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This is to certify that the
dissertation entitled

EARLY AND LATE MOVERS IN
DOMESTIC AND INTERNATIONAL MARKET ENTRY

presented by

Bernadine Johnson Dykes

has been accepted towards fulfillment
of the requirements for the

Doctoral degree in Strategic Management

 

 

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EARLY AND LATE MOVERS
IN DOMESTIC AND INTERNATIONAL MARKET ENTRY

By

Bernadine Johnson Dykes

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Strategic Management

2009

ABSTRACT

EARLY AND LATE MOVERS
IN DOMESTIC AND INTERNATIONAL MARKET ENTRY

By

Bernadine Johnson Dykes

In this three-essay dissertation, I apply the behavioral theory of the ﬁrm (Cyert &
March, 1963) and Hofstede’s national culture dimensions (Hofstede, 1980, 2001) to
predict the relative timing in which ﬁrms enter a new product market and a new
geographic market, respectively. I also examine the effect of the diverse modes of
imitation on the relationship between early market entry and ﬁrm performance. The
results from Study I indicate that market entry timing is inﬂuenced by ﬁrm level factors,
such as prior performance, slack resources and prior entry experience, but in ways that
are sometimes contrary to theory. The results from Study 11 indicate that market entry
timing is also inﬂuenced by societal level factors at the single and multi-ﬁrm level of
analysis. However, as Study 111 suggests, the influence of the different modes of
imitation on the relationship between ﬁrm performance and early market entry remains
unclear. I test these ideas on sample of ﬁrms that entered the mutual fund industry and
the project-ﬁnance market. In multiple ways, this dissertation adds to the extensive body
of work in strategic management and international business regarding market entry and

points to several opportunities for future research.

ACKNOWLEDGEMENTS

I thank my dissertation committee, Dr. Gerry McNamara (chair), Dr. Linn Van Dyne,
Dr. Yuri Mishina and Dr. G. Tomas Hult, for their support and guidance in completing
my degree and dissertation. I thank my husband (Roy), family, friends, Dr. Matthew
Anderson, Dr. Ernest Betts, Dr. Christopher Porter and all of my cohort members in the
doctoral program for their support and encouragement during my tenure at Michigan
State. Finally, I thank the members, afﬁliated faculty and support staff of the

Management Doctoral Students Association of the PhD Project.

iii

TABLE OF CONTENTS

LIST OF TABLES ........................................................................... v
LIST OF FIGURES ........................................................................... vi
INTRODUCTION ............................................................................. 1

STUDY 1: Behavioral Perspectives on the Order of Market Entry. . . . . . . . . . . . . . .......18

STUDY II: The Cultural Determinants of the Order of Market Entry ................ 59
STUDY III: The Consequences of Firm Imitation on Early Entrant Performance
................................................................................................................................. 105
DISSERTATION CONCLUSION ....................................................... 140
APPENDIX A: Mutual Fund Category Titles .......................................... 142
APPENDD{ B: Project Finance Countries .............................................. 144
APPENDIX C: Factor Analysis Details .................................................. 146
REFERENCES ............................................................................... 156

iv

LIST OF TABLES

Table 1: Descriptive Statistics I ............................................................ 42
Table 2: Descriptive Statistics: Continuous versus Spline function ................... 43

Table 3: Random-Effects Tobit Regression Predicting Order of Entry: Historical
Aspirations ..................................................................................... 44

Table 4: Random-Effects Tobit Regression Predicting Order of Entry: Social
Aspirations ..................................................................................... 45

Table 5: Random-Effects Tobit Regression Predicting Order of Entry: Spline

Function ........................................................................................ 46
Table 6: Descriptive Statistics 11: Hofstede Dimensions ................................ 92
Table 7: Descriptive Statistics: House Dimensions ...................................... 93
Table 8: Descriptive Statistics: Hofstede versus House ................................. 94
Table 9: Descriptive Statistics: Cultural Diversity Models ............................. 95

Table 10: Random Effects Tobit Regression Results Predicting Order of Entry:
Hofstede Dimensions ......................................................................... 96

Table 11: Random Effects Tobit Regression Results Predicting Order of Entry:
House Dimensions ............................................................................. 97

Table 12: Random Effects Tobit Regression results Predicting Order of Entry:

Cultural Diversity .............................................................................. 98
Table 13: Descriptive Statistics 111 ........................................................ 135
Table 14: HLM Results Predicting Total Net Assets ................................... 136

Figure 1:
Figure 2:
Figure 3:
Figure 4:
Figure 5:
Figure 6:
Figure 7:

Figure 8:

LIST OF FIGURES

Full Model ........................................................................ 150
Hypothesis 1 ...................................................................... 15 l
Hypothesis 2 ...................................................................... 152
Hypothesis 3 ...................................................................... 153
Hypothesis 4 ...................................................................... 154
Interaction between experience and social relative performance .......... 155
Interaction between slack and historical relative performance ............. 156
Interaction between slack and social relative performance ................. 157

vi

INTRODUCTION

One of the classic areas of study in the strategic management literature is the order in
which ﬁrms enter new markets', and the advantages and disadvantages that subsequently
ensue to early and late entering firms (Lieberman & Montgomery, 1988). Economics and
competitive dynamics form the foundation of the arguments used in this stream of
literature. Combined, these literatures suggest that order of entry dynamics develop
through a process that begins with an unspeciﬁed environmental change (e. g., a change in
the economic or social environment). This change creates opportunities for ﬁrms to
compete in order to gain the economic and social advantages of new market opportunities
as they evolve continuously in the environment (Schumpeter, 1934). When ﬁrms earn
proﬁts from capitalizing on these opportunities, other ﬁrms are motivated to enter the
same market. However, markets are imperfect, so heterogeneity in terms of resources,
capabilities and information exist between organizations. As a result, some ﬁrms are
more proﬁcient at identifying, responding and capitalizing on market opportunities than
other ﬁrms. As a result, these pioneer fu'rns2 gain advantages, such as superior returns,
customer loyalty, advanced technologies or reputation capital (Lieberman &
Montgomery, 1988, 1998). However, rival or late moving ﬁrms often imitate the actions

of pioneer ﬁrms, and thus the advantages earned by the pioneer ﬁrms diminish.

The empirical research on order of market entry has focused on four main areas: (1)
the conceptual foundation of order of entry dynamics, (2) the antecedents of order of

entry behavior (i.e., which ﬁrms tend to enter markets early versus late), (3) the context

 

' Order of entry refers to the relative timing of a firm’s entrance into a market. Throughout the document,
order of entry is a continuous variable.
2 I use the terms “pioneer” and “early moving” ﬁrm interchangeably to refer to a unique entrant in a market.

in which order of entry dynamics exist and (4) the advantages and disadvantages that
accrue to ﬁrms that act early or late on new market opportunities. I review these four
areas in turn followed by a discussion of how this dissertation extends each of these

areas.

