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A Meta-Analysis of the Determinants of Organic Sales Growth

S. Cem Bahadir

Assistant Professor of Marketing

University of South Carolina

Moore School of Business

1705 College Street

Columbia, SC, 29208

Phone: 803-777-6842

cem.bahadir@moore.sc.edu

Sundar Bharadwaj

Professor of Marketing

Emory University

Goizueta Business School

1300 Clifton Road

Atlanta, GA 30322

Phone: 404-727-2646

sundar_bharadwaj@bus.emory.edu

Michael Parzen

Associate Professor of Marketing

Emory University

Goizueta Business School

1300 Clifton Road

Atlanta, GA 30322

Phone: 404-727-6110

michael_parzen@bus.emory.edu

* The authors would like to thank Jade S. Dekinder, Ajay Kohli, Kapil Tuli, the participants of

the 2005 INFORMS Marketing Science Conference at Emory University, and the 36th

Annual

Haring Symposium at Indiana University for their comments on an earlier version of this paper.

To be published in the International Journal of Research in Marketing

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A Meta-Analysis of the Determinants of Organic Sales Growth

We present the results of a meta-analysis on drivers of organic sales growth, conducted using a

Hierarchical Bayes estimation technique. Based on a comprehensive review of a diverse set of

literatures on organic sales growth, we identify eleven drivers of organic sales growth

performance of firms: (i) innovation, (ii) marketing orientation, (iii) advertising, (iv)

interorganizational networks, (v) entrepreneurial orientation, (vi) management capacity, (vii)

firm age, (viii) firm size, (ix) competition, (x) munificence, and (xi) dynamism. Among the

variables that are under a manager’s control, innovation, advertising, market orientation,

interorganizational networks, entrepreneurial orientation, and managerial capacity serve as

positive drivers of organic growth. Older firms and firms operating in dynamic and competitive

environments face constraints in terms of organic growth. We find that the omission of

marketing variables in empirical models biases the elasticities of eight of the drivers of organic

growth. Three characteristics of the study design affect the elasticity of organic growth drivers:

using cross-sectional data instead of panel data, using growth rates instead of absolute change as

operationalization of growth and using market share instead of sales as a measure of revenues.

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Growth is an issue of paramount importance to Wall Street. Companies that grew slower

than GDP were five times less likely to survive the next business cycle compared to firms that

grew faster than GDP (Baghai, Smit, & Viguerie, 2007). Consequently, managers have been

motivated by boards of directors to deliver growth. Firms follow either one or a combination of

organic and inorganic growth strategies. Organic growth involves developing new products

and/or taking existing products to new markets. In contrast, inorganic growth involves acquiring

businesses or strategic assets rather than building these assets internally to generate growth. For a

variety of reasons, such as slack resources (cash on hand) or lack of internal capabilities, many

firms opt to follow inorganic growth strategies. Over the years, the inorganic growth strategy has

proven to be very risky and expensive to implement. Acquirers face severe integration challenges

following mergers and acquisitions (e.g., Capron, Dussauge, & Mitchell, 1998; Homburg &

Bucerius, 2005). Integration and other problems surrounding M&As cause a value loss for the

acquirer (Billett, King, & Mauer, 2004; Hackbarth & Morellec, 2008). Consequently, these

challenges imposed by inorganic growth have encouraged managers to generate growth

organically. The question that immediately follows is: what are the drivers of organic growth?

Many researchers have studied the drivers of organic firm growth (e.g., Nobeoka &

Cusumano, 1997; Parthasarthy & Sethi, 1993). The theoretical and empirical work has been

dispersed across disciplines such as business, economics, and sociology, leading to heterogeneity

in model specification, research methods and samples studied. Such variability constrains the

ability of any one discipline to integrate empirical findings without examining the impact of the

heterogeneity in study design characteristics on the determinants of organic firm growth.

The objective of this paper is to integrate empirical findings on the drivers of organic

firm growth. In this paper, we use sales or market share growth of the firm generated from

organic sources as a proxy for organic growth1. A meta-analysis has not yet been conducted

solely on the drivers of organic growth, despite its theoretical and practical importance. Capon,

1 In this paper, we use the terms organic growth, organic sales growth and sales growth interchangeably.

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Farley and Hoenig (1990) provide the only meta-analysis that includes firm growth as a variable

of interest among a broader set of drivers of performance. Moreover, they do not distinguish

between the different measures of growth. Weinzimmer, Nystrom and Freeman (1998) argue that

alternate measures of growth may lead to asymmetric findings on the impact of drivers of growth

because the processes of growth in assets, employees and sales may differ. In addition, the

accumulation of empirical research on growth over the past 15 years holds the potential for

generalizations on new drivers of organic growth.

This research study contributes to the field by presenting the results of two sets of

analyses. The first set of meta-analysis results pertains to the mean elasticity of organic growth

drivers. Researchers have frequently examined eleven constructs across disciplines as

antecedents of organic growth: (i) innovation, (ii) marketing orientation, (iii) advertising, (iv)

interorganizational networks, (v) entrepreneurial orientation, (vi) management capacity, (vii)

firm age, (viii) firm size, (ix) competition, (x) munificence, and (xi) dynamism. The meta-

analytic results indicate that the signs of all the elasticities are consistent with the theory. For

example, innovation, advertising, and market orientation have positive growth elasticities. It is

noteworthy that management capacity and entrepreneurial orientation have effect sizes

comparable to those of innovation and advertising, although more empirical work is needed on

management capacity and entrepreneurial orientation. Among the marketplace factors,

dynamism has the highest impact on firm growth, highlighting the impact of volatile

environments on firm growth.

A second meta-analysis pertains to study characteristics that explain the variability in

mean elasticity parameters. In meta-analysis convention, parameters vary around a non-zero

mean with identifiable systematic components in this variability (Farley & Lehmann, 1994). We

test for the impact of a diverse set of moderators of these effect sizes, namely, model

specification, research environment, estimation method, and measurement. We examine the

impact of omitting various antecedents of firm growth (e.g., firm size) from the model

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specification on the elasticity of the other determinants of organic sales growth. Our findings

indicate that the omission of marketing variables (e.g., advertising) creates substantial biasing

effects on the elasticities of eight of the firm growth determinants. The results on research

environment, estimation method, and measurement provide guidance for future research by

alerting researchers to the impact of various study design choices. For example, using market

share growth instead of sales growth as the measure of growth affects the elasticities of all the

marketplace factors included in the meta-analysis.

In the following section, we discuss the theoretical lenses used to examine organic

growth. Then, we discuss the framework of our study and the meta-analysis procedures.

CONCEPTUAL BACKGROUND

Theories that explain why and/or how firms grow focus either on internal firm-specific

factors or on marketplace/environmental factors as drivers of a firm’s organic growth. The

studies in the first category focus on the firm’s strategies, processes, and characteristics as

primary sources of growth. In contrast, the research focusing on the marketplace examines the

competitive and other dynamics as the primary factor behind a firm’s organic growth. Next, we

discuss the theories in each category and the growth determinants based on these theories.

Firm-Focused Theories of Growth

Endogenous Growth Theory: Evolutionary Routines & The Resource Based View. At

different levels of analysis, a stream of theories focusing on strategic resources and processes is

proposed to explain growth. Based on a country level of analysis, Romer (1986) argues that

long-run growth is driven by accumulation and transmission of knowledge. At the firm level,

early work by Penrose (1959) viewed the growth of a firm as a function of managerial resources,

since managers are a body of tacit knowledge about the workings of the firm and the best

utilization of internal assets and competencies. Subsequently, theoretical research under the

same umbrella has emphasized theories focusing on organizational routines, which are defined

as the storage of an organization’s operational knowledge (e.g., Nelson & Winter, 1982).

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Researchers considered these routines and the firm’s ability to renew these routines according to

market dynamics to determine its growth. Finally, building on these two earlier firm level views

of growth, the resource-based view of the firm (RBV) defines firms as a collection of strategic

resources, assets, and skills/capabilities (e.g., Amit & Schoemaker, 1993; Bharadwaj,

Varadarajan & Fahy, 1993) that collectively determine its growth.

Researchers have also examined a wide variety of constructs, rooted in endogenous

growth theory, evolutionary and resource-based approaches, as predictors of firm growth. Across

disciplines, the most commonly studied construct is innovation, which refers to all the activities

that a firm engages in to develop new products and services (Scherer, 1965). Innovation is

viewed as a fundamental process or as an organizational capability that generates organizational

knowledge. This strategic routine generates knowledge, which leads to new products, greater

organic growth and subsequently better financial performance (Geroski, 1989; Stremersch &

Tellis, 2004).

In marketing, researchers have examined the process of obtaining, disseminating, and

responding to market information (i.e., market orientation) as an important antecedent factor to

organic growth. This literature views market orientation as a strategic routine of the company

that generates market knowledge critical to firm growth (Bharadwaj, Clark & Kulviwat, 2005).

Market-oriented firms are likely to serve their customers better than their competitors by using

the information they collect (Jaworski & Kohli, 1993). Consequently, market-oriented

companies should grow at higher rates than their competitors (Gotteland & Boule, 2006).

Marketing’s impact on growth is not restricted to market orientation. Marketing

expenditures enable the firm to build strategic assets such as brands (Srivastava, Shervani, &

Fahey, 1998). Advertising enables companies to inform customers of new products, or to

highlight superior product/service attributes, which contributes to sales growth. An emphasis on

marketing investments even helps firms grow sales and share in recessionary market conditions

(Srinivasan, Rangaswamy & Lilien, 2005).

