The moderating effect of firm customer orientation and firm product orientation on the relationship between CSP and CFP

THE MODERATING EFFECT OF

FIRM CUSTOMER ORIENTATION

AND FIRM PRODUCT

ORIENTATION ON THE

RELATIONSHIP BETWEEN CSP

AND CFP

Saskia Grant, 11390735

MSc in Business Administration – Strategy track Amsterdam Business School

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Using data from the MSCI KLD database on firms’ corporate social performance, this thesis

examines whether the primary customer orientation (business to business or business to

consumer) and primary product orientation (search goods or experience goods) of a firm has a moderating effect on the relationship between a firm’s social and financial performance. Corporate social performance is measured through data from the MSCI KLD database, while

firm financial performance is measured as firm return on assets. The results of the study are not statistically significant. According to this study, a firm’s primary customer orientation and product orientation does not have a statistically significant moderating effect on the

relationship between firm social and financial performance.

2

S

O

This document is written by Student Saskia Grant who declares to take full responsibility for the

contents of this document.

I declare that the text and the work presented in this document are original and that no sources other

than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the

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T

Search goods and Experience goods ... 12

Hypotheses ... 14

Data and methods ... 15

Sample selection ... 15 Variables ... 16 CFP ... 16 CSP ... 16 Moderators... 17 Control variables ... 17

Issues with the data ... 22

Sample data and descriptive statistics ... 23

Results ... 23

Data description ... Fel! Bokmärket är inte definierat. Variables ... Fel! Bokmärket är inte definierat. Control variables ... Fel! Bokmärket är inte definierat. Results ... Fel! Bokmärket är inte definierat. B2B vs B2C ... Fel! Bokmärket är inte definierat. Search goods and Experience goods ... Fel! Bokmärket är inte definierat. Conclusion ... 28

General conclusion ... 28

Limitations and future research ... 28

4

I

BACKGROUND

The question of “doing good” has for the past century evolved dramatically. From altruistic acts conducted by individuals or organized by community-serving organizations, governments or

churches, corporate social responsibility is now a new area of business, from which firms are

able to attain competitive advantages both through purely economic gains from innovative ways

of doing business, as well as more intangible or indirect gains from improvements in brand and

reputation, and new angles of differentiation.

The debate concerning whether socially responsible activities could truly be justified by

corporations in terms of profit-maximization was sparked by Friedman (1962; 1970), who

firmly stated that “a corporation’s social responsibility is to make a profit”. He thereby initiated

the challenge between scholars to be able to either prove or disprove the existence of a positive

relationship between CSP and CFP (Griffin & Mahon, 1997).

However, the road to consensus has been riddled with contradictions and obstacles. Cochran

and Wood (1984) conducted a comprehensive meta-analysis resulting in the showing of very

weak, albeit positive, ties between CSP and CFP. As of the late 1990’s, a definitive consensus

had yet to emerge (Griffin & Mahon, 1997).

Following the increased managerial and academic attention to corporate social responsibility

(CSR), and especially its relationship to corporate financial performance (CFP), extensive

research has been conducted with the purpose to shed light on the impact of corporate social

performance (CSP) on CFP (for example Aguinis & Glavas, 2012; Griffin & Mahon, 1997;

Peloza, 2009; Orlitzky et. al., 2003; Dixon-Fowler et. al., 2012; Grewatsch & Kleindienst,

5 by studies such as those by Dixon-Fowler et. al. (2012), Orlitzky et. al. (2003), and Peloza

(2009).

Barnett and Salomon (2012) suggest that future studies should investigate the contingencies

underlying the CSP-CFP relationship. They point out that financial benefits from CSP can be a

result of decreased costs in for example production or efficiency improvements, rather than an

ability to increase revenue by investing in CSP. Brammer and Millington (2008:1341) mention in their research that CSP is a “rich and multidimensional construct that embodies a wide range of corporate behavior”, and that analyses of the CSP-CFP relationship could therefore be extended to many different dimensions of CSP, other than corporate philanthropic donations

which their study focuses on. Similarly, Hawn and Ioannou (2016) believe in an importance of

future studies recognizing and exploring the assortment of CSR activities that companies

perform, and treating CSP as a manifold construct.

