Conceptual Background ...................................................................................................... 10

2.3 Model Development ............................................................................................................ 17

2.4 Data and Method ................................................................................................................. 27

2.5 Results ................................................................................................................................. 34

2.6 Discussion ........................................................................................................................... 40

CHAPTER 3: Portfolio Management in Sustainability Strategy and Firm Performance ............. 45

3.1 Introduction ......................................................................................................................... 46

3.2 Conceptual Background ...................................................................................................... 51

3.3 Data and Method ................................................................................................................. 65

3.4 Results ................................................................................................................................. 74

3.5 Discussion ........................................................................................................................... 77

CHAPTER 4: CONCLUSION ..................................................................................................... 83

APPENDIX ................................................................................................................................... 86

REFERENCES ............................................................................................................................. 92

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LIST OF FIGURES

Figure 1.1 Multi-dimensionality of Sustainability .......................................................................... 3

Figure 2.1The Four Mechanisms .................................................................................................. 10

Figure 2.2 Pairwise Correlation between CSR and Financial Performance ................................. 16

Figure 2.3 Pairwise Correlation between CSR and CSI ............................................................... 17

Figure 2.4 Impulse Response Functions ....................................................................................... 38

Figure 2.5 Forecast Error Variance Decomposition Results after 5 Years ................................... 39

Figure 3.1 Conceptual Framework ............................................................................................... 59

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LIST OF TABLES

Table 2.1 Strength and Concern Items of the KLD Social Rating Database ................................ 28

Table 2.2 Descriptive Statistics ..................................................................................................... 32

Table 2.3 Summary of Unit Root and Stationarity Tests of the Variables ................................... 34

Table 2.4 Estimation Results of Contemporaneous Effects .......................................................... 36

Table 2.5 Estimation Results from the Reduced-form Panel VAR Model ................................... 37

Table 2.6 Forecast Error Variance Decomposition Results after 5 Years .................................... 40

Table 3.1 Example of CSR/CSI Practice Portfolios ..................................................................... 69

Table 3.2 Descriptive Statistics ..................................................................................................... 72

Table 3.3 Hypotheses Testing Results .......................................................................................... 75

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ACKNOWLEDGMENTS

First of all, I would like to start by thanking my advisor, Rajdeep Grewal, for all his help

(including his gentle push), support, and patience throughout my five years in the PhD program

at Penn State. Raj is an exceptional mentor and I am sure that I would not be able to complete the

journey of Ph.D. without his help. I also want to thank other committee members: Hari Sridhar,

Duncan Fong, Saurabh Bansal, and Susan Xu for their guidance, suggestions, and feedback. I

feel sorry for the loss of Dr. Xu and hope she rests in peace. In addition, I would like to express

my appreciation to every faculty member and staff in the marketing department for the help and

encouragement.

I also owe a big thanks to my great colleagues whom I met during the Ph.D. program: to

name a few, Frank Germann, for helping me with developing research ideas and writing, Chen

Zhou, for being a great friend and peer mentor, Aditya Gupta, for being a great officemate, friend,

and peer reviewer, and M. K. Chin, for being a great friend and now a family. I feel so lucky to

meet you all. I also want to thank all Hoopers members and Yonsei Alumni group members for

making my life in State College exciting.

Finally, I would like to thank my parents, Seong Chul Kang and Jin Haeng Lee, who

always provided support, encouragement, and love, and my brother, Ji Hoon Kang, who cares his

little brother all the time. They have been a source of my encouragement. I love you all. Last but

not least, I would like to thank my beloved wife, Yang-Seon Kim, who makes me smile every

day. I will always love you and be grateful to you.

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Chapter 1

INTRODUCTION

Recent changes in the business environment have drawn researchers and practitioners’

attention to the topic of sustainability. For example, Tom Falk, Chairman and CEO of Kimberly-

Clark (K-C), stated that “Sustainability is an essential part of how we operates... The

sustainability movement is gaining momentum as more companies around the world implement

initiatives centered on environmental practices” in their annually issued sustainability report.1

Sustainability refers to “development that meets the needs of the present without compromising

the ability of future generations to meet their own needs” (World Commission on Environment

and Development 1987). Since this definition of sustainability seems to be too abstract for the

purpose of management decision making, Dow Jones Sustainability Indexes defines

sustainability in more practical way, “A business approach that creates long-term shareholder

value by embracing opportunities and managing risks that derived from economic,

environmental, and social developments.” This definition emphasizes two critical aspect of

sustainability: (1) two ways of achieving sustainability in business (i.e., embracing opportunities

and managing risks), and (2) the three pillars of sustainability the which are profit (i.e.,

economic), planet (i.e., environmental), and people (i.e., social).

1.1 Corporate Social Responsibility and Irresponsibility

Sustainability in business can be achieved in two ways: (1) by engaging in more socially

responsible practices, and (2) by minimizing social misbehavior that harms social welfare. The

former corresponds to engaging in corporate social responsibility (CSR), and the latter

1 http://www.sustainabilityreport2010.kimberly-clark.com/index.asp

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corresponds to reducing corporate social irresponsibility (CSI). For example, environmental

aspect of sustainability can be achieved by developing new recycling program, which is a CSR

practices. On the other hand, it can also be achieved by reducing the emission level of ozone

depleting chemicals, which is a CSI practice.

It is important to distinguish the ways of achieving sustainability for two reasons. First,

the benefits from engaging in CSR practices and minimizing CSI practices are different.

Engaging in CSR practices can benefit firms by trust, customer satisfaction, and positive attitude

toward company (e.g., Homburg, Stierl, and Bornemann 2013; Luo and Bhattacharya 2009;

Brown and Dacin 1997) because these firms meet not only societal and ethical obligations but

also philanthropic obligations. In contrast, minimizing CSI practices leads to minimum

penalization rather than positive rewards to the firm. In other words, reducing CSI practices is

perceived as societal and ethical obligations and meeting these obligations may be considered as

an extra effort that should be rewarded by stakeholders. Second, a firm’s decision to engage in

CSR practices and/or to reduce CSI practices can be interdependent. For instance, a firm may

decides to engage in CSR practices to compensate its recent socially irresponsible behavior. Or, a

firm may decide not to reduce its level of CSI practices engagement because it believes that its

previous CSR practice engagement creates good-will that will work as insurance against negative

publicity. In short, engaging in CSR and reducing CSI are two different ways to achieve

sustainability goal in business.

1.2 Triple Bottom Line (TBL)

In contrast to the previous belief of capitalism that firm’s responsibility is limited to

economical aspect, Elkington (1998) introduces Triple Bottom Line (TBL) principle which

views firm’s responsibility from three different angles: economic prosperity (profit),

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environmental quality (planet), and social equity (people). According to stakeholder theory

(Freeman 1984), firms are linked with many groups that can affect and are affected by firm

actions. Since companies have become more powerful and influential to the community and

environment than before, firms have to be more responsible for their actions. Thus, he argues

that firms should focus on the interdependencies among social, environmental, and economical

aspects to develop sustainable competitive advantage. First, the economic dimension focuses on

value creation and enhanced financial performance to satisfy the shareholders. Second, the

environmental dimension focuses on preserving environmental resources by, for example,

pollution prevention, clean energy, and recycling through corporate environmental management

(e.g., Bansal 2005). Finally, the social dimension focuses on firm activities that have impact on

society such as charitable giving, support for housing, and volunteer programs (e.g., Wood 1991).

To be considered as a responsible firm in terms of sustainability, the firm should meet all these

triple bottom lines. In short, sustainability is not uni-dimensional but multi-dimensional. Figure

1.1 displays the multi-dimensionality of sustainability.

Figure 1.1 Multi-dimensionality of Sustainability

Environmental

Social

Economic

CSI CSR

ty Sustainability

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Firms’ performances in these three dimensions can serve as integral market-oriented resources,

capabilities, and competitive advantage (e.g., Barney 1991; Hunt and Morgan 1995; Jaworski

and Kohli 1993). Further, this competitive advantage can develop into a sustainable competitive

advantage (e.g., Day and Wensley 1988) which leads to the greater firm performance.

1.3 Research Directions

Although the issue of sustainability has been drawn tremendous amount of attention from

academics as well as practitioners, previous research has not looked at the multi-dimensional

aspect of sustainability and its dynamic effect on firm performance. For example, researchers

have employed a global conceptualization of corporate social performance and inherently

assume that the effect of one CRI practice can be wiped out by engaging in one CSR practices

(e.g., Hillman and Keim 2001). In addition, most research in marketing has focused one societal

issue area of CSR such as “Green Marketing” or “Cause-related Marketing” rather than all three

pillars of sustainability (e.g., Lichtenstein, Drumwright, and Braig 2004; Robinson, Irmak, and

Jayachandran 2012).

To fill this gap, I look at the sustainability from the top and suggest critical findings in

my dissertation. In Chapter 3 (essay 1), I examine the dynamic relationship among corporate

social responsibility (CSR), corporate social irresponsibility (CSI), and firm performance.

Specifically, I focus on how CSR and CSI relate to firm performance. In Chapter 4 (essay 2), I

examine what types of CSR and CSI portfolios promise the best firm performance. Specifically,

building on financial portfolio theory, I seek to provide guidance on efficient sustainability

practice engagement management across three pillars of sustainability.

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Chapter 2

PERFORMANCE IMPLICATIONS OF CORPORATE SOCIAL

RESPONSIBILITY AND IRRESPONSIBILTY

ABSTRACT

We address the questions of whether and how Corporate Social Responsibility (CSR)

relates to firm performance, and, in so doing identify four mechanisms pertaining to this

relationship that have been proposed in the literature: (1) slack resources lead to CSR, i.e., slack

resources mechanism (2) CSR improves performance, i.e., good management mechanism, (3)

CSR makes amends for past Corporate Social Irresponsibility (CSI), i.e., penance mechanism,

and (4) CSR insures against subsequent CSI, i.e., insurance mechanism. To provide economic

foundations for CSR, we propose that firms exert CSR efforts as well as CSI efforts, where CSR

efforts promote social causes and CSI efforts reduce the probability of CSI incidents. With this

bifurcation of efforts we propose an economic theory model that builds on theory of the firm

primitives to demonstrate the complex interplay among CSR efforts, CSI efforts, and firm value.

To empirically model the complex dynamic interplay among CSR, CSI, and firm value (Tobin’s

q) and test for the four mechanisms, we propose a structural panel vector autoregression model to

empirically assess the four mechanisms. Results from panel data on over 4,500 firms across 19

years suggests that firms benefit financially from CSR and that CSI antecedes CSR, i.e., we find

empirical support for the good management and penance mechanisms. Our research adds to the

extant CSR literature by laying out the theoretical foundations for firms engaging in CSR and

CSI and demonstrating that CSR provides direct and indirect value to the firm.

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2.1 Introduction

Corporate social responsibility (CSR) – company actions that advance some social good

beyond that which is required by law (e.g., McWilliams and Siegel 2001) – continues to draw

interest from practitioners and academics alike. In 2008, The Economist (2008) reported that

about 56% of 1192 global executives surveyed considered CSR a “high” or “very high” priority

for their company, up from 34% just three years earlier. Of these same informants, 69% expected

CSR to be a “high” or “very high” corporate priority by 2011. Against this backdrop, most of the

extant academic CSR-related research has scrutinized the conception that companies do “well”

by doing “good” (e.g., McWilliams et al. 2006; in fact Margolis et al. (2007) use 167 empirical

studies in their meta-analysis that link organizational CSR and financial performance). However,

the debate on how doing “good” and doing “well” converge has yet to be resolved (e.g., Hull and

Rothenberg 2008; Mackey et al. 2007). Specifically, the following four mechanisms have been

proposed regarding the relationship between CSR and (positive) firm performance:

1. Slack Resource Mechanism: Companies engage in CSR because they are doing well

financially and have slack resources (e.g., McGuire et al. 1988).

2. Good Management Mechanism: CSR is part of “good management” and thus

improves financial performance (e.g., Freeman 1984).

3. Penance Mechanism: CSR acts as a form of penance to offset past Corporate Social

Irresponsibility (CSI)2(e.g., Kotchen and Moon 2012).

4. Insurance Mechanism: CSR builds a reservoir of goodwill that softens the blow if and

when things go wrong, i.e., CSR provides an insurance mechanism against CSI (e.g.,

Minor and Morgan 2011).

The good management, penance, and insurance mechanisms, explicitly or implicitly,

postulate positive effects of CSR on firm performance whereas the slack resource mechanism

suggests a positive effect of firm performance on CSR. In other words, while not mutually

2 We define CSI as incidents that appear to hurt the social good, i.e., the antipode of CSR. BP’s Deepwater Horizon

oil spill in 2010 is an example of a CSI incident.

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exclusive, good management and slack resource mechanisms propose reverse causal paths.

Likewise, while also not mutually exclusive, penance and insurance mechanisms again propose

reverse causal paths. Indeed, the penance mechanism suggests that firms engage in CSR in time t

to offset CSI that occurred in time t-1 whereas the insurance mechanism proposes that firms

engage in CSR in time t–1 to insure against CSI in time t.

The key purpose of this study is to unravel these four mechanisms and thus further the

debate on how doing “good” and doing “well” converge. Moreover, while addressing the how

question, we also shed light on the at least equally important question of whether doing "good"

and doing "well" converge. We find that the answer to the whether question appears to be “yes”;

hence, the answer to the how question has substantial managerial and academic significance.

Of note is that the CSR literature provides empirical support for each one of the four

mechanisms; however, a limitation of the literature is that it has not yet studied the four

mechanisms simultaneously. Indeed, the studies only examine one, and at the most two of the

mechanisms (i.e., slack resource and good management) at a time. Such an approach of studying

one or two mechanisms at a time is problematic as important concomitant effects among the four

mechanisms cannot be addressed thus resulting in either a partial picture of the phenomenon or

worse yet, false statistical findings. Also, most studies have employed econometric models that

are simplistic and correlational (and not causal) in nature. Highlighting the issues regarding these

models, Margolis et al. (2007, p. 27) urge that “causal mechanisms need to be […] tested”.

Similarly, King and Lenox (2001, p. 107) suggest that if “one cares merely about correlation and

little about causation, these correlative studies are informative [….]. From the perspective of

corporate managers and policy analysts, however, the distinction is critical.” In this study, we

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follow an integrative approach and examine all four mechanisms simultaneously. We also use

what we deem to be appropriate econometric techniques to test causal relationships.

Moreover, in the extant literature, there is no economic theoretical foundation for the

study of CSR. Such a foundation helps develop “a better understanding of economic activities

and outcomes” (Kreps 1990, p. 7). We thus develop an economic theory model which (1) builds

on theory of the firm primitives to view the firm as maximizing net present profit and (2) uses

optimal control theory to model the costs associated with CSR efforts and CSI efforts as well as

the evolution of sales with CSR efforts and CSI efforts. In contrast to the current singular

conceptualization of CSR efforts, we propose that firms exert CSR efforts as well as CSI efforts,

where CSR efforts promote social causes (e.g., for every pair of shoes that Toms sells, it donates

one to a child in need which is known as the “one for one” model), and CSI efforts reduce the

probability of CSI incidents (e.g., Exxon Mobile reinforces the hulls of its crude oil

transportation ships to reduce the probability of oil leaks when accidents occur).3 Our economic

theory model demonstrates a complex interplay among CSR efforts, CSI efforts, and firm value4

and thus suggests that our empirical model specification should capture the simultaneous

interplay among these three variables. Further, recognizing (1) that our annual data encompasses

more than 4,500 firms across 19 years (i.e., large cross section and small time series), (2) that

there is a need to model the interplay among CSR, CSI, and firm value simultaneously, and (3)

the possibility of contemporaneous effects among the three variables in our annual data, we

propose and estimate a structural panel vector autoregressive (SPVAR) model.

3 We include CSI efforts in our economic theory model to capture the costs associated with avoiding CSI. In our

econometric model, however, we model level of observed CSI and not CSI efforts (similar to CSR where the theory

model deals with CSR efforts and the empirical model with level of observed CSR). CSI and CSI efforts are, of

course, related, and a firm’s CSI is largely a manifestation of its CSI efforts (i.e., the lack thereof). We also note

that, throughout the manuscript, when we use the term CSI (CSR) we refer to level of CSI (CSR). 4 We conceptualize firm value as net present value of current and future profits and operationalize it as Tobin’s q.

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Our results show support for good management and penance mechanisms; thus, our

empirical findings suggest that (1) firms benefit financially from CSR, i.e., CSR leads to positive

financial performance and (2), CSI antecedes CSR temporally, i.e., firms seem to use CSR to

offset past CSI. Thus, our findings suggest that beyond the overt performance implications, CSR

might also have a more subtle and covert impact on a firm’s performance by potentially

offsetting and/or attenuating the negative effects of past CSI. This latter effect has often been

overlooked in the evaluations of the relationship between CSR and financial performance.

As we elaborate in the final section of the manuscript, we contribute to the extant CSR

literature in five important ways: First, we summarize the varying propositions of how CSR and

firm performance converge into four mechanisms, and, more importantly, we test these four

mechanisms simultaneously. Second, we separate CSI from CSR and integrate the two constructs

into the overall “CSR-CSI-financial performance” framework. Third, our economic theory model

provides a theoretical basis for the development of future econometric models that seek to study

the dynamic relationship among CSR, CSI, and firm performance. Fourth, we provide empirical

evidence that CSR has a positive impact on firm performance. We note that the SPVAR model

allows us to make causal claims regarding the relationship between CSR and firm performance.

And finally, we show that CSI tends to temporally antecede CSR; thus, we provide some

evidence for the notion that firms use CSR (at least partially) to offset past CSI.