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Firms form relationships with a wide variety of entities outside their organizational

boundaries (e.g., other firms, suppliers, buyers, financial institutions) (Park & Luo, 2001).

Participating in networks and engaging in collaboration with various entities provide the

following advantages to the firm: they (a) allow the firm to reach out to key resources from its

environment, such as information, access, capital, goods and services; (b) enable the firm to

reduce transactions by information sharing and development of norms; and (c) enable firms to

protect their customer bases from competitive threats by raising entry barriers to key suppliers

through coordination (Gulati, Nohria, & Zaheer, 2000). As a result of these benefits, the linkages

between the firm and external entities can be viewed as strategic assets. Access to key resources

such as technology and marketing expertise, as well as the capability to coordinate with

suppliers, will lead firms to higher sales growth rates via faster and more effective new product

development and market responsiveness (Lee, Johnson & Grewal, 2008).

Finally, drawing on the endogenous growth theory, several researchers argue that

organizational culture and firm orientation can also be regarded as strategic assets of firms (Hitt,

Ireland, Camp, & Sexton, 2001). Specifically, entrepreneurial orientation, defined as the extent

to which the firm has a propensity to take risks, innovate and be proactive (Lumkpin & Dess,

1996), is studied as a predictor of organic growth. Firms that are entrepreneurially oriented will

be able to determine business opportunities better and faster than their competitors (e.g., Lee,

Lee, & Pennings, 2001). Such companies are less reluctant to take risks so they will be more

agile than less entrepreneurial firms in taking advantage of business opportunities. Consequently,

they are expected to grow faster than their competitors.

Coordination and Adjustment. Penrose (1959) proposed one of the earlier theories

specific to firm growth, with a focus on coordination and adjustment issues. This work

emphasizes one factor, namely management resource, as an important predictor of growth above

all other factors. In fact, as highlighted by Trau (1996) and Mahoney and Pandian (1992), the

Penrosian view considers the growth of the firm to be limited in the long-run only by its internal

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management resources. The underlying logic behind this is that total management capacity is

divided between running the company in its current state and exploring new business

opportunities. In order to capitalize on this new growth, firms need to hire new managers. New

managers, however, have to be trained by existing managers. As a paradoxical consequence,

existing managers become more time-constrained. These managerial diseconomies of growth

(called the “Penrose effect”) suggest that a firm’s long-term growth will be affected by the skills

and the number of its managers.

Researchers have also investigated coordination and adjustment issues from an ownership

perspective (e.g., McEachern, 1978). The fundamental argument is that misalignment of

managers’ and owners’ interests will adversely affect firm growth. Managers may seek strategies

to secure their employment as opposed to those that achieve optimal growth. Usually, self-

interest seeking managerial behavior leads to higher-than-optimal growth (e.g, Williamson,

1964) due to managers’ preference for expanding the firm to reduce its likelihood of bankruptcy.

Organizational Theory. The organizational theory approach explains firm growth as a

function of organizational characteristics. According to this approach, organizational

characteristics affect how agents make decisions. One such characteristic is the age of the

organization. Firms may suffer from being “young” due to lack of reputation or they can suffer

from being “old” due to the inability to adapt to changing environmental conditions (the liability

of senescence, Barron, West & Hannan, 1994). Researchers have tested the relationship between

firm age and growth in order to provide empirical evidence for these arguments.

Organizational theorists have also investigated the firm size and growth relationship

extensively. There are divergent views on this relationship. For example, some theorists argue

that larger firms tend to be more bureaucratic than smaller firms. Bureaucracy prevents

organizations from acting on opportunities (Haveman, 1993). Therefore, they grow at lower rates

than smaller organizations. However, others argue that larger organizations have more slack

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resources, enabling them to withstand changes in the environment (Chandy & Tellis, 1998).

These slack resources should be available to generate higher growth.

Gibrat (1931) argues that growth is proportional to size and that the factor of

proportionality is random. In other words, proportional growth rates are independent of size

(Barron, West & Hannan, 1994; Sutton, 1997). Numerous studies have tested this theory, called

“Gibrat’s Law” (e.g., Evans, 1987; Geroski, 2005; Lucas, 1978), and the results have been

mixed.

Environment-Focused Theories of Firm Growth

Industrial Organization. Industrial organization (I-O) theory explains firm growth largely

based on industry structure (Scherer 1980; Bharadwaj & Varadarajan, 2004). According to the I-

O school, firm-specific advantages will be competed away over time. Therefore, a firm’s organic

growth primarily depends on industry characteristics and how the company positions itself vis-à-

vis the industry structure. Researchers have primarily focused on competitive intensity in the

industry as a predictor of growth. As the competition intensifies, firms find it challenging to

achieve high sales growth rates.

Population Ecology. While most of the work in organization theory focuses on

organizational characteristics, some organizational theorists propose environmental

characteristics other than competition as predictors of firm growth. These characteristics are

usually associated with theories of population ecology and/or resource dependence. The most

commonly discussed environmental dimensions are munificence, dynamism, and complexity

(Aldrich, 1979). Munificence is defined as the availability of environmental resource to support

growth (Dess & Beard, 1984). For example, a firm will be able to invest in new products,

operational or service processes, and achieve higher growth rates in an industry with an

abundance of credit as opposed to in an industry where such credit is unavailable.

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Environmental dynamism refers to volatility and instability in an industry (Aldrich,

1979). In highly dynamic environments, firms find it difficult to predict customer preferences

and demand, which is likely to lead to inefficient decisions in new product development and

marketing. Consequently, firms are not able to achieve high growth rates. Environmental

complexity refers to the heterogeneity and range of an organization’s activities in relation to its

environment (Child, 1972). Organizations competing in industries that require many different

inputs or that produce many outputs should find resource acquisition or disposal of output more

complex than organizations competing in industries with fewer inputs and outputs (Dess &

Beard, 1984). Consequently, the difficulty of operating in an environment of heterogeneous

inputs and/or outputs should inhibit firm growth.

EMPIRICAL LITERATURE REVIEW

Sampling Frame

We collected studies published between 1960 and 2008 that examined the relationship

between an independent variable and firm-level sales/market share growth using regression

analysis. We excluded the studies that examined sales/market share growth in M&A settings.

Effectively, we only included studies that examined growth from organic sources. We searched

databases such as ABI Inform, Business Source Premier and JStor by using keywords. We also

incorporated the studies from Capon, Farley and Hoenig (1990) that conformed to our sampling

frame. These studies were published in a variety of disciplines: (1) marketing – Journal of

Marketing, Journal of Marketing Research, Journal of Academy of Marketing Science,

Marketing Science, International Journal of Research in Marketing, Journal of Product

Innovation and Management, (2) management and sociology – Academy of Management

Journal, Administrative Science Quarterly, Management Science, Journal of International

Business Studies, Strategic Management Journal, Journal of Business Venturing, Journal of

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Business Research, Organization Science, Entrepreneurship Theory & Practice, (3) economics –

Journal of Industrial Economics, American Economic Review, Journal of Applied Econometrics,

Applied Economics, and (4) political science – Journal of Political Science. We also searched

the UMI Dissertation database to overcome publication bias problems and included unpublished

dissertations that studied organic growth as a dependent variable (see Appendix).

Through a review of the literature on the determinants of organic sales growth, we

identified eleven antecedent variables that have been frequently studied, namely: (i) innovation,

(ii) market orientation, (iii) advertising, (iv) interorganizational networks, (v) entrepreneurial

orientation, (vi) management capacity, (vii) firm age, (viii) firm size, (ix) competition, (x)

munificence, and (xi) dynamism (Table 1). Innovation is one of the most frequently studied firm-

specific determinants of organic sales growth. The frequency with which inter-organizational

networks was studied as an antecedent construct underscores the increasing interest in social

network theory as an explanatory mechanism for organic growth. The literature survey also

highlights a need for more research examining the impacts of advertising and market orientation

on organic growth. Of the three variables that capture various facets of the environment,

competition and munificence are the most common empirically-examined antecedent variables in

relation to organic growth. The frequency with which these variables are studied highlights the

dominance of environment characteristics in explaining firm growth.

Basic Results

We performed a count of positive and negative coefficients of the eleven constructs

following the approach of Capon et al. (1990) — see Table 2. Most of the directional

relationships conform to extant theory. Innovation positively affects organic growth in

accordance with economic theories of growth (e.g., Solow, 1956; Winter, 1987). Both simple

count and significance patterns confirm the extant theory on market orientation and advertising

as antecedents of organic growth. For example, we did not encounter a single negative

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significant parameter estimate of the market orientation construct. The mean elasticity of

management capacity does not conform to extant theory, which posits that firms should grow at

higher rates as their management capacity increases (Penrose, 1959). This surprising result may

be due to study design characteristics such as omission of relevant variables or temporal

specification, which we investigate in the moderator analysis. However, the majority of

significant management capacity estimates are positive.

We use elasticities from regression models in the subsequent analysis. If a study uses the

logarithm of sales growth as the dependent variable and the logarithm of the independent

variable, we use the coefficient of the independent variable as it stands. We multiply the beta

coefficients by the ratio of the means of the relevant independent and dependent variable (i.e.,

Mean of X / Mean of Y) to compute elasticities if the model is linear (Tellis, 1988). The mean

elasticities of environment-related variables correspond with existing theory. The significance

patterns of the competition and munificence variables also conform to the existing theories of

environment-firm growth relationships. For example, researchers across disciplines have argued

for the positive impact of resource availability on firm growth (e.g., Rajan & Zingales, 1998;

Beck, Levine, & Loayza, 2000) and the large majority of significant munificence estimates are

positive. A considerable number of environmental dynamism estimates are surprisingly positive.