In another study, Dixon-Fowler et. al. (2012) pose the question “When does it pay to be green?”

in an attempt to go beyond the question of whether it does pay at all – a question whose answer

their meta-analytic review concludes is generally “yes”. They further state that research on the

CSP-CFP relationship shows that it is moderated by various contingencies, and suggest that

future research efforts be directed to studying new and additional moderators in order to achieve

a higher understanding of this relationship. Specifically, they mention looking into conditions

and contingencies that managers can use to guide activities to achieve the greatest benefits in

financial performance while supporting the environment. This suggests relevance of looking

into moderating contingencies that managers can control through strategic management and

decision making.

Grewatsch and Kleindienst (2015) propose that including theories from strategic management

into the research on the CSP-CFP relationship may greatly advance our understanding of it. The

6 attributed to, firstly, research by Lee (2008), pointing out that CSR theory has evolved hand in

hand with strategic management theory, taking overall influence from that major stream of

research. Secondly, looking at the central questions to strategic management research as seen

in Farjoun (2002), namely, identifying what affects firm strategy, and finding explanations and

determinants of firm performance, CSR is an inextricable part of them, considering its connection to firms’ internal and external environments, as well as its resources and capabilities. As such, it is central in the creation of a firm’s strategy.

MOTIVATION AND CONTRIBUTION

As exemplified above, there seems to be substantial ground for furthering the research of the

relationship between CSP and CFP through evaluating and examining the contingencies that

underlie it. Furthermore, treating CSP as a multi-dimensional construct by including in the

measurement several different kinds of social responsibility and performance, as suggested by

Brammer and Millington (2008) may serve as a means of extending the research area and adding

new knowledge. Lastly, as suggested by Grewatsch and Kleindienst (2015), anchoring the

research direction to the field of strategic management and especially, as pointed out by

Dixon-Fowler et. al. (2012), business contingencies that are a result of actively made strategical

management choices, may serve as a way of advancing our understanding of the CSP-CFP

relationship.

This study attempts to extend the existing body of research on the relationship between

corporate social and financial performance. In order to do so, I will use data from the MSCI

KLD database on firms’ corporate social performance, completed with financial data from the

database Orbis, to examine whether the primary customer orientation of a firm (business to

7 experience goods) has a moderating effect on the relationship between a firm’s social and financial performance.

The contributions made to existing research in the CSP-CFP field of study by this paper are the

following. Firstly, the paper aims to extend the body of research looking into the contingencies

that underlie the CSP-CFP relationship, particularly, the contingencies primary customer orientation, and primary product type orientation. This has, to the author’s knowledge, not been done before. Secondly, the paper will treat corporate social responsibility as a

multi-dimensional construct by measuring its performance using all social performance areas

measured in the MSCI KLD database, treating them as a comprehensive score for the firm.

Finally, the paper aims to provide further insight into the linkage between strategic

management, active strategic business choices made by firm management, and how those can

act as contingencies potentially moderating the CSP-CFP relationship, shedding light on how

strategic choices made by the firm in both areas concerning social responsibility and areas of

business strategy such as choice of customer orientation and product orientation may affect one

another.

STRUCTURE

This study is structured as follows: The study begins with a literature review, covering the main

insights of existing literature on the relationship between CSP and CFP, as well as looking into

literature on search goods, experience goods, B2B firms and markets, and B2C firms and

markets. The purpose of the latter parts is to display any potential similarities and differences

in market conditions and customer relations for firms primarily selling search goods or

experience goods, and for firms selling primarily to private consumers, or to other businesses.

8 the data and methods used to try them. Subsequently, the results of the regression analyses are

presented, followed up by a presentation of conclusions drawn from said results. Finally, the study’s limitations are discussed, alongside of recommendations for future research.

L

THE CSP-CFP RELATIONSHIP

In the modern business climate, all firms are increasingly being encouraged by their

stakeholders to demonstrate that their policies and operations live up to, and exceed, a range of

social and ethical norms held by society. Succeeding in this endeavor has shown to help build

a positive firm reputation. Hur, Kim and Woo (2013) studied the relationship between CSR and

corporate brand credibility, -equity, and -reputation, showing that socially responsible corporate

action did indeed have a direct positive effect on both corporate reputation and brand credibility.