We proceed as follows: We first elaborate on the four mechanisms that have been

proposed in the extant literature. Next we present the building blocks of our microeconomic

theory model (which is detailed in Appendix A) and then develop an empirical model

specification that allows us to examine the dynamic interplay among CSR, CSI, and firm

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performance. Subsequently, we present our data and empirical results. We conclude with a

discussion of the theoretical and managerial implications, as well as limitations, of our research.

2.2 Conceptual Background

The extant literature posits four mechanisms of how CSR and firm performance relate to

each other. As shown in Figure 2.1, the first two mechanisms are concerned with the direct link

between a firm’s CSR and its financial performance whereas the last two mechanisms examine

the link between CSR and CSI, where the performance implications of CSR are implicit in the

latter two mechanisms.

Figure 2.1The Four Mechanisms

Slack Resource Mechanism

A plethora of studies have examined the direct link between a firm’s CSR and its

financial performance (for a survey see Margolis et al. 2007). Some of these studies posit that

firms engage in CSR because they are doing well financially. Generally referred to as the slack

resources mechanism, supporters of this link tend to argue that good financial performance

provides firms with slack resources which, in turn, provides the firms with the opportunity to

Financial Performance CSR

Mechanism 1

Slack Resources:

Financial performance in (t-1)

causes CSR in t

time (t)CSI CSR

Mechanism 3:

Penance:

CSI in (t-1) causes CSR in t

time (t)

Financial Performance CSR

Mechanism 2:

Good Management:

CSR in (t-1) causes financial

performance in t

time (t)CSICSR

Mechanism 4:

Insurance:

CSI in t causes CSR in (t-1)

time (t)

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invest in CSR related activities, such as community relations (e.g., Waddock and Graves 1997).

In addition, proponents of the slack resources mechanism tend to view CSR activities are

voluntary, meaning that managers have a high flexibility to initiate or cancel them. Accordingly,

they argue that a firm’s decision to invest in CSR activities largely depends on the availability of

excess cash (McGuire et al. 1988). Similarly, advocates of the slack resources mechanism also

tend to believe that CSR related activities are not critical to the success of the company, i.e., that

they fall under the category of discretionary spending, and are hence especially sensitive to the

existence of slack resources (e.g., McGuire et al. 1988).

Supporters of the slack resources mechanism have used various examples to support their

mechanism. For example, Waddock and Graves (1997) reported that IBM had significant

philanthropic programs during good economic times but canceled many of those programs when

the going got tougher. Also, arguably the most cited scholarly article in support of the slack

resources mechanism is the one by McGuire et al. (1988) in which they find that a firm’s prior

performance is more closely related to its CSR than subsequent performance. Others, such as

Preston and O’Bannon (1997), conclude that the relationship between CSR and financial

performance is bi-directional, while Scholtens (2008) reports that only a few studies have used

CSR as the dependent and financial performance as the independent variable. Thus, empirical

substantiation of the slack resources mechanism, besides anecdotal evidence such as the IBM

example mentioned above, is relatively sparse.

Good Management Mechanism

Most of the extant empirical CSR related research has scrutinized the conception that

companies do “well” by doing “good” (e.g., McWilliams et al. 2006; Margolis et al. 2007). Of

these studies, roughly 50% find a positive relationship between CSR and financial performance,

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25% find no relationship, 20% find mixed results, and 5% find a negative relationship (Margolis

and Walsh 2001; Scholtens 2008).5

Proponents of the doing “well” by doing “good” viewpoint argue that the cost of CSR is

lower than the benefits that accrue from it (e.g., Hull and Rothenberg 2008) and that it is simply

a part of good management to engage in CSR. They suggest, for example, that superior CSR can

attract and retain quality employees (e.g., Greening and Turban 2000), enhance the morale,

productivity and satisfaction of employees (e.g., Waddock and Graves 1997), reduce costs by

increasing operational efficiencies (e.g., Hart and Ahuja 1996), increase customer satisfaction

(e.g., Luo and Bhattacharya 2006), and help the firm market its products (e.g., Fombrun 1996).

Advocates of this link have proposed that CSR can become a source of competitive advantage

due to, for example, the resulting positive stakeholder perceptions of the firm (e.g., Hull and

Rothenberg 2008). Accordingly, scholars who support the positive effect of CSR on financial

performance have argued that CSR improves stakeholder relationships, which leads to positive

firm performance (Freeman 1984; Hillman and Keim 2001).6

Penance Mechanism

Historically, most research has examined the slack resource and\or good management

mechanisms to address how CSR and firm performance relate to each other; more recently,

however, scholars are beginning to explore other mechanisms. Building on Heal (2005), who

proposed that CSR is a program of actions for firms to reduce externalized costs, Kotchen and

Moon (2012) argue that firms engage in CSR as a form of penance to offset its past CSI.

5 Financial performance is treated as the dependent and CSR as the independent variable in most of these studies.

6 As indicated above, a small fraction (i.e., about 5%) of the studies that have examined the CSR – financial

performance link reason that CSR results in negative financial performance (e.g., Margolis and Walsh 2001;

Scholtens 2008). Advocates of this negative effect usually argue that CSR unnecessarily raises a firm’s costs (e.g.,

Aupperle et al. 1985; McWilliams and Siegel 1997), and that it draws resources away from the core areas of

business (e.g., Jensen 2002) which ultimately results in subpar performance.

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Specifically, Kotchen and Moon (2012) argue that CSR is a type of Coasian solution that allows

firms to efficiently reduce externalized costs, i.e., costs that the firm has caused through CSI but

that it does not pay back in full. CSR allows the firm to make amends for the “unpaid bill”.

For example, in the case of an oil spill, the company that caused the spill usually only

pays a fraction of the long-term costs that accrue from the spill, largely because it is impossible

to estimate the precise long-term costs and damage caused. Or when firms treat their workers

poorly, it is difficult to gage the negative ripple-effect that this poor treatment can have on the

individual workers, their family, and the communities they live in. Yet, there is sufficient

empirical evidence that shows that firms are penalized if they are perceived as not holding their

end of the bargain, and, conducting their business in ways that conflict with social norms and

values. For example, following the Deepwater Horizon oil spill in 2010, US public opinion polls

were extremely critical of BP’s initial response to the spill, and sales at BP gas stations declined

by as much as 40% (WSJ, 2010). Thus, Kotchen and Moon (2012) argue that firms have an

incentive to engage in CSR because it acts as a penance mechanism that allows the firm to

compensate for externalized costs stemming from past CSI. The performance implications of this

approach, of course, are implicit.

Insurance Mechanism

Similar to the penance mechanism, advocates of the insurance mechanism argue that it is

imperative to consider CSR and CSI as separate constructs. Moreover, proponents of the

insurance mechanism also view CSR as a strategic mechanism that can protect against CSI (e.g.,

Fombrun et al. 2000; Peloza 2006; Minor 2011; Minor and Morgan 2011). The difference

between the two mechanisms, however, is that the proponents of the insurance mechanism posit

that CSR should not be used as a form of penance to atone for CSI but rather as insurance against

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CSI. Thus, compared to penance mechanism, where CSI in time t-1 causes CSR in time t,

proponents of the insurance mechanism propose that potential CSI in time t should cause CSR in

time t-1. The insurance mechanism is conceptually grounded in the literature that suggests that a

firm’s good reputation can serve as an intangible asset in times of crises and attenuate negative

stakeholder responses to bad news (e.g., Jones et al. 2000; Schnietz and Epstein 2005); i.e., CSR

presumably helps build a reservoir of goodwill among the firm’s stakeholders that endows the

firm with idiosyncrasy credits that act as safeguards, i.e., as an insurance, when bad things, i.e.,

CSI, happens.

Klein and Dawar (2004) provide empirical support for the notion that CSR acts as an

insurance mechanism against CSI. Specifically, they found that CSR attenuates negative

consumer responses in the case of a product harm crisis. Further, Minor (2011) and Minor and

Morgan (2011) propose that the primary role of CSR is to increase a firm’s value by insuring the

firm against potential losses caused by CSI. The performance implications of this mechanism are

thus again implicit; while the returns to CSR during “normal times” might be insignificant, the

financial benefits of CSR during adverse events can be substantial.

Summary and Initial Evidence

Although four mechanisms regarding the relationship between CSR and firm

performance have been proposed in extant literature, there has been no attempt to simultaneously

study these mechanisms. The majority of the literature focuses on one mechanism at a time

(typically on the direct influence of CSR on firm performance outcomes) and thus only paints a

partial picture for the influence of CSR. Further, the dichotomization of CSR into CSR and CSI

is also a recent phenomenon, where the recognition is explicit that a good deed need not

completely write off a bad deed. Also, no attempt has been made to provide a microeconomic

15

basis for CSR and CSI that would layout the primitives for the importance of these

organizational activities. Finally, as King and Lenox (2001) observe, the methods used in the

extant CSR literature have been either too simplistic, perhaps because they focus on only one

mechanism, or simply inappropriate (also see Margolis et al. 2007; Scholtens 2008).

To further corroborate the misgiving that can stem from correlational analysis focusing

on only one relationship at a time, we resort to model free analysis of our data. As we elaborate

subsequently, we have firm level annual data for up to 19 years (i.e., unbalanced panel) from

multiple sources on number of CSR actions, CSI incidents, firm performance (Tobin’s q), and

other variables. In Figure 2.2 we show the pairwise correlation between a firm’s lagged CSR (i.e.,

t-1), measured using the CSR ratings provided by the Kinder, Lydenberg, and Domini (KLD)

Social Ratings Database, and its current financial performance (i.e., t) as well as the pairwise

correlation between a firm’s CSR (i.e., t) and its lagged financial performance (i.e., t-1).

As can be seen, the correlation between lagged CSR and financial performance (solid line)

is positive from 1992 to 2002 and close to zero from 2003 to 2009. Thus, one might conclude

that firms benefit financially from CSR (i.e., that the data provides support for the good

management mechanism) given the positive correlation between the two constructs up until 2002.

However, a look at the dotted line in Figure 2.2 reveals that the correlation between lagged

financial performance (i.e., t-1) and CSR (i.e., t) looks quite similar. Indeed, financial

performance in t-1 is positively correlated with CSR from 1992 to 2005. Hence, based on this

“evidence”, one might conclude that companies engage in CSR because they are doing well

financially (i.e., support for the slack resource mechanism).

16

Figure 2.2 Pairwise Correlation between CSR and Financial Performance

Similarly, in Figure 2.3 we present the correlation between CSR and CSI. Specifically,

the solid line in Figure 2.3 shows the pairwise correlations between a firm’s lagged CSR (i.e., t-1)

and its CSI (i.e., t), as measured by the KLD Social Ratings Database. As can be seen, the

correlation between lagged CSR and CSI (solid line) increases from 0.03 in 1993 to 0.43 in 2003,

and remains at around 0.40 until 2009. Further, the dotted line in Figure 2.3 shows the pairwise

correlation between CSR (i.e., t) and lagged CSI (i.e., t-1) and shows a very similar pattern. Thus,

paradoxically, these patterns suggest that firms increasingly tend to do both “good” and “bad”,

and, it hence seems sensible to examine which comes first – CSI or CSR? We note that the

correlational analysis again does not shed much light on whether CSR antecedes CSI or vice

versa.

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

1992 1994 1996 1998 2000 2002 2004 2006 2008

Co

rrel

ati

on

Year

lag(CSR) & Tobin's q

17

Figure 2.3 Pairwise Correlation between CSR and CSI

Thus, we seek to provide a microeconomic basis for the relationship among CSR, CSI,

and firm performance and to develop an empirical model specification that would allow us to test

for the existence of the four mechanisms simultaneously.

2.3 Model Development

We begin by overviewing our theoretical model (which is an exercise in spirit similar to

Hanssens and Ouyang 2001), where for exposition purposes, the details are in Appendix A. The

objective of the theory model is to provide a microeconomic foundation for the CSR-CSI-

performance framework. We then elaborate on our empirical model specification – where we

recognize the structure of our data and our research objectives of simultaneously studying the

four mechanisms. We end this section by discussing our estimation approach for the SPVAR

empirical model specification we need for our research.

Theory Model

Building on the primitives from the theory of the firm (e.g., Becker 2007), we outline an

optimal control theory based model in Appendix A. In this model, we suggest that efforts related

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

1992 1994 1996 1998 2000 2002 2004 2006 2008

Co

rrel

ati

on

Year

lag(CSR) & CSI lag(CSI) & CSR

18

to CSR and CSI influence sales and that there are costs associated with these efforts. Thus, as is

customary in optimal control theory (e.g., Kamien and Schwartz 1991) and recent applications of

the theory in marketing (e.g., Sridhar et al. 2011), we specify an infinite horizon problem in

which a firm maximizes its profits subject to constraints emanating from the sales evolution

function.

For the specific case of CSR, the uniqueness arises in the specification of costs and the

sales evolution function. For costs, we include direct costs associated with (1) CSR efforts and (2)

CSI efforts, as is usually done (e.g., Naik and Raman 2003), but we also include (3) costs

associated with CSI incidents, such as oil spills as in the case of BP. First, costs associated with

CSR are the direct costs the firm expends on CSR activities such as donating to a charitable

cause (e.g., Coca Cola donating bottled water during hurricane Katrina). It is important to note

that the costs of CSR activities are private information for the firm, while the outcomes are

publically observed; however, efforts should drive outcomes. Thus, our theory model focuses on

efforts and our empirical model on outcomes; the link between efforts and outcomes should be

apparent.

Second, similar to CSR costs, costs associated with CSI are also direct costs for CSI

activities. Further, here again efforts are private information for the firm while level of CSI is

publically observed. The distinction between CSR and CSI is in the outcomes; while CSR are

primarily actions, CSI are principally incidents. CSI incidents, such as oil spills, are truly

exogenous but firms can spend efforts to reduce the probability of such incidents. For example,

Nike could spend efforts on sustainability officers that scrutinize all important organizational

decisions such that it can ensure that its outsourcing manufacturing partners do not use child

19

labor.7 Nonetheless, CSI efforts should relate directly to CSI incidents and thus, the link between

theory model that focuses on CSI efforts and the empirical model that focuses on CSI actions and

incidents is similar to that for CSR.

Third, truly unique to our research context (see Appendix A for details), we model the

costs associated through the expected loss and buffering effects of CSR efforts and CSI efforts.

Three features of this specification are worth noting: (1) the buffering effects of effort increase as

efforts increase; (2) we allow for the possibility that the effect of the efforts can be reduced or

enhanced; and (3) we also allow for the possibility of difference in the buffering effects of CSR

efforts and CSI efforts.

For the evolution of the sales function, as is customary (e.g., Naik and Raman 2003), we

specify the rate of change in sales as a function of past sales and efforts in CSR and CSI; further,

we suggest that the efficacy of CSR efforts and CSI efforts depends on the level of sales such

that larger firms with higher sales tend to receive greater scrutiny. This model specification of

costs and sales evolution reveals that there is a complex interplay among CSR efforts, CSI efforts,

and firm sales and net present value of profits.

Empirical Model Specification

Based on the economic model specification, it is apparent that an ideal dataset would

have information on firm sales and profits as well as CSR and CSI efforts over time. However,

as we elaborate subsequently, we have annual panel data on firms, where we observe firm value

(Tobin’s q to be specific) and the attained levels of CSR and CSI as opposed to CSR efforts and

CSI efforts. In the spirit of econometric models, which are “concerned with the empirical

7 The distinction between CSR efforts and CSI efforts is of theoretical importance; however, it is not always possible

to isolate effort as either CSR or CSI. For example efforts concerning sustainability officers can be seen as CSR

efforts or CSI efforts. Nevertheless, for vast majority of efforts, it is easy to classify them as either CSR efforts or

CSI efforts.

20

estimation of economic relationships,” we follow Intriligator (1983, p. 182) in that “the theory of

the phenomena under investigation is developed into a [economic] model which is further

refined into an econometric model.” As the theoretical model recognizes the endogeneity of firm

value and CSR efforts and CSI efforts, the level of CSR and CSI, which are attained by CSR

efforts and CSI efforts, also become endogenous and are determined by firm value (i.e., net

present profits that reflect the difference between sales and costs for an infinite horizon). Thus,

we seek to specify a system of equations in which Tobin’s q, CSR, and CSI influence each other

over time. In addition, the panel data model specification is required since our data set contains a

large number of firms (approximately 25,600 overall) which exceed the time dimension (19

years) (Baltagi 2005). Recognizing that we have data on a large number of firms as well as a

system of equations, and that we seek to model dynamics, we employ a panel vector

autoregression (VAR) specification (e.g., Holtz-Eakin et al. 1988, Love and Zicchino 2006).

Further, we require a model specification that incorporates contemporaneous effects among the

endogenous variables. As we have annual-level data, it seems reasonable to assume that there are

contemporaneous effects among some of the focal variables (i.e., CSR, CSI, and firm value).

Hence, we employ a SPVAR specification where a vector of endogenous variables is linearly

represented by its current and lagged effects (e.g., Cooley and Dwyer 1998, Enders 2004).

