The Antecedents and Consequences of
Marketing Budget Expenditures
Ofer Mintz
June 2015
Ofer Mintz (omintz@lsu.edu) is Assistant Professor of Marketing, E. J. Ourso College of
Business, Louisiana State University, Baton Rouge, LA 70803. The author would like to thank
Dan Rice, Stephanie Mangus, and McDowell Porter for their helpful feedback.
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The Antecedents and Consequences of Marketing Budget Expenditures
ABSTRACT
While progress has been made on continuous calls from MSI and ISBM on the consequences, or
value relevance of marketing budget expenditures, surprisingly, less attention has been paid to
developing a comprehensive conceptual model that accounts for both its antecedents and
consequences. Incorporating theories from across the different business disciplines and insights
garnered from 20 managerial interviews, this work takes a holistic approach to suggest that the
firm’s resources, top management decision characteristics, and environmental contingencies
influence marketing expenditures, which in turn impacts firm performance. Employing data
merged from seven secondary datasets, the empirical results show that (i) marketing
expenditures are driven in a broad sense mostly by the firm’s top management characteristics and
least by its environmental contingencies, and (ii) not accounting for antecedents when
investigating its consequences can lead to spurious results. Theoretical and managerial
implications of the conceptual model, its empirical results, and additional analysis of alternate
performance and marketing expenditure measures are further discussed.
Keywords: marketing budget, marketing strategy, marketing-finance interface,
firm value
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1. Introduction
Marketing efforts cannot succeed and firm performance will not reach their objectives without a
sensible marketing budget (Best 2009). However, since it requires an understanding of a variety
of customer, firm, and industry characteristics (Farris and Buzzell 1979), managers view
determining the marketing budget as an arduous task which they typically approach with great
uncertainty (Kotler and Keller 2012). Therefore, even though determining the marketing budget
is of paramount importance to firms (Fischer et al. 2011), the majority of companies struggle to
set their budget and claim to not use best practices (Doctorow et al. 2009).
Surprisingly, given such difficulties and its impact on most things that constitute
marketing, apart from some seminal works in the 70’s and 80’s (e.g., Buzzell and Gale 1987,
Farris and Buzzell 1979, Lilien 1979) that used PIMS and survey data but were restricted in the
antecedents and consequences of marketing considered and did not investigate marketing’s effect
on stock market performance, the field has relatively ignored conducting new or follow-up
investigations to construct a comprehensive theory-based conceptual model of antecedents, or
conditions, that increase or decrease marketing budget sizes. This is even more surprising given
recent questions about marketing’s importance to the firm and the evolution of the business and
marketing environment after these seminal studies were conducted, i.e., the digital revolution and
marketing’s shift towards greater accountability (e.g., see Kumar 2015 for a review).
Thus, in this work I propose a theory-based conceptual framework of antecedents and
consequences of marketing budget expenditures (MBE). Given their limitations in resources and
decision making, (i) firms develop allocation strategies based on a combination of functional
value and need in order to manage internal competing demands for resources like monetary
budgets (Pfeffer and Salancik 1978); (ii) top management teams (TMTs) help resolve such
3
allocation decisions based on their own personalized understanding of the situation and the
pressures they face (Hambrick and Mason 1984); and (iii) firms and TMTs also often rely on
their external environments to aid in their decisions (Donaldson 2001). Hence, I take a holistic
approach incorporating resource dependency, upper echelons, and contingency theories to
systematically account for various drivers of MBE. A conceptual model of antecedents is
proposed based on the firm’s (a) resources, which includes factors such as firm diversification,
cash holdings, level of innovation, capital intensity, and customer concentration, (b) TMT
decision characteristics like institutional investor ownership concentration, marketing executive
presence, executive compensation, and past product and stock market performance, and (c)
environmental contingencies such as industry average marketing spending, concentration, and
turbulence, and economy-wide market growth and investor sentiment level.
In addition, I link MBE to marketing’s value relevance to the firm, i.e., its consequences
to firm performance, which has continuously has been an important research topic for marketing
practitioners, researchers, and lecturers (e.g., see MSI 1996-2014 Research Priorities, ISBM
2008-2014 B-to-B Marketing Trends). A hypothesis is developed in accordance with information
economics theory linking MBE with stock market performance. Unlike the majority of studies in
the marketing-finance literature (e.g., see Srinivasan and Hanssens 2009 for a review) that focus
on consequences of MBE but mostly overlook, exclude, or limit analysis of antecedents of MBE
to a few variables or specify fixed industry and/or year effects as control variables, this work
accounts for the possibility of MBE being endogenous by considering the aforementioned
various antecedents of MBE.
Therefore, the primary theoretical contribution of this work towards marketing science is
to develop a model of antecedents and consequences of MBE based on resource dependency,
upper echelons, contingency, and information economics theories, which is more comprehensive
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than previous marketing-finance interface studies that limit their analysis to a few variables, and
considers a more updated and comprehensive theory-based model in comparison to seminal
papers from the 70’s and 80’s. To test the conceptual model, I analyze data that spans over a
decade and is merged from seven secondary datasets with variants of two-stage least squares
fixed-effects models that differ on whether MBE are considered endogenous or exogenous. The
empirical analysis finds that upper echelons is the most useful theory in explaining the
antecedents of MBE, but resource dependency and contingency theories are also helpful, while
information economics theory is useful for understanding the consequences of MBE.
The main managerial implications of understanding the antecedents and consequences of
MBE are to help managers decide on how much to spend on marketing, identify which
conditions associate with less firm MBE, and recognize how such spending affects stock market
performance. For example, I identify conditions in which firms appropriate less on MBE, such as
in less diverse firms with fewer institutional owners that operate in industries with less average
MBE spending, and methods that can help increase MBE in such situations like increasing longterm executive compensation and better utilization of manufacturing capacity. In addition, I find
that MBE has a positive relationship with firm stock returns when one accounts for its
antecedents, but a negative relationship with firm stock returns when one does not account for its
antecedents. I find further evidence that MBE has different signed and level of significance
effects on firm performance in models that do and do not account for MBE’s antecedents across
a number of MBE and firm financial performance metrics. Hence, the overall methodological
implications of this work are to demonstrate that MBE vary beyond just industry and time period
effects, and not controlling for its endogeneity as is common in the marketing-finance literature
may be producing biased and spurious results.
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2. Conceptual Framework
2.1. Background
In this section I discuss the two-step research process employed to develop my proposed
conceptual model of antecedents and consequences of MBE.
2.1.1. Literature Review. First, I review relevant literature across the different fields of
business. In the marketing literature, two streams of research have investigated antecedents of
MBE. The first stream, in contrast to the present work, focuses on providing a detailed
assessment of the impact of one or only a few antecedents. For example, Mizik and Jacobson
(2003) investigate how the trade-offs between marketing and R&D spending impacts a firm’s
stock market returns, Kurt and Hulland (2013) examine the role that corporate financial
resources play in determining MBE in IPO and SEO settings, and Chakravarty and Grewal
(2011) look at how past stock market performance influences current MBE. 1 Findings from this
stream of research suggest that (i) multiple firm attributes involving firm resources such as R&D
intensity, cash holdings, and capital intensity, TMT decision characteristics such as ownership
characteristics, executive compensation, and past performance pressure, and environmental
contingencies like economic conditions are likely to impact MBE; and (ii) one should account
for these various factors in determining a comprehensive framework of antecedents of MBE.
In contrast to the first research stream on MBE, the second stream takes a more
comprehensive viewpoint to describe its antecedents. For example, Lilien (1979), Lilien and
Weinstein (1984), Farris and Buzzell (1979), and Buzzell and Gale (1987) suggest amongst other
1
There is also a related vast literature stream on antecedents of specific marketing mix expenditures. For example,
in research on antecedents of advertising budget expenditures, Joseph and Richardson (2002) investigate the roles of
cash holdings and type of managerial ownership, Currim et al. (2012) examine the impact of executives’ long versus
short-term compensation, and Steenkamp and Fang (2011) investigate the impact of economic contractions.
However, these and other works on antecedents of different types of marketing mixes do not have the goal of
proposing a comprehensive conceptual model of antecedents and consequences of MBE.
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variables that level of innovation (or R&D intensity), capital intensity, customer concentration,
past firm performance, industry concentration, and market growth influence MBE. The results of
this stream of research (i) also suggest that a comprehensive framework is appropriate for
detailing antecedents of MBE; and (ii) that a conceptual framework of drivers of MBE will need
to encompass firm resources, TMT decision characteristics, and external economy-wide and
industry variables.
Outside of marketing, i.e., in management, finance, accounting, and economics, there
have been numerous studies on firm resource allocation, spending decisions, and firm decision
making. Most relevant to this research are (i) Wan et al. (2011), who look at how differing levels
of firm diversification influence the efficiency of resource allocation practices; (ii) Lang et al.
(1991) and Richardson (2006), who each examine how cash holdings impact firm investment
decisions; (iii) Chung and Zhang (2011) and Hartzell and Starks (2003), who inspect institutional
investors role in firm decisions; and (iv) DiMaggio and Powell (1983) and Donaldson (2001),
who discuss the importance for institutions to follow industry and environmental norms. Overall,
while these studies do not specifically investigate MBE, their theoretical principles are useful for
my proposed conceptual model (discussed in section 2.2.).