Conceptual Foundation

The work of Joseph Schumpeter (1934, 1950, 1986) forms the foundation of order of
market entry research. Schumpeter (1934) described capitalism as an evolutionary
process. From his viewpoint, this evolutionary process is fueled by the opening of new
markets (or the creation of new technologies) that replace or revolutionize existing
economic structures (Schumpeter, 1934, 1950, 1986). In other words, similar to other
members of the Austrian School of Economics (Jacobson, 1992), but unlike traditional
economic theorists, Schumpeter saw innovation (i.e., the entrepreneurial discovery and
exploitation of new markets, new technologies, new product uses, new distribution
channels, etc.) rather than monopoly power as the foundation of superior ﬁrm proﬁts.
Schumpeter’s argument was that competition based on innovation requires more cost and
quality advantages than competition based on monopoly power, and thus competition

based on innovation is more likely to generate superior proﬁts.

To quote Schumpeter (1950: 84), “it is not price competition which counts, but the
competition from the new commodity, the new technology, the new source of supply, the
new type of organization-competition which commands a decisive cost or quality

advantage and which strikes not at the margins of the proﬁts and the outputs of the

existing ﬁrms, but at their foundations and very lives. This kind of competition is more

eﬁective than the other ..... ”

Further, Schumpeter and other Austrian economists emphasized that ﬁrm action rests
upon gaining knowledge or information about innovative opportunities that others may

have overlooked (Jacobson, 1992).

Schumpeter’s arguments suggest that ﬁrms that enter a new market (or develop a new
product, ﬁnd a new supply source, etc.) earlier than other ﬁrms obtain superior returns
(Schumpeter, 1934). The superior proﬁts that innovative ﬁrms gain from entering new
markets motivate other fmns to enter the market in an attempt to dethrone the innovator
and obtain the same proﬁts. Schumpeter named the process whereby new markets,
commodities or innovations destroy existing ones, “creative destruction” (Schumpeter,
1934). The process of “creative destruction” suggests that the need to innovate stimulates
ﬁrm behavior in a competitive context. Without innovation, ﬁrms are likely to be
replaced by other ﬁrms that discover (or create) new markets. The process of creative
destruction highlights the interdependence of ﬁrm strategy and action, and ultimately
performance, and points to the fact that a fmn’s strategy is often, in part, a function of the

strategy of its rivals.

Despite its Schumpeterian origins, the literature on order of market entry has

conceptual limitations. First, the literature on order of market entry has relied almost

exclusively on the arguments based in Austrian economics,3 which limits the ability of
scholars to make different predictions and explanations regarding order of market entry
behavior. For example, agency theory (Jensen & Meckling, 1976) may suggest that top
management team compensation or board structure may inﬂuence market entry timing,
yet Austrian economic thought does not directly consider corporate governance factors to
explain entry behavior. Second, the literature on order of market entry under-speciﬁes
the manner in which ﬁrms actually discover new markets. Speciﬁcally, this literature
assumes that certain ﬁrms use their unique proﬁciencies to detect market opportunities
more effectively than other ﬁrms (Lieberman & Montgomery, 1988), yet the decision-
making process that leads to the discovery of these opportunities is unclear.4 Finally,
although logic would suggest that ﬁrms consider the risks of market entry, the literature
on order of entry pays limited attention to the downside potential of the market

opportunities on which ﬁrms capitalize to gain pioneering advantagess

Antecedents of Order of Market Entry Behavior

Research regarding the antecedents to the timing of market entry has primarily
examined the ﬁrm-level attributes and characteristics that predict whether ﬁrms will enter
markets earlier or later than other ﬁrms. Existing research has found several ﬁrm
characteristics to be important. For example, this stream of literature suggests that ﬁrm
size (Fuentelsaz, Gomez, & P010, 2002; Haveman, 1993a; Khorana & Servaes, 1999;

Mascarenshas, 1992; Teece, 1986; Wemerfelt & Kamani, 1987), prior performance

 

3 For an exception, see Lambkin (l988).

4 The literature on the diffusion of innovations suggests that ﬁrms discover new technologies through
research and development (Rogers, 2003).

5 The literature on the diffusion of innovations recognizes that new technologies are costly and that they do
sometimes fail (Rogers, 2003).

(Bolton, 1993; Fuentelsaz, Gomez, & P010, 2002), prior experience (Klepper & Simons,
2000), speciﬁc organizational skills (Robinson, Fomell, & Sullivan, 1992) and certain
types of ﬁrm resources (Frynas, Mellahi, & Pigman, 2006; Schoenecker & Cooper, 1998)
may predict which ﬁrms act early or late in a competitive setting. Among the most
researched characteristics (i.e., ﬁrm size, prior performance and firm assets), the only
consistent ﬁnding suggests that early moving ﬁrms tend to have more or better

(technological and political) resources than late moving ﬁrms.

The Context of Order of Market Entry Behavior

Scholars have examined the ongoing process of moves and counterrnoves implicit
within an order of entry context in many, but primarily domestic settings, such as new
product or new technology markets.6 For example, order of entry dynamics exist in the
mutual fund (Makadok, 1998), intemet commerce (Lieberman, 2007),
telecommunications (Lee, Smith, Grimm, & Schomburg, 2000), radio/television (Klepper
& Simons, 2000), banking (Dos Santos & Peffers, 1995), computer (Schoenecker &
Cooper, 1998) and consumer goods (Robinson & Fomell, 1985) industries. Further,
order of entry dynamics also exist within waves of domestic mergers and acquisitions

(Carow, Heron, & Saxton, 2004; McNamara, Haleblian, & Dykes, 2008).

Order of entry dynamics also occur in international business environments (Fuentelsaz
et al., 2002; Mascarenshas, 1992; Pan & Chi, 1999; Pan, Li, & Tse, 1999). Taken

together, the ﬁndings in the international business research suggest that multinational

 

6The literature has not made a distinction between these different contexts in terms of predicting early or
late market entrance.

ﬁrms enter developed and developing countries to gain access to new sources of demand
and factor inputs. Multinational ﬁrms that enter these countries earlier than other ﬁrms
are able to pre-empt the nation’s valuable resources and capabilities, and hence earn
higher returns. Nonetheless, research on order of entry studied in an international
business context has not fully considered how the cultural factors that are unique to an

international business context may shape early or late market entry.