To summarize, our model specification incorporates three key features: (1) the dynamic

interplay and endogeneity among CSR, CSI, and firm value, (2) a large number of firms

compared to a short time dimension, and (3) possible contemporaneous effects due to the annual

level data. Hence, we specify the relationship among CSR, CSI and firm value through the

following structural panel VAR model:

21

(1) [

] ∑ [

] [

]

where

[

]

and is the firm index, { } is the time period index, , , and

are the endogenous variables in the system, , is

the matrix which indicates the contemporaneous relationships among the endogenous

variables, where , and represent the contemporaneous effect of CSI on CSR, the

contemporaneous effect of CSI on Tobin’s q, and the contemporaneous effect of CSR on Tobin’s

q, respectively, are the coefficient matrices of the lags of the endogenous variables

where ,

, , and

are associated with the four mechanisms, i.e., slack

resources, good management, penance, and insurance mechanism, respectively, is the number

of lags, is the vector of the control variables for firm at time , are the

coefficients of the control variables, is the unobserved firm-specific fixed effect, and

[

]

is the error term for the system of equations. Also the

composite error term [

]

is expressed as the usual ‘fixed effect’

decomposition where . Further, we assume that is independently and

identically distributed across and with the assumption that ( | )

and, ( | ) {

}

22

where is a positive definite matrix.8

Compared to the reduced-form panel VAR specification, our structural panel VAR model

is multiplied by the matrix to account for potential contemporaneous effect. Specifically, we

assume that CSI has a contemporaneous effect on CSR (i.e., ), but not the other way

around. This seems reasonable as firms frequently engage in CSR activities shortly after

committing CSI to attenuate (e.g., following its toy recall in late 2007, Mattel started testing

every production batch of toys for containing potentially dangerous levels of chemicals and

toxins) negative consumer responses (please see discussion above). However, we do not see any

reasons why firms would engage in CSI activities immediately after engaging in CSR activities.

Further, we also assume that CSI has a contemporaneous effect on firm value (i.e., ), but

not vice versa. Firms’ stock prices and/or sales often decrease after a significant CSI incident

(e.g., BP’s stock price decreased after the oil spill in 2010). Yet, current firm value should not

affect the current level of CSI. Finally, we assume that CSR has a contemporaneous effect on

firm value (i.e., ), but not vice versa. Firms evaluate and plan their CSR activities and

goals annually and do not change their CSR strategy based on short-term financial performance.

In contrast, as shown by Cellier and Chollet (2011)’s event study, CSR announcements can

influence short-term financial performance. In summary, we let the matrix be a lower triangle

matrix since we believe there are no contemporaneous effects on CSI from CSR and firm value

and on CSR from firm value. Hence, we use this recursive causal ordering to identify the

structural parameters in the matrix .

Estimation Procedure

8 This assumption ensures that the equation-by-equation estimator (e.g., Arellano and Bond 1991; Blundell and

Bond 1998) is asymptotically equivalent to the corresponding system-of-equations estimator of panel VAR models

(Cao and Sun 2011).

23

We follow a two-step approach to estimate the structural VAR model (e.g., Blanchard

and Perotti 2002; Blanchard and Quah 1989; Sims 1980). That is, we first estimate the reduced-

form VAR model and then estimate the structural parameters from the variance-covariance

matrix of residuals from the reduced-form VAR estimation.

First, we multiply equation (1) by (the inverse of the contemporaneous effect matrix)

and derive the reduced-form representation of equation (1).

(2) [

] ∑ [

] [

]

where, [

] [

] , , , and

[

]

.

From the mapping between and , we can derive the relationship between the

variance-covariance matrix of the reduced-form residual ( ) and the variance-covariance matrix

of the structural-form residual ( ) such that . Second, we obtain estimates of

and by using the estimate of . In general, several identification restrictions are needed since

there are knowns (the distinct elements of ) and unknows (the

elements of and the distinct elements of ). For identification, we set the

diagonal elements of equal to 1 by scaling and let be the lower triangle matrix based on our

recursive causal ordering. In other words, we impose some short-run restrictions which rely on

the contemporaneous effect assumptions. In addition, as suggested in the literature (e.g.,

Bernanke 1986; Sims 1986), we assume that is a diagonal matrix. Thus, our SPVAR model is

just identified and we can obtain structural parameter estimates of and .

24

When estimating equation (2), we have to consider the following three econometric

issues: (1) endogeneity, (2) unobserved heterogeneity, and (3) dynamic panel bias. First, CSR,

CSI and financial performance are assumed to be endogenous because, as discussed above, the

causality may run in both directions. That is, doing “good” may lead to doing “well” financially

and/or vice versa. Similarly, companies may be doing “good” to protect the firm against

subsequent mishaps and/or, alternatively, to compensate for their past mishaps. In other words, it

is possible that our variables of interest can be explained by their lagged values and/or by the

lagged values of the respective other endogenous variables (Pauwels et al. 2004). These potential

feedback effects among the regressors may be correlated with the error term. Dynamic panel

GMM estimators such as the Blundell and Bond (1998) estimator account for this type of

endogeneity that arises from direct and indirect feedback effects among the regressors.

Specifically, by using lagged values of the endogenous regressors and lagged first-difference

scores of the regressors as additional instruments, the endogenous variables become pre-

determined and are therefore not correlated with the error term (Arellano and Bond 1991;

Arellano and Bover 1995; Holtz-Eakin et al. 1988).

Second, unobserved heterogeneity may play a critical role in determining a firm’s CSR

and CSI scores. For example, companies operating in the oil and gas industry may engage in

more environmentally harmful activities than companies that operate in the food and beverage

industry. Thus, we need to control for this unobserved heterogeneity to detect the true

relationship among CSR, CSI and financial performance. We use first-differencing which allows

us to control for unobserved heterogeneity stemming from firm-specific and industry-specific

effects (Cameron and Trivedi 2005). In short, by first-differencing, time-invariant firm-specific

and industry-specific effects are removed. In other words, the unobserved firm-specific effect

25

cancels out in the model and, considering the iid assumption of , our estimation gives

consistent slope estimates.

Third, as we will show, our panel data has a relatively short time series dimension (T) and

a large cross-sectional dimension (N). Estimating equation (2) using a first-difference ordinary

least square approach or a least square dummy variable approach would give inconsistent and

biased estimates (i.e., result in a dynamic panel bias; Nickell 1981). That is, in the dynamic panel

models, the first-difference OLS estimator is inconsistent because the regressors include lagged

dependent variables (Cameron and Trivedi 2005). In contrast, the dynamic panel GMM estimator

allows us to overcome the dynamic panel bias. To obtain consistent estimates, Anderson and

Hsiao (1981) proposed an instrumental variable (IV) approach that estimates the first-difference

model using the lags of the dependent variable as an instrumental variable. Later, Holtz-Eakin et

al. (1988) and Arellano and Bond (1991) extended Anderson and Hsiao’s (1981) idea and

proposed a panel GMM estimator using not only the additional lags of the dependent variables

but also the lags of the difference of the dependent variables as instruments. Subsequently,

Arellano and Bover (1995) and Blundell and Bond (1998) developed a system GMM estimate

which uses lags of differences for equations in levels and also lags of levels as instruments for

equations in first differences. They showed that an efficiency gain in estimation is possible even

when the time series is nearly unit root.

Thus, we use the Blundell and Bond (1998) estimator to deal with the weak instruments

problem in first-differenced models as well as the dynamic panel bias which is a common

problem in small and large panels such as the one we are using. We also note that the

Blundell and Bond (1998) estimator has been widely used to test causal relationships when using

26

panel data (e.g., Huang et al. 2008). In the following, we briefly present the Blundell and Bond

(1998) estimator as well as the moment conditions.

We first derive the first-difference of equation (2) to remove the unobserved firm-specific

time invariant effect .

(3) [

] ∑ [

]

where is the first-difference operator.

Note that is cancelled out since ＝ ( ) . For

identification, we assume the standard initial conditions on

that for and (e.g., Ahn and Schmidt 1995). The standard

moment condition is the orthogonality condition between the dependent variable and the lagged

error term: ( ) . In addition, we impose two extra

moment conditions for the GMM estimation. These are T-3 linear moment conditions:

( ) for and =0. Due to these two moment conditions, the

lagged differences of the dependent variable can be used as a possible instrument. In general, the

asymptotically efficient GMM estimation based on the set of moment conditions is as follows:

Let where is a matrix of stacked coefficients of lagged

dependent variables. The GMM estimator of , where denotes the column

stacking operator, is given by (Cao and Sun 2011; note that we modified their equation (7)),

( ) (( ) )

where

∑

∑

(

∑

)

27

is a identity matrix, is the Kronecker product, is the weight matrix, and are

consistent estimates of the first-differenced residual obtained from a preliminary consistent

estimator (this is known as a two-step GMM estimator). is a matrix with -

th row , , is a vector with -th row , and is the

instrument matrix such that,

(

)

where (

) ].

To estimate the dynamic relationship among CSR, CSI and financial performance, we

calculate the orthogonalized impulse response function. To do so, we need to estimate the

covariance matrix of the error . The estimator of is given by,

∑∑

where ∑ ( ) , and

are the averages of the dependent variables and the control variables over time, respectively.

2.4 Data and Method

KLD Social Ratings Database

Our data comes from two sources: We first obtained corporate social performance data

from the Kinder, Lydenberg, and Domini (KLD) Social Ratings Database. This database has

been widely used in the academic literature (e.g., Hull and Rothenberg 2008; Kotchen and Moon

28

2012), and it provides annual data on firms’ performance in seven social issue areas, including

community, corporate governance, diversity, employee relations, environment, human rights and

product quality and safety. Further, the KLD database provides multiple indicators regarding a

firm’s strengths and concerns in each of the seven social issue areas. For instance, the

community area consists of 8 strength indicators (e.g., charitable giving, support for housing,

support for education) and 6 concern indicators (e.g., investment controversies, negative

economic impact). Table 2.1 lists all strength and concern indicators across the 7 issue areas.

Altogether, the database covers approximately 80 indicators.

Table 2.1 Strength and Concern Items of the KLD Social Rating Database

Qualitative Issue

Area

Type Categories # of

Categories

Corporative

Governance

Strengths

Limited Compensation

Ownership Strength

Transparency Strength (added ’05)

Political Accountability Strength (added ’05)

Other Strength

5

Concerns

High Compensation

Ownership Concern

Accounting Concern (added ’05)

Transparency Concern (added ’05)

Political Accountability Concern (added ’05)

Other Concerns

6

Community

Strengths

Charitable giving, Innovative giving

Non-US Charitable giving

Support for Housing

Support for Education (added ’94)

Indigenous Peoples Relations (added ’00,

moved ’02)

Volunteer Programs (added ’05)

Other Strength

8

Concerns

Investment Controversies

Negative Economic Impact

Indigenous Peoples Relations (’00-’01)

Tax Disputes (added ’05)

Other Concerns

5

Diversity

Strengths

CEO

Promotion

Board of Directors

8

29

Work/Life Benefits

Women & Minority Contracting

Employment of the Disabled

Gay & Lesbian Policies

Other Strength

Concerns

Controversies

Non-Representation

Other Concerns

3

Employee

Relations

Strengths

Union Relations

No-Layoff Policy (ended ’94)

Cash Profit Sharing

Employee Involvement

Retirement Benefits Strength

Health and Safety Strength

Other Strength

7

Concerns

Union Relations

Health and Safety Concern

Workforce Reductions

Retirement Benefits Concern (added ’92)

Other Concerns

5

Environment

Strengths

Beneficial Products and Services

Pollution Prevention

Recycling

Clean Energy

Communications (added ’96, moved ’05)

Property, Plant, and Equipment (ended ’95)

Management Systems

Other Strength

8

Concerns

Hazardous Waste

Regulatory Problems

Ozone Depleting Chemicals

Substantial Emissions

Agricultural Chemicals

Climate Change (added ’99)

Other Concerns

7

Human Rights

Strengths

Positive Record in South Africa (’94-’95)

Indigenous Peoples Relations Strength (added ’02)

Labor Rights Strength (added ’02)

Other Strength

4

Concerns

South Africa (ended ’94)

Northern Ireland (ended ’94)

Burma Concern (added ’95)

Mexico (’95-’02)

Labor Rights Concern (added ’98)

Indigenous Peoples Relations Concern (added ’00)

Other Concerns

7

Product

Strengths

Quality

R&D/Innovation

Benefits to Economically Disadvantaged

Other Strength

4

30

Concerns

Product Safety

Marketing/Contracting Concern

Antitrust

Other Concerns

4

Consistent with Kotchen and Moon (2012), we consider all strength indicators as CSR

and all concern indicators as CSI of the firm. This approach of treating the strength and concern

indicators as separate items is also in line with Mattingly and Berman (2006) who show that the

strength and concern indicators are divergent constructs and should not be combined.

Further, the KLD database provides a yearly binary summary of a firm’s strengths (i.e.,

CSR) and concerns (i.e., CSI) for each indicator belonging to the seven social issue areas. For

example, if a firm has consistently given over 1.5% of trailing three-year net earnings before

taxes to charity, then the “charitable giving” CSR indicator for the firm and year is coded as 1,

otherwise 0.

To determine each firm’s CSR in a given year, we followed Kotchen and Moon’s (2012)

approach and summed up the firm’s scores of all “strength” items in and across all seven issue

areas. We repeated the same procedure to determine each firm’s CSI in a given year summing up

the scores of all “concern” items in and across all seven issue areas. Thus, for each year, we

calculated two scores for each firm – one representing the firm’s overall CSR and the other one

its overall CSI. We note that this procedure places equal weight on each item. Then, we created

the standardized overall CSR and CSI scores for each firm and year.

We standardized the scores for two reasons: First, some of the items were added and/or

removed over the years. Thus, the total number of “strengths” and “concerns” varies over time.

Second, the number of companies included in the KLD database also varies over time. To

minimize the effect of different samples sizes and to make the CSR and CSI variables

comparable across the years, we used their standardized scores in our analysis.

31

The KLD data begins in 1991, and we used the complete KLD dataset up until 2009.

Thus, all firms for which KLD provides data for the time period between 1991 and 2009

constituted our initial sample. As mentioned above, the number of companies included in the

KLD database is not constant over time. Instead, the KLD database includes approximately 650

firms from 1991 to 2000, approximately 1,100 firms from 2001 and 2002 and approximately

3,100 firms from 2003 onwards. We included all available KLD data in our sample.

Financial Variables

We obtained financial performance data as well as control variables for as many of our

initial sample firms and years as possible using COMPUSTAT, our second data source. We

selected Tobin’s q as our financial performance measure because it is a market-based measure

which reflects the investors’ long-term expectation of the firm’s future earnings (Miller 2004). In

contrast to short-term marketing efforts, such as promotion, the financial benefits of CSR

activities might only manifest over time. For example, Cox et al. (2004) argue that improved

corporate social performance should lead to significant financial gains only in the long run.

Hence, compared to accounting-based financial performance measures such as return-on-asset

(ROA) or return-on-equity (ROE) which only capture short-term performance, Tobin’s q is a

more appropriate financial performance measure to understand the benefits as well as potential

costs of a firm’s social performance. We calculated Tobin’s q using the method proposed by

Chung and Pruitt (1994).

Further, Steenkamp and Fang (2011) suggest that it is important to control for firm size,

market share, and market concentration (i.e., Herfindahl-Hirschmann index; HHI) when studying

firm performance. Firm size might have a positive impact on market profitability (Boulding and

Staelin 1990), and we hence include the natural log of the number of employees (in million) in

32

our model. Likewise, market share might positively impact profitability (Szymanski et al. 1993),

and we thus include market share, which we calculated as the firm’s sales divided by the sales of

all firms in the firm’s industry (i.e., the same four-digit SIC code in COMPUSTAT), in our

model. Finally, we control for the degree of market concentration by including the normalized

HHI9 in the model. Large HHI values indicate a higher market concentration, and larger values

of HHI have been found to correlate positively with firm profitability (Lipczynski et al. 2005).

We retrieved the pertinent data to calculate the control variables from COMPUSTAT.

Our final sample, which is an unbalanced panel with time gaps, consists of approximately

4,500 firms, 25,000 data points, and data from 1991 to 2009. In addition, our sample firms are

publically traded firms from a wide range of industries. We present the descriptive statistics in

Table 2.2.

Table 2.2 Descriptive Statistics

Variables Mean Std. Dev. Min Max

Corporate Social Irresponsibility (CSI) 1.772 1.893 0 18

Corporate Social Responsibility (CSR) 1.468 2.043 0 22

Tobin’s q 1.669 1.999 0.030 148.802

Number of Employees (1,000s) 16.785 54.086 0.00 2100

Market Share 0.105 0.186 -0.295 1

HHI(Market Concentration Index) 0.212 0.188 0.014 1

The summary statistics are based on 4,539 firms. The statistics for CSR and CSI are based on data before

standardization.

Data Analysis Approach

We performed our analysis as follows: First, we tested for stationarity and unit roots.

Second, we estimated the dynamic panel GMM model using the Blundell and Bond estimator.

Third, we applied these estimators to each equation in the reduced-form panel VAR system and

9

where is the number of firms in the market, and ∑

where is the market share of firm .

33

recovered the contemporaneous effects based on the identification restrictions. Fourth, we

estimated the dynamics of the carryover effects (over time) using generalized impulse response

functions. Finally, we estimated the relative importance of the variables using generalized

forecast error variance decomposition (Pesaran and Shin 1998).

Stationarity in Time Series

To ensure that our analysis does not produce spurious results, we used the Augmented

Dickey-Fuller (ADF; Dickey and Fuller 1979) to examine stationarity and unit root for each time

series to determine whether the underlying data generation process of each variable is evolving

over time or is stationary (Granger and Newbold 1974; Hanssens et al. 2001). Since we have

unbalanced panel data with time gaps, the Im-Pesaran-Shin (2003) unit root test, which has been

widely used to test individual unit root processes in unbalanced panel data, cannot be used.

Hence, we used the ADF test to examine the null hypothesis of unit root. In addition, we tested

stationarity after first differencing as our model estimation is based on the first-difference.