In summary, integrating these literatures suggests that (a) an investigation on antecedents
would need to be comprehensive in nature, as MBE would be influenced by multiple firm
attributes, and (b) that firms appear to typically allocate resources across the firm based on
functional value and power, environmental contingencies, and TMT characteristics.
2.1.2. Managerial Interviews. To gain a managerial perspective, I conducted 20
qualitative interviews with managers from a variety of levels, firms, and industries.2 The main
2
Each interview was conducted by the author, lasted 20-30 minutes, and centered around a set list of questions and
appropriate unscripted follow-up questions related to firm MBE practices. The managers came from a variety of
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insights garnered from the interviews (a) reaffirmed the importance of proposing a
comprehensive model of drivers of MBE since a more narrow approach would not account for
enough of the variation in their practices; (b) suggested that firms’ resources, TMT decision
characteristics, and environmental contingencies were indeed the most important drivers of
MBE; and (c) adjusted the preliminary model to include certain TMT characteristics like
presence of a marketing executive and economic environmental contingencies such as investor
sentiment in addition to excluding certain variables from the original literature review based
model. The interviews also revealed that all their firms budget marketing dynamically
throughout the year as conditions dictate, and not just statically at once based on the prior year.
Hence, the conceptual model links antecedents and consequences of MBE from the current fiscal
year (except for recent past performance), a position corroborated in recent popular press
business articles (e.g., McKee 2014).
2.2. Conceptual Framework
On the basis of the aforementioned multidisciplinary literature review and 20 managerial
interviews, I propose the conceptual model in Figure 1. Since I identify a large number of
antecedents and because my focus is on establishing a model of the antecedents and
consequences of MBE, I only concentrate on the main effects of the relationships. In Table 1, I
provide definitions and operational measures of each variable.
My main variable of interest is MBE, which is defined as the firm’s marketing to sales
(M/S) ratio. Briefly, I provide the theoretical rationale for the conceptual model of antecedents
and consequences of MBE and the inclusion / exclusion of the proposed variables as follows.
First, firms need to acquire and manage resources to sustain a competitive advantage in the
levels (i.e., director and VPs of marketing, CMOs, CEOs, etc.), firms (i.e., large and small, successful and
unsuccessful, differentiated and low cost focused, etc.), and industries (i.e., concentrated and fragmented, growing
and declining, manufacturing, and services, etc.).
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market (Barney 1991) since they guide the selection and effectiveness of its strategic choices
(Wernerfelt 1984). However, it is not plausible for firms to possess endless amounts of resources
(Amit and Schoemaker 1993), and hence organizations must confront numerous and frequently
incompatible demands of resource allocation from a variety of actors (Oliver 1991). As a result,
resource dependency theory (RDT) takes the perspective that firms develop allocation strategies
based on a combination of functional value, need, and thus power in order to manage such
internal competing demands for resources (Hillman et al. 2009, Pfeffer and Salancik 1978).
Therefore, I employ RDT as an internally focused perspective to consider why five variables,
i.e., firm diversification, cash holdings, focus on innovation, capital intensity, and customer
concentration, would influence MBE. I specifically include these variables since prior studies
(e.g., Farris and Buzzell 1979, Verhoef and Leeflang 2009, Wan et al. 2011) have shown them to
be influencers of functional power, which according to RDT should impact MBE allocation.
Second, TMTs have discretion to make decisions that they believe will lead to better
performance on topics like resource allocation decisions (Hult 2011). Upper echelons theory
suggests that TMTs make such decisions for the firm on the basis of their personalized
understanding of the situation (Hambrick and Mason 1984); but because TMT members have
bounded rationality cognitive constraints, such major decisions are heavily influenced by the
executives’ own experiences, characteristics, and pressures from inside and outside the firm
(Finkelstein et al. 2009). Thus, I consider five extensively studied (e.g., Chakravarty and Grewal
2011, Hartzell and Starks 2003, Lilien 1979) TMT decision characteristics, i.e., institutional
ownership concentration, marketing executive presence, executive compensation, and pressure
from past product and stock market performance.
Third, given that firms typically have limitations in resources and decision making,
contingency theory posits that firms also often rely on external environments to develop their
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resource allocation strategies (Hofer 1975). This tactic is important because it helps firms (a)
gain legitimacy by mimicking industry norms of resource allocation (DiMaggio and Powell
1983) and (b) adjust resources contingent on conditions to better performance (Donaldson 2001).
Therefore, based on previous studies investigating how environmental contingencies affect
marketing and firm strategies (e.g., Baker and Wurgler 2006, Homburg et al. 1999, Steenkamp
and Fang 2011) and my managerial interviews, I include industry average marketing spending,
concentration, and turbulence, and economy-wide market growth and investor sentiment level.
Finally, information economics theory suggests that marketing efforts signal to the
marketplace information about the firm’s products that customers and investors evaluate and
screen to determine whether it matches their wants and needs (Spence 1973, Stigler 1961).
Therefore, I link the impact of MBE on firm stock market performance, and consider results
analyzing alternative financial market performance in the robustness section.
3. Hypotheses
3.1. Antecedents of Marketing Budget Expenditures
3.1.1. Firm Resources. Resource dependency theory (RDT) suggests that because
resources such as monetary budgets are scarce, firms actively manage and control resource flows
across the firm (Pfeffer and Salancik 1978). RDT further posits that central to organizational
decisions on how to distribute such resources is the influence of functional and departmental
power, which is generated based on the firm’s dependence of each function and the department’s
ability to create value to the firm (Hillman et al. 2009). Hence, I employ RDT to help understand
why five firm characteristics may influence MBE.
First, I consider firm diversification. Firms diversify into different business segments in
order to take advantage of greater economies of scale (Capon et al. 1988) and production (Rao et
al. 2004), with departments across the firm also obtaining greater experience and knowledge
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(Fang et al. 2011). From a resources based perspective, firms diversify because it allows them to
maximize their resources across several business segments to realize additional returns (Wan et
al. 2011); thus, in firms with greater firm diversification, assets like marketing typically are more
specialized and efficient with its value increasing by the level of its use (Kirca et al. 2011).
Further, because of its knowledge of different customer’s wants and needs across multiple
markets, the marketing function often acts as a valuable conduit linking the firm and its multiple
markets, which leads it to have greater influence (Boyd et al. 2010). As a result of its value to the
firm and its resultant power and influence, RDT suggests that marketing would be appropriated
greater resources in more diversified firms. Hence, I expect a positive relationship between firm
diversification and MBE.
Second, I consider cash holdings as a percentage of the firm’s total sales. Hoarding cash
and not spending available capital on potentially successful investment projects is not optimal for
a firm (Jensen 1986). Thus, when firms possess additional cash, they are likely to over-invest and
even spend such cash flows on non-value maximizing projects (e.g., Lang et al. 1991,
Richardson 2006). From an internal resource allocation viewpoint, within greater cash holding
firms there will be less determination of overall spending levels based on internal functional
power and influence since these firms do not value cash as such a rare and indispensable
resource (Joseph and Richardson 2002). Instead, when firms possess greater cash flows they will
have more free cash to allocate across the firm regardless of functional power and influence
(Tellis 1997). Consequently, I expect a positive relationship for cash holdings and MBE.
Third, I discuss a firm’s focus on innovation, defined as the firm’s level of R&D intensity
(Nath and Mahajan 2008). On the one hand, as previously mentioned, firms do not possess
endless amounts of resources, so some firms may not be able to devote enough resources to
maximize both R&D and marketing types of expenditures (Amit and Schoemaker 1993) and will
11
need to balance resources between the two types of expenditures (Mizik and Jacobson 2003). On
the other hand, in order to sustain a competitive advantage, firms with greater innovation and
R&D need to utilize marketing’s skills and experience in segmentation, targeting, and
positioning to identify market opportunities quickly and correctly (Varadarajan and Clark 1994).
As a result, marketing has been found to possess more influence in such firms (e.g., Nath and
Mahajan 2011, Verhoef and Leeflang 2009), and thus, in line with RDT, I expect firms with a
greater focus on innovation to spend more on MBE.
Fourth, I discuss the effect of capital intensity, which is defined as the ratio of
manufacturing capacity to sales (Farris and Buzzell 1979). I expect a positive relationship
between capital intensity and MBE for two reasons. First, when a firm’s capital intensity is
greater, its products are typically more complex with greater costs of labor and production to
serve customers (Dutta et al. 1999). To maintain and increase future sales of such products, firms
need to keep customers more continually satisfied (Mittal et al. 2005) and informed (Lilien 1979)
by reinforcing product quality with marketing efforts (Farris and Buzzell 1979). As a result, from
an RDT viewpoint, marketing is expected to receive more resources in firms with greater capital
intensity than in firms with fewer capital intensity that are less dependent on marketing for future
sales. Second, firms with greater capital intensity have less absorbive capacity in their
operations, leading firms to need to allocate more of their resources towards marketing
capabilities so their outputs can reach desired performance (Narasimhan et al. 2006).
Consequently, because of its value, according to RDT, marketing would command more power
and influence, and is expected to receive greater MBE in firms with greater capital intensity.
Finally, I consider level of customer concentration. With greater levels of customer
concentration, firms need to focus more of their resources to those limited number of powerful
customers (Boyd et al. 2010). However, marketing typically is more useful when it is reaching
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mass audiences then when it is targeting only a smaller amount of potential customers (Farris
and Buzzell 1979), and hence there is less need for greater MBE (Lilien 1979). Consequently, I
expect less MBE with greater customer concentration. For efficiency purposes, I present each of
the five firm resource expectations in Table 2, but summarize them here in one hypothesis.