While volatility in the marketplace puts a significant pressure on most of the firms’ growth,

several firms apparently achieve high growth by benefiting from the volatility.

The magnitude of the mean elasticities provides insight into the role of organic growth

determinants. However, there remains a reasonable amount of variability in the magnitude and

the sign of the elasticities across studies (see Table 2). We conduct a moderator analysis to

explain the variability in elasticities following the meta-analysis convention. In the following

section, we describe the meta-analysis procedure and the estimation approach.

META-ANALYSIS METHODOLOGY

Moderators

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The meta-analysis literature identifies four broad categories of moderators: (i) model

specification – omission of relevant explanatory variables, (ii) research environment (iii)

estimation procedure, and (iv) measurement method (Assmus, Farley, & Lehmann, 1984; Farley,

Lehmann, & Sawyer, 1995). In order to take stock of frequently studied variables, and following

meta-analysis convention to avoid sample size problems, we only included independent variables

that are utilized in at least 10 regression models (Capon, Farley, & Hoenig, 1990). We also

provide information on less frequently tested variables (see Table 2).

Model Specification Moderators

Omitting variables that are related theoretically and empirically to both the outcome and

included exogenous variables can contribute to the variance in the elasticities. We study a wide

variety of constructs that are sometimes omitted from organic growth models, namely, firm age,

firm size, innovation, marketing, interorganizational networks, knowledge endowment, and

environment characteristics. The growth literature considers all of these variables to be relevant

for explaining organic firm growth. When one of these variables is omitted from an organic

growth model, the estimated elasticity of the included exogenous variables could be biased (i.e.,

differ from the estimate based on a correctly-specified model). The direction of the estimation

bias is determined by the sign of the impact of the omitted variable on organic growth and the

sign of the correlation between the omitted variable and the included exogenous variables

(Farley, Hoenig, Lehmann, & Szymanski, 2004).

Research Environment

Industry. Studies of organic growth vary in the extent of industry heterogeneity in the

sample. Some studies focus on a single industry, whereas others focus on a cross-section of firms

from different industries in their sample. Industries demonstrate high variability in growth rates

and independent variables (e.g., R&D expenditures). Furthermore, industries differ in terms of

their institutional environments.

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Country. The country from which the data is sampled will likely impact the effect size as

a result of differences in markets, institutional environments (e.g., government regulations) and

business environments (e.g., Farley & Lehmann, 1994). However, we do not hypothesize the

directionality of country effects on elasticities because each country’s rules and regulations may

impact the cause-effect relationship differently.

Startups/New Ventures. Most of the earlier work on firm growth focuses on well-

established firms (e.g., Scherer, 1965). More recently, researchers have started examining startup

firms and/or new ventures (e.g., DeKinder & Kohli, 2008). Established and startup companies

are likely to be different on many levels, such as in the productivity of R&D or the influence of

the top management team on firm performance. Therefore, samples that constitute only startup

companies will likely produce different results than mixed samples.

Common Source. Researchers have focused on various sources of common method bias,

i.e., common rater effects, item characteristic effects and item context effects (e.g., Rindfleisch,

Malter, Ganesan, & Moorman, 2008). Of these effects, we are only able to test common rater

effects due to limited data availability. Common rater bias can work bi-directionally depending

on the process in operation. As a result of consistency (e.g., Heider, 1958) or social desirability

(e.g., Crowne & Marlowe, 1964), the covariance between two constructs may be either inflated

or attenuated.

Estimation Method

Data Structure. The cross-sectional time series (panel) data enhances the ability to

control for cross-section specific (firm-specific) and/or time effects. Controlling for firm-specific

and/or time effects may lead to different results than not accounting for them. Therefore, we

include the nature of the data structure as a moderator.

Estimation. A wide variety of estimation techniques are used in the empirical work on

organic growth. The most commonly used estimation technique is ordinary least squares.

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However, recently, researchers have increasingly been using fixed-effect or random-effect

estimators.

Endogeneity. The determinants of organic firm growth may also be affected by the nature

of the growth experienced in the time period. For example, high growth in a period may lead to

greater R&D or advertising expenditures in the following period. Some studies address this issue

by measuring the independent variables at time t and growth at time t+1, while others use

contemporaneous dependent and independent variables. Utilization of contemporaneous

variables may lead to biased effect sizes due to endogeneity.

Dependent Variable Specification. There are several different methods of modeling

growth. In a majority of the studies, growth rate is used as the dependent variable (e.g., Scherer,

1965), whereas in others absolute change is used as the dependent variable (e.g., Eisenhardt &

Schoonhoven, 1990).

Measurement Method

Dependent Variable Measure. Some studies use sales growth whereas others use market

share growth as a measure of organic growth. Market share growth may be affected more by the

competitive dynamics among firms. Consequently, we include the dependent variable type as a

moderator in the analysis.

Construct Specific Measurement. The measurement of a construct is likely to have a

significant influence on effect sizes. For example, some studies use an input measure of

innovation (e.g., Odagiri, 1983), whereas others use an output measure (e.g., Geroski, Machin, &

Walters, 1997). We test for measurement differences across studies pertaining to each construct.

Furthermore, we also code marketing-related measurement differences where the nature of the

construct lends itself to such a categorization. For example, firms form various types of network

links with institutions in their environments. We test whether the type of network linkage

(marketing vs. other types of linkages) affects the interorganizational networks’ elasticity. We

provide the coding regimen of all the moderators in Table 3.

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Data Analysis

Dependent Variable. We use elasticities from the regression models as the dependent

variables in the meta-analysis (e.g., Bijmolt, Van Heerde, & Pieters, 2005). In the estimation, we

also use the standard error of beta coefficients as variance components. Inclusion of standard

errors in meta-analysis enables the analysis to incorporate more information on the distribution

of the reported effect sizes (e.g., Bell, Chiang, & Padmanabhan, 1999) We transformed the

standard errors of beta coefficients by multiplying them with the ratio of the Mean of X / Mean

of Y to ensure compatibility of standard errors with the elasticities.

Estimation Technique. The vast majority of the studies included in the database report

multiple model specifications. We use all the relevant models from each study to include as

much information as possible on the variable of interest. Consequently, the data has a multi-level

structure. A number of prior meta-analysis studies treat the data from these studies as

independent and use an OLS approach to analyze the data (e.g., Capon, Farley, & Hoenig, 1990).

Bijmolt and Pieters (2001) provide simulation results that suggest that hierarchical modeling

achieves “the highest consistency of parameter estimates” (p.166) of all the estimation

techniques (e.g., OLS). Therefore, we adopt a hierarchical modeling approach to address the

multi-level nature of the data.

The general form of model specifications across empirical sales growth studies from

which we collected the data for the dependent variable for our analysis is:

Yijk = aijkZijk+ bijkXijk + eijk (1)

where Yij is sales growth of the jth

model in the ith

study for elasticity k, Z represents all

variables included in a sales growth model along with the variable of interest X (e.g.,

innovation), and bijk is the kth

elasticity of interest (e.g., advertising) in the jth

model of study i.

Recall that we have 11 distinct variables of interest, so k takes on values between 1 and 11. We

use bijk as the dependent variable in the analysis described below.

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Specifically, assume that the estimated effect size bijk (kth

elasticity of interest in the jth

model of study i), is equivalent to a true effect size βijk plus an error estimate εijk, where the errors

are assumed to be independent and normally distributed with known variance

s2

ijk. We use the reported standard errors (t-statistics) of beta coefficients as the known variance

component (e.g., Montgomery & Srinivasan, 1994):

bijk= βijk + εijk , εijk ~ N (0, s2

ijk) (2)

The mixed model assumes that the effect size parameter is a function of known study

characteristic M and random error:

βijk = M′ijk γ + δi , δi ~ N (0,τ2) (3)

Note that δi represents a random “researcher effect” that controls for individual-specific effects

along with moderators. The random effects control for commonality within a study, as well as

the dependence of observations across models within each study. Combining these two

equations, we obtain:

bijk = M′ijk γ + δi + εijk (4)

We use a hierarchical Bayesian technique to estimate the model. We estimate this model

separately for each of the 11 sets of elasticities. Two alternatives to this approach are random

intercept and iterative-GLS methods. Both of these methods require relatively larger sample

sizes to achieve asymptotic normality. We, however, are faced with small sample sizes for some

of the bivariate relationships (e.g., dynamism with n = 63), which prevents us from using these

techniques. We estimate Equation (4) for each set of elasticities that we examine.

We use a non-informative prior for j

γ , with 2~ (0, )j jN dγ where j

d → ∞ , following

DuMouchel (1994). For the unknown standard deviation, τ , we assume a log-logistic prior:

τ ~ π(τ) , π(τ) = (s0 / s0 + τ)2 and (s0)2= K / Σ(si)

2 (5)

where K is the total number of studies collected for the elasticity of interest.

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The advantages of using a log-logistic prior are: (i) it has a proper density (ii) the density

has a maximum at τ = 0, which incorporates the possibility that there is no study-specific

heterogeneity, and (iii) the density is highly dispersed (DuMouchel, 1994). A recent simulation

of the prior’s effect on inferences also highlights the strengths of log-logistic priors in

hierarchical models with heterogeneity in sample size and within-study variance (Lambert,

Sutton, Burton, Abrams, & Jones, 2005).