Peloza (2006) shows that not only do CSR investments act as a source of novel competitive

advantage for corporations, they may also act as a safeguard for existing competitive advantages through the so called “reputation mechanism”, i.e. the concept that enhanced firm reputation through the conducting of CSR activities helps the firm endure consequences of negative CSR.

This notion is supported in research by Doh et. al. (2010), that show that firms’ CSR reputation can moderate investors’ reactions to positive and negative social responsibility occurrences with the firm. Investing in socially responsible activities can protect a firm’s reputation against

stakeholder reactions to scandals or socially irresponsible activities, and protects the value of

the reputational capital of the firm (Brammer & Pavelin, 2005).

In their 2011 article, McWilliams and Siegel investigated among other phenomena how a firm

9 some of this value can be captured through the positive influence of producing social goods on

firm reputation. Good firm reputation constitutes a strategic resource, since it may entail

increased revenues through the possibility of improved consumer loyalty or premium pricing.

McWilliams and Siegel (2011) also note that an enhanced firm reputation through high CSP

may lower personnel costs by constituting intrinsic motivation for the employees, as well as lowering the firm’s cost of capital thanks to CSR activities bringing down the firm’s risk profile for investors.

According to Godfrey (2005), stakeholders interpret good firm reputation gained from high

CSP as evidence of good character when they decide how much to punish a company following

socially irresponsible behavior.

The greater the extent of a firm’s dependence on good reputation for financial performance, the more the firm will likely suffer financially from any loss in reputation. When customers cannot

properly assess the attributes of a product prior to purchase due to lack of information, as is the

case with experience goods, they are forced to rely on corporate reputation when making their

judgement (Schinietz & Epstein, 2005). Therefore, firms that rely more heavily on reputation,

such as sellers of experience goods, have a larger incentive to engage in CSR activities that

strengthen CSP. On the other hand, firms that depend less on their reputation will not engage

in insurance-motivated CSR because it has far less to lose if a scandal were to occur.

Trust is an essential resource in business, and CSP has been shown to create a trusting

relationship between a company and its customers. The attribute brand credibility, shown by

Hur, Kim and Woo (2013) to be positively influenced by corporate social activities, refers to the extent to which the firm’s customers (potential and present) perceive its level of trustworthiness. Pivato et. al. (2008) have also been able to confirm this relationship, by

demonstrating in their research that companies with a perceived high CSP by consumers do

10 customers to a greater extent need to simply trust the seller and its information, have a greater

reliance on their reputation, and will thusly have a greater tendency to invest in CSR to gain a

higher CSP (Brammer & Pavelin, 2005).

Companies that are socially responsible are distinguished from their competitors, meaning CSP

can be used as an investment in differentiation strategy (Pivato et. al., 2008). This gives the

firm the ability to charge a premium price for its products. CSP can also be used as a credible

signal for quality and reliability (Hur, Kim & Woo, 2013).

B2B VS B2C

An essential component of firm strategy is the management of its relationships with its primary

group of buyers. The distinction between buyers as firms and buyers as consumers is important

to make when looking at a supplier-buyer relationship, since research has shown that

business-to-business (B2B) markets and business-to-consumer (B2C) markets have certain dynamics

that differ between them, and that should have an impact on the behavior of the supplying firm.

Buyers in B2B markets tend to perceive the value of a product or service according to other

attributes than buyers in B2C markets. B2B buyers look primarily at economic and functional

attributes, taking more of a cost-benefit approach, and are often even considered to be guided

solely by rational criteria when appreciating the value of a purchase. It is important to note,

however, that recent studies do still indicate a certain influence of noneconomic aspects when

establishing long-term buyer-supplier B2B relationships. (Mencarelli & Rivière, 2015).

Another of these differing dynamics is the influence of trust in the buyer-supplier relationship. Lynch and de Chernatony’s 2004 study presents the traditional perception of trust’s role in B2C and B2B relationships, and attempts to change it. In the study, the authors look at branding

11 and B2B markets. This type of branding is actually more durable than branding based on

concrete factors such as price or quality, and is less likely to suffer from competitive erosion.

As a matter of fact, branding based on concrete, functional values, can both be subject to high

competitive pressure, and be easily imitated. Consequently, branding based on intangible

factors may be more likely to act as a source of sustainable competitive advantage.