Impulse Response Functions and Variance Decomposition

To examine the dynamic effect of one endogenous variable on another, we use impulse

response functions (IRFs). Generalized IRFs (Dekimpe and Hanssens 1999; Pesaran and Shin

1998) trace the effect that a one unit (e.g., one standard deviation) shock to one variable in the

system has on another variable over subsequent time periods while holding all other variables’

shocks equal to zero. We derived the generalized orthogonal IRFs by using the panel GMM

estimates and the covariance matrix of the equation residuals ( . Further, we used Monte Carlo

simulation to obtain upper and lower 95% confidence bands (see Doan 1992 for details). Finally,

we used the generalized forecast error variance decomposition (Pesaran and Shin 1998) to

34

examine the relative importance, i.e., effect size, of one variable in forecasting another (Grewal

et al. 2001).

2.5 Results

We present the results as follows: (1) unit root tests, (2) SPVAR estimates, (3) IRF

results, and (4) generalized forecast error variance decomposition estimates.

Stationarity and Unit Root Tests

As we show in Table 2.3, the null hypothesis of unit root is rejected for all three variables

of interest. Further, ADF test statistics of the first-differenced CSR, CSI and Tobin’s q variables

are -16.75, -16.09, and -15.28, respectively (p < .05); thus, our focal variables are difference

stationary.

Table 2.3 Summary of Unit Root and Stationarity Tests of the Variables

Variable Statistics P-value Conclusion

-16.089 0.000 Stationary



We use three lags in the ADF regression. The reported statistic is the inverse normal Z statistic. Choi’s

(2001) simulation results suggest that this statistic offers the best trade-off between size and power.

Model Estimation and Optimal Lags Selection

In the first step of our two-step estimation procedure, we estimated a reduced-form VAR

model (i.e., equation 2) using the Blundell and Bond estimator. We first determined the optimal

number of lags , where, consistent with research on dynamic panel data models (e.g., Huang et

al. 2008), we use the statistic (where stands for the order of autocorrelation) suggested by

Arellano and Bond (1991). The idea behind the statistics is that the residual from the dynamic

panel data model should be free of serial autocorrelation. Doornik et al. (2006) suggest that, if

the error term is not serially correlated, there would be significant and negative first order

35

serial correlation in the differenced residuals (i.e., ) and no significant second order

serial correlation. Thus, we examined the and statistic, and obtain the optimal number of

lags when the statistic is negative and statistically significant and the statistic is not

statistically significant. The last two rows of Table 2.5 illustrate the first and second order serial

autocorrelation results. In the equation in which CSR is the dependent variable, the statistic is

negative and statistically significant ( = -13.436) while the statistic is not significant at the

0.01 level ( = 2.339) when the number of lags specified is 3. Similarly, in the equation in

which CSI is the dependent variable, a three lags specification satisfies the criterion suggested by

Arellano and Bond (1991) ( = -15.567 and = 2.397). The three lags specification in the

equation in which Tobin’s q is the dependent variable is also sufficient to remove the serial

autocorrelation in the residual. Thus, the optimal lag length for our dynamic panel data model is

three.

In the second step of our two-step process, we estimated the contemporaneous effects

from the variance-covariance matrix of residuals from the reduced-form panel VAR model. The

results in Table 2.4 suggest that as the level of CSI increases, the level of CSR increases

( ); thus, it seems that firms engage in CSR activities reactively to cope with

their CSI activities. Further, the level of CSR has a positive impact on firm value (

) which suggests doing “good” leads to doing “well” immediately. The

contemporaneous effect of CSI on firm value is not statistically significant (

.

36

Table 2.4 Estimation Results of Contemporaneous Effects

Independent Dependent

CSI CSR Tobin’s q

CSI 1 0.23419(0.00954) 0.07487(0.01371)

CSR 0 1 0.16147(0.01843)

Tobin’s q 0 0 1

Standard errors are reported in parentheses. They are calculated based on 1,000 bootstrap samples.

We present the estimation results from the reduced-form panel VAR model with a three

lags specification in Table 2.5. We first discuss the results for the control variables. As can be

seen in Table 2.5, firm size (i.e., number of employees) does not have a significant effect on CSR.

This result is analogous to Kotchen and Moon (2012)’s finding. However, firm size does have a

positive effect on CSI ( = 0.597). Further, market concentration does not play a

significant role in forecasting CSR, CSI, and Tobin’s q after controlling for other variables.

Finally, market share has a positive impact on CSR ( = 0.3944) and CSI ( = 0.4578).

It is difficult to understand the effect of one endogenous variable on another variable by

merely looking at the estimates in Table 2.5. For example, the second column of Table 2.5 shows

the estimation result when the dependent variable is CSI. All lagged CSR variables are

significant. However, the coefficients of and are negative whereas the coefficient

of is positive. In short, to understand the dynamic effect of the lagged independent

variables on the dependent variable, we need to investigate the IRFs.

37

Table 2.5 Estimation Results from the Reduced-form Panel VAR Model


CSI(t) CSR(t) Tobin’s q(t)

CSI(t-1) .6386*** -.0287* .0275

CSI(t-2) .0415*** .0169 .0153

CSI(t-3) -.0035 -.0127 -.0339*

CSR(t-1) -.0611*** .7676*** -.0568**

CSR(t-2) .0799*** .0406*** .0428*

CSR(t-3) -.0371** -.0051 -.0671**

Tobin’s q(t-1) -.0018 -.0120 .6731***

Tobin’s q(t-2) .0033 .0203** .0118

Tobin’s q(t-3) .0137** .0096 .0939*

Log(Emp) .0597*** .0273 .0959

HHI .1964* .1902* .9495*

Market Share .4578** .3944** .4944

AB Test (m1) -15.567*** -13.436*** -5.4425***

AB Test (m2) 2.3971** (p-value:

0.0165)

2.3393** (p-value:

0.0193)

-.62535

***, **, and * represent 1%, 5%, and 10% significance level, respectively

Impulse Response Functions

As we are interested in testing the four mechanisms, i.e., whether and how doing “good”

and doing “well” converge, we focus on the relationship between CSR and CSI and the

relationship between CSR and financial performance for generating IRFs. In Figure 2.4, we

present the graphs of the IRFs for these two relationships along with the 95% confidence bands

generated by the Monte Carlo simulation. Panel A in Figure 2.4 shows the relationship between

CSR and financial performance, where Graph 1 of Panel A shows the response of financial

performance to a CSR shock. In support of the good management mechanism, Graph 1 shows

that CSR influences financial performance positively in the short run (0.07) and this positive

effect fades away after about a year. Graph 2 shows that a shock to Tobin’s q does not seem to

38

have any impact on CSR as the 95% confidence band always contains zero. Hence, the slack

resource mechanism is not supported by our results.

Panel B in Figure 2.4 depicts the relationship between CSR and CSI. As Graph 1 in Panel

B shows, CSR has a negative impact on CSI in the short run (-0.026), and this negative impact

wears out after about 2 years. Thus, the insurance mechanism which argues that CSR antecedes

CSI in a positive manner is not supported. In contrast, in support of the penance mechanism,

Graph 2 in Panel B shows that there is a large and positive impact of CSI on CSR that lasts for

about 4 years.

Figure 2.4 Impulse Response Functions

Panel A: Impulse Response Functions for the Dynamic Relationship between CSR and Financial

Performance

39

Panel B: Impulse Response Functions for the Dynamic Relationship between CSR and CSI

The graphs show the 2.5% and 97.5% confidence band (dashed line) on either side of the

response function (solid line).

Figure 2.5 Forecast Error Variance Decomposition Results after 5 Years

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

CSI after 5 years CSR after 5 years Tobin's q after 5

years

Per

cen

tage o

f V

ari

an

ce

Tobin's q

CSR

CSI

40

Table 2.6 Forecast Error Variance Decomposition Results after 5 Years


CSI(t+5) CSR(t+5) Tobin’s q(t+5)

CSI 99.75% 6.16% 0.75%

CSR 0.17% 93.69% 0.53%

Tobin’s q 0.08% 0.15% 98.72%

Forecast Error Variance Decomposition

To assess the relative importance of one variable when forecasting the other variable, we

use forecast error variance decomposition. We present these decompositions for a five year

period in Table 2.6 and Figure 2.5, which show that CSI is mainly explained by its lag values

(99.75%) with CSR and financial performance jointly explaining only 0.25% of the forecast error

variance in CSI. In contrast, CSR is explained by its lag values (93.69%) and CSI (6.16%) but

Tobin’s q explains only 0.15% of CSR. Finally, as would be expected, past Tobin’s q explains

most variance in current Tobin’s q (98.72%) with CSR accounting for 0.53% and CSI for 0.75%

of variance.

2.6 Discussion

Various mechanisms of how CSR and positive firm performance converge have been

proposed in the literature. Some scholars argue that CSR results in positive firm performance and

that it is thus a manifestation of the firm’s good management (e.g. Freeman 1984; Hull and

Rothenberg 2008). Others, however, argue that a firm’s slack resources result in CSR; i.e., these

scholars argue that CSR does not impact financial performance but that financial performance

leads to CSR (e.g., McGuire et al. 1988). More recently, scholars have posited that CSR acts as a

form of penance and can at least partially offset CSI (e.g., Kotchen and Moon 2012). In contrast,

scholars also posit that CSR provides an insurance mechanism against CSI (i.e., CSR antecedes

CSI temporally) (e.g., Peloza 2006; Minor and Morgan 2011). While these four mechanisms are

41

not mutually exclusive, the good management and slack resources as well as penance and

insurance mechanisms do propose reverse causal paths. The primary goal of our study is to

unravel these four mechanisms and identify (1) whether and (2) how firm performance and CSR

converge.

Implications

We believe that our contributions to the extant CSR literature are on five dimensions:

First, the literature on why firms engage in CSR is quite scattered. We summarize the varying

propositions and integrate the same into four mechanisms. More importantly, we test these four

mechanisms simultaneously. Extant literature examines only one or at best two mechanisms

simultaneously; such an approach, however, is cumbersome as it does not permit addressing

concomitant effects among the four mechanisms resulting in either a partial picture or even false

statistical findings.

Second, we separate CSI from CSR, and we also integrate the CSI construct into the

overall “CSR-CSI-financial performance” framework, which is also new in the literature.

Separating CSI from the CSR construct is meaningful because failure to do so can effectively

cancel out important granularities in the data. We note that many past studies have simply

averaged a firm’s CSR and CSI scores into one overall CSR score. Moreover, separating CSI

from CSR is critical to shed light on the question of how CSR and financial performance

converge. Using comprehensive data, we show that one reason of why firms engage in CSR

seems to be that they use it to offset past CSI, i.e., that the penance mechanism is at play.

Third, our economic theory model provides a theoretical basis for the development of

empirical models that seek to examine the dynamic relationship among CSR, CSI, and financial

performance. The model also provides the basis for the link between organizational efforts and

42

the level of CSR and CSI that researchers typically empirically study. We believe that our

economic theory model, which is based on theory of the firm primitives, would provide bases for

future theoretical and empirical research.

Fourth, we provide empirical evidence that CSR has a positive impact on firm

performance. Although this finding is documented in the extant literature (e.g., Margolis et al.

2007), we augment the extant literature in two important directions. First, we model the four

mechanisms simultaneously thus allowing for the possibility of finding evidence for each of the

mechanism and correcting for the possibility of any statistical simultaneity bias. Second, we use

the SPVAR model for a large number of firms (around 4500) over a period of 19 years; thus the

SPVAR specification enables us to control for unobserved heterogeneity (through fixed effects)

and correct for endogeneity (using an adapted Blundell and Bond estimator), in addition to

modeling simultaneity; further, the large data set provides confidence in the findings. Thus, we

do establish causality for the influence of CSR on firm performance (as assessed by Tobin’s q).

In the process, we provide fodder for Margolis et al.’s (2007, p. 4) assertion that “if only doing

“good” could be connected to doing “well”, then companies might be persuaded to act more

conscientiously.”

Fifth, we provide support for the penance mechanism such that firms have a tendency to

invest in CSR after CSI has occurred. Thus, CSR might also have more subtle and covert

performance implications not captured by the positive direct link between CSR and firm

performance; CSR seems to attenuate negative effects stemming from CSI. Our secondary data,

of course, does not allow us to say for certain that firms in fact use CSR to offset previous CSI.

However, the fact that CSI is a strong driver of CSR provides at least some face validity for the

43

notion (i.e., penance mechanism). It also sheds light on the direction of causality behind the

relatively large correlation between CSR and CSI shown in Figure 2.3.10

Limitations and Further Research

While we believe that we have broken some new ground with this research, there are

clear limitations, some of which might provide fruitful avenues for future research. For example,

a potential limitation that we must acknowledge is the CSR and CSI data that we employ.

Although the KLD data has been used extensively in extant research, the CSR and CSI measures

are based on somewhat subjective rather than objective evaluations. Future research should try to

replicate our findings using different CSR and CSI data. Unfortunately, as we write this, we are

not aware of a source that could provide such data (other than KLD, of course).

Further, although we believe that the identification restrictions we imposed to estimate

the SPVAR model are sensible, we need to acknowledge that our results are sensitive to them

(e.g., Canova et al. 1993; Uhlig 2005). We note that we conducted a survey among 31 US-based

senior executives to validate our identification restrictions. The survey results provided strong

support for our assumptions. For example, to support our argument that firm value does not have

a contemporaneous effect on CSR, we asked the executives how often they change their CSR

strategy. 87.5% of the respondents reported that they change their CSR strategy “annually” or

even less frequent, and that short-term firm value does not affect their firm’s CSR.

Conclusion

Scholars have scrutinized the conception that companies do “well” by doing “good” for

many years (e.g., McWilliams et al. 2006). However, the debate on whether and how doing

10 Our data also provides evidence that firms do not tend to use CSR to insure against CSI (i.e., insurance

mechanism). Otherwise, we should see in our data that CSR positively drives CSI, which we do not. In fact, our data

suggests that past CSR has a slight negative impact on current CSI, which seems sensible. We note, however, that

this non-finding does not suggest that the insurance mechanism would not work. It simply suggests that few firms

make use of it.

44

“good” and doing “well” converge has yet to be resolved (e.g., Mackey et al. 2007). In this study,

we provide strong evidence that CSR has a positive effect on firm performance, and we also

show that firms tend to increase their CSR after CSI has occurred. We believe that these findings

are a significant contribution to the extant CSR literature.

45

Chapter 3

Portfolio Management in Sustainability Strategy and Firm Performance

ABSTRACT

Firms can engage in various Corporate Social Responsibility (CSR) practices, including

charitable giving, environmental protection, and employee empowerment, among others.

Moreover, firms can also direct their attention to avoiding Corporate Social Irresponsibility

(CSI), such as accounting irregularities, investment controversies, and health and safety

concerns. Yet, evidence suggests that many firms struggle to decide which CSR and CSI

avoidance practice(s) they should engage in, partially because there are so many different

options. Against this backdrop, we argue that a firm’s CSR and CSI avoidance practices can be

viewed akin to financial investment portfolios, and, borrowing from portfolio theory, develop

hypotheses on how firms should manage their CSR and CSI portfolios. We test our hypotheses

using panel data from over 3,000 firms across 11 years (2001 – 2011) and employ a dynamic

panel data model that controls for unobserved heterogeneity and endogeneity. Our findings

suggest that firms should strive to engage evenly in CSR practices across several societal issue

areas rather than heavily engage in multiple CSR practices in a few societal issue areas. Further,

firms should avoid CSI whenever they can. However, if that is not possible, firms should focus

on minimizing business- related (as opposed to philanthropic-related) CSI practices.

46

3.1 Introduction

Recent changes in the business environment have increased calls for firms to expand their

responsibilities and to use firms’ resources to alleviate a wide variety of social and

environmental problems (Hillman and Keim 2001). These calls are in line with sustainability,11

which is an approach firms are increasingly adopting to conduct business to deliver not only

economic benefits but also social and environmental benefits worldwide (Elkington 1998). The

sustainability movement is gaining momentum as firms try to build better relationships with

stakeholders such as employees, customers, community residents, and non-profit organizations

(NGO), because firms have realized that better relationships with stakeholders lead to increased

shareholder values (Hillman and Keim 2001; Freeman 1984). Responding to this shift of

momentum, the topic of sustainability has drawn particular attention from academic researchers

in business and related fields (e.g., Brown and Dacin 1997; Banerjee, Iyer, and Kashyap 2003;

Chabowski, Mena, and Gonzalez-Padron 2011; Hombug, Stierl, and Bornemann 2013).

Due to the strategic importance of sustainability in business, sustainability now reaches to

C-Suite: Chief Sustainability Officer (CSO). The title of CSO started to appear around 9 years

ago in the US and is increasingly being adopted globally (Acre 2011). According to the article by

Forbes (2011), 29 publically traded firms such as Coca Cola, AT&T, DuPont, and Kellogg have

appointed CSOs. The CSOs, along with other sustainability executives, typically focus on two

strategic functions: (1) they explore business opportunities from sustainability practices that will

increase shareholder values, and (2) they review firms’ management and monitor their impacts

and risks to firm reputation, ultimately, firm profitability (Acre 2011). These roles of CSOs are

challenging and complex for several reasons. First, the number of sustainability practices

11

We define sustainability as a business approach that creates long-term shareholder value by enhancing strengths

(i.e., engaging in corporate social responsibility (CSR) practices) and reducing concerns (i.e., minimizing corporate

social irresponsibility (CSI) practices) that derive from economic, environmental and social developments.