Order of Market Entry and Firm Performance

Finally, the relationship between order of entry and ﬁrm performance has received the
most empirical examination in this stream of literature. Specifically, the literature on
order of entry proposes that ﬁrms that pioneer gain abnormal returns, whereas late or
copy-cat ﬁrms earn lower returns (Carow et al., 2004; Dos Santos & Peffers, 1995;
Lambkin, 1988; Lieberman & Montgomery, 1988, 1998; Makadok, 1998; Mascarenshas,
1992; McNamara etal., 2008; Robinson & Fomell, 1985; Sinha & Noble, 1997). Early-
movers gain their competitive advantage through technological leadership, buyer
switching costs, pre-emption of assets (Lieberman & Montgomery, 1988, 1998) or the
political resources of the ﬁrm (Frynas et al., 2006). Speciﬁcally, early movers can gain
beneﬁts through learning advantages if the learning is kept proprietary and if the ﬁrm can
maintain its leadership in the market (Lieberman & Montgomery, 1988). Early movers
may establish brand loyalty as buyer preferences evolve in a manner that favors the initial
position of the pioneer. Early movers can also seize proﬁts by gaining control of
valuable and scarce resources. If selected and used effectively, these resources can be a

source of sustainable competitive advantage (Barney, 1991; Penrose, 1959; Wemerfelt,

1984). Finally, early movers can gain advantages if they leverage their political
resources (i.e., resources that allow the ﬁrm to use the political process for its gain)

(Frynas et al., 2006).

Early mover advantages appear to be difﬁcult to sustain, however. First, based on a
meta-analysis, early mover advantages appear to be more sustainable when market share
is used as a measure of performance as opposed to proﬁtability or survival (Vanderwerf
& Mahon, 1997). Further, later movers can capitalize on the (1) resolution of
uncertainties, (2) discontinuities in the marketplace or (3) incumbent inertia and thereby
diminish the advantages earned by the early moving ﬁrm (Lieberman & Montgomery,
1988). Later movers can also use their own resources and strategies to obtain market
success (Cho, Kim, & Rhee, 1998; Sharnsie, Phelps, & Kuperman, 2004). Most
importantly, late movers may erode the advantages gained by early movers by imitating
the behavior of early moving ﬁrms (Lee et al., 2000; Sinha & Noble, 1997 ).
Nevertheless, this literature has ignored the multidimensional nature of ﬁrm imitation
(Haunschild & Miner, 1997), which limits the ability of strategic management scholars to

fully understand the effects of imitation on the sustainability of early mover advantages.

Study Purpose

In this dissertation, I extended the order of market entry research in all four areas of
inquiry (i.e., conceptual foundation, antecedents, context and order of entry-performance
relationship). In particular, the main purpose of this dissertation is to address the

following questions: (1) how does a ﬁrm’s decision-making processes and national

culture shape order of market entry? and (2) does the performance of early moving ﬁrms
erode based on diverse forms of imitation? I examined both parts of this research
question in three separate studies. Figure 1 is an illustration of the full set relationships
that I tested. The studies in this dissertation focus on entrance into new product markets

in domestic and international settings.

Overview of Study 1

The purpose of Study 1 is to apply the behavioral theory of the ﬁrm (Cyert & March,
1963) to predict the order in which ﬁrms will enter a new market. I base Study I on three
premises: (1) introducing a new theoretical framework to the research on order of entry
may enrich the current understanding of market entry behavior (2) ﬁrms actively search
for new markets and (3) market opportunities contain risks and uncertainties. Given its
focus on search and risk7, the behavioral theory of the ﬁrm (Cyert & March, 1963)
provides a framework to predict order of entry behavior in terms of the decision
processes through which ﬁrms search for and consider the risks associated with market
entry opportunities. Further, because the behavioral theory of ﬁrm (Cyert & March,
1963) considers performance compared to a speciﬁc benchmark, instead of absolute
performance, Study I provides an opportunity to resolve the inconsistent ﬁndings

regarding the relationship between prior performance and order of entry.

The behavioral theory of the ﬁrm suggests that relative performance predicts future

behavior. In particular, decision-makers compare their performance to an aspiration or

 

7 Although the behavioral theory of the ﬁrm can be applied to risk-taking, its original conceptualization
focused on search and organizational change. Later works (i.e., Singh (1986) and Bromiley (1991))
incorporated elements of risk.

target level of performance relative to their historical performance and relative to the
performance other fums in their social comparison group (Cyert & March, 1963). Both
comparison processes may reveal discrepancies between a ﬁrm’s aspiration level and its
actual performance. A performance discrepancy leads ﬁrms to search for opportunities
with varying levels of risk in order to resolve the discrepancy. In general, performance
below aspirations leads to “problemistic” search and risk seeking behavior, whereas
performance above aspirations tends to result in risk averse behavior (Cyert & March,
1963; Kahneman & Tversky, 1979).8 By considering market entry in terms of search
behavior, the behavioral theory of the ﬁrm (Cyert & March, 1963) suggests that furns
deliberately search for opportunities to enhance their performance, whereas the existing
market entry literature is relatively silent about how ﬁrms identify such opportunities.
Further, the behavioral theory of the ﬁrm suggests that ﬁrm action in a competitive
setting is not only the result of ﬁrms seeking to outperform their rivals through
innovation, but such action is also the result of “boundedly” rational decision—makers
using their routines to resolve a discrepancy in their performance. Finally, by
conceptualizing market entry as a trade-off between risk and return, rather than as an
opportunity, the behavioral theory of the ﬁrm supplies a theoretical framework to predict
when ﬁrms are likely to enter a market earlier or later than other ﬁrms based on their

search behavior and risk preferences.

In addition, research on order of entry has produced mixed results regarding the

relationship between prior performance and the tendency of ﬁrms to enter a market or

 

8 Throughout the document, performance relative to aspirations is a continuous variable.

adopt an innovation earlier or later than other ﬁrms. Speciﬁcally, one set of ﬁndings
suggests that substandard prior performance may lead to the early adoption of an
innovation (Bolton, 1993). Another set of ﬁndings suggests that substandard prior
performance leads to late adoption or imitation of an industry practice (Kraatz, 1998),
while other work found no relationship between prior performance and entry timing
(Fuentelsaz et al., 2002). By using the behavioral theory of the ﬁrm (Cyert & March,
1963), I have an opportunity to resolve these mixed ﬁndings by modeling not just prior
absolute performance, but rather performance relative to social and historical aspiration
levels to predict order of entry into a new market. Consistent with behavioral theory
logic, I also propose that slack may have a main effect on entry timing. Finally, the
equivocal ﬁndings in this area of research suggest that moderating factors may be
inﬂuencing these relationships. Therefore, I also propose that prior entry experience and
organizational slack moderate the relationship between entry timing and performance
relative to aspiration levels. Speciﬁcally, high levels of prior entry experience may
weaken, but slack may strengthen the relationship between performance relative to
aspirations and order of entry, and thus prior entry experience and slack may modify the
decision-making process that is implicit within the logic of the behavioral theory of the

ﬁrm.