We intend to obtain the posterior distribution of γ and τ .We implement a burn-in period

of 5,000 iterations and approximately 150,000 iterations to monitorγ and τ . In order to check

the convergence, we follow Cowles and Carlin’s (1996) suggestion to use multiple convergence

criteria. First, we check the time series plot of the posterior distribution of γ . Second, we

calculate Brooks and Gelman’s (1998) convergence statistic based on multiple chains. We used

three chains with different starting values drawn from an over-dispersed distribution compared to

the target distribution. All of the variables achieved convergence. We report results from the

75,001-150,000 iterations window that provide support for convergence. We also report the OLS

results for comparative purposes; the results are generally consistent.

RESULTS

Firm-Focused Determinants of Growth

Innovation. The intercept of the innovation is positive, as posited by various theories of

firm growth (posterior mean of γ = 0.60). Several moderators have a significant biasing effect on

the effect size of innovation elasticity (Table 4). The omission of other marketing variables (e.g.,

advertising) inflates the elasticity of the innovation variable (posterior mean of γ =0.05), which

underscores the importance of modeling innovation and marketing efforts simultaneously.

Furthermore, the innovation elasticity is sensitive to model specification, as demonstrated by the

significant impact of omission of several variables on innovation elasticity (e.g., significant

posterior means of γAge = 0.24, γNetwork = -0.05, γMarketing = 0.05).

19

Also, sampling from countries other than the USA decreases the elasticity of the

innovation variable (posterior mean of γ =-0.62). Country characteristics have a significant

impact on innovation processes and innovation outcomes (Tellis, Prabhu, & Chandy, 2009) and

the meta-analysis results suggest that such differences affect the relationship between innovation

and organic growth.

Market Orientation. The intercept of the market orientation elasticity is positive

(posterior mean of γ = 0.20), consistent with the research into market orientation and firm

performance (e.g., Jaworski & Kohli, 1993; Slater & Narver, 1994). We find that Narver and

Slater’s (1990) market orientation scale entails a greater market orientation elasticity than Kohli

and Jaworski’s (1990) market orientation measure. This difference may stem from the focus of

two operationalizations. The Kohli and Jaworski measure focuses more on the processing and

response to a wider set of market information, while the Narver and Slater operationalization

focuses more narrowly on customers and competitors. The results indicate that the extent to

which a firm focuses on its customers and competitors may have a greater effect on its growth

compared to its focus on general market information and perhaps a broader set of stakeholders.

Advertising. The intercept of advertising spending is significant and positive (posterior

mean of γ = 0.38). The omission of other marketing variables attenuates the advertising

elasticity. Usually firms implement an integrated marketing strategy that requires them to

allocate marketing resources across the marketing mix. Consequently, the failure to control for

firms’ other marketing actions affects estimates of the advertising elasticity.

Use of estimation techniques more sophisticated than ordinary least squares leads to

estimates of greater advertising elasticity. More advanced estimation techniques seem to allow

the researcher to model the relationship between advertising and organic growth more

accurately.

Interorganizational Networks. The intercept of interorganizational networks is positive

(posterior mean of γ = 0.26), consistent with network theory predictions. The omission of

20

marketing variables attenuates the interorganizational network elasticity (see Table 5). It appears

likely that firms that have strong internal marketing capabilities may be less motivated to

cooperate with other companies. The magnitude of the elasticity indicates that returns from such

investment are not easy to capitalize on (Xiong & Bharadwaj, 2009)

Using network measures other than the number of linkages that a firm has with other

institutions leads to smaller effect sizes. Some researchers focus solely on whether or not a firm

engages in networking activities. Using the number of linkages a firm establishes with other

institutions captures the range of resources and opportunities to which the firm is exposed.

Entrepreneurial Orientation. The intercept of entrepreneurial orientation elasticity is

positive and significant (posterior mean of γ = 1.13). We find significant moderator effects on the

entrepreneurial orientation variable. The omission of marketing variables decreases the

entrepreneurial orientation elasticity (posterior mean of γ = -0.66) due to a negative correlation

between entrepreneurial orientation and marketing variables (see Table 5). Prior theoretical

guidance on this issue remains sparse. Only Matsuno, Mentzer, and Ozsomer (2002) report a

positive relationship between entrepreneurial proclivity and market orientation. However, it may

not be possible to generalize this relationship according to other marketing efforts. For example,

firms that are entrepreneurial in orientation may differ in strategic emphasis and may devote

more resources to R&D than to advertising.

Management Capacity. The intercept of management capacity elasticity is positive and

significant (posterior mean of γ = 0.70). The omission of marketing variables inflates the

management capacity elasticity, which indicates a positive association between management

capacity and marketing efforts. However, some sparse empirical evidence also indicates a

negative association (see Table 5). It is possible that the association between management

capacity and marketing actions hinges upon the type of the marketing strategy or action.

Firm Age. The intercept of firm age elasticity is negative and significant (posterior mean

of γ = -0.96), lending support to the “liability of senescence” argument. The omission of

21

marketing variables inflates firm age elasticity (posterior mean of γ = 0.08). Older firms may

leverage their established reputation, whereas younger firms need to be more aggressive in

promoting their products.

Firm Size. The intercept of the firm size elasticity is positive and significant (posterior

mean of γ = 0.26), lending support to arguments linking firm size to slack resources. Consistent

with the findings of Chandy and Tellis (2000), the meta-analysis results suggest that size may

indeed be an asset that enables organic growth. The use of an asset or employee-based measure

of size, as opposed to a sales-based measure of size, decreases the size elasticity (posterior mean

of γ = -0.68). This may be a critical issue depending on the objective of the analysis. If the

objective is to test the moderator effect of size on a relationship, then researchers need to be

cautious about how they operationalize size in their study.

Environment-focused Determinants of Growth

Competition. The intercept of the competition elasticity is negative (posterior mean of γ =

-0.05), consistent with research that competition hurts growth (e.g., Kornelis, Dekimpe &

Leeflang, 2008). However, the effect size is small. The omission of marketing variables

attenuates the competition elasticity (posterior mean γ = -0.20). This is not surprising as firms

intensify their marketing efforts in competitive environments. We also find that measures of

competition other than the number of competitors (such as intensity of price wars) generate

smaller competition elasticities.

Munificence. The intercept of munificence elasticity is positive (posterior mean of γ =

0.06). The munificence elasticity is sensitive to several estimation methodology moderators.

Specifying munificence contemporaneously with growth inflates the munificence elasticity

(posterior mean of γ = 0.21). This may be due to the failure to address the potential endogeneity

between munificence and firm growth. Similarly, cross-sectional data structures lead to larger

effect sizes (posterior mean of γ = 0.17). Not taking unobserved heterogeneity across firms into

account in the estimation may be causing the larger effect sizes of the munificence variable.

22

Dynamism. The intercept of dynamism elasticity is significant and negative (posterior

mean of γ = -0.61), consistent with theory. Methodologically, obtaining dynamism and growth

information from different sources inflates the dynamism elasticity (posterior mean of γ = 0.30),

which suggests that the elasticity is lower when using a common source of data.

DISCUSSION & IMPLICATIONS

We synthesize a wide variety of research studies on organic sales growth drivers by

conducting a quantitative review of empirical studies. Moderator analyses performed using a

hierarchical Bayes model provides support for firm- and environment-focused growth theories.

Below, we discuss implications of the results.

Research Implications

Model Specification. The omission of marketing variables leads to biased estimates of

eight elasticities (innovation, advertising, interorganizational networks, entrepreneurial

orientation, management capacity, firm age, and competition). This finding highlights the

association of marketing strategy with various aspects of firms’ other activities and marketplace

characteristics as they impact firm growth. Thus, it is essential to control for marketing variables

in organic growth models.

The omission of interorganizational network variables leads to biased estimates of

innovation, management capacity, and dynamism sales growth elasticities. Firms engage in

forming links in the environment to overcome internal resource limitations (e.g., innovation

capabilities). Therefore, when a construct of interest, such as innovation, motivates a firm’s

networking behavior, inclusion of networking in the growth model becomes critical.

Research Environment. Sampling from countries other than the USA affects innovation,

firm age, and firm size elasticities. The results suggest that differences in markets and

institutional environments impact the magnitude of elasticities. It is therefore essential in cases of

cross-country samples to account for country level heterogeneity by using control variables or

hierarchical modeling.

23

Estimation Method. Testing models using a cross-sectional data structure leads to higher

sales growth elasticities of five variables (innovation, interorganizational networks, management

capacity, firm age, munificence). The cross-sectional data imposes two types of endogeneity

concerns, namely, failure to account for feedback loops and failure to account for unobserved

heterogeneity. In the absence of panel data, researchers can address the feedback loop concern by

specifying appropriate lagged measures of independent variables while Bayesian techniques can

be used to address unobserved heterogeneity.

Study Design. Two study design characteristics emerge as the most common moderators,

specifically, the dependent variable specification and the dependent variable measure. Whether

the dependent variable is operationalized in absolute terms versus growth rate is a significant

moderator of five elasticities (innovation, entrepreneurial orientation, management capacity, firm

age, and munificence). The direction of the impact depends on the variable (positive for

innovation and firm age, negative for others). Therefore, using an absolute measure of growth

instead of growth rate may increase or decrease the elasticity of the variables of interest.

Using market share growth instead of sales growth is a significant moderator of the

magnitude of innovation, management capacity, firm size, competition, munificence, and

dynamism effect size elasticities. It is noteworthy that three of the five constructs are related to

marketplace characteristics. Competition and dynamism elasticities increase and munificence

elasticity decreases when market share growth is used as the dependent variable. By

construction, market share growth is affected more by the competitive dynamics than sales

growth.