H1: Firms with greater firm diversification, cash holdings, innovation focus, and capital
intensity, and less customer concentration will have greater marketing budget expenditures.
3.2. TMT Decision Characteristics. TMTs are often responsible for designing
strategies, setting budgets, and allocating resources across the firm (Lehmann and Reibstein
2006). Upper echelons theory suggests that TMT executives making such decisions are typically
rationally bounded, and can be influenced by their own experiences, characteristics, and
pressures (Hambrick and Mason 1984). Therefore, consistent with prior literature in marketing,
management, finance, and accounting (e.g., Bushee 1998, Lakonishok et al. 1994, Nath and
Mahajan 2008), and upper echelons theory, I consider five TMT characteristics as potential
drivers of MBE.
First, I include a firm’s level of institutional investor ownership, i.e., its institutional
investor ownership concentration (Hartzell and Starks 2003). Given institutional investors large
role in the market, previous research has shown institutional investors have the power to
influence firm decisions such as R&D expenditures, managerial compensation schemes, and
corporate governance (e.g., Chung and Zhang 2011, Hartzell and Starks 2003), and indicates that
they would be likely to impact MBE. Such investors, like hedge, mutual, and pension funds
typically are more experienced, sophisticated professional traders who have strict fiduciary
responsibilities and greater accountability which precludes them to sell stocks which can gain
longer term value (Chung and Zhang 2011). Consequently, this greater sophistication,
experience, market power, and accountability enables them to actively monitor and discipline
13
managers to act in the firm’s long-term best interests (Bushee 1998), which should encourage
managers to spend more on MBE. Previous research has also demonstrated that institutional
investors are drawn to firms with greater liquidity and certainty in their future returns (Gompers
and Metrick 2001), and that investors in general are attracted to more attention-grabbing stocks
(Barber and Odean 2008). Hence, institutional investors are likely to pressure firms to
appropriate more MBE because it leads firms to garner more attention, which would then create
greater firm liquidity and long-term value for stocks since it would create a greater market of
potential buying investors. Therefore, I expect firms with a greater concentration of institutional
owners to be pressured into spending more on MBE.
Next, I discuss the presence of a marketing executive as a top-5 paid executive in the
firm. Firms employ a CMO or other high ranking marketing executives to reduce the uncertainty
the TMT faces in marketing areas (Nath and Mahajan 2008). Thus, the presence of a top-5 paid
executive with a marketing title gives marketing a powerful voice in the firm (Boyd et al. 2010)
and should impact how the TMT perceives marketing and allocates resources towards the
department (Hambrick and Mason 1984). Consequently, in firms with a top-5 paid employee
with a marketing job title, I expect greater MBE. Third, I consider long versus short-term
executive compensation. With greater equity, or long-term based compensation, TMTs are more
likely to make budget and allocation decisions that match shareholder’s best interests (Galbraith
and Merrill 1991), whereas with greater bonus, or short-term based compensation, TMTs are
more likely to pursue myopic, quick profit strategies such as cutting MBE (e.g., Mizik and
Jacobson 2007; Mizik 2010). In an empirical test, Currim et al. (2012) find an increase in long
versus short-term compensation positively associates with increased advertising spending, hence
I expect a similar positive association with MBE.
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Finally, I consider past product market and stock market return performance. Investors
typically base their assessments of expected future returns based on the prior year’s return
(Lakonishok et al. 1994). Thus, when firms experience greater product and financial market
performance in the prior fiscal year, investors expect firms to keep increasing their returns in the
short-term (Burgstahler and Dichev 1997). Not beating or matching such expectations can lead to
punitive repercussions which financially punish firms and deteriorate TMT reputation and
compensation (Chakravarty and Grewal 2011). Hence, TMTs feel immense pressure from
financial markets to improve their short-term returns when their firms experience greater prior
year returns (Zhang and Gimeno 2010), and previous research across the different business
disciplines (e.g., Graham et al. 2005, Mizik 2010) suggest that TMTs often actively engage in
myopic earnings management in an attempt to improve such expectations by altering their
investments, strategic objectives, and budgetary expenditure allocation. Because of its lack of
measurement and accountability, marketing is often one of the first firm expenditures to be
reduced when TMTs act myopically (Markovitch et al. 2005), so I expect a negative relationship
between past product market and stock market return performance and MBE.
H2: Firms with greater concentration of institutional owners, a top-5 paid executive with a
marketing title, more long-term based executive compensation, and worse product and stock
market performance will have greater marketing budget expenditures.
3.3. Environmental Contingencies. According to contingency theory, a firm’s strategy,
decision making, and resource allocation is contingent on broad financial market and economic
conditions (Donaldson 2001, Hofer 1975). In other words, contingency theory suggests that
firms continually adapt their structures, strategies, and allocation to "fit" the environment
(Atuahene-Gima and Murray 2004). Hence, in the following section, I discuss the effect of five
widely considered industry and economy-wide external environments in the marketing literature.
15
First, I consider industry average M/S ratio. Many market research companies like
Nielsen, IRI, and Kantar Media monitor and disseminate industry MBE information. Although
knowledge of such industry norms is not always efficient since it can encourage herding
behavior (Homburg et al. 1999), it does provide firms benchmarking opportunities to improve
marketing strategies to enhance their performance (Atuahene-Gima and Murray 2004).
Consequently, in accordance with contingency theory, firms in industries with greater M/S ratios
will conform to these norms, and are expected to spend more on MBE. Second, I consider level
of market turbulence. Marketing’s role in the firm expands in turbulent markets since it
significantly contributes in helping the firm adapt to quickly shifting consumer preferences
(Homburg et al. 1999). Thus, I expect with more industry volatility, more MBE. Third, I include
industry concentration. When the industry is less concentrated, or more fragmented, firms must
worry about “breaking through the clutter” with their marketing mix efforts because of the large
number of competitors in their industry (Mintz and Currim 2013). In order to “break through the
clutter” to reach their customers, firms are expected to spend more on MBE. Therefore, I expect
firms in less concentrated industries to spend more on MBE.
Fourth, I consider financial market growth. In economic contractions, proactive firms can
increase the efficiency and consequences of their MBE with more spending towards their efforts
in comparison to firms in economic expansions (Steenkamp and Fang 2011). By increasing such
expenditures, firms in economic contraction conditions can also help mitigate increasingly
competitive private share brands, who acquire market share and profits from national brands
(Lamey et al. 2007). Thus, according to contingency theory, firms in financial market decline
(growth) conditions should adjust to appropriate additional (fewer) funds to MBE. Fifth, I
consider financial market sentiment. With worse sentiment in the financial markets, investors
value more short-term performing stocks that have tangible, assured assets (Baker and Wurgler
16
2006). Marketing’s strength on the other hand is in creating long-term, intangible assets
(Srinivasan and Hanssens 2009), hence I expect firms to allocate less expenditures towards
marketing in years with worse financial market sentiment.
H3: Firms in industries with greater average M/S ratios, more turbulence, and less market
concentration and in economic conditions with worse market growth and better investor
sentiment levels will have greater marketing budget expenditures.
3.4. Consequences of Marketing Budget Expenditures
In line with previous marketing-finance interface research examining marketing’s value
relevance to the firm’s financial performance, I consider the impact of MBE on stock market
returns. While prior empirical studies provide mixed results on the effects of MBE on financial
market valuation,3 I expect a positive association because of the following. Investors
predominantly do not like uncertainty in their investments and are risk averse (Day and Fahey
1988, Srivastava et al. 1998). Information economics theory suggests that marketing efforts
signal to the marketplace information about the firm’s products that customers and investors
evaluate and screen to determine whether it matches their wants and needs (Spence 1973, Stigler
1961). Consequently, when a firm spends less on marketing its message is less clear, leading to
greater variability in consumer and market expectations (Mizik 2010), worse downside firm
brand value dispersion (Luo et al. 2013), and an increase in the risk of the firm’s stock
(McAlister et al. 2007). In contrast, when a firm spends more on marketing, it sends a clear
message to consumers and the market about the state of the firm and its products (Srivastava et
al. 2006). Therefore, even though some investors may initially characterize MBE as costs and
3
Prior empirical tests on marketing’s effect on market valuations have been mixed, as some studies find that an
increase in advertising spending from the previous year associates with increased firm valuations (Currim, Lim, and
Kim 2012; Joshi and Hanssens 2010; Kim and McAlister 2011), some find that a decrease in marketing spending
from the previous year associates with greater short-term stock market returns (Mizik 2010, Mizik and Jacobson
2007), and others find that marketing’s effects depends on the specific type of expenditure (Srinivasan et al. 2009).
17
not as investments (Rust et al. 2004), I expect a positive relationship between MBE and stock
market returns.
H4: Greater marketing budget expenditures will associate with better stock market performance.