In Study I, I make ﬁve contributions to the literature. First, by applying the behavioral
theory of the ﬁrm, I use a unique theoretical framework that makes predictions about
order of market entry, particularly as it relates to a ﬁrm’s search processes and risk

preferences. Second, existing theories and perspectives in the management literature

10

focus primarily on why and which ﬁrms may follow or act late in a new market setting.
However, the behavioral theory of the ﬁrm provides a more complete perspective on
entry behavior because its arguments helped to examine which ﬁrms may act early and
which ﬁrms may act late in a market entry context. Third, by applying the behavioral
theory of the ﬁrm in an order of entry context, I examine whether performance
discrepancies may trigger innovative behaviors. Finally, given that the existing literature
has produced unclear results regarding the relationship between prior performance and
order of entry, I attempt to reconcile these mixed ﬁndings and thereby establish a more

sound understanding of the prior performance-order of entry relationship.

I organize Study 1 as follows. First, I provide a brief overview of the behavioral
theory of the ﬁrm. Second, I discuss market entry in terms of search and risk. I then
discuss the importance of prior performance in the study of strategic management and
some of the empirical ﬁndings related to prior performance. Next, I review the literature
on the behavioral theory of the ﬁrm. Finally, I develop the hypotheses, present results
and discuss the ﬁndings and areas for future research. I test the hypotheses for this study
on a sample of 102 publicly traded, financial institutions that entered the equity mutual

fund market in the United States between 1980 and 2006.

Overview of Study 11
In Study 11, I examine how national culture predicts order of entry decisions for single
firms and teams of ﬁrms doing business in international markets. In Study II, I

emphasize the fact that national culture is important because it affects ﬁrm action and

11

decision-making. More importantly, in Study 11, I highlight the fact that very few studies
have examined how order of entry behavior varies if it occurs across rather than within
diverse economic or social contexts. This suggests that although scholars have studied
order of entry in international settings, the cultural implications of this setting needs
further investigation. In other words, although current work supports the existence of
order of entry behaviors and the advantages of pioneering activities (Carow et al., 2004;
Dos Santos & Peffers, 1995; Lambkin, 1988; Makadok, 1998; Mascarenshas, 1992;
McNamara et al., 2008; Robinson & Fornell, 1985), most studies have been conducted in
a domestic or single country setting. As a result, these studies ignore the multi—cultural
inﬂuences that permeate the strategic decision-making processes of most contemporary
multinational organizations.9 Further, I recognize that, in an international business
setting, teams of multinational ﬁrms may make order of entry decisions as they seek to
spread the risks associated with investing in new markets. Therefore, the behavior of

multinational teams of ﬁrms is an important area of study.

In the current business environment, multinational organizations compete for proﬁts
across multiple geographic markets. In this setting, Porter (1990) argues that the
traditional sources of competitive advantage (i.e., economies of scale, labor costs, etc.)
are ﬂawed and that a new perspective that focuses on the national environment is
necessary in order for ﬁrms to succeed in international competition. Speciﬁcally, Porter
(1990) identiﬁed the national environment as a source of competitive advantage.

“Differences in national values, culture, economic structures, institutions and histories all

 

9 A multinational organization is an organization with operations in more than one country.

12

contribute to competitive success” (Porter, 1990: 19). That is, the national environment
in which a ﬁrm or industry operates determines its resources, shapes how it will act, and
thus perform, in international competition. Therefore, given that national culture is an
important element of the national environment that inﬂuences organizational behavior
(Hofstede, 1983), national culture may predict the competitive behavior of ﬁrms in an

order of entry context.

In this vein, international business scholars have tested the inﬂuence of the national
environment surrounding order of entry (Isobe, Makino, & Montgomery, 2000; Pan &
Chi, 1999; Song, Di Benedetto, & Zhao, 1999). The ﬁndings from this line of research
suggest that order of entry advantages exist within a variety of international settings and
that managerial perceptions of such advantages vary across countries due to cultural
differences. However, what remains an open question is how national culture speciﬁcally
determines the tendency to pursue (or disregard) order of entry advantages, which aspects
of national cu1ture have the most inﬂuence on such tendencies and how these tendencies
or behaviors vary across international markets. Further, given that multinational
organizations not only make autonomous order of entry decisions that are inﬂuenced by
their own national culture, but also make such decisions jointly with other firms from
diverse cultural environments, the inﬂuence of national culture on market entry decisions

becomes even more complex.

In Study II, I make four contributions to the literature. First, I examine how national

culture shapes competitive behavior. Second, Western experience and data as well as

13

assumptions based in Western culture still primarily guide the literature on order of entry
and its associated advantages (Lieberman & Montgomery, 1998). Therefore, by
examining order of entry decisions across diverse cultural settings, I broaden the
applicability of this stream of literature and respond to a call to consider the implications
of Westem-based theories and frameworks at the international level (Boyacigiller &
Adler, 1991; Hofstede, 1991). Third, by testing the inﬂuence of team and independent
cultural effects on order of entry decisions, I extend the understanding regarding the
relationship between national culture and strategic action for individual ﬁrms and teams
of ﬁrms. Finally, although the inﬂuence of national culture on entry mode choice
(Hennart & Larimo, 1998; Kogut & Singh, 1988) has been tested (i.e. the inﬂuence of
national culture on how ﬁrms enter markets), the direct impact of national culture on
when ﬁrms may enter a market in international settings has not been examined. Given
that it is not only the choice of entry mode, but also the timing of entry that can inﬂuence
a ﬁrm’s outcomes, a study that tests the inﬂuence of national culture on order of entry
decisions allowed me to extend the understanding of order of entry and its ensuing

advantages for international business scholars.

I organize Study II as follows. First, I review the conceptual framework that guides
this study as well as the stream of literature on national culture, including empirical
ﬁndings. Second, I develop hypotheses related to how national culture inﬂuences which
individual ﬁrms and which teams of ﬁrms enter markets early and which ones enter late.
Third, I discuss the data and methods used to test the hypotheses. I conclude with a

discussion and opportunities for future research. I test the hypotheses developed in this

14

study on a sample of 1,000 project-based investments across 48 countries from 1980-
2003. Project-based investments are discrete forms of foreign direct investment, which
multinational corporations primarily use to ﬁnance infrastructure projects across the

globe.