Managerial Implications

Financial analysts view organic growth in firms (especially sales) as a critical metric

because firms with higher (sales) growth receive higher valuations (Brailsford & Yeoh, 2004).

Not surprisingly, managers are interested in the question: what drives organic growth? A

consistent finding across disciplines is the impact of innovation on organic growth (e.g., Odagiri,

24

1983; Park & Luo, 2001). Our findings indicate that among marketing variables, innovation has

the largest positive impact on organic growth. There are three important caveats for managers to

consider. First, organic sales growth returns to innovation investments are likely to be achieved

over a longer time horizon than other typical marketing instruments such as advertising

investments (e.g., Erickson & Jacobson, 1992), primarily due to long product development

cycles. Second, the effect size is larger in US markets compared to others. This result suggests

that returns to innovation in terms of organic growth are greater in the US, which has important

policy implications for managers and national governments. Third, managers would benefit from

thinking about innovation in tandem with marketing. Our findings indicate that omitting

marketing variables from the model biases the effect size of innovation. Correspondingly,

advertising has the second largest impact on firm growth. The two results together suggest that

value creation (through innovation) needs to be capitalized by means of value communication

(through advertising) in order to generate organic growth.

Entrepreneurial orientation has the highest effect size elasticity, among the 11 drivers of

organic growth in the study. In light of this finding, we highlight the relationships among the

growth determinants. Innovation and marketing initiative are two fundamental ways to generate

revenue growth. Entrepreneurial orientation, market orientation, and management capacity can

be viewed as the precursors of innovation. In addition, they can be seen as factors that affect the

intensity and the quality of innovation and marketing processes. For example, promotion of

entrepreneurial behavior in a firm can trigger the development of new product proposals, which

would increase the firm’s odds of identifying growth options. Empirical evidence supports the

argument that market orientation’s impact on firm performance operates through the firm’s

innovative activity (Han, Kim & Srivastava, 1998). In order to create an organizational

environment that fosters growth, managers should focus on creating an entrepreneurial and

market-oriented environment.

Firms benefit more from monitoring managerial capacity vis-à-vis firm size.

25

At the same time, consistent with some recent findings in marketing (but inconsistent with

popular management literature), firm size also enables organic growth. It appears that larger

firms have the management capacity and slack resources needed to pursue the growth

opportunities. However, if a company has the optimal number of managers just to run the

existing operations, then growth projects may not receive sufficient management attention

(Penrose, 1959). A related issue is the combined experience of the management team. Generating

organic growth requires a solid managerial knowledge base, which in turn highlights the

importance of training and career planning of managerial talent.

Finally, the environment in which a firm operates both enables and constrains a firm’s

organic growth. Our results suggest that firms should select non-dynamic and munificent

industry environments in which to compete in order to maximize organic growth. Moreover,

while competitive environments hurt organic growth, they are not significant in terms of impact.

Future Research Directions

The quantitative review of the literature on organic firm sales growth points to several

research directions. First, the link between marketing actions and firm organic sales growth is

not sufficiently studied. While firm level studies relating marketing actions (e.g., advertising) to

stock price and/or firm risk provide indirect evidence for the relationship between marketing

strategy and organic growth, more work is required on mediating variables such as cash flow

growth between firm actions and firm value (Rust, Ambler, Carpenter, Kumar, & Srivastava,

2004).

Second, there is need for more theoretical development in the organic growth area. As

we identified in the meta-analysis, various variables are proposed as determinants of organic

growth. Future work can study the interrelationships among these variables to specify mediating

and moderating processes. For example, researchers can study the chain of effects between

entrepreneurial orientation, innovation, and organic growth. Similarly, the interaction between a

firm’s marketing actions and networking initiatives could be studied. Marketing researchers have

26

examined the relationship between marketing actions and firm performance, but the interaction

between marketing actions and interorganizational networks could provide insights into a firm’s

initiatives to overcome marketing deficiencies.

27

Table 1. Organic Firm Growth Theories & Determinants

Theory Key Determinant

Variable

Operationalization

Firm-focused Theories

Endogeneous Growth Theory:

Evolutionary Approach and RBV

Innovation

Market Orientation

Advertising

Entrepreneurial Orientation

R&D Intensity, number of new

products

Generation, dissemination of, and

response to market information

(Kohli & Jaworski 1990).

Customer & competitor

orientation (Narver and Slater

1990)

Advertising intensity

Combination of three dimensions:

innovativeness, proactiveness,

and propensity to take risks

Investments in new businesses

Coordination and Adjustment Management Capacity Managers’ experience

Top management team size

Management Control* Owner vs. management control

Organization Theory Firm Age Number of years since founding

Org. Theory and Stochastic

Approach (Gibrat’s Law)

Firm Size

Total sales, assets, or employees

Environment-focused Theories

Industrial Organization Competition Number of competitors

Intensity of competitive actions

Population Ecology Munificence Resource availability

Demand growth

Population Ecology Dynamism Demand volatility

Change in customer preferences,

technology

Population Ecology Complexity* Heterogeneity in inputs and

outputs

*These variables are excluded from the empirical analysis due to a limited number of empirical studies.

28

Table 2. Count Analysis of Organic Sales Growth Determinants

Variable

n Study + - + and

Significant

- and

Significant

Mean

Elasticity

Standard

Deviation of

Elasticities

Innovation 169 24 114 55 66 14 0.020 0.290

Market Orientation 58 11 49 9 27 0 0.313 0.317

Advertising 128 8 89 39 47 8 0.081 0.233

Interorganizational

Networks 207 22 131 74 75 23 0.022

0.275

Entrepreneurial

Orientation 57 15 54 2 22 0 0.157

0.283

Management Capacity 62 16 30 32 9 3 -0.032 0.181

Firm Age 178 35 65 112 32 57 -0.157 0.373

Firm Size 339 55 241 98 123 30 0.170 0.411

Competition 95 19 48 46 19 34 -0.060 0.411

Munificence 104 24 73 31 38 7 0.123 0.322

Dynamism 63 16 29 34 6 10 -0.029 0.142

n: Number of elasticities

29

Table 3. Moderators

Moderator Coding Regimen

Model Specification Firm Age “0” if included ,“1” if omitted

Firm Size “0” if included, “1” if omitted

Innovation (e.g., R&D expenditures) “0” if included, “1” if omitted

Marketing (e.g., advertising expenditures) “0” if included, “1” if omitted

Interorganizational Network “0” if included, “1” if omitted

Entrepreneurial Orientation “0” if included, “1” if omitted

Management Capacity “0” if included, “1” if omitted

Environment (e.g., competition)

“0” if included, “1” if omitted

Research Environment

Multi-Industry Sample “0” if single industry sample, “1” otherwise

Non-USA Sample “0” USA, “1” otherwise

Startup “0” if a sample of established and startup firms, “1” if only startups

Estimation Method

Cross-Sectional Data “0” if cross-sectional time series data, “1” if cross-sectional

Estimation “0” if OLS, “1” otherwise (e.g., random effects)

Temporal Specification “0” if independent variable is lagged, “1” otherwise

Measurement

Dependent Variable Specification “0” if DV is growth rate [(St-St-1)]/(S(t-1)], “1” otherwise

Dependent Variable Measured as Market

Share Growth

“0” if sales growth, “1” if market share growth

Multiple Data Sources “0” if dependent and independent variable information is collected

from a common source, “1” otherwise

Output Innovation Measure “0” if input related measure such as R&D intensity, “1” if output

related measure such as number of new products

Market Orientation Single Measure “0” if a composite measure of market orientation, “1” otherwise

Market Orientation Type “0” if Kohli & Jaworski measure, “1” if Narver&Slater measure

Network Measure “0” if measure is based on “number of linkages”, “1” otherwise

Non-Marketing Network “0” if marketing-related linkages, “1” otherwise

Entrepreneurial Orientation “0” if composite measure based on three dimensions: innovativeness,

proactiveness, propensity to take risks, “1” otherwise.

Management Capacity Measured as Size “0” if top management team experience, “1” if top management size

Non-Marketing Management Experience “0” if marketing-related experience, “1” otherwise

Firm Size Measured as Assets “0” if sales, “1” if assets or employees

Competition “0” if number of competitors, “1” otherwise

Munificence “0” if resource availability, “1” industry growth

Dynamism “0” if demand volatility, “1” otherwise

30

Table 4. The Impact of 11 Drivers of Organic Growth

Moderators Innovation Market

Orientation Advertising

Interorganizational

Networks

Entrepreneurial

Orientation

HB OLS HB OLS HB OLS HB OLS HB OLS

Intercept -0.60** -0.58*** -0.20 -0.27 -0.38** -0.32** -0.26 -0.15 -1.13** -1.03

Model Specification

Firm Age -0.46** -0.55*** -0.23 -0.22* -0.31 -0.40 -0.09 -0.30** -0.28** -0.27**

Firm Size -1.27** -0.35** -0.09 -0.09 -0.28** -0.29** -0.07 -0.06 -0.69 -0.46

Innovation -0.06 -0.11 -0.00 -0.11 -0.01 -0.26*** -0.05 -0.14

Marketing -0.05** -0.02 -0.25** -0.23** -0.08** -0.22*** -0.66** -0.60

Interorganizational Networks -0.05** -0.30*** -0.02 -0.04

Entrepreneurial Orientation -0.42** -0.08 -0.01 -0.15 -0.04 -0.27**

Management Capacity -0.14** -0.29*** -0.38** -0.42*** -0.22 -0.08 -0.01 -0.26

Environment -0.01 -0.12* -0.09 -0.04 -0.00 -0.22*** -0.02 -0.27

Research Environment

Multi-Industry Sample -0.54** -0.01 -0.19 0.26*** -0.03 -0.12 -0.03 -0.28*** -0.45 -0.48*