47

increases and their priorities are changing because the policy, consumer awareness and needs,

and firm’s strategic direction change. For example, Greg Morris, head of environment at

Newcrest Mining, stated that “The environmental skill set changes. In the beginning, you had to

know how to grow trees and plant grass. Then you had to know how to manage waste, water, and

other impacts. Now you have to know how to translate the issues and ideas into workable

solutions in a broader policy context.” Second, it is hard to identify each sustainability practice’s

strategic priority due to the difficulty in quantifying intangible effects and predicting customer

responses. According to the 2012 Sustainability & Innovation Global Executives Study, 37% of

survey respondents identified that the most significant obstacles to evaluating the business case

for sustainability-related strategies is competing priorities (Kiron et al. 2012). In addition,

according to The Sustainability Executives: Profile and Progress report, sustainability

executives responded that ‘choice, paring, and specialization’ is one of the specific challenges

ahead (PwC 2012). Specifically, sustainability executives wanted answers to the questions such

as “We can’t do everything, so how do we prioritize our efforts?” and “What adds value?” Third,

sustainability practices not only have to compete among themselves but also with other critical

marketing instruments such as advertising and research and development (R&D) because of

limited resources (Friedman 1970; Luo and Bhattacharya 2009). In sum, although firm’s

sustainability practice engagement management has become one of the critical strategic issues,

there has been scarce guideline for managing a wide range of sustainability practices.

In the similar vein, extant research has not provided holistic view of sustainability

practice engagement management. Rather, most extant studies either focus on only one societal

issue area (e.g., Klassen and McLaughlin 1996; Lichtenstein, Drumwright, and Braig 2004;

Robinson, Irmak, and Jayachandran 2012) or look at the global sustainability performance (e.g.,

48

Brown and Dacin 1997; Wagner, Lutz, and Weitz 2009; Luo and Bhattacharya 2009). For

example, Klassen and McLaughlin (1996) focus on environmental management, and investigate

the effect of environmental strength and concern on financial performance. Luo and

Bhattacharya (2009) investigate the relationship between firm-idiosyncratic risk and corporate

social performance (CSP) which refers to “a company’s overall performance in these diverse

corporate prosocial programs” (p. 201). However, these studies failed to consider the

interdependence in returns and risks from diverse sustainability practices. For instance, engaging

in multiple practices (e.g., pollution prevention, recycling, using clean energy) in environment

area may increase consumers’ awareness on firm’s green image and provide higher returns than

engaging in one practices in three different societal issue areas (e.g., environment – recycling,

employee relations – no-layoff policy, community – charitable giving). In addition, previous

research has shown that each CSR practice has distinct benefit mechanism depending on whether

the practice targets primary or secondary stakeholders (Homburg, Stierl, and Bornemann 2013).

Hence, they argue that expressive customer outcomes from different sustainability practices vary.

In short, researchers have neglected the interdependence and synergy effect among a wide

variety of sustainability practices and previous research has not been able to shed lights on the

effective sustainability practice management. Indeed, Chabowski, Mena, and Gonzalez-Padron

(2011) argue that “the influence of multiple sustainability-focused marketing assets on financial

returns” has not been examined thoroughly (p.66).

To answer these calls, the purpose of this paper is to provide insights into effective

strategies to manage a wide variety of sustainability practices. Specifically, building on financial

portfolio theory (Markowitz 1952), we propose three portfolio descriptors which capture the

characteristics of a firm’s sustainability practice engagement: breadth, depth and the ratio of

49

philanthropic/business-related practices. We posit that the effective sustainability practice

engagement decisions can be made in the similar manner to find optimal composition of assets in

financial portfolio for several reasons. First, both involve the choice decisions among diverse

sustainability practices and financial assets (Hillman and Keim 2001). Second, like financial

assets, sustainability practices create returns such as consumer loyalty and high purchase

intentions through psychological processes and these returns are interdependent (e.g., Du,

Bhattacharya, and Sen 2007; Sen, Bhattacharya, and Korschun 2006). Third, similar to financial

assets’ risks, sustainability practices also have inherent risks such as high operating costs and

green product failure.

Further, building on stakeholder theory, we hypothesize and test the relationship between

three portfolio descriptors and firm performance. First, we argue that the breadth of CSR

practices increases firm performance whereas the breadth of CSI practices decreases firm

performance. We also predict that the depth of CSR practices mitigates the positive effect of

CSR breadth on firm performance. In addition, with respect to the negative effect of CSI breadth,

we argue that this negative effect is less profound when the ratio of philanthropic vs. business-

related CSI practices is high. Lastly, we predict that the depth of CSI practices mitigates the

positive moderating effect of the ratio of philanthropic vs. business-related CSI practices on the

negative link of the breadth of CSI – firm performance. In our analysis, we combine the KLD

Social Rating Database, which provides annual snapshot of firm’s sustainability practices in

seven societal issue areas, with financial performance data from COMPUSTAT over the period

2001-2011. We employ dynamic panel data analysis which controls for unobserved

heterogeneity and endogeneity issues to test our hypotheses in rigorous way. In CSR practices,

although we do not find positive effect of the breadth of CSR on firm performance, we find a

50

mitigation effect of the depth of CSR on the link of the breadth of CSR – firm performance. In

CSI practices, we find that the breadth of CSI negatively impacts firm performance while its

effect is positively moderated by the ratio of philanthropic vs. business-related CSI practices.

Moreover, we find negative three-way interaction among breadth, depth, and philanthropic vs.

business-related practice ratio for CSI. In short, we find that three sustainability practice

portfolio descriptors, i.e., breadth, depth, and philanthropic vs. business-related practice ratio,

can effectively predict firm performance and the interactions among them are critical.

We seek to make several important contributions to both academia and practitioners. First,

we advance research on CSR by adapting a portfolio prospective which enables us to capture the

interdependence and synergy among a wide variety of sustainability practices. We propose three

simple sustainability practice engagement portfolio descriptors and provide holistic view of the

relationship between sustainability practice engagement and firm performance. Second, while

most CSR and sustainability research has extensively conceptualized CSR globally or looked at

only one societal area of CSR (e.g., Klassen and McLaughlin 1996; Brown and Dacin 1997;

Lichtenstein, Drumwright, and Braig 2004; Wagner, Lutz, and Weitz 2009), we distinguish

sustainability into positive practices (i.e., CSR) and negative practices (i.e., CSI), and further into

business-related practices and philanthropic-related practices. We extend CSR and sustainability

research by examining the link between specific types of sustainability practices and financial

outcome. Third, we contribute to the marketing-finance interface literatures by investigating the

impact of sustainability practice portfolio on stock market-based long term measure of a firm

value. Further, we provide insights into the debate on the role of CSR in firm performance. We

suggest that not only the breadth but also the depth and philanthropic vs. business-related

practice ratio should be considered. Finally, we provide managerial guidelines for the effective

51

sustainability practice engagement management under the condition of limited resources. Based

on our findings, we propose two simple rules: (1) be the generalist in CSR practices, and (2)

minimize business-related CSI practices first, but don’t let the philanthropic-related CSI

practices stand out.

The remainder of this article is organized as follows: in the following section, we provide

an overview of sustainability, stakeholder theory, and financial portfolio theory. Based on the

overview, we propose three descriptors of firm’s sustainability practice engagement portfolio.

Then, we predict the relationship between these sustainability practice portfolio descriptors and

firm performance. Next, we test our hypotheses empirically while describing our data, variable

operationalization, and modeling issues. We conclude with a discussion of our findings, and

outline the implications of our research as well as the limitation of the current study.

3.2 Conceptual Background

In this section, we first provide a theoretical background of stakeholder theory and

portfolio theory relating to CSR/CSI practices. Based on portfolio theory and stakeholder theory,

we propose three sustainability practice engagement portfolio descriptors –portfolio breadth,

portfolio depth, and the ratio of philanthropic/business-related sustainability practices. Then, for

each of CSR/CSI practice, we discuss the role of these descriptors on firm performance.

CSR, CSI, and Stakeholder Theory

Sustainability can be achieved in two ways: (1) by engaging in more socially responsible

practices, and (2) by minimizing societal misbehaviors. These two ways correspond to corporate

social responsibility (CSR) and corporate social irresponsibility (CSI), respectively. CSR refers

to firm’s pro-social practices, ranging from corporate governance and green marketing to any

activities that advance social welfare beyond that which is required by law (McWilliams and

52

Siegel 2001; Luo and Bhattacharya 2009). In contrast, CSI refers to corporate anti-social

behaviors, ranging from tax disputes and labor right concern to any practices that appear to hurt

the social goods (Kotchen and Moon 2012).

While various theoretical bases such as the resource-based view (Barney 1986; McGuire

et al. 1988) , risk management theory (Godfrey 2005), and institutional theory (Handelman and

Arnold 1999), have been applied to understand the effect of firm’s sustainability practices to firm

performance, we primarily use stakeholder theory (Freeman 1984) in our research because our

research focuses on investigating the link between sustainability practice engagement and firm

performance outcome, operationalized as Tobin’s q. Stakeholder theory provides an appropriate

explanation for the linkage between stakeholder-directed activities and firm performance

(Donaldson and Preston 1995; Freeman 1984). Specifically, stakeholder theory argues that

stakeholder-directed activities such as firm’s CSR practices should create benefits to

shareholders which, in turn, should enhance firm performance (Bhattacharya, Korschun, and Sen

2009; Jones 1995). Moreover, Homburg et al. (2013) illustrate that different CSR practices have

different impacts on consumers’ mindset, and further, organizational customer outcomes in B2B

context. In fact, stakeholder theory proposes that the benefits from stakeholder-directed activities

are different from one stakeholder groups to another depending on stakeholders’ interests. For

example, primary stakeholders, who engage in market exchange with a firm, such as employees

would value a firm’s CSR practices such as no-layoff policy and employee involvement. In

contrasts, secondary stakeholders, who influence or are influenced by a firm’s behavior but are

not engaged in direct market exchange, such as the community would be more interested in

secondary stakeholder-directed practices such as charitable giving and a firm’s volunteer

programs. Similarly, primary stakeholders may not concern deeply about criticism by NGOs

53

which is one of CSI practices in community area,12

while primary stakeholders concern such as

workplace health and safety program may not be a critical problem to secondary stakeholders.

Since we argue that, depending on the target stakeholder group, CSR/CSI practices may have

different impacts on firm performance, we rely on stakeholder theory.

Portfolio Theory

Financial portfolio theory describes the effective way to construct investment portfolio

that balances the cash flow opportunities (return) and the cash flow vulnerability (risk) by

diversification on stocks and bonds (Markowitz 1952). The key tenet of financial portfolio theory

is to compose portfolio with various stocks and bonds, each of which has low total correlation

with the total return. By doing so, investors can still enjoy positive cash flow opportunities while

smooth out negative cash flow vulnerability from unanticipated environmental and economic

changes because diversified stocks act differently to these market changes.

Financial portfolio theory has been applied in several other business domains including

marketing. For example, Tarasi et al. (2011) adopt portfolio theory in customer management and

demonstrated that the efficient customer portfolio consistently yields low risk. Bordley (2003)

uses portfolio theory in the context of a firm’s product portfolio. Grewal et al. (2008) illustrate

the relationship between new product development portfolio and firm performance in the

pharmaceutical industry. Sridhar et al. (2014) investigate manager’s advertising investments

decision through the lens of portfolio theory. In many marketing applications, researchers have

looked at the number of portfolio components (e.g., new products, media outlets) and variability

among portfolio components which represent the degree of diversification in portfolio (e.g.,

Bordley 2003; Grewal et al. 2008).

12

For example, SodaStream has been criticized by Oxfam for its operations in the occupied West Bank. However,

SodaStream’ Palestinian employees who work at the factory said that they are just trying to make a living regardless

of the political issue and they just wanted to work and live (International Business Times 2014).

54

Sustainability Portfolio Strategy

In this section, we argue that top management’s (e.g., CSO) strategic decision to engage

in certain sustainability practices resembles investor’s financial portfolio decision. According to

the report from Acre (2011), the major role of CSO is to explore sustainability practices that

should make a significant difference to shareholder value, and at the same time, to manage the

impacts and risks of sustainability-related concerns (i.e., CSI). This strategic decision by CSO is

similar to investor’s financial portfolio decision with respect to (1) choices of assets, (2) returns,

and (3) risks.

First, similar to the financial portfolio decision which involves choice of various types of

stocks and bonds, sustainability practice engagement management decision involves choices of

sustainability practices within and among various societal issue areas such as corporate

governance, environment, and community. Indeed, recent changes in the business environment

have increased calls for firms to expand their responsibilities and to use their resources to

alleviate a wide variety of social and environmental problems (Hillman and Keim 2001). For

example, traditionally, firms have dealt with sustainability issues such as employee safety in

employee relations area (e.g., Buehler and Shetty 1974; Foote 1984), product safety in product

area (e.g., Siomkos 1999; Chen, Ganesan and Liu 2009), and pollution reduction in environment

area (e.g., Nehrt 1998). In addition to these sustainability practices, as consumers’ awareness on

environmental issues has been growing and firms recognition on the importance on

environmental practices as the sources for future corporate competitiveness (Nidumolu, Prahalda,

and Rangaswami 2009; Sharma, Mehrotra, and Krishnan 2010), firms are now engaging in more

and more environmental practices such as preventing water depletion, recycling, and building

sustainable management systems. Moreover, firms’ efforts to reach out community and the

55

emergence of cause-related marketing (e.g., Arora and Henderson 2007; Krishna and Rajan 2009)

added another dimension of sustainability practices. Overall, as shown in Table 2.1, our KLD

database covers approximately 80 sustainability practices. In addition, these sustainability

practices differ in their implementing costs and expected benefits or returns, as we describes in

next paragraph.

Second, like assets in financial portfolio, sustainability practices generate the returns and

they are interdependent. Previous research has shown that firm’s sustainability practice

engagements create the returns. For example, it has been shown that sustainability practice

engagements have positive influence on psychological outcomes such as trust (e.g., Homburg,

Stierl, and Bornemann 2013; Vlachos et al. 2009), customer satisfaction (e.g., Luo and

Bhattacharya 2009), attitude toward the company (e.g., Brown and Dacin 1997), customer-

company identification (e.g., Lichtenstein, Drumwright, and Braig 2004), and resistance to

negative company information (e.g., Klein and Dawar 2004). Further, these engagements result

in behavioral outcomes such as increase in consumer loyalty (e.g., Du, Bhattacharya, and Sen

2007; Maignan, Ferrell, and Hult 1999), higher purchase intention (e.g., Sen, Bhattacharya, and

Korschun 2006), and higher willingness to pay (e.g., Auger et al. 2003). In addition, the returns

of sustainability practices are interdependent. In other words, engaging in one CSR practices can

affect the returns from the other CSR/CSI practices. For instance, Klein and Dawar (2004) found

that CSR mitigate the negative consumer responses to the product harm crisis. Similarly, if the

firm has bad sustainability reputation in general while well in one or two practices, the returns

from the CSR practices for this firm may be lower than the level of the returns for the firm

whose sustainability reputation is good.

56

Third, sustainability practices are inherently risky as assets in financial portfolio. The risk

is characterized by the uncertainty in the returns of the practices. For example, although firms

have gone through intensive market research and R&D process, firms’ practices to develop and

sell green products can go wrong. According to the article by Clifford and Martin (2011), Green

Works, which is Clorox’s environmental-friendly cleaning line, experienced sharp sales decrease

in 2009 not because of the product failure but because of the recession and price-conscious

customers. In addition, although firms invest huge amount of money in sustainability practices

which target secondary stakeholders such as community and third-parties,13

the returns from

these sustainability practices are hard to be measured and it takes time to understand the realized

returns. According to the 2012 Sustainability & Innovation Global Executive Study, 22% of

executives and managers said there are no revenues from sustainability for our company, and 14%

of them said sustainability is pure philanthropic investments which subtract profits (Kiron et al.

2012). Also, accidental events such as Deep Water Horizon Oil Spill by British Petroleum (BP)

in 2010 can increase the vulnerability in the returns of other sustainability practices.

In sum, given the similarity of sustainability practice engagement decision to financial

portfolio decisions in terms of assets, returns, and risks, we argue that portfolio theory principles

can be applied to the decision of developing effective sustainability practice engagement

management.

Constructs

Before delving into our hypotheses, we propose three constructs that capture the structure

of sustainability practice portfolio, namely CSR/CSI breadth (number of engaged practices),

CSR/CSI depth (variation in engaged practices among different societal areas), and the ratio of

13

For example, Johnson & Johnson spent 588.1, 603.3, and 706.1 million dollars on charitable giving in 2009, 2010,

and 2011, respectively, which account for 3.7%, 3.6%, and 5.7% of each year’s pretax income.

57

philanthropic vs. business-related CSR/CSI practices (focus on philanthropic-related practices

compared to business-related practices). Compared to assets in financial portfolio, the challenge

in assessing sustainability practice portfolio stems from the fact that there are no objective

measures of the potential returns and risks from sustainability practices. Thus, we rely on simple

portfolio descriptors which have been frequently used in empirical works (e.g., Bordely 2003;

Grewal et al. 2008; Wassmer 2010; Sridhar et al. 2014).