Traditionally, research in this area has been focused on B2C-firms, leading to an unfounded

impression of emotion-based branding not being as important for B2B-firms, that are assumed

to be more rational and knowledge-based in their decision-making than final consumers.

However, as Lynch and de Chernatony point out in their study, strong B2B brands conducting

emotion- and value-based brand activities incorporating for example trust building, may

achieve an important point of differentiation among functionality-focused rivals, as well as

create emotional ties with customers and stakeholders. The role of trust and emotion-based

selling points in B2B vs B2C markets is thus perhaps transforming.

Following this first indication of somewhat of a distinction between B2B and B2C

buyer-supplier relationships along the lines of more utilitarian to more emotionally driven, additional

differing dimensions shall be added. The utilitarianism of B2B markets has been observed in

the product attributes and decision-making processes of both buyers and sellers. In these

markets, the products tend to be more complex and technically advanced, and buying processes

correspondingly tend to be longer and more formal, as opposed to B2C markets where buying

processes are fast and impulsive to a much larger extent (Swani, Brown & Milne, 2013). Due

to the complexity of B2B products and buying processes, firms operating in these markets

perceive higher levels of risk in the buyer-supplier relationship, and look for long-term and

collaborative relationships in order to mitigate that risk. This also contributes to the higher level

of cognition used in buying processes in B2B markets, as well as a stronger emphasis on

12 where appeals are more emotional and commercialistic, less cognition is used in the buying

processes, and relationships are shorter and one-sided. (Swani, Brown & Milne, 2013).

Findings by Brammer and Pavelin (2005) suggest that firms that have a greater reliance on their

reputation in order to achieve a high financial performance have a greater tendency to invest in

their social responsibility. For firms operating in industries where trust is an important

parameter in the firm-customer relationship, reputation is expected to be of more importance.

Examples of such industries named by Brammer and Pavelin (2005) are retailing and consumer

goods – in other words, B2C industries.

Following that same line of reasoning, while still being very important for business-to-business

firms, reputation insurance is of higher value to business-to-consumer firms, since they have a

larger exposure to their customers and thusly face a larger risk of suffering negative media

exposure or boycotts by consumers (Peloza, 2006). The different characteristics of

buyer-supplier relationships in B2B and B2C markets can have important consequences for the

implementation of socially responsible strategies and activities. Comparatively, B2B firms have

fewer incentives to engage in CSR than B2C firms, which face more pressure from consumers

and media due to their higher visibility towards external stakeholders (Hoejmose, Brammer &

Millington, 2012).

These dynamics mentioned above lead to the reasoning that a firm’s primary customer orientation (B2B or B2C) may act as a moderator in the relationship between CSP and CFP.

SEARCH GOODS AND EXPERIENCE GOODS

All goods possess different characteristics that translate into different degrees of difficulty and

cost in assessing the dominant features of the product. This allows for an, albeit slightly crude,

13 most efficient way of obtaining information on the product’s dominant features is through inspection prior to the purchase, or through experiencing it after purchase. (Lancaster, 1966;

Nelson, 1970). A third product group is identified by Darby and Karni (1973), the so-called

credence goods. This group pertains to products whose attributes and dominant features cannot

be evaluated even after purchase. A distinction is also often made between durable and

non-durable goods (Liebermann & Flint-Goor, 1996; Nelson, 1970; Nelson, 1974).

For search goods, information to evaluate a product sufficiently can be found in its advertising.

If the advertised information is incorrect, the buyer will be able to tell so prior to purchase. For

experience goods, the advertised information about a product has to be strengthened or further

confirmed, since the buyer cannot evaluate its validity prior to purchase. This is done in part

through higher advertising expenditures from experience good brands, but also much through

advice and recommendations obtained by the buyer from his or her surroundings. (Nelson,

1970; Nelson, 1974). Liebermann and Flint-Goor (1996) further show that functional and

rational appeals should be stressed in the advertising of durable goods and experience goods,

while a more emotional appeal should be employed when advertising non-durable goods, search

goods and credence goods, for the advertising to reach the furthest success.