4. Empirical Analysis
4.1. Data
4.1.1. Database. Based on the multidisciplinary literature review, managerial interviews,
and theories employed in the conceptual model development, I needed to create a database from
a number of sources to empirically test my conceptual model. As a result, I merged the following
seven secondary datasets: 1) CRSP Stock Market Index, which provides monthly stock marketlevel information; 2) CRSP, monthly firm-level stock data; 3) Thomson Reuters Institutional
(13f) Holdings: Stock Ownership Summary, quarterly firm-level institutional ownership
modifications; 4) ExecuComp, annual firm-level top-5 paid executive compensation data; 5)
Compustat Annual, annual 10-K based firm-level information; and 6) Compustat Historical
Industry Business Segments and 7) Compustat Historical Customer Segments, which provide
annual 10-K based firm-level operating segment information. Data was available between the
years of 1998 and 2013. Each dataset was cleaned prior to the data merge to remove observations
that contained missing data on any of the measures employed for this work. Industry measures
were classified by their GIC Sub-Industry code, which MCSI and S&P use as a taxonomy to
classify 154 different industries, rather than more traditional SIC or NAICS codes because of
better data availability. For variables that require such industry calculations, I computed each
measure prior to the data merge to maximize each dataset’s information.
4.1.2. Variable Operationalization. The operational measures for the variables
displayed in Figure 1 are taken from a variety of extant literatures. My main variable of interest
is MBE, which is measured by the firm’s marketing to sales (M/S) ratio (Lilien 1979, Lilien and
18
Weinstein 1984, Buzzell and Gale 1987). The operational basis for this metric emanates from the
following three-step process. First, to obtain a raw MBE value, I follow previous marketing
studies (e.g., Dutta et al. 1999, Fang et al. 2011, 2015, Kurt and Hulland 2013, Luo 2008, Mizik
2010, Mizik and Jacobson 2007, Narasimhan et al. 2006, Swaminathan and Moorman 2009,
Xiong and Bharadwaj 2013) by subtracting R&D expenses from selling, general, and
administrative (SG&A) expenses values from the Compustat Annual dataset. Dutta et al. (1999,
p. 556) argue that SG&A is “a good proxy for the amount the firm spends on its market research,
sales effort, trade promotion expenses, and other [marketing] related activities.” Then, to further
account for general and administrative costs included in the SG&A based measure such as
recovery of allowances for losses, foreign currency adjustments, bad debt, and legal, severance,
and staff expenses, I subtract out foreign exchange income (FCA), special items (SPI), total staff
expense (XLR), commissions and fees paid (CFPDO), and broker/dealer (CFBD), real estate
(CFERE), and other commissions and fees (CFO).4 Last, I scale this resultant value by the firm’s
sales to obtain an M/S ratio.
To conserve page space, I now briefly summarize the dataset and literature sources for
measures of the antecedents and consequences of MBE, and refer the reader to Table 1 and Web
Appendix A for detailed descriptions of the definitions and operational measures. First, for firm
resource variables, cash holdings (Joseph and Richardson 2002), innovation (Nath and Mahajan
2008), and capital intensity (Farris and Buzzell 1979) are each scaled by firm sales, and
computed from Compustat Annual. Firm diversification is the entropy level of concentration (cf.
Palepu 1985) calculated from the different business segments listed in Compustat Historical
4
The administrative costs accounted for in CFBD, CFERE, CFO, CFPDO, FCA, SPI, and XLR include variables in
SG&A such as bad debt, legal, severance, and staff expenses, commissions, discontinued operations, foreign
currency adjustments, recovery of allowances for losses, and restaurant and retail companies’ preopening, closing,
and rental costs.
19
Industry Business Segments; and customer concentration is taken from whether a firm listed a
customer segment in the Compustat Historical Customer Segments (Boyd et al. 2010). Second,
for TMT decision characteristics, level of institutional investor ownership is based on firm 13f
quarterly filings listed in the Thomson Reuters Institutional Holdings dataset (Hartzell and Starks
2003); marketing executive presence (Boyd et al. 2010) and executive long vs. short-term equity
to bonus ratio (Currim et al. 2012) are both computed from ExecuComp; and product market
performance is the firm’s ROA, computed from Compustat Annual (Luo and Bhattacharya
2006). Third, for industry-wide environmental contingencies, average M/S ratio (Boyd et al.
2010), turbulence (Carpenter and Westphal 2001), and concentration (Kurt and Hulland 2013)
are each computed from Compustat Annual; and for economy-wide environmental
contingencies, market growth (Steenkamp and Fang 2011) and market sentiment (Baker and
Wurgler 2006) are computed from the CRSP Stock Market Index. Last, the operational measure
for stock market performance is calculated by compounding the firm’s monthly stock return for
the fiscal year, based on CRSP data (Currim et al. 2012). Past stock market return is analogous,
except based on the year prior to the current fiscal period.
4.1.3. Descriptive Statistics. The merged dataset employed for empirical analysis spans
over a decade from 2000 to 2012, and consists of 2,174 firm-year observations from 369 firms
and 53 industries overall, and 39-260 firms and 18-47 industries per year. Panel A of Table 3
provides a summary. The data on MBE provides good news for marketers, with the average firm
spending around $1.10 million on raw MBE and having a 0.23 M/S ratio. As shown in Panel B,
the average firm in the sample increased its yearly M/S ratio until the recession that transpired in
2008, but the ratio mostly recovered by 2012. In Panel C, I provide mean and standard deviation
descriptive statistics for each variable. To briefly summarize, the average firm tends to be
successful with a 0.13 stock return, and has a 0.33 cash holdings to sales ratio, 0.08 innovation
20
level (R&D to sales ratio), and 0.22 capital expenditures to sales ratio. Around a quarter of the
firms (23%) have a marketing executive as a top-5 paid employee and the average executive’s
compensation skews more long-term with a higher bonus to equity ratio (0.74). However, as
shown in Panel C, there is high variance in amongst the firm resources, TMT decision
characteristics, and environmental contingency variables in the sample. In summary, the data
reported in Table 3 on MBE has face validity and should be very useful to serve as benchmarks
for marketing practitioners and academics.
4.2. Econometric Model
Following the conceptual framework, I formulate the following two-stage econometric model for
firm i at time t with MBE specified as an endogenous regressor as:
(1) 𝑀𝐵𝐸𝑖𝑡 = 𝛽0 + ∑5𝑑=1 𝛽𝑑 𝐹𝑟𝑚𝑅𝑒𝑠𝑑,𝑖𝑡 + ∑5𝑤=1 𝛽𝑤+5 𝑇𝑀𝑇𝐶ℎ𝑟𝑤,𝑖𝑡 + ∑5𝑧=1 𝛽𝑧+10 𝐸𝑛𝑣𝐶ℎ𝑟𝑧,𝑖𝑡 + 𝜀𝑖𝑡
(2) 𝑆𝑡𝑘𝑅𝑡𝑛𝑖𝑡 = 𝛼0 + 𝛼1 𝑀𝐵𝐸𝑖𝑡 + ∑8𝑐=1 𝛼𝑐+1 𝐶𝑡𝑙𝑉𝑎𝑟𝑐,𝑖𝑡 + 𝜀𝑘,𝑖𝑡
where MBE is the firm’s M/S ratio; FrmRes are the five firm resources variables (firm
diversification, cash holdings, innovation, capital intensity, and customer concentration);
TMTChr are the five TMT managerial characteristics (institutional ownership concentration,
marketing executive presence, executive long vs. short-term compensation, past product market
performance, and past stock market return); and EnvChr are the five environmental
characteristics (industry M/S ratio average, industry turbulence, industry concentration, market
growth, and market sentiment level). In equation 2, StkRtn is the firm’s stock market return,
calculated following Currim et al. (2012) as {∏12
𝑡=1(1 + 𝑖𝑡 )}-1, where 𝑖𝑡 is the stock return for
firm i in month t of the fiscal year. CtlVar are eight variables included as controls of firm stock
return (firm diversification, cash holdings, innovation, capital intensity, marketing executive
presence, past product market performance, market growth, and market sentiment).
21
Estimation of the model faces several challenges. First, the proposed conceptual model
examines total MBE and not changes or differences between yearly MBE (which as further
discussed in the discussion section would require a different conceptual model); consequently,
the use of first-differenced models such as dynamic GMM is limited and theoretically at odds
with the goals of this paper. Second, the sample does not contain many observations per firm,
which limits the applicability of VARX models. Third, in each of the 20 interviews, managers
indicated that their firm appropriated MBE dynamically throughout the year as conditions
dictate, and not just at the beginning of the fiscal year based on the prior year. Thus, it may be
inappropriate to specify the model with a lagged structure for variables other than past product
market and stock market performance.