First, Portfolio Breadth refers to the number of engaged CSR/CSI practices by a firm in a

given year. The greater the number of engaged CSR/CSI practices, the broader is a firm’s

CSR/CSI portfolio breadth. Specifically, if a firm’s CSR portfolio breadth is broad, this firm

engages in diverse range of sustainability practices. In contrast, if a firm’s CSI portfolio breadth

is broad, this means that a firm does harm to social welfare. In short, this construct represents the

size of the portfolio in the sustainability practice engagement. Second, Portfolio Depth refers to

the variance in strategic engagement in the sustainability practices. This captures the extent of

concentration on a few societal issue areas (e.g., environment) compared to other areas (e.g.,

corporate governance, community, and so on) in terms of engaged CSR/CSI practices. The

greater the concentration on a few societal areas over other areas, the deeper is a firm’s CSR/CSI

portfolio depth. In details, if a firm’s CSR portfolio depth is deep, this firm put more emphasis

on selected societal issue areas than other areas. CSI portfolio depth is deep if a firm fails to

comply with sustainability issues in a few societal issue areas while it meets sustainability

requirements in other areas. Third, we define the ratio of philanthropic vs. business-related

CSR/CSI practices as the extent to which strategic engagement varies across philanthropic-

related CSR/CSI practices over business-related CSR/CSI practices. Following Hombug, Stierl,

and Bornemann’s approach (2013), we distinguish firms’ CSR/CSI practices into two parts:

58

philanthropic-related practices and business-related practices. We define philanthropic-related

CSR/CSI practices as CSR/CSI practices which involve philanthropic obligations to secondary

stakeholders such as the community and non-profit organizations (Carroll 1991; Peloza and

Shang 2011). In contrast, we refer to CSR/CSI practices which target at primary stakeholders as

business-related CSR/CSI practices. These business-related CSR/CSI practices are related to the

societal and ethical obligations of the firm (Carroll and Shabana 2010; Carroll 1991). Hence, the

less emphasis on philanthropic obligations relative to societal and ethical obligations, the smaller

is the ratio of philanthropic vs. business-related practices.

Hypothesis

We develop hypotheses on the relationship between the descriptors of sustainability

practice engagement portfolio and firm performance. In essence, our theoretical framework

predicts (1) the impact of the CSR/CSI breadth on firm performance, and (2) the moderating

roles of the CSR/CSI depth and the ratio of philanthropic vs. business-related CSR/CSI practices

on the relationship between the CSR/CSI breadth and firm performance.

In essence, we expect that broad range of CSR practice engagement should lead to a

positive firm performance while broad range of CSI practice engagement should result in

negative firm performance. We also expect that the depth of CSR practices play a negative

moderating role on the (positive) effect of the breadth of CSR practices on firm performance,

whereas we assume that the ratio of philanthropic vs. business CSI practices mitigates the

negative impact of the breadth of CSI practices on firm performance. Finally, we expect that the

mitigating role of the ratio of CSI practices on the effect of the breadth of CSI practices on firm

performance becomes less prominent when the depth of CSI practices increases. The overview of

hypotheses is shown in Figure 3.1.

59

Figure 3.1 Conceptual Framework

Role of the breadth of CSR/CSI practices

We first focus on the link between the breadth of CSR/CSI practices and firm

performance. With respect to CSR practices breadth, the link between good sustainable moves

and firm performance has been widely studied (e.g., Brown and Dacin 1997; McWilliams et al.

2006; Margolis et al. 2007). Roughly speaking, 50% of empirical studies on this link have found

a positive relationship. For example, researchers have found that CSR delivers benefits to the

firm through quality employees attraction and retention (e.g., Greening and Turban 2000), cost

reduction form increased operation efficiencies (e.g., Hart and Ahuja 1996), customer

satisfaction and loyalty (e.g., Du, Bhattacharya, and Sen 2007), customer-company identification

(e.g., Lichtenstein, Drumwright, and Braig 2004), trust (e.g., Vlachos et al. 2009), and favorable

purchase intention (e.g., Du, Bhattacharya, and Sen 2007). In addition, researchers have argued

that CSR may create “good will” which works as protection mechanism against firm reputation

crisis (e.g., Klein and Dawar 2004; Godfrey 2005). For example, Klein and Dawar (2004)

provide empirical support that CSR provides resistance to negative company information. In the

Depth of CSR Practices H3(-)

CSR Phil/Biz Ratio

Control Variables

Breadth of CSR Practices

Firm Performance

Breadth of CSI Practices

H1 (+)

H2 (-)

Depth of CSI Practices

CSI Phil/Biz Ratio H4(+)

H5 (-)

60

similar vein, Luo and Bhattacharya (2009) argue that higher corporate social performance lowers

firm-idiosyncratic risk with all else (i.e., advertising spending and R&D spending) being equal.

Hence, we suggest that the link between the breadth of CSR practices and firm performance is

positive.

Regarding the breadth of CSI practices, we postulate that the more a firm engages in CSI

practices, the lower is firm performance. By not pursuing sustainability, firms may experience

substantial direct/indirect losses and high volatility. For example, vast literature on product

recall has been documented negative abnormal return due to the product safety issue. Chu, Lin

and Prather (2005) found that the recall announcement induces significant negative abnormal

return especially in drugs/cosmetics industry. Similarly, Thomsen and McKenzie (2001) found

significant shareholder losses from food companies when a recall is involved in serious food

safety hazards. In addition, engaging in CSI practices may incur penalties and fines. For instance,

British Petroleum (BP) paid 4.5 billion dollars in government penalties due to the Deepwater

Horizon Oil Spill in 2010, which caused harm to the environment around the Gulf of Mexico

(Isidore, Riley, and Frieden 2012). This incident also induced loss of sales at BP gas stations by

as much as 40% (Rudolf 2010). Furthermore, engaging in CSI practices such as child labor

issues may negatively affect firm reputation and potential customer loss because consumers

believe that firms need to meet not only business obligations but also societal and ethical

obligations. Thus, we posit that the link between the breadth of CSI practice and firm

performance is negative.

H1: The breadth of firm’s CSR practices is positively related to firm performance.

H2: The breadth of firm’s CSI practices is negatively related to firm performance.

Moderating role of CSR depth on the CSR breadth-firm performance link

61

We expect that the strength of the effects of the breadth of CSR on firm performance is

context dependent. In particular, we posit that an increase in the depth of CSR leads to weaker

link of the CSR breadth and firm performance. In other words, if a firm concentrates a few

sustainability societal issue areas and shallow in other areas, its performance would be lower

than balanced CSR practice engagement approach across multiple societal issue areas.

The effectiveness of the CSR breadth on firm performance should decrease as the depth

of CSR practices increases for three reasons. First, focusing on a few societal issue areas may

results in unsuccessful appeal to certain stakeholder groups. For example, Homburg, Stierl, and

Bornemann (2013) distinguish CSR engagement into two facets, business practice CSR

engagement which targets at primary stakeholders and philanthropic CSR engagement which

targets at secondary stakeholder based on stakeholder theory (Freeman 1984). They further argue

that different CSR engagements facilitate different psychological outcomes. Business practice

CSR engagement increases consumer trust, while philanthropic CSR engagement induces

customer-company identification. Hence, to concentrate on only business practice CSR

engagement may not be able to achieve customer-company identification whereas to focus on

only philanthropic CSR engagement cannot build trust in consumers’ mind. Second, not only

business practice CSR engagement but also philanthropic CSR engagement provides explicit

benefits to firms. For example, Zhang et al. (2010) argue that firms may take advantage of their

philanthropic engagement against their competitors in highly competitive market. Third, there

exist interdependencies not only between the firm and various stakeholder groups but also

among stakeholder groups themselves (Bhattacharya and Korschun 2008). Previous research in

marketing (e.g., Luo and Bhattacharya 2009) has argued that being a customer (i.e., primary

stakeholder) is only one part of a personal identity; the same person can also be the part of non-

62

profit organizations. Hence, being the generalist in CSR engagement may create synergy from

the interdependencies among stakeholder groups themselves rather than being the specialist. In

sum, we posit that the positive link between the breadth of CSR practices and firm performance

is negatively moderated by the depth of CSR.

H3: The depth of firm’s CSR practices negatively moderates the positive relationship

between the breadth of CSR practices and firm performance.

Moderating role of the ratio of philanthropic vs. business-related CSI practices

Although we believe that an increase in the breadth of CSI leads to negative firm

performance, we argue that this negative effect is mitigated by an increase in the ratio of

philanthropic vs. business CSI practices. To elaborate, we argue that when a firm engages in a

few sustainability concerns in philanthropic-related issues such as a criticism by NGOs but not in

business-related issues such as employee health and safety concerns or product safety concerns,

its firm performance would be less panelized by stakeholders compared to a firm which engages

in CSI practices mainly in business-related issue areas.

The negative effect of the CSI breadth on firm performance should be mitigated as the

ratio of philanthropic vs. business-related CSR practices increases, because philanthropic-related

sustainability concerns are less critical to primary stakeholders than business-related

sustainability concerns. Indeed, Maignan, Ferrell, and Ferrell (2005) suggest that “stakeholder

research indicates the treatment of customers and employees has the most influence on firm

performance” (p. 958). Customers are the ones who engage in direct market exchange with firm

and create demand, and employees are the ones who produce the goods and services for the

company and create supply (Clarkson 1995). Also, investors are the ones who affect firm’s stock

market values. Thus, when a firm’s CSI practices are highly relevant to the concerns of these

63

primary stakeholders, its performance would decrease much sharper through sales decrease,

labor unions’ protest, and stock price decrease (e.g., Pruitt and Peterson 1986; Salin and Hooker

2001). In addition, business-related CSI practices may involve in direct losses such as fines or

factory operation suspensions (e.g., BP’s Deepwater Horizon Oil Spill). In contrast, secondary

stakeholders are the ones who “are not engaged in transactions with the corporation” (Clarkson

1995, p. 107) and philanthropic-related CSI practices in general do not bear direct losses (e.g.,

Sodastream’s controversy with Oxfam). Hence, we posit that expected loss from philanthropic-

related CSI practices would be less than that from business-related CSI practices, and hence, the

breadth of CSI may induce less decrease in firm performance with higher ratio of philanthropic

vs. business-related CSI practices.

H4: The ratio of firm’s philanthropic vs. business-related CSI practices positively

Interaction among three sustainability practice portfolio descriptors in CSI practices

Building on the mitigation effect of the philanthropic vs. business-related ratio in CSI

practices on the link of breadth of CSI-firm performance (H4), we develop an argument for the

three-way interaction among breadth, depth and the ratio of philanthropic vs. business-related

practices in CSI practices.

For a given level of the CSI breadth, an increase in the depth of CSI practices with higher

ratio of philanthropic vs. business-related practices means that a firm reduces the number of

engaged CSI practices in business-related practices and adds the same number of CSI practices

in philanthropic-related practices. That is, the number of business-related CSI practices decreases

while the number of philanthropic-related practices increases, and this change increases overall

disparity among CSI practice engagement (i.e., increase in depth). For example, if a firm changes

its CSI practice engagement from ‘two from corporate governance issue areas (e.g., high CEO

64

compensation, transparency concern) and two from community issue areas (e.g., investment

controversies, indigenous peoples relations)’ to ‘one from corporate governance issue area (e.g.,

transparency concern) and three from community issue areas (e.g., investment controversies,

indigenous peoples relations, negative economic impact)’, both the CSI depth and the ratio of

philanthropic vs. business-related CSI practices increase while its CSI breadth stays the same as

four.

We argue that simultaneous increase in both the CSI depth and the ratio of philanthropic

vs. business-related CSI practices (at a given level of the CSI breadth) decreases the

effectiveness of the positive moderating role of the ration of philanthropic vs. business-related

CSI practice on the breadth of CSI – firm performance link. We expect this negative three-way

interaction in CSI practices because simultaneous increase in both the depth and the

philanthropic vs. business ratio could provide stronger signal to stakeholders that a firm

intentionally fails to meet philanthropic obligations. If the ratio of philanthropic vs. business-

related CSI practices is high and the depth of CSI is low, stakeholders (especially primary

stakeholders) may suspect that firm’s poor philanthropic performance may be the outcome of

other decisions and unavoidable consequence. For example, if a firm engages in one employee

relationship concern and one community concern, stakeholders may not believe that this is an

intentional strategic move to focus only on primary stakeholders’ concern. However, if a firm

engages two community concerns while none in other societal issue areas, this may create a

suspicion that a firm only focuses on primary stakeholders’ concern and ignores secondary

stakeholders’ concern, and may further form negative firm image because of the

interdependencies among stakeholders. In addition, simultaneous increase in both the depth and

the philanthropic vs. business ratio may increase probability that stakeholders would aware

65

firm’s poor philanthropic performance. Thus, this suspicion would further weaken the positive

moderating effect of the ratio of philanthropic vs. business-related practices.

H5: The deeper a firm’s depth of CSI practices, the weaker is the positive moderating

effect of the ratio of philanthropic vs. business-related CSI practices on the breadth of CSI – firm

performance link.

3.3 Data and Method

Data Sources

To test the hypotheses, we assemble the secondary data sets from two sources:

KLD Social Rating Database, and COMPUSTAT. We use Kinder, Lydenberg, and Domini

(KLD) Social Ratings Database as the data source for firms’ CSR and CSI performance,14

which

has been widely used in the academic literature (e.g., Hull and Rothenberg 2008; Kotchen and

Moon 2012). The KLD database provides a firm’s yearly binary (0\1) CSR/CSI practice

performance indicators across seven social issue areas, including community (e.g., charitable

giving / negative economic impact), corporate governance (e.g., transparency strength /

transparency concern), diversity (e.g., gay & lesbian policies / controversies), employee relations

(e.g., union relations / health & safety concern), environment (e.g., pollution prevention /

hazardous waste), human rights (e.g., labor rights strength / labor rights concern) and product

quality/safety (e.g., benefits to economically disadvantaged / product safety concern). Compared

to other widely used database for sustainability research such as Fortune’s MAC source which

provides survey-based aggregated corporate social performance rating (e.g., Luo and

Bhattacharya 2009; Houston and Johnson 2000), the KLD database is more suitable for our

research purpose because the disaggregated nature of CSR/CSI practice performance indicators

14

Consistent with Kotchen and Moon (2012), we consider all strength indicators as CSR and all concern indicators

as CSI of the firm.

66

allows us to construct the firm’s sustainability portfolio variables. In Table 2.1, we list all

CSR/CSI indicators across the seven social issue areas.

We focus on the data after 2001 and onwards since the number of companies in the KLD

database is not consistent over time and the main structural change happened in 2001.

Specifically, the KLD database covers approximately 650 firms from 1991 to 2000, while it

covers approximately 1,100 firms from 2001 and 2002 and approximately 3,100 firms from 2003

onwards. Also, some of the CSR/CSI practices were added, deleted or relocated from one social

issue area to another, yet the data structure after 2001 and onwards has been more stable than

before.

We obtain financial performance data as well as control variables from COMPUSTAT

Industrial Annual database. After concatenating the two databases, the final sample consists of

26698 observations, representing 3238 firms from 2001 to 2011. These firms are publically

traded firms from a wide range of industry.

Variable Operationalization

Dependent Variable

We use a firm’s Tobin’s q (TQ) as the measure of firm performance. Tobin’s q is a

market-based measure which captures the investors’ long-term expectation of the firm’s future

earnings (Miller 2004) and it has been regarded as a robust measure of firm performance (Mittal

et al. 2005). We choose to use Tobin’s q as our dependent variable for two reasons. First, in

contrast to short-term marketing efforts such as promotion, firms’ CSR/CSI practices may take

time to be realized by stakeholders and to change firms’ reputation as well as their financial

performances. For example, Cox, Brammer, and Millington (2004) argue that improved

corporate social performance should lead to significant financial gains only in the long run.

67

Second, Tobin’s q is not vulnerable to the distortion from tax laws or latitude in interpreting

regulations (Anderson et al. 2004). In other words, Tobin’s q is not affected by accounting

convention and can be used to compare firm performances across industries (e.g., Lee and

Grewal 2004). Thus, we argue that Tobin’s q is the best measure to test the link between firm

performance and sustainability practice engagement portfolio strategies. We calculate Tobin’s q

using the method proposed by Chung and Pruitt (1994).

Independent Variables

To provide concise picture of sustainability practice engagement, we propose three

descriptors of sustainability practice engagement portfolio, namely portfolio breadth (number of

different CSR/CSI practices that firm engages in), portfolio depth (variation in engaged CSR/CSI

practices in seven societal issue area), and philanthropic/business ratio (the ratio of engaged

philanthropic-related CSR/CSI practices over business-related CSR/CSI practices).

Portfolio Breadth of CSR/CSI (Breadth). Portfolio breadth refers to the number of

CSR/CSI practices that a firm engages in. Hence, the greater the number of engaged CSR/CSI

practices by a firm, the wider is its portfolio breadth. We operationalize portfolio breadth of CSR

(CSI) as the number of engaged CSR (CSI) practices across seven societal issue areas.

Specifically,

∑ (1)

where I is an indicator function which is set to 1 if the firm engages in particular CSR (CSI)

practices and 0 otherwise, and J is the total number of CSR (CSI) practices in the KLD database

in particular year. For example, Whole Foods Market, Inc. engaged in one CSR practice (limited

compensation) in corporate governance area, two CSR practices (promotion, gay & lesbian

policy) in diversity area, one (employee involvement) in employee relations and one (beneficial

68

products and services) in environment area in 2002. Thus, as shown in Table 2, Whole Foods

Market, Inc.’s portfolio breadth of CSR practices is five in 2002.

Portfolio Depth of CSR/CSI (Depth). Portfolio depth refers to the extent to which the

number of engaged CSR/CSI practices varies across different societal issue areas. The greater the

number of engaged CSR (CSI) practices in one societal issue area compared to other six areas,

the deeper is a firm’s portfolio depth of CSR (CSI) practices. We operationalize portfolio depth

of CSR (CSI) practices as the variance in the ratio of engaged CSR (CSI) practices over total

number of CSR (CSI) practices in particular societal issue areas. Specifically,

∑

∑ ( )

(2)

where

(3)

Although the data structure for CSR/CSI practices in seven societal issue areas is stable

in our final dataset, there are still some changes in total number of CSR/CSI practices in

particular societal issue area over time because of adding/removing practices or changing

societal issue area of particular practice from one to another. Hence, we look at the variance in

ratio instead of absolute number of practices.