Siegel and Vitalino (2007) have indeed been able to empirically show that firms that sell

experience goods (and credence goods) are in fact more likely to engage in socially responsible

activities than firms that sell search goods. This affirms the impact of information asymmetry

and the role of corporate social responsibility in a firm’s differentiation strategy when

discerning differences between the selling of different types of goods.

Grewatsch and Kleindienst (2015) emphasize the potential strategic benefits of CSP for firms

selling experience goods and credence goods rather than search goods. They claim that the

benefits of CSP for these firms could be especially great, since their customers have a higher

14 2015 review on moderators and mediators in the CSP-CFP relationship, Grewatsch and

Kleindienst subsequently propose that scholars look into the role of product type as a moderator

in that relationship.

HYPOTHESES B2B vs B2C:

B2B and B2C firms are influenced differently by factors affected or replaced by corporate social

performance, such as firm-customer trust, and firm reputation. Traditionally, these factors were

less important for B2B firms, that have been seen to have customer relationships more based

on facts and functionality, while end-consumer facing B2C firms have been more affected by

customer trust and firm reputation due to their typically high visibility and more emotion-driven

selling tactics. However, this traditional view is gradually changing, and research has shown

that emotional values are increasingly important for not only B2C-markets, but for B2B as well

(Lynch & de Chernatony, 2004). Therefore, this paper asks the question of whether CSP, a

factor that affects customers emotionally e.g. by increasing their trust in a firm, may see its effect on CFP moderated by a firm’s primary customer orientation, even though emotional factors such as trust are becoming more equally important for both B2B and B2C firms.

H1a: A firm’s primary customer type orientation in terms of B2B (wholesale) vs. B2C (retail)

will have a moderating effect on the relationship between the firm’s CSP and CFP.

Search vs Experience:

As stated previously, the need for, and creation of, trust in the customer-firm relationship is a

15 between firms selling search goods and firms selling experience goods as well. Experience

goods are more difficult for the customer to evaluate in terms of quality and product attributes,

and thus seems to warrant a stronger need for trust building activities from the firm towards the

customers. Firms selling experience goods are more likely to engage in CSR, and the strategic

benefits of conducting CSR activities is greater for firms selling experience goods than for firms

selling search goods. This line of reasoning results in the following two hypotheses:

H2a: A firm’s primary product type orientation in terms of experience vs. search goods will

have a moderating effect on the relationship between the firm’s CSP and CFP.

H2b: Firms that primarily sell experience goods will have a stronger positive relationship

between CSP and CFP than firms that primarily sell search goods.

D

SAMPLE SELECTION

For my first initial sample, I used the Orbis database. From there I was able to create four large

sample groups, one for each firm type I wanted to investigate. I used the SIC-code system to

select firms corresponding to a certain type, looking at wholesale and retail firms as

SIC-categories, and finding firms dealing in search goods and experience goods, respectively, by

selecting industry groups corresponding to the two types of goods as described in the research

literature on consumer goods. Additionally, I conditioned the data selection to contain only

publicly listed firms, and only firms located in the United States. The subsequent data output

contained much of the financial information I was interested in as well. All data in the sample

pertains to the period 2007-2015, since these were the years for which the most information

16 VARIABLES

CFP

I will use an operating performance measurement of financial performance and look at ROA

(return on assets). I will use average ROA for the period 2007-2015. CFP is a dependent variable, that according to the relationship for this study is dependent on the values of the firms’ CSP.

CSP

The independent variable of the studied relationship is CSP. In order to collect information on

the corporate social performance of the sample firms, I used a list of the ticker symbols for all

firms in my original data set from Orbis and entered these in a query to the MSCI KLD database.

Using information from the MSCI KLD database is widely recognized as a valid method for

measuring CSP, and has been employed in studies such as Doh, Howton, Howton and Siegel

(2010), Hillman and Keim (2001), McWilliams and Siegel (2000), Strike et al. (2006), and

Waddock and Graves (1997). The MSCI KLD database evaluates the social performance of

publicly listed companies in the United States. The assessment of CSP is based on the input of

expert panels, company surveys and publicly disclosed information, among other sources. The

social performance of the firms in the database is assessed through scores along 13 dimensions

of social responsibility or irresponsibility (named as strengths and weaknesses, respectively).