Fourth, the focal dependent variable MBE in equation 1 is measured as a share of sales,
whose values are restricted between 0 and 1. Therefore, to extend its values outside the 0 and 1
range and to have the dependent variable function more like a normally distributed value, I
perform a logit transformation of this measure. Hence, equations 1 and 2 are re-written with
MBE logit-transformed across the system of equations to ensure consistency in the measure:
𝑀𝐵𝐸
(3) ln ((1−𝑀𝐵𝐸𝑖𝑡 )) = 𝛽0 + ∑5𝑑=1 𝛽𝑑 𝐹𝑚𝑅𝑒𝑠𝑑,𝑖𝑡 + ∑5𝑤=1 𝛽𝑤+5 𝑇𝑀𝑇𝐶ℎ𝑟𝑤,𝑖𝑡 + ∑5𝑧=1 𝛽𝑧+10 𝐸𝑛𝑣𝐶ℎ𝑟𝑧,𝑖𝑡
𝑖𝑡
𝑀𝐵𝐸
(4) 𝑆𝑡𝑘𝑅𝑡𝑛𝑖𝑡 = 𝛼0 + 𝛼1 ln ((1−𝑀𝐵𝐸𝑖𝑡 )) + ∑8𝑐=1 𝛼𝑐+1 𝐶𝑡𝑙𝑉𝑎𝑟𝑐,𝑖𝑡 + 𝜀𝑘,𝑖𝑡
𝑖𝑡
Fifth, there may be concerns about unobserved, observed, within-firm, and between-firm
heterogeneity. However, the model accounts for observed, within-firm, and between-firm
heterogeneity with the inclusion of a comprehensive set of firm, TMT, industry, and economywide variables. To account for unobserved heterogeneity, I apply a fixed-effects transformation
of equations 3 and 4, which time-demeans the data by subtracting the average value for each
variable per firm from each variable per firm-observation (Wooldridge 2002). Consequently, this
22
transformation also eliminates the intercept term from each equation and the 46 firms that only
have 1 observation. Thus, equations 3 and 4 are re-written accordingly, with 𝑋̅ indicating the
firm’s average value per variable:
𝑀𝐵𝐸𝑖𝑡
)−
(1−𝑀𝐵𝐸𝑖𝑡 )
(5) (ln (
̅̅̅̅̅̅̅
𝑀𝐵𝐸
𝑖𝑡
ln ((1−𝑀𝐵𝐸
)) = ∑5𝑑=1(𝛿𝑑 𝐹𝑚𝑅𝑒𝑠𝑑,𝑖𝑡 − 𝛿𝑑̅̅̅̅̅̅̅̅̅
𝐹𝑚𝑅𝑒𝑠𝑑,𝑖 ) + ∑5𝑤=1(𝛿𝑤+5 𝑇𝑀𝑇𝐶ℎ𝑟𝑤,𝑖𝑡
̅̅̅̅̅̅̅ )
𝑖𝑡
−𝛿𝑤+5 ̅̅̅̅̅̅̅̅̅̅̅
𝑇𝑀𝑇𝐶ℎ𝑟𝑤,𝑖 ) + ∑5𝑧=1(𝛿𝑧+10 𝐸𝑛𝑣𝐶ℎ𝑟𝑧,𝑖𝑡 − 𝛿𝑧+10 ̅̅̅̅̅̅̅̅̅̅
𝐸𝑛𝑣𝐶ℎ𝑟𝑧,𝑖 ) + (𝜀𝑖𝑡 − 𝜀̅𝑡 )
(6) (𝑆𝑡𝑘𝑅𝑡𝑛𝑖𝑡 − ̅̅̅̅̅̅̅̅̅
𝑆𝑡𝑘𝑅𝑡𝑛𝑖 ) = 𝜕1 (ln ((
̅̅̅̅̅̅̅
𝑀𝐵𝐸𝑖𝑡
𝑀𝐵𝐸
) − ln ( ̅̅̅̅̅̅̅𝑖𝑡 )) + ∑8𝑐=1(𝜕𝑐+1 𝐶𝑡𝑙𝑉𝑎𝑟𝑐,𝑖𝑡
1−𝑀𝐵𝐸𝑖𝑡)
(1−𝑀𝐵𝐸𝑖𝑡 )
−
𝜕𝑐+1 ̅̅̅̅̅̅̅̅̅
𝐶𝑡𝑙𝑉𝑎𝑟𝑐,𝑖 ) + (𝜀𝑖𝑡 − 𝜀̅𝑡 )
Sixth, there exists concerns of heteroskedasticity and autocorrelation, so to account for
this I compute heteroskedastic and autocorrelation-consistent (HAC) variance estimates, which
provide more conservative standard errors but do not alter the original parameter estimates
(Baum et al. 2007). Seventh, because of the possible similarity of variables involved,
multicollinearity could pose issues with the estimation. However, I find only 2 of 105 correlation
coefficients (2%) in the correlation matrix shown in Web Appendix B are greater than 0.40 and
all variance inflation scores are below 2. Thus, estimation is not expected to suffer from
multicollinearity (Hair et al. 2009). Finally, I need to test for various diagnostics that could
impede a two-stage least squares fixed effects model, such as (i) over-identification (SarganHansen test), (ii) excluded (Angrist-Pischke), under (Anderson canonical correlations test) and
weak identification (Craig-Donald statistic) of instruments employed in the MBE equation but
not in the stock return equation, and (iii) weak instrument robust inference for testing
significance of the MBE endogenous regressor in the joint model estimation (Anderson-Rubin
and Stock-Wright tests). However, none of these issues appear to be problematic.
5. Results
5.1. Hypotheses Testing
23
In Table 4, I provide the estimation results of the two-stage least squares fixed effects model
(M1). First, I discuss hypotheses testing of antecedents of MBE. I begin with firm resources. As
hypothesized in H1, I find firms that have greater (i) business diversification (p<.01), (ii)
innovation focus (p<.05), and (iii) capital intensity (p<.01) have greater MBE. In contrast, I do
not find cash holdings or level of customer concentration to significantly impact MBE.
Consequently, H1 is moderately supported (3 of 5 expectations). Second, for TMT decision
characteristics, I find MBE is greater when (i) institutional investors have greater ownership
concentration (p<.05), (ii) executives have more long vs. short term compensation (p<.05), and
when firms have worse (iii) past product market and (iv) stock market performance (both p<.01).
However, I do not find support of a relationship between the presence of a marketing executive
as a top-5 employee and MBE. Hence, I find H2 is mostly supported (4 of 5 expectations). Third,
for environmental contingencies, I find that a firm has greater MBE only when the firm’s
industry has (i) greater average industry M/S ratio and (ii) worse market growth (both p<.01).
Consequently, H3 is partially supported (2 of 5 expectations). Finally, I find MBE has a positive
association with stock market returns (p<.01). Hence, H4 is supported.
In summary, I find that TMT decision characteristics have the greatest impact on MBE,
followed by firm resources, and environmental contingencies. In addition, I find that MBE has a
positive effect on firm stock returns. In other words, the results show that upper echelons and
RDT are more useful than contingency theory in explaining MBE, and that information
economics theory is useful in explaining MBE’s effect on firm performance. Consequently, the
results suggest that understanding (i) the TMTs decision characteristics related to marketing
allocation and (ii) the marketing function’s power, influence, and ability to demonstrate value are
particularly important traits in determining how firms allocate MBE, while (iii) the role of
industry and economy-wide external contingencies are still important but less useful.
24
5.2. Results Analyzing Only the Consequences of Marketing Budget Expenditures
In order to compare results from my focal model (M1) that considers both the antecedents and
consequences of MBE (equations 5 and 6) vs. the current marketing-finance interface literature
that typically only considers the consequences of MBE (equation 6), I estimate a second model
(M2). Interestingly, in M2 which does not account for the antecedents of MBE, I find a
significant negative effect between MBE and firm stock market return, which is in contrast to the
significant positive effect between MBE and firm stock market return found in M1. This is
important to note since current marketing-finance models that focus solely on the consequences
of MBE and often neglect and overlook its antecedents may be producing biased results.
5.3. Additional Analysis
To provide additional analysis and to account for possible alternative explanations of antecedents
and consequences of MBE, I now report further robustness tests.
What if there exists a non-linear effect on MBE? I test whether several variables (i.e.,
cash holdings, innovation, capital intensity, and institutional investor ownership) exhibit
decreasing returns of scale on their effect on MBE by adding additional quadratic terms for each
variable into equation 5.5 However, I do not find support that any of these variables exhibit
decreasing returns to scale.
What if alternative measures of marketing expenditures are employed? To assess
robustness of my conceptual model, I employ four alternative metrics of MBE: (i) the traditional
MBE measure of SG&A-R&D divided by sales, (ii) M/S ratio normalized by industry, (iii) raw
MBE, i.e., not scaled by sales, and (iv) share of overall marketing voice in the industry. There
are four interesting findings (see Web Appendices C and D for results). First, I find that cash
5
Because each measure is restricted in its values between 0 and 1, I first logit transform the variables, and then
square these terms.
25
holdings now associates with greater MBE when employing the (i) traditional MBE and (ii) M/S
ratio normalized by industry measures; hence, H1 is now supported for cash holdings. Second, I
find that marketing executive presence positively impacts MBE when MBE is operationalized by
(i) raw marketing expenditures and (ii) share of industry voice, adding further support to H2.
Third, I find that industry turbulence positively impacts share of industry voice; and industry
concentration negatively impacts M/S ratio normalized by industry. Consequently, H3 is now
supported for both these variables. Fourth, consistent with my earlier finding, I find that each
alternative MBE metric exhibits a different significant effect on a firm’s stock market return
when estimating models accounting for antecedents of MBE (equation 5 and 6) than when not
accounting for antecedents of MBE (only equation 6).
What if an alternative measure of industry is employed? I expand the definition of an
industry from GIC Sub-Industry to GIC Industry in order to capture greater variation in the
industry-wide environmental contingency variables. The results (see Web Appendix C)
employing an expanded industry classification are similar to our original classification except
institutional investor ownership is no longer found to be significant.