Philanthropic/business ratio of CSR/CSI (Phil/Biz). Philanthropic/business ratio of

CSR/CSI refers to a firm’s emphasis on philanthropic-related CSR (CSI) practices compared to

its emphasis on business-related CSR (CSI) practices. If a firm engages high in business-related

CSR (CSI) practices and low in philanthropic-related CSR (CSI) practices,

philanthropic/business ratio is low and it suggests that the firm focuses on CSR (CSI) practices

that mainly influence primary stakeholders rather than secondary stakeholders. Following

Homburg, Stierl, and Bornemann’s (2013) approach, we divide seven societal issue areas in the

KLD database into two parts: community as philanthropic-related societal issue area vs.

69

corporate governance, diversity, employee relations, environment, human rights, and product as

business-related societal issue areas. Specifically, we define philanthropic/business ratio of CSR

(CSI) as:

(4)

We provide examples of firm’s sustainability portfolio strategy descriptors for six

randomly selected firms in our data, over three different years in Table 3.1. Over the years,

selected firms’ portfolio breadth of CSR/CSI practices, the depth of CSR/CSI practices, and the

ratio of philanthropic/business-related CSR/CSI practices have been changing over time.

Table 3.1 Example of CSR/CSI Practice Portfolios

Panel A: Examples of CSR Practice Portfolios

Company

SIC

Portfolio Breadth Portfolio Depth Philanthropic/Business

Ratio

2002 2006 2011 2002 2006 2011 2002 2006 2011

Johnson & Johnson 2834 7 16 19 0.128 0.235 0.309 0.889 0.324 1.219

Valero Energy Co 2911 3 4 7 0.131 0.162 0.205 2.667 1.619 0

Goodyear Tire &

Rubber Co 3011 3 2 9 0.131 0.076 0.232 0 0 0

Whole Foods Market 5411 5 7 11 0.116 0.147 0.244 0 0.810 0.65

Allstate Corp 6331 5 4 11 0.189 0.131 0.297 1.333 4.857 0.65

Microsoft Co 7372 5 9 15 0.108 0.208 0.213 1.333 1.388 1

70

Panel B: Examples of CSI Practice Portfolios

Company

SIC

Portfolio Breadth Portfolio Depth Philanthropic/Business

Ratio

2002 2006 2011 2002 2006 2011 2002 2006 2011

Johnson & Johnson 2834 6 9 3 0.366 0.255 0.197 0 0 0

Valero Energy Co 2911 5 8 6 0.176 0.215 0.360 1.813 1.107 5

Goodyear Tire &

Rubber Co 3011 4 9 6 0.115 0.155 0.399 2.417 0.969 5

Whole Foods Market 5411 2 3 2 0.104 0.131 0.189 0 0 0

Allstate Corp 6331 6 7 1 0.173 0.292 0.126 1.45 0 0

Microsoft Co 7372 4 4 4 0.192 0.192 0.207 0 0 0

Control Variables

Previous research has suggested that it is important to control for several factors which

might be associated with firm performance. Boulding and Staelin (1990) suggest that firm size

might have a positive impact on firm performance. Thus, we include the firm’s total assets

(Assets) and the natural log of the number of employees (in million; log(Emp)) in the model.

Also, various researches have suggested that advertising spending increases the sales and

profitability (e.g., Villanueva, Yoo, and Hanssens 2008; Naik, Mantrala, and Sawyer 1998).

Krasnikov and Jayachandran (2008) demonstrate a critical role of research and development

(R&D) on firm growth and performance. In addition, Steenkemp and Fang (2011) suggest that it

is important to control for market share when studying firm performance. Hence, we control for

advertising-to-sales ratio (AD), R&D-to-sales ratio (R&D) and market share (MS). Moreover, we

control for state dependence in firm performance by including the first-lag of Tobin’s q in the

model. Finally, we include year dummy to control for time trend and firm specific fixed-effect to

control for firm’s idiosyncracy. Note that firm fixed-effect term is also included in our model to

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capture unobserved heterogeneity in firm performance, yet this term drops out since our model is

first-differenced model. We present the descriptive statistics of the key variables Table 3.2.

Model Specification

To test our hypotheses of the relationship between sustainability portfolio strategy and

firm performance, we examine the following model for firm i in year t:

(5)

In equation (5), captures the inertia of firm performance. The terms represent

the main effects of three sustainability practice engagement portfolio descriptors, the breadth of

CSR (H1) / CSI (H2), the depth of CSR/CSI, and philanthropic/business ratio of CSR/CSI,

respectively. The terms represent the two-way interaction between the breadth and the

depth of CSR (H3 / CSI, the breadth and philanthropic/business ratio of CSR/CSI (H4 , and the

depth and philanthropic/business ratio of CSR/CSI, respectively. Three-way interaction effects

among portfolio breadth, depth, and philanthropic/business ratio of CSR/CSI (H5) are captured

by the terms and , respectively. The effects of control variables such as total assets,

advertising spending, R&D spending, market share, and number of employees are captured by

the terms , respectively, and the vector and capture year fixed effects and firm

fixed effects, respectively. Finally, is a normally distributed error term which is independent

and identical across firm i and year t.

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Table 3.2 Descriptive Statistics

Variable Correlation

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

1. Tobin’s q(TQ) 1.000

2. Breadth (CSR) -0.024 1.000

3. Breadth (CSI) -0.080 0.373 1.000

4. Depth (CSR) -0.026 0.883 0.290 1.000

5. Depth (CSI) -0.047 0.155 0.714 0.093 1.000

6. Phil/Biz (CSR) -0.052 0.025 0.053 -0.082 0.101 1.000

7. Phil/Biz (CSI) -0.058 0.106 0.028 0.123 -0.010 -0.014 1.000

8. Total Assets -0.078 0.326 0.264 0.262 0.146 0.094 0.046 1.000

9. Advertising-to-sales Ratio 0.101 0.037 -0.012 0.027 -0.010 0.009 -0.010 -0.007 1.000

10. R&D-to-sales Ratio 0.009 0.000 -0.001 -0.001 -0.003 -0.006 -0.004 -0.002 -0.002 1.000

11. Market Share -0.035 0.240 0.221 0.208 0.089 0.009 0.019 0.083 0.008 -0.007 1.000

12. Number of Employees

(natural log) (Emp) -0.173 0.425 0.397 0.391 0.168 0.043 0.042 0.214 0.010 -0.014 0.417 1.000

Mean 1.618 1.332 1.922 0.050 0.107 0.699 0.451 11338.35 0.012 2.381 0.089 0.867

Standard Deviation 1.926 2.241 1.849 0.062 0.079 0.942 1.245 75067.46 0.046 178.420 0.177 1.926

Estimation Strategy

When estimating equation (5), we consider the following econometric issues to provide a

robust and consistent evaluation on the effect of sustainability portfolio strategy on firm

performance: (1) endogeneity, and (2) unobserved heterogeneity. We overcome these

econometric issues by using Arellano-Bond (1991) estimator.

First, sustainability practice engagement portfolio and firm performance are assumed to

be endogenous because firm may decide its sustainability practice engagement portfolio based on

its actual performance or other unobserved factors. For example, Luo and Bhattacharya (2009)

argue that there may be reverse causality concern such that “firms that are performing well with

lower firm-idiosyncratic risk are more likely to engage in CSR” (p. 205). This reverse causality

argument suggests that the regressors may be correlated with the error term in equation (5).

Hence, following Arellano and Bond (1991), we use dynamic panel GMM estimator to account

for this type of endogeneity. Specifically, our panel data structure allows us to use lagged values

of the endogenous regressors as instruments. Due to these instruments, the endogenous variables

become pre-determined and not correlated with error term (Arellano and Bond 1991; Arellano

and Bover 1995; Holtz-Eakin et al. 1988).

Second, time-invariant unobserved firm characteristics may play a crucial role in firm

performance. For example, industry specific political economic factors such as industry

protection can influence firm performance differently across the firms from different industry. If

the unobserved heterogeneity is not controlled, then the estimation results would be inconsistent

and unreasonable (Wooldridge 2010). To control for this time-invariant unobserved

heterogeneity, we use first-differenced model which removes time-invariant firm-specific effects

by canceling out the term in equation (5).

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3.4 Results

Hypotheses Testing

To test the hypotheses, we rely on the dynamic panel estimator results. We present the

estimation results in Table 3.3. Overall, our model is significant ( statistic = 1260.02, p <

0.000). In addition, to check the validity of our model, we test the autocorrelation of the residuals

and over-identification restriction for the instruments. The AR(2) test for autocorrelation of the

residuals suggests that the differenced residuals do not exhibit significant AR(2) behavior (z

value = -0.98, p = 0.326). The over-identifying restrictions test result suggests that the

instruments are valid ( statistic = 258.73, p = 0.159). Thus, we conclude that our model is

valid and our dynamic panel estimation is meaningful. We now discuss hypothesis-testing results

followed by the effects of the control variables.

Main effects. For the main effects of the CSR/CSI breadth, we do not find a support for

H1, but we find a support for H2. The effect of a firm’s CSR breadth is positive but not

significant ( = 0.030, not significant [ns]), while the effect of a firm’s CSI breadth is negative

and significant ( = -0.036, p < .01). This result suggests that shareholders have negative

expectations of a firm’s future cash flows when firm’s portfolio breadth of CSI is high. However,

the limited information on the number of engaged CSR practices does not have significant

impact on shareholders’ expectations. Notably, the main effects of the portfolio depth of CSR

( = 0.682, ns) / CSI ( = 0.141, ns) and philanthropic/business ratio of CSR ( = 0.011, ns) /

CSI ( = 0.001, ns) on firm performance are not significant. These results are in line with the

findings that the influence of negative CSR performance (i.e., CSI practices) is much stronger

than that of positive CSR performance (i.e., CSR practices) in reducing information asymmetry

in investors (e.g., Cho, Lee, and Pfeiffer 2012).

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Table 3.3 Hypotheses Testing Results

Variables Hypotheses Model 1

Estimate Std. Err

Breadth (CSR) H1 0.0300 0.0247

Breadth (CSI) H2 -0.0361***

0.0129

Depth (CSR) 0.682 0.581

Depth (CSI) 0.141 0.288

Phil/Biz Ratio (CSR) 0.0114 0.0171

Phil/Biz Ratio (CSI) 0.0014 0.0075

Breadth × Depth (CSR) H3 -0.186***

0.0678

Breadth × Depth (CSI) 0.0783 0.0637

Breadth × Phil/Biz Ratio (CSR) -0.0043 0.0136

Breadth × Phil/Biz Ratio (CSI) H4 0.0139**

0.00662

Depth × Phil/Biz Ratio (CSR) -0.507 0.349

Depth × Phil/Biz Ratio (CSI) -0.0514 0.0416

Breadth × Depth × Phil/Biz Ratio (CSR)

0.0667 0.0708

Breadth × Depth × Phil/Biz Ratio (CSI) H5 -0.0883***

0.0342

Total Assets 6.10e-08 2.71e-07

Advertising-to-sales Ratio -0.416 0.844

R&D-to-sales Ratio -0.000038***

0.000014

Market Share -0.163 0.292

Number of Employees -0.305***

0.0497

Lag(Tobin’s q) 0.141***

0.0498

Firm Fixed Effects First-differenced

Time Fixed Effects Included

- Test Statistics (d.f. = 29) 1260.0

Arellano-Bond test for AR(2)

-0.98 (p-value: 0.326)

Sargan test of over-identifying restrictions

258.73 (p-value: 0.159)

*p< 0.10; **p< 0.05; ***p < 0.01.

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Moderating effects. To test the moderating effects of the depth and philanthropic/business

ratio on the breadth of CSR/CSI practices, we create the interaction terms by multiplying mean-

centered independent variables as suggested in the literature (e.g., Homburg, Stierl and

Bornemann 2013). Our analysis result shows a negative moderating effect of the depth on the

link between sustainability portfolio breadth of CSR practices and firm performance ( = -0.186,

p < .01), in support of H3. In other words, the higher the variance in a firm’s sustainability

practice engagement portfolio, the weaker the positive effect of portfolio breadth of CSR

practices on firm performance. However, we do not find a significant moderating effect of the

depth of CSI practices on the relationship between the breadth of CSI and firm performance (

= 0.078, ns). For the moderating effect of philanthropic/business ratio on the link between the

breadth of CSR practices and firm performance, we do not find a significant two-way interaction

for CSR ( = -0.004, ns), while we find a significant positive moderating effect of

philanthropic/business ratio for CSI ( = 0.014, p < .05). This finding suggests that the

negative effect of portfolio breadth of CSI practices on firm performance is mitigated when a

firm has high philanthropic/business ratio, in support of H4. All other two-way interactions are

not significant.

Finally, turning to the three-way interaction, although we do not find a moderating effect

of philanthropic/business ratio of CSR practices on the negative moderating effect of the depth

on the breadth of CSR – firm performance relationship ( = 0.067, ns), we find a negative

moderating effect of the depth on the positive moderating effect of philanthropic/business ratio

on the breadth of CSI – firm performance link ( = -0.088, p < .05), providing support for H5.

To elaborate, the mitigation effect of philanthropic/business ratio of CSI practices on the breadth

of CSI – firm performance link become less strong when a firm’s sustainability practice

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engagement portfolio depth of CSI practices increases. Thus, simultaneously pursuing for high

depth, high philanthropic/business ratio sustainability portfolio can actually be harmful for

reducing the negative impact of the breadth of CSI on firm performance.

Control variables. In terms of control variables, we do not find significant effects of the

firm’s total assets ( = 0.000, ns), advertising spending ( = -0.416, ns), and market share

( = -0.163, ns). However, interestingly, we find negative effect of R&D spending ( = -

0.00004, p < .01) on firm performance. Previous research has pointed out that stakeholder may

perceive a trade-off between firm’s sustainability practices and other key strategic marketing

levers such as R&D because firms have limited resources (Sen and Bhattacharya 2001). In

addition, Luo and Bhattacharya (2009) found that the simultaneous pursuit of corporate social

performance, advertising, and R&D leads to increase firm idiosyncratic risk. In line with

previous research, we also find that the effect of R&D spending is negative after accounting for a

firm’s sustainability practice engagement portfolio strategy. The year fixed effects which

captures temporal variation in firm performance are significant for all years except 2009.

3.5 Discussion

Sustainability continues to draw special attentions from practitioners and academics alike.

Sustainability is considered a “high” or “very high” priority for the firms (The Economist 2008)

and it now reaches to C-Suite level: Chief Sustainability Officer. As firms engage in a wide

range of sustainability practices in the area of economic, social, and environmental development,

CSOs often encounters complex and challenging decision of how to strategically engage in

various sustainability practices and to prioritize firm’s efforts in these sustainability practices.

The goal of this research is to provide guideline on how firms should manage their sustainability

practice engagement portfolio to increase their firm performance. To provide a holistic view of

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sustainability practice engagement, we utilize three constructs, i.e., breadth, depth, and the ratio

of philanthropic vs. business-related practices, for CSR and CSI practices and empirically test

the roles of these constructs on firm performance. We show that the deep engagement in a few

CSR practices mitigates the positive impact of the breadth of CSR on firm performance. In

addition, we show that a firm’s high concentration on philanthropic-related CSI practices

compared to business-related CSI practices mitigates the negative impact of the breadth of CSI

on firm performance. Yet, simultaneous increase in the breadth, depth, and philanthropic vs.

business-related ratio in CSI practices incur negative impact on firm performance. We now

discuss the implications of our finding.

Theoretical Implication

Our research contributes to prior CSR and sustainability research in several ways. First,

building on financial portfolio theory (Markowitz 1952), we advance research on CSR by

examining the link between a firm’s holistic engagements in a wide range of sustainability

practices and firm performance. Although prior studies examine the relationship between CSR

and firm performance, most of the studies either focus on only one part of CSR (e.g., Klassen

and McLaughlin 1996; Lichtenstein, Drumwright, and Braig 2004; Robinson, Irmak, and

Jayachandran 2012) or look at the impact of aggregated sustainability performance on firm

performance (e.g., Brown and Dacin 1997; Wagner, Lutz, and Weitz 2009; Luo and

Bhattacharya 2009) which fail to capture the interdependence and synergy among various

sustainability practices. In contrast, our portfolio theory based approach enables us to capture

these interdependence and synergy effect. In other words, this study provides a theoretical

framework for a simultaneous investigation of multiple sustainability practice engagement. Thus,

we respond to calls to the argument that “the influence of multiple sustainability-focused

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marketing assets on financial returns” has not been examined thoroughly (Chabowski et al. 2001,

p.66).

Second, while most research in marketing has conceptualized CSR globally or has looked

at one societal area of CSR (Homburg, Stierl, and Bornemann 2013), we refine this approach by

(1) separating out CSI practices from CSR and (2) distinguishing CSR/CSI practices into two

distinct groups which are philanthropic-related and business-related. In this paper, we look at the

portfolio descriptors for both CSR and CSI and investigate the holistic framework of “CSR-CSI-

firm performance,” which is new in the literature. Indeed, while researchers who utilized global

conceptualization of CSR (e.g., Hillman and Keim 2001) inherently assumed that the effect of

one CSI practices can be canceled out by another CSR practices (e.g., if the breadth of CSR is

equal to the breadth of CSI, then a firm’s global CSR score is 0), we show that the effect of one

CSI practice may not be wiped out by engaging in one CSR practice since we find support for H2

but not for H1, which suggests the separation of CSI from CSR. Further, we find that the effects

of CSR and CSI practices on firm performance are differently moderated by their own depth and

the ratio of philanthropic/business-related practices. In addition, we introduce the ratio of

philanthropic/business-related practices as one of the key descriptor of sustainability practice

portfolio based on stakeholder theory. Thus, we partially answer calls to examine the link

between specific types of CSR and customer outcomes (Barnett 2007). Our research

demonstrates that the link between the breadth of CSR and firm performance is not affected by

the type of CSR, while the negative relationship between the breadth of CSI and firm

performance is mitigated by the type of CSI. That is, if a firm engages in multiple CSI in

philanthropic-related cases, the customers would less panelize the firm compared to the situation

of multiple CSI in business-related practices.