Overview of Study [11

Finally, in Study III, I examine how the performance of early moving ﬁrms decreases
based on three modes of imitation. Research on order of entry advantages has long
acknowledged the contingent nature of the abnormal earnings garnered by early moving
ﬁrms (Kerin, Varadarajan, & Peterson, 1992; Szymanski, Troy, & Bharadwaj, 1995).
For example, early mover advantages may be contingent on the early mover’s advertising
intensity, quality of products and services, and resource endowments. Similarly, early-
mover advantages may also depend upon the pioneer’s strategy or the technologies that
the pioneer owns (Coeurderoy & Durand, 2004; Durand & Coeurderoy, 2001).
Primarily, however, this stream of research has argued that early-mover advantages are
contingent upon the ability of late movers to imitate or copy the behavior of early moving
ﬁrms. As a result of this imitative behavior, early-mover advantages are eroded as other
ﬁrms enter the same market, or develop a similar product, strategy or practice (Lee et al.,
2000; Lieberman & Montgomery, 1988). By responding quickly through imitation, rival
ﬁrms scale the barriers to entry and thereby diminish the superior proﬁts that early

moving ﬁrms accrue.

15

However, the literature on the acquisition and sustainability of early mover advantages
has treated the act of imitation in a rather simplistic and uni-dimensional manner. In
reality, not all imitation is the same. Indeed, inter-organizational imitation has three
modes that can operate simultaneously among groups of organizations: frequency-based
imitation, trait-based imitation and outcome-based imitation (Haunschild & Miner, 1997).
Frequency-based imitation occurs when a large number of other firms imitate the actions
of the early moving ﬁrm. Trait-based imitation occurs when ﬁrms imitate the behavior of
early moving ﬁrms based on the speciﬁc characteristics of the early moving ﬁrms, such
as the size, success or reputation of the ﬁrm. Outcome-based imitation occurs when
ﬁrms imitate a practice or structure of the early moving ﬁrms based on the apparent
positive outcomes that early moving ﬁrms have received from employing this practice or
structure. Although each mode of imitation has a different operationalization, each mode
has its theoretical foundation in institutional theory (DiMaggio & Powell, 1983; Meyer &

Rowan, 1977).

Given the multi-faceted nature of ﬁrm imitation, it may have different effects in a
competitive context. Further, the three modes of imitation may have different effects
when compared to each other. Therefore, in Study III, I attempt to address two issues.
First, how is the performance of early moving ﬁrms eroded based on the (a) the number
of imitators (frequency-based imitation), 0)) the characteristics of the early moving ﬁrm
(trait-based imitation) and (c) what practice used by the early moving ﬁrm is being
imitated (outcome-based imitation)? Second, what is the comparative effect of each

mode of imitation on the performance of early moving ﬁrms?

16

In Study 111, I seek to make three contributions to the literature. First, I extend the
understanding of ﬁrst-mover advantages by further specifying the conditions in which
ﬁrst-mover advantages may erode through imitation by late moving ﬁrms. Second, I
specify the consequences of the diverse modes of imitation, whereas the current literature
has primarily focused on the antecedents or consequences of a generic form of imitative

behavior. Finally, I investigate the comparative strength of each mode of imitation.

I organize Study III as follows. First, I review the literature on organizational
imitation, which has primarily been discussed in the organizational sociology research.
Second, I describe the three modes of imitation. Third, I develop and test the hypotheses.
Finally, I discuss ﬁndings and areas for future research. I test the hypotheses for this
study on a sample of 470 international equity mutual funds that were opened in the

United States between 1985 and 2006.

17

BEHAVIORAL PERSPECTIVES ON THE ORDER OF MARKET ENTRY

THEORY AND HYPOTHESES

The traditional economic perspective on order of market entry has portrayed the
relative tinting of market entry as the outcome of a competitive process whereby certain
ﬁrms use their superior knowledge or proﬁciencies to see and exploit market
opportunities before rival ﬁrms are able to do so. From this perspective, the process by
which ﬁrms gain knowledge about new markets or deploy their proﬁciencies to identify
such markets is unclear. Further, this perspective primarily views market entry as a
potential gain for entering ﬁrms. However, to fully capitalize on the beneﬁts of a new
market, the ﬁrm must ﬁrst search for the market opportunity. In addition, market entry is
a strategic decision that ﬁrms make. Thus, once a ﬁrm detects a new market, it may also
consider the entry decision in terms of the risks and returns associated with such a
decision. By employing the behavioral theory of the ﬁrm (Cyert & March, 1963), I use a
behavioral perspective to explain and predict how ﬁrms use their routines to search for
new markets and how ﬁrms consider risk in order to predict order of entry into a new
market. Moreover, by applying the behavioral theory of ﬁrm (Cyert & March, 1963), I
have the opportunity to reconcile the inconsistent ﬁndings regarding the relationship
between prior performance and market entry timing by examining not just absolute prior
performance, but performance relative to a ﬁrm’s aspiration levels. Further, in line with
behavioral theory predictions, I hypothesize a direct relationship between organizational
slack and order of market entry. Finally, I seek to further reﬁne and extend the

understanding related to the inﬂuence of relative performance on market entry timing by

18

suggesting that high levels of prior entry experience and organizational slack may act as
moderating variables by weakening or strengthening the relationship between relative

performance and order of market entry, respectively.

The Behavioral Theory of the Firm

The behavioral theory of the ﬁrm is one of the classic theories related to the study of
organizations. In their seminal work, Cyert and March (1963) portray organizations as
goal-oriented, routine-based entities that use simple decision rules to adapt to variation in
the environment. Among many things, the behavioral theory of the ﬁrm proposes that
performance compared to a ﬁrm’s aspiration or target level of performance, not just
absolute prior performance, predicts future ﬁrm behavior (Cyert & March, 1963).10
Based in social comparison theory (Festinger, 1954),11 these aspiration levels develop as
ﬁrms compare themselves to others and their own historical performance. Uncertainty in
the environment evokes greater levels of social comparison (Pfeffer, Salancik, &
Leblebici, 1976). If the firm determines that its position is less than its aspired to or
target level of performance, it attempts to adjust its position by searching for alternatives
that reduce the performance discrepancy (Cyert & March, 1963). This comparison
process leads ﬁrms to accept varying levels of risk to either protect or enhance their

relative position (Bromiley, 1991; Singh, 1986).

Market Entry, Search and Risk

'0 Recent work suggests that organizations also form aspirations based on ﬁrm size (Greve, 2008).
H Social comparison theory (Festinger, 1954) has been supported empirically at the individual and ﬁrm
level of analysis, respectively (Moschis, 1976; O'Reilly, Main, & Crystal, 1988).