What if alternative firm performance metrics are employed? To assess whether the
contradictory results of consequences of MBE in M1 and M2 is exclusively related to my
compounded stock market return firm performance measure, I estimate additional models with
five alternative financial market performance metrics; i.e., (i) book to market value, (ii) market
value, (iii) sales, (iv) trading volatility, and (v) Tobin’s Q. Specifically, I substitute each metric
one-at-a-time for stock market return in equations 5 and 6 while holding all else the same.
Results for all the five alternative metrics except for market value show different effects on firm
financial performance when estimating models accounting for antecedents of MBE (equation 5
and 6) than when not accounting for antecedents of MBE (only equation 6). In other words, I
26
find that the parameter estimate of MBE on four of the five additional firm performance metrics
switch in significance level and/or in signs when comparing models that do and do not account
for MBEs antecedents, which adds robustness to the aforementioned findings with stock return.
6. Discussion
This work builds on seminal research conducted in the 70’s and 80’s (e.g., Buzzell and Gale
1987, Farris and Buzzell 1979, Lilien 1979) to examine antecedents and consequences of
marketing budget expenditures (MBE). Its goals were (1) to provide an updated and more
comprehensive theoretical framework than previous marketing-finance interface studies that
either do not investigate or just limited their analysis of antecedents of MBE to a few variables;
and (2) to compare models of firm financial market performance that do and do not account for
the antecedents of MBE. The main results summarized in Table 2 suggest that firms appropriate
marketing spending in accordance with (a) resource dependency theory since firms spend more
on MBE when marketing demonstrates value, need, and hence influence in the firm, such as
when the firm has greater levels of diversification, innovation, and capital intensity; (b) upper
echelons theory because firms spend more on MBE when the TMT comprises of a greater
concentration of institutional owners, is incentivized by more long-term compensation, and faces
less earnings pressure based on worse product market and stock market; and (c) contingency
theory as more spending on MBE is dependent on the level of industry average MBE spending
and economy-wide market declining conditions. The results also suggest in accordance with (d)
information economics theory that MBE signal to customers and investors clarity about the state
of the firm and its products, which associates with greater stock market performance. In addition,
the results across a variety of MBE and firm performance metrics show that models which do not
27
account for the antecedents and consequences of MBE produce inconsistent results with models
accounting for both.
Based on such results, I now focus on theoretical and managerial implications. By
blending rationale from resource dependency, upper echelons, and contingency theories, I
propose a conceptual framework that systematically accounts for various drivers of MBE such as
how marketing’s ability to demonstrate value for the firm impacts its resource allocation, why
TMT characteristics and the internal and external pressure TMTs face influences budgetary
decisions, and what is the role of industry and economy-wide environmental contingencies given
resource and managerial limitations. The empirical analysis finds that upper echelons theory is
the most useful theory in describing the antecedents of MBE, but there is broad support for each
theory across a variety of MBE metrics and model specifications. Such findings demonstrate the
importance of accounting for various firm resource allocation strategies, decision making, and
environmental attributes when determining MBE, and help extend the marketing-finance
literature by showing that MBE vary beyond accounting for only industry and year fixed effects.
Future research can also use this framework as a theoretical building block for models of either
antecedents or consequences of marketing efforts.
In addition, this research provides further insights on marketing’s value relevance to the
firm, addressing a continuously debated topic. First, I find a positive significant relationship
between MBE and stock market return when accounting for MBE’s antecedents. Consequently,
this helps demonstrate that marketing does indeed provide value relevance to the firm. Second,
when not accounting for MBE’s antecedents, I find a negative significant relationship between
MBE and stock market return, which is in contrast to the result when accounting for MBE’s
antecedents. I find further robustness for this result of different significant effects in models that
do and do not account for MBE’s antecedents when employing several variants of MBE and firm
28
financial performance measures. Such results demonstrate that researchers need to account for
MBE’s various antecedents or endogeneity when examining its consequences on firm financial
performance or risk having conflicting and spurious results about MBE’s impact. This finding
also may help explain why prior empirical tests on marketing’s effect on financial market
valuations have been mixed, as the majority of these studies have not taken a comprehensive
viewpoint to account for MBE’s antecedents.
The main managerial implications of this work are to help managers decide on how much
to spend on marketing and to show how such spending relates with firm financial performance.
First, in Table 3, I provide MBE benchmark information averaged across a large sample of firms
and years, which should help provide managers a starting point for determining their MBE.
Second, based on the results of my analysis, I identify conditions such as when firms have less
diversification, focus on innovation, and operate in industries that spend less on average on
MBE, where firms are likely to spend less on MBE. However, I find that by spending less on
MBE, these firms also attain worse stock market performance. Therefore, I recommend methods
identified through my analysis such as increasing long-term executive compensation and better
utilization of manufacturing capacity to help such firms build an organizational environment that
will be more conducive towards appropriating further MBE funding.
This work also adds to the debate about the consequences of myopic management
practices by examining trade-offs when appropriating funding to MBE. The results show that
better recent performing firms who pay their executives with a more short-term focus are in fact
acting myopically by spending less on MBE. However, the results also demonstrate that such
myopic behavior is not even beneficial over a relatively short timeline (i.e., monthly
compounded returns for a year). Therefore, a recommendation for stakeholders that are
concerned about their firms acting myopically is to solicit greater institutional investor
29
ownership concentration within their firm. While often characterized as traders, and not owners,
who focus on their own myopic short-term rates of return, results from my analysis shows that
firms with greater institutional investors spend more on MBE, which adds further support to the
growing viewpoint that institutional investors often counteract myopic management practices by
actively monitoring and disciplining managers to act in the firm’s long-term best interest (e.g.,
Bushee 1998).
My study has limitations that offer potential research opportunities. First, the merged
dataset used is based on larger, public, and more profitable firms, a common bias when working
with secondary datasets. While I control for firm characteristics like cash holdings and scale
several variables by firm sales, a future study could explore whether similar findings occur with
smaller and/or privately held firms. Second, I do not investigate what causes firms to change
their MBE. A future study could build a conceptual model of what causes anticipated or
unanticipated and reactive or proactive changes to the expenditures, and what performance
consequences such changes would create. This study could also examine whether investors truly
understand and correctly evaluate the actual reasons for why firms change their MBE, as
suggested by the efficient market hypothesis. Third, I am unable to examine how different types
of internal budgetary processes such as ad hoc, top-down, bottom-up, use of marketing mix
modeling, etc. influences the size of MBE. Fourth, while I investigate antecedents and
consequences of MBE, I am unsure their allocation. A common challenge with each of these
limitations that may hinder future research is in the collection of appropriate data and the
integration of theories and literatures across different disciplines. However, I hope this work and
these suggestions can act as a building block for such work.
30
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Figure 1. Conceptual Framework















Firm Resources
Firm Diversification
Cash Holdings
Innovation
Capital Intensity
Customer Concentration
Top Management
Decision Characteristics
Institutional Own. Concentration
Marketing Executive Presence
Executive Compensation
Past Product Market Performance
(ROAt-1)
Past Stock Market Performance
(Stock Returnt-1)
Environmental Contingencies
Industry Average Marketing
Budget Expenditures / Sales
Industry Turbulence
Industry Concentration
Market Growth
Market Sentiment Level
Marketing Budget
Expenditures
Firm Stock Market
Performance
36
Table 1. Variables, Definitions, and Dataset Sources
Variable
Definition
Dataset
Source(s) of
Inspiration or
Operationalization
Created from
Compustat
Annual
Dutta et al. (1999);
Fang et al. (2011,
2015); Kurt &
Hulland (2013);
Luo (2008); Mizik
(2010); Mizik &
Jacobson (2007);
Narasimhan et al.