Non-USA Sample -0.62** -0.22*** -0.16 -0.24* -0.13 -0.16*** -0.15 -0.32*** -0.40 -0.45

Startups -0.03 -0.08 -0.01 -0.08 -0.54 -0.57

Estimation Method

Cross-Sectional Data -0.56** -0.30*** -0.01** -0.06

Estimation -0.02 -0.52*** -0.18 -0.26*** -0.14** -0.19*** -0.10 -0.29**

Temporal Specification -0.00 -0.24*** -0.00 -0.01 -0.27 -0.11

Measurement

DV Specification -0.63** -0.24** -0.09 -0.32** -0.01 -0.11 -0.19 -0.45*** -0.92** -0.91

DV as Market Share Growth -0.63** -0.09 -0.41 -0.71*** -0.13 -0.25 -0.06 -0.44** -0.45 -0.19

Multiple Data Sources -0.05 -0.27*** -0.25 -0.57*** -0.11 -0.23** -0.76* -1.01**

Output Innovation Measure -0.03 -0.19***

M. Orientation Single Measure -0.01 -0.18

Market Orientation Type -0.13*** -0.06

Network Measure -0.01** -0.15*

Non-Marketing Network -0.00 -0.08

Entrepreneurial Orientation -0.35 -0.40

τ2 0.15 0.14 0.01 0.08 0.001

For HB: *5%-95% interval does not include zero, **2.5%-97.5% interval does not include zero, For OLS: *p<0.1, ** p<0.05, *** p<0.01

31

Table 4. The Impact of 11 Drivers of Organic Growth (Continued)

Moderators Management

Capacity Firm Age Firm Size Competition Munificence Dynamism

HB OLS HB OLS HB OLS HB OLS HB OLS HB OLS

Intercept -0.70** -0.64** -0.96*** -1.20*** -0.26* -0.32** -0.05 -0.19 -0.06 -0.06 -0.61** -0.64**

Model Specification

Firm Age -0.09 -0.04 -0.23** -0.12*** -0.04 -0.34 -0.01 -0.19*** -0.43 -0.39

Firm Size -0.32** -0.32*** -0.30 -0.39*** -0.71** -0.26 -0.02 -0.02 -0.47 -0.31

Innovation -0.00 -0.11** -0.00 -0.04 -0.03 -0.03 -0.00 -0.06 -0.01 -0.09 -0.09** -0.14*

Marketing -0.44** -0.46*** -0.08* -0.16* -0.03 -0.03 -0.20** -0.16 -0.06 -0.45*** -0.03 -0.05

Interorg. Network -0.35** -0.17* -0.00 -0.04 -0.02 -0.01 -0.02 -0.12 -0.06 -0.24*** -0.52** -0.83***

Entrepreneurial Orient. -0.01 -0.00 0.05 -0.37*** -0.01 -0.02 -0.13 -0.06 -0.12** -0.06 -0.05** -0.08

Management Capacity -0.00 -0.35* -0.00 -0.10* -0.09 -0.00 -0.03 -0.16* -0.17 -0.23

Environment -0.18* -0.21** -0.01 -0.06 -0.00 -0.10**

Research Environment

Multi-Industry Sample -0.61** -0.63*** -0.01 -0.01 -0.16 -0.11** -0.04 -0.29 -0.13 -0.65*** -0.34 -0.27

Non-USA Sample -0.04 -0.07 -0.25** -0.20* -0.23* -0.01 -0.32 -0.05 -0.11 -0.49*** -0.24 -0.36***

Startups -0.01 -0.04 -0.01 -0.15** -0.05 -0.04 -0.00 -0.22 -0.05 -0.16 -0.05 -0.11

Estimation Method

Cross-Sectional Data -0.33** -0.27** -0.58** -0.28 -0.03 -0.02 -0.12 -0.02 -0.17* -0.13 -0.26 -0.31

Estimation -0.02 -0.08 -0.61** -0.09 -0.17** -0.14*** -0.01 -0.12 -0.05** -0.16* -0.1 -0.03

Temporal Specification -0.38** -0.23* -0.11 -0.14 -0.08 -0.24*** -0.19 -0.04 -0.21** -0.45*** -0.00 -0.03

Measurement

DV Specification -0.45** -0.38* -0.51* -0.44*** -0.02 -0.39*** -0.06 -0.09 -0.15** -0.15** -0.03 -0.06

DV as Market Share

Growth

-0.05** -0.19*** -0.09 -0.25** -0.68** -0.23*** -0.19** -0.11 -0.37** -0.38*** -0.12** -0.10*

Multiple Data Sources -0.20 -0.12 - 0.10 -0.02 -0.17** -0.14** -0.25 -0.01 -0.11 -0.07 -0.30** -0.22

Mng. Capacity as Size -0.02 -0.01

Non-Marketing Mng.

Experience

-0.45 -0.54

Firm Size as Assets -0.68** -0.29***

Competition -0.15** -0.37**

Munificence -0.10 -0.25***

Dynamism -0.08 -0.04

τ2 0.001 0.12 0.27 0.19 0.03 0.001

For HB: *5%-95% interval does not include zero, **2.5%-97.5% interval does not include zero, For OLS: *p<0.1, ** p<0.05, *** p<0.01

32

Table 5. Correlations a

1 2 3 4 5 6 7

1. Innovation 1

2. Marketing Variablesb 0.166 1

3. Interorganizational

Networks

0.051 -0.003 1

4. Entrepreneurial Orientation 0.160 0.680 c 0.158 1

5. Management Capacity 0.267 -0.030 c 0.145 0.166 1

6. Firm Age 0.032 0.029 0.067 0.021 0.095 1

7. Firm Size 0.075 -0.022 0.092 0.170 0.146 0.189 1

8. Competition -0.069 0.041 0.018 -0.020 0.001 0.026 0.089

9. Munificence 0.089 0.001 -0.025 0.138 0.183 0.041 0.034

10. Dynamism 0.041 0.095 0.020 0.136 0.083 -0.052 -0.003 a We report correlations that are relevant for the moderators analyses. For example, we use

“marketing variables” as an aggregate moderator to test the impact of inclusion/omission of

marketing variables on the other variables’ elasticities. Therefore, we report the correlations

among the marketing variables and other variables. b For the purposes of brevity, the correlation between advertising and other marketing variables

is not included in the table. The correlation coefficient is 0.03. c These correlations are included only for illustration purposes. They are collected from a very

small number of research studies due to limited data availability. Thus, they are not conclusive in

terms of the relationship they represent.

33

Table 6. Summary of Findings Moderator Elasticity Direction of

the Effect

Explanation Suggestion

Omission of

Marketing

Variables

Innovation

Advertising

Interorganizational Networks

Entrepreneurial Orientation

Management Capacity

Firm Age

Competition

+

-

-

-

+

+

-

Marketing is a key component of a firm’s

overall strategy. Both organizational and

environmental factors are related to marketing

initiatives of firms. Therefore, omission of

marketing variables leads to biased parameter

estimates.

Across constructs of interest, it is

critical to control for marketing

strategy / actions of firm in sales

growth models.

Omission of

Interorganizational

Networks

Innovation

Management Capacity

Dynamism

-

-

+

Firms engage in forming links with other

organizations in the environment to overcome

internal resource limitations (e.g., R&D

capabilities, managerial expertise).

It is essential to check if a construct

of interest (e.g., R&D) is a

motivating factor for a firm’s

networking initiatives.

Sampling from

countries other than

USA

Innovation

Firm Age

Firm Size

-

-

+

Institutional environments are different across

countries. Sampling from emerging or

developing countries affects parameter

estimates.

Researchers may consider using

control variables to account for

country characteristics. In cases of

cross-country sampling, researchers

can use hierarchical models.

Cross-sectional

Data Structure

Innovation

Interorganizational Network

Management Capacity

Firm Age

Munificence

+

+

+

+

+

Cross-sectional data structure inflates the

parameter estimates of 5 of the 11 drivers of

organic growth due to lack of accounting for

unobserved heterogeneity.

The results reiterate the importance

of examining for unobserved

heterogeneity. They also suggest that

researchers should be confident of

the results from using cross-sectional

data in situations where alternative

data collection techniques may not be

feasible or appropriate.

Absolute Change in

Sales as Growth

Specification

Innovation

Entrepreneurial Orientation

Management Capacity

Firm Age

Munificence

+

-

-

+

-

Growth rate specification is independent of the

magnitude of sales whereas absolute difference

is not. This difference leads to different sales

growth parameter estimates.

Absolute change in sales may be

more appropriate in a startup context

where firms do not generate sales

initially. Otherwise, it is essential to

conduct robustness checks for

different growth specifications.

Market Share

Based Growth

Measure

Innovation

Management Capacity

Firm Size

Competition

Munificence

Dynamism

-

+

+

+

-

+

By construction, market share growth is

affected more by the competitive dynamics

than sales growth. The fundamental difference

between two measures leads to different

parameter estimates.

Researchers may benefit from using

market share growth when they are

interested in capturing competitive

dynamics as part of the growth

process.

34

REFERENCES

Aldrich, H. (1979). Organizations and environments. Englewood Cliffs: Prentice Hall.