19

Firm entry into a new market involves organizational search and risk. In order to
enter a new market, an organization must ﬁrst discover the new market opportunity. T 0.
do so, the ﬁrm may need to search the environment broadly and discriminately in order to
identify the best opportunity. Then, in order to capitalize on the opportunity, the ﬁrm
may need to change its current strategy, re-arrange its organizational structure, invest in
new research and development or challenge the status quo. Therefore, new market entry
takes time and resources away from existing strategies and tactics. This change in focus
and resources towards a new or different strategy is laced with uncertainty and risk
because the outcomes associated with new market entry may be unknown. Further, as
population ecology theorists have long argued, organizational change is difﬁcult and
gradual because organizations are subject to strong inertial forces (Harman & Freeman,
1977, 1984). These forces prevent the organization from making radical changes based
on threats or opportunities in the environment. Ultimately, organizational change is
precarious because the ﬁrm may not survive or remain competitive after the change

(Amburgey, Kelly, & Barnett, 1993; Harman & Freeman, 1977, 1984).

Despite the challenges associated with market entry, it is still a common occurrence
(Geroski, 1995). However, ﬁrms enter new markets at varying times. Certain ﬁrms see
opportunities more quickly than other fums, and subsequently embrace the risk and
uncertainty associated with moving into new markets, and thus gain early moving
advantages (Lieberman & Montgomery, 1988, 1998). Conversely, other ﬁrms may fail to
detect new markets in a timely manner or simply prefer to enter late or imitate the

behavior of other market entrants because imitation allows ﬁrms to “free ride” on the

20

learning and experience gained by other ﬁrms. Therefore, imitation or late entry is a less
opportune, but safer, less costly and less risky form of market entry. Nevertheless, the
empirical ﬁndings are reasonably consistent in terms of the consequences of the order of
market entry. '2 Firms that enter markets early receive higher ﬁnancial returns than ﬁrms
that enter later (Carow et al., 2004; Dos Santos & Peffers, 1995; Lambkin, 1988;
Mascarenshas, 1992; McNamara et al., 2008; Robinson & Fomell, 1985; Sinha & Noble,
1997). Given these polar outcomes, one way to consider market entry timing (or order of
entry) is to interpret it as an outcome of how ﬁrms search for new markets and how ﬁrms
perceive the risks associated with market entry. Therefore, based on its consideration of
search and risk in decision-making, the behavioral theory of the ﬁrm may provide

important predictions regarding the timing of market entry.

Prior Performance and Market Entry

Although much of the literature in strategic management concentrates on the factors
that predict the subsequent performance of the ﬁrm, the effect of prior performance on
future organizational behaviors is an important area of inquiry. Similar to the literature
on performance feedback in the organizational behavior literature, much of the literature
regarding prior performance and subsequent ﬁrm behavior assumes that actors learn what
actions to take in the future based on the feedback they receive from past and current
actions and behaviors. Based on this feedback, actors are motivated to adjust their
actions and behaviors to achieve desirable outcomes. Financial performance is a relevant

and salient feedback mechanism for organizations because investors, management and

 

'2 In a meta-analysis, VanderWerf and Mahon( 1997) ﬁnd that order of entry advantages are more
signiﬁcant when market share, instead of proﬁtability or survival, is used as the performance measure.

21

other stakeholders view performance as a signal of the ﬁrm’s potential for growth or
stability into the future. In the strategic management literature, prior performance has
been shown to predict multiple behaviors, including corporate illegal behavior (Baucus &
Near, 1991), partner selection in strategic alliances (Li & Rowley, 2002), CEO
succession (Dalton & Kesner, 1985), risk taking (Bromiley, 1991; Singh, 1986) and

corporate social responsibility (McGuire, Sundgren, & Schneeweis, 1988).

Existing research has also examined the relationship between prior performance and
the tendency of ﬁrms to adopt an innovation or enter a market earlier or later than other
ﬁrms, but with mixed results. Speciﬁcally, this line of research has found that low prior
performance causes ﬁrms to act either in a leading or imitative fashion (i.e. act early or
late). For example, Bolton (1993) found that substandard prior performance led to the
early adoption of a technological innovation among a group of research and development
organizations across multiple industries. Alternatively, Kraatz (1998) found that
substandard performance led to the imitation or late adoption of an adaptive change in a
network of colleges and universities. Finally, others found that prior proﬁtability had no
signiﬁcant impact on the speed or timing of bank entry into a new geographic market

(Fuentelsaz et al., 2002).

Because the behavioral theory of the ﬁrm models relative performance, its arguments
may help to resolve the inconsistent empirical ﬁndings regarding the relationship
between prior performance and market entry timing. However, certain factors may

moderate the relationship. In particular, prior entry experience and organizational slack

22

may moderate the relationship between performance relative to aspirations and order of
market entry because prior experience and slack may inﬂuence the decision calculus that
is embedded within the logic of the behavioral theory of the ﬁrm. Speciﬁcally, prior
entry experience may lead to over-reliance on existing routines, and thus early market
entry. On the other hand, organizational slack may create complacency, and thus later
market entry. Therefore, performance relative to aspirations moderated by prior entry
experience and slack may adjust a ﬁrm’s tendency to continue operating under its

existing market entry timetable.

The Behavioral Theory of the Firm Revisited

The behavioral theory of the ﬁrm (BTOF) suggests that decisions are the result of
ﬁrms searching for a solution to a problem (Cyert & March, 1963; March & Simon,
1958). The problem arises because the ﬁrm experiences a gap between its aspired to or
target performance level and its actual (or expected) performance level. A gap or
attainment discrepancy between the aspiration level and actual performance leads to a
search to resolve the discrepancy. Models that focus on attainment discrepancy provide
the best description of the aspiration formation process compared to rational, economic
models (Lant, 1992). Performance below aspiration levels leads to “problemistic” search
to identify opportunities that may raise the organization’s performance level. Slack
search, which occurs when ﬁrms have excess resources, leads to experimentation with
novel approaches. Search ends when the ﬁrm ﬁnds a “satisﬁcing” option or when the
ﬁrm adjusts its aspiration levels. The behavioral theory of the ﬁrm assumes that (1) ﬁrm

behavior is based on routines (i.e., action entails matching procedures to circumstances)

23

(2) firm behavior is goal-oriented, path dependent and rule-based (Levitt & March,
1988), (3) attainment discrepancies are repairable and (4) decision makers are not always
aware of all possible alternatives due to cognitive and information limitations, and thus

are “boundedly” rational (Cyert & March, 1963; March & Simon, 1958).