(2006);
Swaminathan &
Moorman (2009);
Xiong &
Bharadwaj (2013)
Created from
Compustat
Historical
Industry
Business
Segments
Palepu (1985)
Compustat
Annual
Joseph &
Richardson (2002)
Created from
Compustat
Annual
Nath & Mahajan
(2008)
Compustat
Annual
Farris & Buzzell
(1979)
Created from
Compustat
Historical
Customer
Segments
Boyd et al. (2010)
Thomson Reuters
Institutional (13f)
Holdings: Stock
Ownership
Hartzell & Starks
(2003)
Marketing Budget Expenditures
Marketing
Budget
Expenditures
(MBE)
Definition: The amount a firm spends on
marketing as a percentage of its sales
Measure: (Selling, general and
administrative expenses [variable XSGA in
database] – research and development
expense [XRD] – broker/dealer commissions
[CFBD] – real estate commissions [CFERE]
– other commissions and fees [CFO] –
commissions and fees paid [CFPDO] –
foreign exchange income [FCA] – special
items [SPI] – total staff expense [XLR]) /
Sales [SALE]
Firm Resources
Definition: Level of total diversification of
firm sales across different business segments
1
Measure: ∑𝐼𝑖(𝑃𝑖 ∗ ln [ ]) where 𝑃𝑖 is
Firm
𝑃𝑖
Diversification business segment i’s share of total firm sales
and I is the firm’s total number of business
segments (Entropy measure)
Definition: All firm’s cash and securities
readily transferable to cash as a percentage
Cash Holdings of firm sales
Measure: Cash and Short-Term Investments
[CHE] / Total Assets [AT]
Definition: Level of R&D expenditures as a
percentage of firm sales
Innovation
Measure: R&D expenditures [XRD] / Sales
[SALE]
Definition: All costs, less accumulated
depreciation, of tangible fixed property used
Capital
in the production of revenue as a percentage
Intensity
of its sales
Measure: Total Net Property, Plant, and
Equipment [PPENT] / Sales [SALE]
Definition: Presence of a major customer
Measure: Whether a company discloses a
Customer
major customer in their 10-K reports as listed
Concentration
in Compustat Historical Segments (Industry),
Customer Segments
Top Management Decision Characteristics
Definition: Level of concentration of a firm’s
Level of
stock that are owned by institutional
Institutional
investors
Ownership
Measure: HHI index of institutional investor
37
Marketing
Executive
Presence
Long vs. Short
Term
Compensation
ownership concentration
Definition: If a marketing executive is one of
top 5-paid firm employees
Measure: Whether a top 5 highest-paid
executive in the firm had a job title with any
of the following (and variations) of the
words: marketing, branding, consumer,
customer, advertising, distribution, place,
pricing, products, or promotions
Definition: Average equity-to-bonus ratio of
firm’s top 5 highest-paid executives
Measure: Average of top 5 highest-paid
executives: (Total equity as percentage of
overall compensation – bonus as percentage
of overall compensation) / (total equity as
percentage of overall compensation + bonus
as percentage of overall compensation)
Definition: Total return on assets in fiscal
year prior to current year
Measure: Income Before Extraordinary
Items t-1[IB] /Total Assetst-1[AT]
Past Product
Market
Performance
(ROAt-1)
Past Stock
Definition: Firm stock market return in
Market
fiscal year prior to current year
Performance
Measure: Compounded monthly stock return
(Stock Return
for the fiscal year prior to current fiscal year
t -1)
Environmental Characteristics
Definition: Industry M/S ratio average
Industry M/S
Measure: GIC Sub-Industry M/S ratio
Ratio Average
average
Definition: Level of competitive instability in
Industry
an industry over a 3 year period
Turbulence
Measure: Absolute change in the GIC SubIndustry HHI over a 3 year period
Definition: Level of sales concentration in
Industry
an industry
Concentration Measure: GIC Sub-Industry HHI
concentration
Definition: Yearly financial market return
Market
Measure: Yearly monthly-average of S&P
Growth
500 index stock returns [SPRTRN]
Definition: Yearly financial market
Market
sentiment level
Sentiment
Measure: Yearly monthly-average S&P 500
Level
index level [SPINDX]
Firm Performance
Stock Market
Definition: Firm stock market return in the
Performance
current fiscal year
(Stock
Measure: Compounded monthly stock return
Returnt)
for the current fiscal year
Summ.
Created from
ExecuComp
Boyd et al. (2010)
Created from
ExecuComp
Currim et al. (2012)
Compustat
Annual
Luo &
Bhattacharya
(2006)
Created from
CRSP
Currim et al. (2012)
Created from
Compustat
Annual
Boyd et al. (2010)
Created from
Compustat
Annual
Carpenter &
Westphal (2001)
Created from
Compustat
Annual
Kurt & Hulland
(2013)
Created from
CRSP Stock
Market Indices
Steenkamp & Fang
(2011)
Created from
CRSP Stock
Market Indices
Baker & Wurgler
(2006)
Created from
CRSP
Currim et al. (2012)
38
Table 2. Summary of Hypotheses when Accounting for Antecedents and Consequences of
Marketing Budget Expenditures
Variable
Antecedents of Marketing Budget Expenditures
Firm Resources
Firm Diversification
Cash Holdings
Innovation
Capital Intensity
Customer Concentration
Top Management Characteristics
Institutional Ownership Concentration
Marketing Executive Presence
Long vs. Short Term Compensation
Past Product Market Performance (ROAt-1)
Past Stock Market Return (Stock Returnt-1)
Environmental Contingencies
Industry M/S Ratio Average
Industry Turbulence
Industry Concentration
Market Return on the S&P 500 Index
Market Sentiment S&P 500 Index Level
Consequences of Marketing Budget Expenditures
Stock Market Return (Stock Returnt)
Hypothesis
If Supported in
Main Model
+
+
+
+
-
Yes
No
Yes
Yes
No
+
+
+
-
Yes
No
Yes
Yes
Yes
+
+
+
Yes
No
No
Yes
No
+
Yes
39
Table 3. Overall Descriptive Statistics by Year
Panel A. Number of Firms and Industries per Year
Year
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Overall
Firms
39
63
80
111
167
182
196
210
207
208
218
233
260
2,174
(369 unique firms)
Industries
18
23
28
33
45
46
45
47
47
47
47
44
47
517
(53 unique industries)
Panel B. Yearly Marketing Expenditures
0.25
0.2
0.15
0.1
0.05
0
M/S Ratio
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
1500
1250
1000
750
500
250
0
Raw Marketing Expenditures (in thousands of dollars)
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Panel C. Descriptive Statistics
Variable
Marketing Budget Expenditures
M/S Ratio
Raw Marketing Expenditures
Firm Resources
Firm Diversification
Cash Holdings
Innovation (R&D Intensity)
Capital Intensity
Customer Concentration
Top Management Characteristics
Institutional Ownership Concentration
Marketing Executive Presence
Long vs. Short Term Compensation
(Equity to Bonus Ratio)
Past Product Market Performance (ROAt-1)
Past Stock Market Return (Stock Returnt-1)
Environmental Contingencies
Industry M/S Ratio Average
Industry Turbulence
Industry Concentration
Market Return on the S&P 500 Index
Market Sentiment S&P 500 Index Level
Firm Performance
Stock Market Return (Stock Returnt)
Mean
Std.
Dev.
.23
1,112,516
.13
290,083
.40
.33
.08
.22
.45
.62
.51
.10
.18
---
.05
.23
.03
---
.74
.07
.20
.44
.08
.58
.24
.07
.30
.00
1,217
.09
.09
.19
.02
178
.13
.43
40
Table 4. Results of Antecedent and Consequences of Marketing Budget Expenditures
Model Number
M1
M2
Antecedents &
Consequences
Type of Model
Consequences
Only
Antecedents of Marketing Budget Expenditures (Equation 5)
Firm Diversification
.06 ***
--Cash Holdings
.03
--Innovation
.88 **
--***
Capital Intensity
.52
--Customer Concentration
.02
--**
Institutional Ownership Concentration
1.09
--Marketing Executive Presence
.00
--**
Long vs. Short Term Compensation
.04
--***
Past ROA
-.49
--Past Stock Market Return
-.08 ***
--***
Industry M/S Ratio Average
1.70
--Industry Turbulence
-.26 *
--Industry Concentration
-.13
--Market Return on the S&P 500 Index
-1.15 **
--Market Sentiment S&P 500 Index Level
-.00
--Consequences of Marketing Budget Expenditures (Equation 6)
Marketing Budget Expenditures
.45 ***
-.08***
*
Firm Diversification
-.05
-.02
Innovation
-1.21 **
-.59**
Capital Intensity
-.32
-.04
Cash Holdings
.03
-.04
Marketing Executive Presence
-.02
-.02
***
Past ROA
-.87
-1.12***
Market Return on the S&P 500 Index
14.43 ***
13.82***
***
Market Sentiment S&P 500 Index Level
-.00
-.00 ***
Model Diagnostics
Total N
2128
2128
Total Firms
323
323
***
Wald χ2 Statistic
731.29
--F Statistic
--66.65***
Overall r2
.04
.17
Г (interclass correlation)
.56
.40
Notes; *p<.10; **p<.05, ***p<.01;
1
Web Appendix A. The Operational Measures, Definitions, and Dataset and Literature
Sources of Variables Employed
Marketing Budget Expenditure Measures. My main variable of interest is MBE, which
is measured by the firm’s marketing to sales (M/S) ratio. The operational basis for this metric
emanates from the following three-step process. First, to obtain a raw MBE value, I follow
previous marketing studies (e.g., Dutta et al. 1999, Fang et al. 2011, 2015, Kurt and Hulland
2013, Luo 2008, Mizik 2010, Mizik and Jacobson 2007, Narasimhan et al. 2006, Swaminathan
and Moorman 2009, Xiong and Bharadwaj 2013) by subtracting R&D expenses from selling,
general, and administrative (SG&A) expenses values from the Compustat Annual dataset. Dutta,
Narasimhan, and Rajiv (1999, p. 556) argue that SG&A is “a good proxy for the amount the firm
spends on its market research, sales effort, trade promotion expenses, and other [marketing]
related activities.” Then, to further account for general and administrative costs included in the
SG&A based measure such as bad debt, legal, severance, and staff expenses, commissions,
discontinued operations, foreign currency adjustments, recovery of allowances for losses, and
restaurant and retail companies’ preopening, closing, and rental costs, I subtract out foreign
exchange income (FCA), special items (SPI), total staff expense (XLR), commissions and fees
paid (CFPDO), and broker/dealer (CFBD), real estate (CFERE), and other commissions and fees
(CFO). Last, I scale this resultant value by the firm’s sales to obtain an M/S ratio.
Antecedents and Consequences of Marketing Budget Expenditure Measures. The
operational measures for the antecedents and consequences proposed in Figure 1 are taken from
a variety of extant literatures. Table 2 provides a summary of the definitions, measures, and
dataset sources. First, for measures of firm resources, firm diversification is the entropy level of
concentration (cf. Palepu 1985) from different business segments listed in Compustat Historical
Industry Segments; and customer concentration is whether a company discloses a major
2
customer in their 10-K reports as listed in Compustat Historical Customer Segments (Boyd et al.