Amit, R., & Schoemaker, P. (1993). Strategic assets and organizational rent. Strategic

Management Journal, 14 (1), 33-46.

Assmus, G., Farley, J., & Lehmann, D. (1984). How advertising affects sales: Meta-analysis of

econometric results. Journal of Marketing Research, 21(1), 65-74.

Baghai, M., Smit, S., & Viguerie, P. (2007). The granularity of growth. McKinsey Quarterly, 2,

40-51.

Barron, D., West, E., & Hannan, M. (1994). A time to grow and a time to die: growth and

mortality of credit unions in New York City. American Journal of Sociology, 100 (2), 381-419.

Beck, T., Levine, R., & Loayza, N. (2000). Finance and sources of growth. Journal of Financial

Economics, 58 (1/2), 261-300.

Bell, D., Chiang, J., & Padmanabhan, V. (1999). The decomposition of promotional response:

An empirical generalization. Marketing Science, 18(4), 504-526.

Bharadwaj, S., Clark, T., & Kulviwat, S. (2005). Marketing, market growth, and endogenous

growth theory: an inquiry into the causes of market growth. Journal of the Academy of

Marketing Science, 33(3), 347-359.

Bharadwaj, S., & Varadarajan, R. (2004). Towards an Integrated Model of Business

Performance. in Review of Marketing, New York: M.E. Sharp.

Bharadwaj, S, Varadarajan R. & Fahy, J. (1993). Sustainable competitive advantage in service

industries: A conceptual model and research propositions. Journal of Marketing, 57 (4), 83-99.

Bijmolt, T., Van Heerde, H., & Pieters, R. (2005). New empirical generalizations on the

determinants of price elasticity. Journal of Marketing Research, 42(2), 141-156.

Bijmolt, T., & Pieters, R. (2001). Meta-analysis in marketing when studies contain multiple

measurements. Marketing Letters, 12(2),157-169.

Billett, M., King, T., & Mauer, D. (2004). Bondholder wealth effects of mergers and

acquisitions: new evidence from 1980s and 1990s. Journal of Finance, 59(1), 107-135.

35

Brailsford, T., & Yeoh, D. (2004). Agency problems and capital expenditure announcements.

Journal of Business, 77(2), 223-256.

Brooks, S.P., & Gelman, A. (1998). General methods for monitoring convergence of iterative

simulations. Journal of Computational and Graphical Statistics, 7(4), 434-455.

Capon, N, Farley, J., & Hoenig, S. (1990). Determinants of financial performance: A Meta-

Analysis. Management Science, 36(10), 1143-1159.

Capron, L., Dussauge, P., & Mitchell, M. (1998). Resource deployment following horizontal

acquisitions in Europe and North America, 1988-1992. Strategic Management Journal, 19(7),

631-661.

Chandy, R. & Tellis, G.J. 2000. The incumbents curse? Incumbency, size and radical product

innovation. Journal of Marketing, 64(3), 1-17.

Chandy, R. & Tellis, G.J. 1998. Organizing for radical product innovation: The overlooked role

of willingness to cannibalize. Journal of Marketing Research, 35(4), 474-487.

Child, J. (1972). Organization structure, environment, and performance: The role of strategic

choice. Sociology, 6(1), 1-22.

Cowles, M.K., & Carlin B. (1996). Markov chain Monte Carlo convergence diagnostics: A

comparative review. Journal of the American Statistical Association, 91(434), 883-904.

Crowne, D., & Marlowe, D. (1964). The approval motive: studies in evaluative dependence.

New York: Wiley.

DeKinder, Jade S., & Kohli A. (2008). Flow signals: How patterns over time affect the

acceptance of start-up firms. Journal of Marketing, 72(5), 84-97.

Dess, G., & Beard, D. (1984). Dimensions of organizational task environment. Administrative

Science Quarterly, 29(1), 52-73.

DuMouchel, W. (1994). Hierarchical Bayes linear models for meta-analysis. NISS Technical

Report Number 27.

Eisenhardt, K.M., & Schoonhoven, C.B. (1990). Organizational growth: Linking founding team,

strategy, environment, and growth among U.S. semiconductor ventures, 1978-1988.

Administrative Science Quarterly, 35(3), 504-529.

36

Erickson, G., & Jacobson, R. (1992). Gaining comparative advantage through discretionary

expenditures: The returns to r&d and advertising. Management Science. 38(9), 1264-1279.

Evans, D.S. (1987). Tests of alternative theories of firm growth. Journal of Political Economy,

95(4), 657-674.

Farley, J., Hoenig, S., Lehmann, D., & Szymanski, D. (2004). Assessing the impact of marketing

strategy using meta-analysis. In C.Moorman, & D.Lehmann (Eds.), Assessing marketing strategy

performance (pp.145-164). Cambridge: Marketing Science Institute.

Farley, J., & Lehmann, D. (1994). Cross-national ‘Laws’ and Differences in Market Response.

Management Science, 40(1), 111-122.

Farley, J., Lehmann, D., & Sawyer, D. (1995). Empirical marketing generalization using meta-

analysis. Marketing Science, 14, G36-46.

Geroski, P. (2005). Understanding the implications of empirical work on corporate growth rates.

Managerial and Decision Economics. 26(2), 129-138.

Geroski, P., Machin, S., & Walters, C. (1997). Corporate growth and profitability. Journal of

Industrial Economics, 45(2), 171-189.

Geroski, P. (1989). Entry, innovation and productivity growth. The Review of Economics and

Statistics.71(4), 572-578.

Gibrat, R. (1931). Les inégalités économiques. Paris: Sirey.

Gotteland, D. & Boule, J.M. (2006). The market orientation-new product performance

relationship: Redefining the moderating role of environmental conditions. International Journal

of Research in Marketing, 23(2), 171-185.

Gulati, R., Nohria, N., & Zaheer, A. (2000) Strategic networks. Strategic Management Journal,

21(3), 203-215.

Hackbarth, D., & Morellec, E. (2008). Stock returns in mergers and acquisitions. Journal of

Finance, 63(3), 1213-1252.

Han, J.K., Kim, N., & Srivastava R.K. (1998). Market orientation and organizational

performance: Is innovation the missing link? Journal of Marketing, 62(4), 30-45.

37

Haveman, H. (1993). Follow the leader: Mimetic isomorphism and entry into new markets.

Administrative Science Quarterly, 38(4), 593-627.

Heider, F. (1958). The psychology of interpersonal relations. New York: Wiley.

Hitt, M., Ireland, D., Camp, M., & Sexton, D. (2001). Strategic entrepreneurship: Entrepreneurial

strategies for wealth creation. Strategic Management Journal, 22(6/7), 479-491.

Homburg, C., & Bucerius, M. (2005). A marketing perspective on mergers and acquisitions:

How marketing integration affects postmerger performance. Journal of Marketing, 69(1), 95-

113.

Jaworski, B., & Kohli, A. (1993). Market orientation: Antecedents and consequences. Journal of

Marketing, 57(3), 53-70.

Kohli, A., & Jaworski, B. (1990). Market orientation: The Construct, research propositions, and

managerial implications. Journal of Marketing, 54(2), 1-18.

Kornelis, M., DeKimpe, M.G. & Leeflang, P.S. H., (2008). Does competitive entry structurally

change key marketing metrics?. International Journal of Research in Marketing, 25(1), 173-182.

Lambert, P., Sutton, A.J., Burton, P.R., Abrams, K.R., & Jones, D.R. (2005). How vague is

vague? A simulation study of the impact of the use of vague prior distributions in MCMC using

WinBugs. Statistics in Medicine, 24(15), 2401-2428.

Lee, C., Lee, K, & Pennings J.M. (2001). Internal capabilities, external networks, and

performance: A study on technology-based ventures. Strategic Management Journal, 22(6/7),

615-640.

Lee, R.P., Johnson, J.L. & Grewal, R. (2008). Understanding the antecedents of collateral

learning in new product alliances. International Journal of Research in Marketing, 25(3), 192-

200.

Lucas, R.E. (1978). On the size distribution of business firms. The Bell Journal of Economics.

9(2), 508-523.

Lumpkin, G.T., & Dess, G.G. (1996). Clarifying the entrepreneurial orientation construct and

linking it to performance. Academy of Management Review, 21(1), 135-172.

38

Mahoney, J., & Pandian R. (1992). The resource-based view within the conversation of strategic

management. Strategic Management Journal, 13(5), 363-380.

Matsuno, K., Mentzer, J., & Ozsomer, A. (2002). The effects of entrepreneurial proclivity and

market orientation on business performance. Journal of Marketing, 66(3), 18-32.

McEachern, W.A. (1978). Corporate control and growth: An alternative approach. Journal of

Industrial Economics, 26(3), 257-266.

Montgomery, D., & Srinivasan, V. (1994). An improved method for meta-analysis with

application to new product diffusion models. Research Paper No.1310. Graduate School of

Business, Stanford University.

Narver, J., & Slater, S. (1990). The effect of a market orientation on business profitability.

Journal of Marketing, 54(4), 20-35.

Nelson, R.R.,& Winter,S.G.(1982). An evolutionary theory of economic change. Cambridge:

Harvard University Press.

Nobeoka K., & Cusumano, M. (1997). Multiproject strategy and sales growth: The benefits of

rapid design transfer in new product development. Strategic Management Journal, 18(3), 169-

186.

Odagiri, H. (1983). R&D expenditures, royalty payments, and sales growth in Japanese

manufacturing corporations. Journal of Industrial Economics, 32(1), 61-71.