Out of the 13 categories, six are exclusively concerns, as opposed to the remaining seven that

are comprised of a strength-dimension and a concern-dimension. Not all firms had a

corresponding CSP value, and those firms were then removed from my data sample in

17 Looking at Doh, Howton, Howton and Siegel (2010), I will follow them in addressing this issue

by summarizing the value of the strengths and concerns for each firm to obtain total scores for the firms’ positive and negative CSR, respectively. Peloza (2006) points to the importance of examining both positive and negative firm behavior in terms of social responsibility, in order

to provide as accurate as possible an estimation of the overall value of CSR in its relationship

to corporate financial performance. Strike et. al. (2006) further emphasize that view in supporting the use of the sums of a firm’s CSR strengths and weaknesses to achieve a comprehensive CSP measure. Consequently, I will use the aggregate score of positive CSR,

measured in total number of strengths, and negative CSR, measured in total number of

concerns, points from KLD to better show the full picture of the companies’ total social

performance.

In order to compensate for missing values from certain years (due to the fact that not all

companies in the study have been active all the time since 2007 and therefore have no values

for the years before they were active) I am using a measurement of CSP as average aggregate

CSP score for the years 2007-2015 or average for the years during which the company has been

active within that time span.

MODERATORS

For the purpose of sorting firms into those selling search goods and those selling experience

goods, I used the three classification tables presented below as starting points from where to

18

Product type classification table, Liebermann and Flint-Goor, 1996

19

Product type classification table, Nelson, 1974

By choosing firms belonging to the SIC-categories corresponding to products listed as either

search goods or experience goods in either of these three tables, I was able to get my final

populations of search good selling firms and experience good selling firms. The benefit of

basing my selection on more than one classification table is that the table presented in Nelson

(1974) has been used as the basis for such product classification since its publication (see for

example Siegel and Vitalino, 2007) and is a solid foundation to build a selection upon. Using

Liebermann and Flint-Goor’s (1996) classification tables gives a modern addition that I believe

20 previous research has set a good example in using both classification tables as grounds for that study’s classification, for example the previously mentioned Siegel and Vitalino (2007).

CONTROL VARIABLES

Since my dependent variable reflects the corporate financial performance of the firms in the

sample, I will in the study control for factors that may systematically affect that performance.

As control variables, I have used firm size, measured as average number of employees over the

period 2007-2015, average ROE (return on equity), average research intensity (calculated as

R&D expenditures divided by sales, expressed as a percentage), average debt ratio (calculated

as long term debt divided by total assets, expressed as a percentage), all for the period

2007-2015, as well as number of buy recommendations from stock analysts, industry classification,

and state, further explained below.

A study by Luo et. al. (2015) looking into the mediating effect by analyst stock

recommendations on the relationship between CSP and financial performance in the shape of

future stock returns shows that strong social performance of a firm may aid in abridging

information asymmetries between the firm and stock analysts. This abridgement, in turn, results

in analysts being more prone to assign “buy” recommendations to firms with high CSP,

entailing more investment in said firms, and thusly stronger financial performance in the shape

of future stock returns. Considering this effect by stock analyst recommendations on the

relationship between CSP and CFP, I have chosen to incorporate the number of analyst buy

recommendations as a control variable in the subsequent regressions.

Research intensity is used for the reason that R&D expenditures are expected to have a positive

impact on firm productivity, and thusly also have a long-term effect on its financial

performance. Barnett et. al. (2012) point out that technological capabilities can create value for

the firm. McWilliams and Siegel (2009) actually state that models leaving out any control for

21 Doh, Howton, Howton and Siegel (2010) propose the use of firm size as a control variable when

examining the CSP-CFP relationship. They use size as a proxy for the overall visibility of the

firm in the marketplace, as well as its presence in said place, with the motive that previous

studies show size being an influencing factor in the information production of the firm.

Furthermore, Stanwick and Stanwick (1998) show empirically that firm size affects its CSP,

providing additional incentive to use firm size as a control variable. Finally, Barnett et. al.

(2012) too recommend the usage of size as a control variable in analyses of the CSP-CFP

relationship, with the purpose of controlling for possible size effects.