When viewed through the lens of prospect theory (Kahneman & Tversky, 1979), the
behavioral theory of the ﬁrm provides predictions regarding the relationship between
relative performance and the inclination of organizations to engage in risk taking
behavior. At the individual level of analysis, prospect theory suggests that how decision-
makers respond to risk and uncertainty is a reﬂection of the framing of the potential
outcomes. Decision-makers tend to be risk averse for positively framed outcomes, but
tend to be risk seeking for negatively framed outcomes (Kahneman & Tversky, 1979).
Similarly, low performing ﬁrms seek risk, whereas high performing furns avoid risk
(Fiegenbaum & Thomas, 1988). Combined, prospect theory and the behavioral theory of
the ﬁrm suggest that ﬁrms with performance above social and historical aspirations are
risk averse because they want to uphold their success, but ﬁrms with performance below
social and historical aspirations are risk seeking because they want to improve their

performance (Cyert & March, 1963; Kahneman & Tversky, 1979).

Most studies based in the behavioral theory of the ﬁrm focus on ﬁrm risk-taking
tendencies as measured by variance in ﬁnancial returns (Bromiley, 1991; Singh, 1986).
However, more recent studies have used the behavioral theory of the ﬁrm to predict

speciﬁc ﬁrm behaviors. For example, radio stations are more likely to change formats

24

when guided by performance relative to social and historical performance levels (Greve,
1998). Based on a sample of Japanese shipbuilders, low performance relative to
aspiration levels leads to increased innovation launches and R&D intensity (Greve,
2003a). However, high performance relative to aspiration levels reduces innovation
launches and R&D intensity (Greve, 2003a). Organizations with inconsistent
performance feedback (i.e., performance levels above historical aspirations, but below
social aspirations and vice versa) are inclined to engage in non-local partnership ties,
whereas those organizations performing near aspiration levels are more likely to re-
connect with prior allies (Baum, Rowley, Shipilov, & Chuang, 2005). Hospitals that
performed below their reference group are more likely to add and delete high technology
in order to increase proﬁts or enhance their image among stakeholders (Ketchen &
Palmer, 1999). Finally, relative performance also inﬂuences the likelihood of ﬁnancial
statement misrepresentation (depending on whether the reference point is social or
historical) (Harris & Bromiley, 2007) and the extent to which railroads learn from

experience, and thus reduce accident costs (Baum & Dahlin, 2007).

Given the shared focus on search and risk between market entry and the behavioral
theory of the ﬁrm, organizational performance relative to aspirations may likely affect the
ﬁrm’s propensity to enter a market earlier or later than other ﬁrms. Performance
compared to aspiration levels can inﬂuence a variety of behaviors. Nevertheless, this
comparison process is likely to predict market entry timing because the timing decision
reﬂects the effectiveness of the ﬁrrn’s search processes and its willingness to accept risk.

Thus, given the comparison process embedded within the logic of the theory, the

25

behavioral theory of the ﬁrm provides a natural setting to more fully investigate the
antecedents of order of entry into a new market. Further, the behavioral theory of ﬁrm
implies that certain ﬁrms will act early or late in a market entry context based on their
relative performance, whereas existing management theories and perspectives, such as
institutional theory (DiMaggio & Powell, 1983; Meyer & Rowan, 1977) and bandwagon
processesl3 (Abrahamson & Rosenkopf, 1993; Banetjee, 1992; Bikchandani, Hirshleifer,
& Welch, 1992; Denevow & Welch, 1996; Palley, 1995; Scharfstein & Stein, 1990)

primarily explain late moving or imitative behaviors.

Performance, Aspirations and Order of Market Entry

According to the behavioral theory of the ﬁrm (Cyert & March, 1963), aspirations or
target levels of performance are derived from two comparisons: (1) comparison to others
in the organization’s social comparison group and (2) comparison to the organization’s
own historical performance. Comparable others and a ﬁrm’s historical performance
serve as benchmarks or reference points against which the organization compares its
current level of performance. '4 In both comparisons, aspirations have a positive bias in
that organizations tend to aspire to higher levels of performance relative to historical
performance or the performance of similar others (Bromiley, 2005). Empirical work
supports a positive relationship between the aspiration levels of the previous period and

those in the current period (Mezias, Chen, & Murphy, 2002).

 

'3 See Study 111 for a more complete description of bandwagons, herd behavior and information cascades.
‘4 Organizations may also compare their performance to a survival level (Chen & Miller, 2007; March &
Shapira, 1987, 1992).

26

Research related to the behavioral theory of the ﬁrm suggests that if performance is
above social or historical aspirations, ﬁrms become risk averse or less willing to change
(Greve, 1998, 2003a; Singh, 1986). Performance above aspirations leads organizations to
engage in risk-averse behavior because the organization wants to protect its successful
position relative to its peers and own prior performance. That is, when performance is
above social or historical aspirations, organizations tend to conserve their resources and
focus on the exploitation of existing systems and strategies (March, 1991). These
arguments suggest that performance above aspirations would lead to late entry into a new
market because late entry would allow the ﬁrm to rely on current modes of operation and

thus face lower risks of failure.

Alternatively, if performance is below social or historical aspirations, organizations
are more willing to take risks in order to improve their performance (Baum et al., 2005;
Bromiley, 1991; Greve, 2003a; Ketchen & Palmer, 1999). When performance is below
social or historical aspirations, organizations engage in problemistic search. Cyert and
March (1963) theorized that problemistic search is local initially (and thus may result in
imitative behavior). If a solution is not found, the search becomes distant. Greve (1998)
also asserts that problemistic search tends to be focused on competitors in the local
market. However, I argue that problemistic search results in a broad search for
opportunities, which results in innovative behavior that improves the ﬁrm’s performance.
This argument is consistent with recent empirical ﬁndings (Baum et al., 2005; Bolton,
1993; Greve, 2003a). The logic is that the further a ﬁrm’s performance is below

aspirations, the larger the adjustments the ﬁrm must make in order to improve

27

performance. Therefore, the ﬁrm must emphasize more exploratory or innovative search.
Further, the pressure to improve performance leads ﬁrms to accept higher levels of risk in
any potential activity. By accepting a higher level of risk, the organization expects to
receive a higher level of return that will boost its performance. These arguments imply
that performance below aspirations would lead to early entry into a new market. This is
because ﬁrms with performance below aspirations would be inclined to search broadly
and thus identify a wide and perhaps better set of market opportunities, and pursue the
higher earnings associated with early entry in order to improve their performance.