2010). Cash holdings are all firm cash and securities readily transferable to cash scaled by firm
sales (Joseph and Richardson 2002), innovation is calculated as the firm’s allocation of research
and development expenses scaled by sales (Nath and Mahajan 2008), and capital intensity is all
costs, less accumulated depreciation, of property used in the production of revenue as a
percentage of its sales (Farris and Buzzell 1979), with each measure calculated from Compustat
Annual. Second, for top management characteristics, level of institutional ownership is the HHI
index of institutional investor ownership concentration as reported in their 13f quarterly filings
listed in the Thomson Reuters Institutional Holdings dataset (Hartzell and Starks 2003).
Marketing executive presence is whether one of the top-5 paid executives in the firm listed in
ExecuComp has a marketing job title (Boyd et al. 2010), and long vs. short-term compensation is
the firm’s top-5 paid executives average equity to bonus ratio (Currim et al. 2012) also from
ExecuComp. Past product market performance is the firm’s return on assets (ROA), computed
by dividing income before extraordinary items divided by total firm assets, with both variables
listed in Compustat Annual (Luo and Bhattacharya 2006).
Third, for environmental contingencies, industry M/S ratio average is the industry firm
average (Boyd et al. 2010), industry turbulence is the absolute change in industry HHI
concentration over the prior 3 year period (Carpenter and Westphal 2001), and industry
concentration is the industry HHI (Kurt and Hulland 2013), with each industry characteristic
calculated in Compustat Annual prior to the full dataset merger. Market growth is computed as
the yearly monthly-averages of the return of the S&P 500 index, and market sentiment is
calculate as the yearly monthly-averages of S&P 500 index level, with both computed from the
CRSP Stock Market Indices. Last, the operational measures for the firm’s stock market return is
3
calculated by compounding the firm’s monthly stock return for the fiscal year, based on CRSP
data (Currim et al. 2012). The measure for past stock market return is analogous, except based
on the year prior to the current fiscal period.
4
Firm
Diversification
Cash
Holdings
-.23 1
Innovation
-.27 .63
Capital
Intensity
Customer
Conc.
Institutional
Own. Conc.
Marketing
Executive
Executive
Comp.
Past
ROA
Past
Stock Return
Industry
M/S Average
Industry
Turbulence
Industry Conc.
Market
Growth
Market
Sentiment Level
Stock Return
MBE
Market
Sentiment Level
Market Growth
Industry
M/S Average
Industry
Turbulence
Industry
Conc.
Past Stk Return
Past ROA
Exec Comp
Mktg Exec
Customer
Conc.
Institutional
Own. Conc.
Capital Intensity
Innovation
Cash Holdings
Firm
Diversification
Web Appendix B. Correlation Matrix
1
1
.01 -.01 -.01
1
-.07 .19 .20 .01
1
-.09 .15 .08 -.02 .01
1
-.02 .01 .01 .00 .07 .03
1
-.02 .05 .07 -.08 -.11 -.05 .06
1
-.10 -.14 -.23 -.07 -.06 -.12 -.02 .07
1
-.01 .06 .00 -.04 .03 -.02 .00 .00 .01
1
-.24 .20 .28 -.17 -.08 .04 -.02 .03 .09 -.04
1
-.09 .07 .03 .06 .05 .06 -.01 -.13 -.01 .03 .00
1
-.08 .00 -.02 .08 .00 -.03 .01 .01 .05 .03 -.17 .34
1
.00 .01 -.01 .00 .03 -.01 .02 .00 -.04 .03 -.01 -.05 -.04
1
-.03 -.03 .01 -.02 -.03 -.01 -.01 .16 .08 .13 -.02 -.10 -.04 .58
1
MBE
-.23 .23 .32 -.16 -.11 .15 -.01 .05 -.07 -.06 .64
-.02 -.11 -.03 -.02
Stock Return
.01 .00 -.03 -.06 .01 -.07 .00 -.10 -.12 -.11 .01
.06 -.02 .36
1
.02 -.05
1
5
Web Appendix C. Additional Analysis of Antecedent and Consequences of Marketing
Budget Expenditures
Model Number
Type of Industry
Classification
Type of M/S
Ratio for MBE
M1
M2
M3
Sub GIC Industries
M4
M5
M6
M7
M8
GIC Industries
New
Traditional
New
Traditional
(MAIN MODEL)
Antec. & Consq. Antec. & Consq. Antec. & Consq. Antec. & Consq
Type of Model
Consq.
Only
Consq.
Only
Consq.
Only
Consq. . Only
Antecedents of Marketing Budget Expenditures (Equation 5)
Firm Divers.
.06***
--.01
--.05**
--.01
--Cash Holdings
.03
--.07***
--.02
--.06***
--***
**
***
**
Innovation
.88
--.74
--.81
--.75
--Capital Intensity
.52***
--.26***
--.63***
--.34***
--Customer Conc.
.02
---.01
--.02
---.01
--Inst. Own. Conc.
1.09**
--.84**
--.85
--.86**
--Mktg. Exec.
.00
--.01
---.01
--.01
--Exec. Comp.
.04**
--.01
--.05***
--.01
--***
***
***
***
ROAt-1
-.49
---.59
---.45
---.54
--Stock Returnt-1
-.08***
---.04***
---.07***
---.04***
--*
***
**
***
Ind. M/S Ratio
1.70
--.43
--1.63
--.40
--Ind. Turb.
-.26*
---.04
---.05
--.04
--Ind. Conc.
-.13
---.01
--.03
--.04
--**
***
Market Return
-1.15
--.04
---1.37
--.13
--Market Sent.
-.00
---.00
--.00
---.00
--Consequences of Marketing Budget Expenditures (Equation 6)
MBE
.45*** -.08***
1.20**
-.07
.79*** -.09***
1.37***
-.09*
Model Diagnostics
Total N
2128
2128
2128
2128
2214
2214
2214
2214
Total Firms
323
323
323
323
344
344
344
344
Wald χ2 Statistic 731.29***
--662.73***
--609.64***
--650.59***
--***
***
***
F Statistic
--66.65
--65.62
--71.07
--69.69***
Overall r2
.04
.17
.01
.17
.01
.17
.01
.17
Г (interclass
.56
.40
.84
.40
.72
.43
.87
.43
correlation)
Notes; Traditional MBE is calculated from Compustat as (SG&A-R&D)/Sales; Control variables of consequences of
marketing budget expenditures not shown to conserve space; Classifying industries by GIC Industries results in a
slightly larger database of 86 firm-year observations and 21 firms because all industries with less than 3 firms were
eliminated from the merged database. *p<.10; **p<.05, ***p<.01;
6
Web Appendix D. Additional Analysis of Antecedent and Consequences of
Alternative Marketing Budget Expenditure Measures
Model Number
M1
M2
M9
M10
M11
M12
M/S Ratio
Mktg. Dev. from
Variable
Raw Mktg Exp.
(MAIN MODEL)
Industry
Antec. & Consq. Antec. & Consq. Antec. & Consq.
Type of Model
Consq.
Only
Consq.
Only
Consq.
Only
Antecedents of Marketing Budget Expenditures (Equation 5)
Firm Divers.
.06***
--.00
--.08 ***
--**
Cash Holdings
.03
--.00
---.27 ***
--**
***
Innovation
.88
--.05
---.03
--Capital Intensity
.52***
--.04 **
---.53 ***
--Customer Conc.
.02
--.00
---.05 **
--Inst. Own. Conc.
1.09**
--.11 **
---1.46 ***
--***
Mktg. Exec.
.00
--.00
--.07
--Exec. Comp.
.04**
--.00
--.25 ***
--ROAt-1
-.49***
---.01
---.06
--***
***
***
Stock Returnt-1
-.08
---.00
---.09
--Ind. M/S Ratio
1.70***
---.25 ***
--.39
--Ind. Turb.
-.26*
---.02 ***
---.56 ***
--Ind. Conc.
-.13
---.02 **
---.08
--**
**
**
Market Return
-1.15
---.11
---1.36
--Market Sent.
-.00
--.00
--.00 ***
--Consequences of Marketing Budget Expenditures (Equation 6)
MBE
.45*** -.08***
-1.35
-.87**
.05
-.18***
Model Diagnostics
Total N
2128
2128
2108
2108
2128
2128
Total Firms
323
323
321
321
323
323
Wald χ2 Statistic 731.29***
--870.23***
--840.95***
--F Statistic
--66.65***
--66.34***
--72.91***
Overall r2
.04
.17
.17
.18
.11
.11
Г (interclass
.56
.40
.44
.43
.43
.50
correlation)
M13
M14
Share of Voice
Antec. & Consq
Consq. . Only
.11 **
-.19 *
.03
.56 **
-.04
-.73
.08 **
-.05
.50 **
.03
-1.75 ***
.65 **
1.17 ***
-2.50 ***
.00 **
-------------------------------
-.05
-.04***
2128
2128
323
323
870.00***
----66.34***
.17
.17
.40
Notes; Control variables of consequences of marketing budget expenditures not shown to conserve space;
Share of Voice is logit-transformed before estimation since its values are restricted between 0 and 1.
*p<.10; **p<.05, ***p<.01;
.39