Additionally, Barnett et. al. (2012) propose using measures of firm debt ratio to control for the

managerial behaviors inherent with varying amounts of debt. On the one hand, a high debt ratio

might incentivize managers to be more disciplined and go to further lengths to make decisions that are truly in the firm’s best interest, proving beneficial for firm financial performance. On the other hand, it may also impose a limitation on managers wanting to innovate and explore

new business strategies and opportunities for the firm, resulting in a negative impact on

financial performance.

In their 1997 review of the CSP-CFP research body, Griffin and Mahon point to industry effects

as a large methodological issue in existing research. They emphasize possible differences

pertaining to for example governmental regulations and public visibility that may have an effect

on either of the variables. In the United States, for example, differing state legislature and

state-specific regulations regarding areas such as pollution control may translate into differences in

CSP and the CSP-CFP relationships for firms operating in different states. Dixon-Fowler also

point to the impact of firm industry, especially concerning media coverage, public pressure, and

industry-specific regulations. Therefore, I looked at industry classification by using the first,

22 differences. Thereby, the study can maintain external validity stemming from several industries,

while still ensuring a certain measure of internal validity by controlling for the industry effect.

Wang and Bansal (2012) mention that regional rules, policies and regulations, as well as local

norms and social pressures, can have an impact on firm CSR activities. State laws and

regulations are probable to infer some impact on financial performance as well, due to factors

such as tax regulation, local resource supply, and infrastructure. Therefore, I have included a

variable showing which state the firm is located in within the US, indicated by a number

between 1-52.

LIMITATIONS AND ISSUES WITH THE DATA

Callan and Thomas (2009) point out that including the MSCI KLD’s “Controversial Business Issues” in the CSP score may give a somewhat distorted score in that the controversial business issues are shown in their study to have a substantially stronger influence on CFP than the other

social performance parameters. This effect was shown especially in those parameters that are

more qualitative in nature, such as human rights.

The classification of firms according to their primary product orientation is, no matter how

objective or scientific one attempts to be, inevitably a question of subjective judgement, due to

the sometimes multi-faceted or innovative and broad way of conducting a business in terms of

product creation and innovation. While my sample was restricted to firms that are not

conglomerates, and whose industry classification codes state clearly a primary product type that

does correspond cleanly to the different goods-type categories, there is no escaping that many

firms are diversified in their operations. This does inescapably entail a certain degree of error

23 SAMPLE DATA AND DESCRIPTIVE STATISTICS

Before running my regression analysis, I made sure that the variable for type was nominal, since there is no interrelation between the types’ numerical categories. I then conducted a z-score transformation of all my variables (since they are all numerical). This is beneficial because my

variables were written in different scales (ROA and ROE are presented as percentages, while

the rest of the variables are absolute values, except for type, state, and SIC-codes, which

represents a category for each numerical value). When variables are presented in different

scales, conducting a z-score transformation puts them all on a common scale, and thereby makes

it easier to do a visual comparison.

This study has a deductive approach, and since the purpose of the study includes the measuring

and analyzing of a large amount of quantitative data, the study is built on a quantitative method

(Bryman & Bell, 2017). Two different regression models were conducted in order to test the

relationships between the variables. These were multiple regression analyses, where 𝑦 varies

depending on how several other variables (several 𝑥) vary. The results show the effect of the

independent variable (CSP) on the dependent variable (CFP) under control of the control

variables (firm type). The regressions have the following equation model:

𝑌_𝐶𝑆𝑃 = 𝛼 + 𝛽₁𝑅𝑂𝐴 + 𝛽₂𝐶𝑆𝑃 ∗ 𝑇𝑦𝑝𝑒 + 𝛽₃𝑅𝐷 + 𝛽₄𝐷𝑅 + 𝛽₅𝐸𝑚𝑝 + 𝛽₆𝑆𝑡𝑎𝑡𝑒 + 𝛽₇𝑆𝐼𝐶

R

24 Presented below are the scatter plots for both regressions. They display that homoscedasticity

is present in both regressions between the predicted dependent variable scores and the errors of

prediction. The presence of homoscedasticity ensures that the risk of making Type I and Type

II errors in the regression is minimized, which entails a higher degree of correctness for the

research findings (Tabachnick & Fidell, 2013).