Park, S.H., & Luo Y. (2001). Guanxi and organizational dynamics: organizational networking in

Chinese firms. Strategic Management Journal, 22(5), 455-476.

Parthasarthy R.,& Sethi, P. (1993). Relating Strategy and Structure to Flexible Automation: A

Test of Fit and Performance Implications. Strategic Management Journal, 14(7), 529-549.

Penrose, E. (1959). The theory of the growth of firm. Oxford: Blackwell.

Rajan, R G., & Zingales, L. (1998). Financial dependence and growth. American Economic

Review, 88(3), 559-586.

Rindfleisch, A., Malter, A., Ganesan, S., & Moorman, C. (2008). Cross-sectional versus

longitudinal survey research: concepts, findings, and guidelines. Journal of Marketing Research,

45(3), 261-279.

39

Romer, P. (1986). Increasing returns and long-run growth. Journal of Political Economy, 94,

1002-1037.

Rust, Roland T., T. Ambler, G. Carpenter, V. Kumar, and R. K. Srivastava (2004), “Measuring

Marketing Productivity: Current Knowledge and Future Directions,” Journal of Marketing, 68(4),

76-89.

Scherer, F.M. (1980). Industrial market structure and economic performance (2nd ed.). Chicago:

Rand McNally.

Scherer, F.M. (1965). Corporate inventive output, profits and growth. Journal of Political

Economy, 73(3), 290-297.

Slater, S.F., & Narver, J. (1994). Does competitive environment moderate the market

orientation-performance relationship? Journal of Marketing, 58(1), 46-55.

Solow, R. (1956). A contribution to theory of economic growth. Quarterly Journal of

Economics, 70(February), 65-94.

Srinivasan, R., Rangaswamy, A. & Lilien, G.L. (2005). Turning adversity into advantage: Does

proactive marketing during a recession pay off? International Journal of Research in Marketing,

22 (2), 109-125.

Srivastava, R. K., Shervani, T.A. & Fahey L. (1998), Market-based assets and shareholder value: A

framework for analysis,” Journal of Marketing, 62(1), 2-18.

Stremersch, S. & Tellis, G.J. (2004). Understanding and managing international growth of new

products. International Journal of Research in Marketing, 21(4), 421-438.

Sutton, J. (1997). Gibrat’s Legacy. Journal of Economic Literature, 35(1), 40-59.

Tellis, G., Prabhu, J., & Chandy R. (2009). Radical innovation across nations: The preeminence

of corporate culture. Journal of Marketing, 73(1), 3-23.

Tellis, G. (1988). The price elasticity of selective demand: A meta-Analysis of economic models

of sales. Journal of Marketing Research, 25(4), 331-341.

Trau, F. (1996). Why do firms grow? ESRC Centre for Business Research, working paper.

Weinzimmer, L., Nystrom, P., & Freeman, S. (1998). Measuring organizational growth: Issues,

consequences and guidelines. Journal of Management, 24(2), 235-262.

40

Williamson, O.E. (1964). The economics of discretionary behavior: Managerial objectives in a

theory of the firm. Englewood Cliffs, NJ: Prentice-Hall.

Winter, S.G. (1987). Knowledge and competence as strategic assets. In D.Teece (Ed.), The

competitive challenge (pp. 159-184). Cambridge: Ballinger.

Xiong, G., & Bharadwaj, S. (2009). Transforming social capital into financial value in IPOs: The

role of absorptive capacity in young technology firms’ B2B relationships. Working paper.

41

42

APPENDIX. Database of Studies

Authors Year Journal 1 2 3 4 5 6 7 8 9 10 11

Ambler, Styles and Xiucun 1999 IJRM + + +

Ang 2008 SMJ + + + +

Autio, Sapienza and Almeida 2000 AMJ +

Bamford, Dean and McDougall 1999 JBV + + + +

Baum, Calabrese and Silverman 2000 SMJ + + + + +

Baum and Silverman 2004 JBV + + + +

Bronars and Deere 1993 AER +

Brush, Bromiley and Hendrickx 2000 SMJ +

Buzzell and Wiersama 1988 SMJ + + +

Chandler 1996 ET&P + +

Chandler and Hanks 1994 ET&P + +

Clark 1984 AER + + +

Collins and Clark 2003 AMJ + +

Covin,Green and Slevin 2006 ET&P + + + +

Covin, Slevin and Heeley 2001 JBR + +

Dowling and McGee 1994 MS + + +

Dussauge, Garrette and Mitchell 2004 SMJ + +

Eisenhardt and Schoonhoven 1990 ASQ + +

Ensley, Pearson and Amason 2002 JBV + + +

Ettlie 1998 MS +

Ferrier, Smith and Grimm 1999 AMJ + + +

Florin, Lubatkin and Schulze 2003 AMJ + + + + +

Franko 1989 SMJ + (1) Innovation (2) Market Orientation (3) Advertising (4) Interorganizational Networks (5) Entrepreneurial Orientation (6) Management Capacity

(7) Firm Age (8) Firm Size (9) Competition (10) Munificence (11) Dynamism

43

APPENDIX. Database of Papers (Continued)

Authors Year Journal 1 2 3 4 5 6 7 8 9 10 11

Gao, Zhou, and Yim 2007 IJRM + + + + + +

Garg, Walters and Priem 2003 SMJ + +

Geroski, Machin and Walters 1997 JIE + +

Greve 1999 ASQ + + +

Greve 2008 SMJ + + +

Grinyer, McKiernan and Yasai 1988 SMJ + + +

Hart and Banbury 1994 SMJ + + +

He and Wong 2004 OS + + +

Heeley 1996 Diss. + + +

Henderson 1999 ASQ + + +

Hirschey 1981 JB +

Ito and Pucik 1993 SMJ +

Jacquemin and Lichtbuer 1973 EER +

Kim 1995 Diss. + + +

Kor 2001 Diss. + + + + +

Kraatz and Zajac 2001 OS + + + +

Kumar, Subramanian and Yauger 1998 JOM + + +

Kwoka 1993 RES + +

Landes and Rosenfield 1994 JIE + +

Lee, Lee and Pennings 2001 SMJ + + + + + + + +

Lumpkin and Dess 2001 JBV + +

Luo, Zhou and Liu 2003 JBR + +

Martin and Grbac 2003 IMM + (1) Innovation (2) Market Orientation (3) Advertising (4) Interorganizational Networks (5) Entrepreneurial Orientation (6) Management Capacity

(7) Firm Age (8) Firm Size (9) Competition (10) Munificence (11) Dynamism

44

APPENDIX. Database of Papers (Continued)

Authors Year Journal 1 2 3 4 5 6 7 8 9 10 11

McGee, Dowling and Megginsion 1995 SMJ + + + +

Miller and Toulouse 1986 MS +

Mishina, Pollock and Porac 2004 SMJ + + + + +

Mitchell and Singh 1993 MS +

Nobeoka and Cusumano 1997 SMJ +

Oczkowski, Farrell 1998 IJRM +

Odagiri 1983 JIE +

Ostgaard and Birley 1996 JBR + + +

Park and Luo 2001 SMJ + + + + + + +

Parthasarthy and Sethi 1993 SMJ +

Pecotich, Laczniak and Inderrieden 1985 IJRM +

Paton 2002 AE + +

Peles 1971 JPE +

Pelham 1999 JBR + + + +

Pelham 1997 JMTP +

Peng 2004 SMJ + + +

Ratana 1999 Diss. + +

Requena-Silvente and Walker 2007 JEB +

Robinson and McDougall 2001 SMJ + +

Rothaermel, Hitt and Jobe 2006 SMJ + + +

Sadler-Smith 2003 JSBM +

Scherer 1965 JPE +

Sharma and Kesner 1996 AMJ + + (1) Innovation (2) Market Orientation (3) Advertising (4) Interorganizational Networks (5) Entrepreneurial Orientation (6) Management Capacity

(7) Firm Age (8) Firm Size (9) Competition (10) Munificence (11) Dynamism

45

APPENDIX. Database of Papers (Continued)

Authors Year Journal 1 2 3 4 5 6 7 8 9 10 11

Shepherd 1972 RES + +

Shipilov 2006 AMJ +

Shipilov and Li 2008 ASQ + + + +

Simsek 2007 SMJ + + + + +

Singh and Mitchell 2005 SMJ + + +

Slater and Narver 1994 JM + + +

Slevin and Covin 1997 JOM + +

Smith et al. 1994 ASQ + + + +

Snell and Youndt 1995 JOM + + +

Stam and Elfring 2008 AMJ + + + + +

Stuart 2000 SMJ + + +

Subramanian and Gopalkrishna 2001 JBR +

Tanriverdi and Lee 2008 AMJ + + + +

Walter, Auer and Ritter 2006 JBV + + + +

Wiklund and Shepherd 2005 JBV + + + + +

Wolff and Pett 2006 JSBM +

Yin and Zajac 2004 SMJ + +

Zahra 1993 JBV + +

Zahra and Bogner 1999 JBV + + + +

Zahra and Garvis 2000 JBV + + +

Zahra and Hayton 2008 JBV + + + + + +

Zahra, Ireland and Hitt 2000 AMJ + + +

Zahra, Neubaum and El-Hagrassey 2002 ET&P + + + (1) Innovation (2) Market Orientation (3) Advertising (4) Interorganizational Networks (5) Entrepreneurial Orientation (6) Management Capacity

(7) Firm Age (8) Firm Size (9) Competition (10) Munificence (11) Dynamism