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Third, we contribute to the marketing-finance interface (Luo and Homburg 2008;

Srivastava, Shervani, and Fahey 1998) by examining the impact of CSR/CSI practices on

Tobin’s q, stock market-based long-term measure of a firm value (Wernerfelt and Montgomery

1988; Miller 2004). In particular, we directly respond to call for the research on the marketing-

finance interface to examine the stock market impact of CSR initiatives on [long-run] firm

valuation by Srinivasan and Hanssens (2009). While prior marketing literature has focused on

the short-term financial measures such as the level of stock return and firm idiosyncratic risk

(e.g., Luo and Bhattacharya 2009), our study suggest a long-term impact of CSR/CSI on firm

valuation. Moreover, we contribute to the debate on whether “doing good” leads to “doing well.”

While roughly 50% of the literature finds a positive relationship between CSR and firm

performance, 25% finds no relationship, 20% find mixed results, and 5% find even a negative

relationship (Margolis and Walsh 2001; Scholtens 2008). Our finding suggests that “doing good”

may lead to “doing well” when the breadth of CSR practices is high and the depth of CSR

practices is low. Further, we suggest that not only the breadth but also the depth and

philanthropic vs. business-related practice ratio should be considered in sustainability-finance

interface.

Managerial Implication

Our research shed lights on the problem of effective sustainability practice management.

Anonymous respondent’s comments from The Sustainability Executive: Profile and Progress

report (PwC 2012) echoes one of the most challenging decisions for sustainability executives:

“You have to make hard choices, and you have to piss some people off, and you have to say that

there are some things that I am not going to do.” In other words, we address managerial

questions of how a manager should prioritize firm’s effort across various sustainability practices

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under the resource constraints. Indeed, Luo and Bhattacharya (2009) argue that “[corporate

social responsibility] competes for the resources that instead could be invested in adverting

and/or R&D” which are key strategic marketing levers (p.204). We advise managers to apply

two simple rules for sustainability practice engagement. First, be the generalist in CSR practices.

Our finding suggests that when a firm increases both the breadth and the depth of CSR practices,

firm performance decreases. Thus, when the resources are limited and a firm may not be able to

increase the number of CSR practice engagement, it would be better to evenly engage in CSR

practices across multiple societal issue areas than engage heavily on multiple CSR practices in a

few societal issue areas. In other words, an effort to build a corporate image of “Green Giant” or

“Ultimate Philanthropist” can be misguided sustainability strategy. Second, minimize business-

related CSI practices first, but don’t let the philanthropic-related CSI practices stand out. Our

findings suggest that less CSI practice engagements lead to better firm performance. Yet, due to

the limited resources, reducing all the CSI practice engagement may not be possible. In this case,

the best sustainability practice engagement strategy would be increase the ratio of philanthropic

vs. business-related CSI practices. However, if high engagement in philanthropic-related CSI

practices speaks out to stakeholders, they would panelize firm performance.

Limitations and Future Research Areas

Although our study presents fruitful insights into sustainability practice engagement

management, there are some limitations. First, although we focus on the link between actual

CSR/CSI practice engagement and firm performance, there may exists a gap between actual

CSR/CSI practice engagement and firm’s CSR/CSI reputation, which may actually drives

customer outcomes such as trust and customer-company identification, and further, firm

performance. In fact, Homburg, Stierl, and Bornemann (2013) found that CSR engagements are

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highly correlated with CSR reputation, but not perfectly. However, since our main research

objective is to provide a management guideline for the effective engagement in various

sustainability practices, we believe that the actual engagement is more appropriate construct than

the reputation. Further research could expand our link and investigate “actual sustainability

engagement – sustainability reputation – firm performance” chain and provide more insights.

Second, although we investigate the relationship between three descriptors of

sustainability practice engagement and firm performance, further research could utilize other

financial indicators. For example, building on the risk management theory, Luo and

Bhattacharya (2009) look at the risk-reduction benefit of corporate social performance.

Recognizing the possible trade-offs between different financial indicators such as risks and

market share growth (Grewal et al. 2008), developing a holistic framework of effective

sustainability practice engagement management regarding different financial goals would be an

interesting extension of this research.

In conclusion, we suggest a guideline for the sustainability practice engagement

management. In particular, our findings show that the generalists in CSR practices are “doing

well.” Also, our research suggests that business-related CSI practices have priorities to be

minimized compared to philanthropic-related CSI practices. We contribute to both ends of our

readers by extending CSR and sustainability research and providing practical insights to

practitioners.

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Chapter 4

CONCLUSION

In my dissertation, I investigate the effectiveness of sustainability strategy. In particular, I

address two interesting questions: (1) how and why firms are engaging in corporate social

responsibility practices (chapter2 - essay 1) and (2) what types of sustainability strategy

portfolios promise the greatest return (chapter 3 - essay 2). The findings from essay 1 suggest

that firms are engaging in CSR practices to compensate for their previous misbehaviors (i.e., CSI)

while engaging in CSR practices promises greater return. In essay 2, the findings provide a

guideline for the effective sustainability practices engagement management with respect to its

portfolio breadth, depth, and philanthropic-focus. Both essays contribute to marketing literatures,

especially CSR and sustainability literatures, and practice in several ways.

First, unlike previous research that studied the effectiveness of overall sustainability

performance or the influence of sustainability performance in one specific societal dimension on

firm value, both essays distinguish the roles of sustainability practices from different

sustainability dimensions and examine the link between multi-dimensional sustainability

practices and firm performance. In particular, in essay 1, I distinguish engaging in CSR practices

from minimizing CSI practices and investigate the dynamic interplay among CSR, CSI and firm

performance. In essay 2, I further divide sustainability practices into business-related and

philanthropic-related practices. This directly responds to calls to the argument that “the influence

of multiple sustainability-focused marketing assets on financial returns” has not been examined

thoroughly (Chabowski et al. 2001, p.66).

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Second, both essays expend current marketing-finance interface literature (e.g., Luo and

Homburg 2008; Srivastava, Shervani, and Fahey 1998) by examining the impact of CSR and CSI

practices on firm performance. While marketing-finance interface literature has revealed the

impact of marketing and other marketing-related functions (e.g., R&D) on firm performance

(e.g., Luo and Homburg 2008; Sridhar et al. 2011), the link between one of the most popular

components of recent marketing (i.e., CSR/sustainability) and financial outcome has not been

fully investigated. In essay 1, I disentangle the dynamic interplay among CSR-CSI-firm

performance by using advanced econometric method, and find a support for the argument that

“doing good” leads to “doing well.” To the best of my knowledge, my essay 1 is the first study

that uses panel vector autoregression model to separate CSI from CSR-firm performance to

understand the true relationship between CSR and firm performance. In essay 2, I take slightly

different stance and propose additional constructs to understand the complex relationship

between CSR-CSI-firm performances.

Third, both essays provide useful managerial implications for CSO and sustainability

executives who face complex sustainability practice management decisions. The findings from

essay 1 provide an insight to CSO and executives that CSR has a positive and long-term effect

on firm performance. This insight can motivate firms to engage in CSR practices. Further, the

findings from essay 2 should help managers decide on their sustainability practice engagement

strategy for both embracing opportunities and managing risks. The findings suggest that, under

the condition of limited resources, managers need to diversify firm’s effort on engaging in CSR

practices and to focus on minimizing business-related CSI practices while not letting the

philanthropic-related CSI practice stand out. These noble findings should guide sustainability

executives when they question about the priority of sustainability practice engagement decision.

85

In summary, my two dissertation essays examine the performance implications of multi-

dimensional sustainability strategy that extant research has so far not considered. I hope that my

first step will provoke additional work in this important area.

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APPENDIX

THEORETICAL MODEL

We rely on the basic economic notion that firms seek to maximize utilities and profits to

develop a theoretical model for the effect of CSR and CSI; specifically, we use primitives from

theory of the firm (e.g., Becker 2007, Chapter 5). Within the theory of the firm framework, profit

maximizing can be subsumed within utility maximization, such that a “firm may act as if it

maximized a utility function that depends not only on its profits, but also on color, sex, and

family background of employees” i.e., CSR and CSI (Becker 2007, p. 70); thus, we focus on

long-term profit maximization. In this framework, we define profits (π) at a given time t as:

(A1) ,

where, and represent revenues and costs respectively.

With the profit function described in equation A1, we seek to develop a dynamic

optimization problem for a firm where the firm chooses an optimal level of efforts for CSR and

CSI. To layout this specification we represent revenues as (where is the product operator):

(e.g., Naik and Raman 2003), where m represents the margins that account for all

expenses other than those associated with CSR and CSI and is the sales at time t; thus can

be seen as the costs associated with CSR and CSI. If ρ represents the discount rate and and

the efforts for CSR and CSI respectively, , then the firms net present value of its

current and future profits ( ) can be given as:

(A2) ∫

∫

∫

Equation A2 represents continuous time infinite horizon profit flows and, as is common

in this literature (e.g., Rao 1986), we will not index variables by t to facilitate reading. As is

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typical in dynamic optimization (e.g., Kamien and Schwartz 1991; Sethi and Thompson 2006),

we seek to maximize equation A2 subject to constraints that emanate due to the dynamic

response function of sales ; that is:

(A3a) ∫

Subject to:

(A3b)

where, is an appropriate dynamic response function for sales.

In the parlance of optimal control theory (e.g., Kamien and Schwartz 1991), S represents

the state variable and r and i are the control variables (i.e., variables that firms make decisions

on). To maximize the system in equation A3, we define the Hamiltonian as:

(A4)

where, is the costate variable.

At optimality, the necessary conditions, which are sufficient when is concave in and

, are (e.g., Kamien and Schwartz 1991):

(A5)

With this dynamic CSR decision problem in place, we now seek to develop the

specification for cost and evolution of sales as specified in

.

Cost Function Specification

There are three components for the CSR relevant costs that we need to consider. First we

need to consider the direct costs associated with CSR efforts r. Firms exert such efforts to

promote social causes; for example, for every pair of shoes that Toms sells, they donate one to a

child in need (known as the “one for one” model). Thus, we use the function to represent

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these costs, where is a typical continuous twice differentiable indirect cost function that

satisfies properties of cost functions (e.g., Kreps 1990, p. 251-253). As costs typically increase

with efforts and are convex, a popular form for costs is quadratic (e.g., Naik and Raman 2003),

such that one could specify

.

Second, similar to CSR efforts r, one needs to consider costs associated with direct CSI

efforts i. Firms exert these CSI efforts to reduce the probability of CSI incidents. For example,

accidents during the transportation of crude oil are imminent; thus, oil firms such as Exxon

Mobile reinforce the hulls of their transportation ships to reduce the probability of oil leaks when

such accidents occur. Thus, CSR efforts represent doing social good, while CSI efforts serve to

reduce harm from CSI incidents. Similar to CSR efforts, we use the function to represent

costs associated with CSI efforts. Again, a quadratic functional form can be used for such costs,

i.e.,

.

Third, we need to consider the costs that firms can expect to incur when CSI incidents

occur; examples of such incidents include oil spills as in the case of BP or unforeseen product

harm crises. As CSI is associated with incidents, we model the costs associated through expected

loss and the buffering effects of CSR and CSI efforts. Expected loss can be seen as a firm-

specific typical or average loss associated with a CSI incident; for example, for a firm in a

particular business, could represent the average over the last 10 years. To specify these

buffering effects we recognize that the buffering effects should increase as efforts increase and

there should be some difference in the buffering between CSR efforts and CSI efforts. Thus, we

specify the costs due to such incidents as: , where . As

efforts r and i are positive and θ and λ are positive, the expression “ ” is positive

and thus as efforts increase, ; thus efforts reduce the costs associated with CSI

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incidents. Further, θ instruments the nature of the buffering effect of efforts; implies that

the level of CSR efforts proportionally reduce expected loss; , implies that the translation

of efforts for expected loss is lower than when ; implies an amplification of effort

such that the translation of effort is higher than . λ ensures that the effects of CSR efforts r

are different from those of CSI efforts i. If λ is less than 1, then CSR efforts buffer more than CSI

efforts; if λ is equal to 1, then both efforts buffer equally; and if λ is greater than 1, then CSI

efforts buffer more than CSR efforts.

With the above discussion, we can write the costs as:

(A6)

Sales Function Specification

As is typical in optimal control applications in marketing, we need to specify the

evolution of the sales function S (e.g., Naik and Raman 2003), which serves as a constraint in the

Hamiltonian specification (equation A3b). Thus, now we elaborate on the functional form of

. We recognize that change in sales (

) should be a function of CSR efforts and CSI

efforts and the level of sales (i.e., consistent with extant research, we do expect persistence in

sales; e.g., Sridhar et al. 2011). Further, it is reasonable to expect that the efficacy of CSR efforts

and CSI efforts depend on the level of sales. For example, larger firms (i.e., firms that have

higher sales) tend to be better known, and better known firms tend to get scrutinized, i.e.,

condemned, more often by the media than smaller firms (e.g., Brooks et al.2003). Thus, it seems

reasonable to assume that larger firms’ CSR efforts and CSI efforts also receive greater scrutiny

by the media. Hence, in our specification for

, we include the interaction between CSR efforts

and sales and CSI efforts and sales, i.e.,:

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(A7)

Profit Maximization

Optimal efforts for CSR and CSI, i.e., r and i respectively, can be obtained by

maximizing infinite horizon profits, i.e., firm value given in equation A2. To maximize firm

value in equation A2, the cost function and evolution of sales function in equations A6 and A7

are substituted into the Hamiltonian in equation A4, which gives us:

(A8) ( ) ( )

With the Hamiltonian in equation A8, the first order conditions would be:

(A9a)

(A9b)

(A9c)

In theory one could solve equations A9a and A9b for efforts r and i as functions of other

parameters including the costate variable . However, due to the presence of the exponent (with

both r and i in the exponent), these equations are referred to as transcendental equations that

require the use of the Lambert W function and thus, numerical methods to solve them (e.g.,

Hayes 2005). To solve for , one has to use the transversality conditions obtained from steady-

state conditions on state and costate variables, i.e.,

and

respectively.

Further, we consider two alternate specifications for costs associated with CSI incidents,

i.e., . For both these specifications, we do not make changes to expected loss but argue

that the buffering effect depends only on CSR efforts r. As expected losses can be seen as a

typical or average firm-specific loss associated with a CSI incident, CSI efforts should reduce the

probability of CSI incidents, and, as a result, the benefits of CSI efforts i are built into . Thus,

91

buffering effects only depend on CSR efforts r. To specify these buffering effects we recognize

that the buffering effects should increase as efforts increase. Thus, there are two options to

specify these efforts: (1) similar to our earlier specification we use the exponential function to

specify and (2) we can use a much simpler inverse function, such that:

, where . In this second specification, as efforts r are positive, the

expected loss is positive, is positive, and the expression

is positive and decreases as

effort r increases; thus CSR efforts reduce the costs associated with CSI incidents. If is less

than 1, then the buffering effect of CSR efforts is amplified, if equals 1 then the CSR efforts

proportionally reduce expected loss, and if is greater than 1, then CSR efforts do not have

much of a buffering effect. The solution to the first specification, where ,

would still involve the Lambert W function and the use of numerical methods. The solution to

the second specification, where

, would lead to a cubic functional form for r and a

linear functional form for i; thus there would be three solutions for r and one for i.

92

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VITA

Charles Kang [email protected]

Education

Smeal College of Business, Pennsylvania State University

PhD in Business Administration

Concentration: Marketing

August 2014 (Expected)

University of Illinois at Urbana-Champaign

Master of Science in Statistics

May 2009

Yonsei University, Seoul, South Korea

B.S. in Business Administration

B.S. in Applied Statistics (Second Major)

February 2006

Research Interests

My research interests are in the area of quantitative marketing strategy. Specifically, I am in

interested in dynamic empirical modeling of issues relating to sustainability strategy in

marketing, and its influence on firm performance.

Working Papers

Kang, Charles, Frank Germann, & Rajdeep Grewal. “Performance Implications of Corporate

Social Responsibility and Irresponsibility”

Kang, Charles, and Rajdeep Grewal. “Portfolio Management in Sustainability Strategy and

Firm Performance”

Sridhar, Hari, Charles Kang, Frank Germann & Rajdeep Grewal. “Portfolio Perspectives on

Organizational Media Advertising Spending ”

Kang, Charles, Rajdeep Grewal & Duncan Fong. “Understanding Common Trends in

Sustainability Strategy”

Academic Honors and Awards

Jerome E. Scott Memorial Scholarship for outstanding doctoral students, Department of Marketing,

Pennsylvania State University (2013)

Haring Symposium Best Discussant Award, Indiana University (2013)

Haring Symposium Fellow, Discussant, Indiana University (2013)

Competitive Smeal Small Research Grant, Smeal College of Business, Pennsylvania State University

(2013, 2014)

Consortium Fellow, INFORMS Marketing Science Doctoral Consortium (2012)

Frank P. and Mary Jean Smeal Endowment Fund Scholarship, Smeal College of Business,

Pennsylvania State University (2009-2011)

Tuition Scholarship, ISBM PhD Seminar Series, Pennsylvania State University (2008)

mailto:[email protected]

Documents

DOING GOOD, DOING BAD, AND DOING WELL: INVESTIGATING …