Firm

type

25 An issue with the data set used for the regression concerning wholesale and retail firms is that

the final set for the primary regression only contained 19 samples. By removing the variable “Research Intensity”, the sample size increased to 152. However, the central results (those pertaining to the potential moderating effect of “Type” on the CSP-CFP relationship) remained the same regardless of sample size, and taking into account the strong recommendation by

McWilliams and Siegel (2009) to include R&D intensity as a control variable, I chose not to

omit it from the regression.

In order to measure any autocorrelation present in the regression analysis, I have conducted a

Durbin Watson test. A Durbin Watson score in the range of 1,5 to 2,5 is considered normal test

statistic values (Field, 2009). As shown below, the Durbin Watson scores for both regressions

26 The regression results are presented in full below, samples presented in the order of first B2B

and B2C firms, and last search good firms and experience good firms. The regressions showed

no significant moderating effect by firm customer orientation or firm product orientation on

either of the studied CSP-CFP relationships.

Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -15,856 7,300 -2,172 0,062 Zscore(CSP) 0,090 0,200 0,300 0,451 0,664 Zscore(Type) 0,236 0,413 0,658 0,572 0,583 Zscore(AvgRD) -84,197 37,886 -0,628 -2,222 0,057 Zscore(AvgDR) -0,015 0,050 -0,133 -0,309 0,765 Zscore(Emp) -0,461 0,629 -0,646 -0,733 0,485 Zscore(State) 0,008 0,135 0,021 0,057 0,956

27

Zscore(SIC2) 0,097 0,151 0,297 0,642 0,539

Zscore(SIC3) -0,149 0,424 -0,475 -0,351 0,735

Zscore(Buyrec) 0,129 0,179 0,639 0,722 0,491

int_1 0,030 0,198 0,091 0,152 0,883

a. Dependent Variable: Zscore(Avg_ROA)

Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 0,269 0,232 1,159 0,248 Zscore(CSP) 0,080 0,945 0,112 0,085 0,933 Zscore(Type) -0,039 0,336 -0,018 -0,117 0,907 Zscore(AvgRD) -0,483 0,124 -0,244 -3,911 0,000 Zscore(AvgDR) 0,547 8,344 0,021 0,066 0,948 Zscore(Emp) -0,046 0,073 -0,053 -0,632 0,528 Zscore(State) 0,133 0,059 0,143 2,252 0,026 Zscore(SIC2) -0,285 0,054 -0,383 -5,300 0,000 Zscore(SIC1) 0,137 0,137 0,069 0,995 0,321 Zscore(Buyrec) 0,104 0,048 0,143 2,154 0,033 int_1 -0,183 2,367 -0,103 -0,077 0,938

a. Dependent Variable: Zscore(Avg_ROA)

The interaction variable int_1 shows how much of the effect of CSP on CFP that is attributable

to the firm having, in the first case, a customer orientation of either B2B or B2C, or in the other

case, having a product orientation of either search goods or experience goods. As can be seen

in both tables, neither of the interaction variables are statistically significant, meaning that none

of the hypotheses can be strengthened. In the regression for type search good firm / experience

28 logically showing that an increase in R&D expenditures translates into higher financial profits

for a firm. What was interesting to see was that also the variable SIC2, representing the second

tier of SIC classification for the firms (the first three numbers, indicating firm industry to a

semi-specific level), has a significant effect on CFP. This may be attributable to previously

mentioned industry effects present in the firm data sample.

C

GENERAL CONCLUSION

The statistical analysis showed no significant results for any of the regressions. It seems that

corporate social responsibility is equally important for the financial performance of all types of

companies. This indicates that customers do not discriminate in their rewarding of social

responsibility of companies, regardless whether these customers are regular people or corporate

clients. Furthermore, it seems as though the importance of corporate social responsibility carries

the same weight for customers when making purchasing decisions, regardless of whether they

are buying a good whose qualities are very easy to determine before purchase, or buying a good

for which they need to rely on qualities such as firm reputation to a larger extent in order to feel

safe enough to go through with the buy.

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Hoejmose, Brammer and Millington (2012) emphasize the role of trust in B2B supply chains

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B2B vs. B2C-orientation of firms with regards to green supply chain management and its

potential effect on corporate financial performance may be an intriguing avenue for future

research. Another interesting avenue is to investigate further how different measures of both

30

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