Therefore,

H1: F irrn performance relative to aspirations is positively related to the timing of market
entry, such that ﬁrms with performance above (below) aspirations will be late (early)

entrants into new markets. (See Figure 2)

The Main Inﬂuence of Firm Slack

Aside from problemistic search, Cyert and March (1963) theorized that slack search
may facilitate organizational change. Slack search results from extra time and resources
that are used for experimentation (Greve, 2003b). Restated, slack creates a willingness to
explore new opportunities (March, 1991) because slack allows for a relaxation of
resource controls. Finally, slack buffers the ﬁrm against environmental uncertainties
(Thompson, 1967). In an order of entry context, slack supports more intensive search
activities (Chen & Miller, 2007; Greve, 2003a) and thus would lead to the early detection

of more or more novel market opportunities. Therefore, ﬁrms with slack resources are

28

more likely to identify entry opportunities earlier than other ﬁrms and be more willing to
accept the risks of early market entry. Therefore,
H2: Slack is negatively related to the timing of market entry (i.e., more slack leads to
early market entry). (See Figure 3)
The Moderating Influence of Prior Entry Experience

Historically, the management literature has argued that organizations learn from their
own experience or though the experience of others (Levitt & March, 1988). Own-
experience learning (i.e., learning curve effects) suggests that cumulative experience can
reduce the costs of production because, over time, ﬁrms experiment with different inputs
and processes. Consequently, they learn which inputs and processes are the most
efﬁcient. Put differently, as ﬁrms gain experience, they become more familiar with
which alternatives may lead to success or failure. Despite the primary focus on
manufacturing entities in early works, recent research provides evidence that experience

improves efﬁciency for service and manufacturing ﬁrms (Darr, Argote, & Epple, 1995;

Yelle, I979).

Theoretically, prior experience is an important factor as it relates to the behavioral
theory of the ﬁrm. A central idea from the theory is that organizations learn from their
past experiences and this learning inﬂuences the likelihood of future behavior (Levitt &
March, 1988). In a behavioral theory of the ﬁrm sense, this means that ﬁrms learn which
adjustments are necessary in order to resolve any discrepancies in their performance
based on their experiences. The more experience a ﬁrm has had, the more likely it is to

have learned the appropriate adjustments. The lessons gained from experience are

29

encoded in routines (Nelson & Winter, 1982), which can then by used by the organization
in the future. Routines change as experiences change. Therefore, varying levels of prior
experience may have different inﬂuences on the adjustments that ﬁrms make in order to

address a performance discrepancy.

In terms of the timing of market entry, existing work suggest that ﬁrms with prior
entry experience in a related industry will enter a market earlier than other ﬁrms (Klepper
& Simons, 2000). That is, prior entry experience has a direct effect on order of market
entry. The argument is that prior entry experience in a related industry provides ﬁrms
with the necessary expertise to be successful, and thus enables the ﬁrm to enter the
related industry earlier than other ﬁrms (Klepper & Simons, 2000). However, prior entry
experience may have moderating effects on the relationship between relative performance
and order of entry because ﬁrms may view experience differently depending on their

performance standing, and hence make different entry decisions.

Speciﬁcally, under conditions of high levels of prior entry experience, ﬁrms are more
likely to rely on their established routines rather than performance cues when making
entry decisions. Again, firms encode the lessons gained from experience in their routines
(Nelson & Winter, 1982). Therefore, higher levels of experience create more established
routines. This is because organizations that gain experience with particularly activities
are likely to develop distinctive competencies in a domain, which then invites further
utilization and routinization (Levinthal & March, 1993; O'Neill, Pouder, & Buchholtz,

1998). Similarly, as organizations gain experience with a particular action, strategy or

30

behavior, they are likely to intensify this behavior (even if the previous outcomes are
unfavorable) (Amburgey & Miner, 1992). The momentum gained from repeating entry
behaviors implants these behaviors within the ﬁrm’s core routines. These core entry
routines may cause fums to ignore any new or discrepant market information because the
ﬁrm is simply following a pattern of established behavior. Therefore, the ﬁrm may
become insensitive to performance cues or any information gained from search activities
that may direct the ﬁrm towards more rational entry decisions. Therefore, prior
experience weakens the relationship between relative performance and order of market

entry. Hence,

H3: Prior entry experience moderates the relationship between relative performance and
order of market entry with a stronger relationship for ﬁrms with low levels of prior entry

experience than ﬁrms with high levels of prior entry experience. (See Figure 4)

The Moderating Influence of Firm Slack

The management literature presents two opposing perspectives regarding
organizational slack: one from the behavioral theory of the ﬁrm (Cyert & March, 1963)
and the other from agency theory (Jensen & Meckling, 1976). Slack is a pool of
resources in an organization that is in excess of the minimum necessary to produce a
given level of organizational output (Nohria & Gulati, 1996). Environmental conditions
and organizational characteristics inﬂuence a fum’s level of slack resources (Sharfman,
Wolf, Chase, & Tansik, 1988). Among its many fImctions, behavioral theorists view

slack as a buffer against the vagaries of the external environment, a resource for creative

31

and innovative experimentation, and a facilitator of strategic change (Bourgeois, 1981;
Cyert & March, 1963; Nohria & Gulati, 1996; Thompson, 1967). Because of its potential
beneﬁts, behavioral theorists view slack as a particularly useful asset for failing ﬁrms
because slack may provide failing ﬁrms with the resources needed to rectify their

performance.

Alternatively, from an agency theory perspective (Jensen & Meckling, 1976), slack
represents waste and managerial self-interest. Agency theorists believe that ﬁrms with
slack resources have been inefﬁcient in terms of their operations and that slack allows
ﬁrms to invest in activities that may not be in the best interest of the ﬁnn’s stakeholders
(Jensen, 1986). Because of its potential to distract managerial attention away from
maximizing shareholder value, agency theorists view slack as harmful to the
organization. This view of slack may be particularly applicable to successful
organizations because successful organizations may discount their infallibility, which
compounds the potential detrimental effects of slack. Given these arguments, slack may
have different implications for the relationship between relative performance and order of

market entry.

First, ﬁrms with performance below aspirations may be faced with the threat of failure
as their performance continues to decline. However, for these ﬁrms, slack provides a
cushion against the uncertainties in the environment (Thompson, 1967) and may provide
the resources needed to search for opportunities to improve the ﬁrm’s performance.

Under these conditions, failing ﬁrms may view their performance discrepancy as a

32

repairable condition. Therefore, these ﬁrms may be more inclined to search broadly for
novel market opportunities. More importantly, they may be more willing to accept the

risks of early market entry, which may then enhance their performance.

Alternatively, ﬁrms with performance above aspirations may view their successful
performance as an opportunity to ignore the interests of the fum’s stakeholders. This is
because, for these ﬁrms, the presence of slack suggests that managers may have the
resources to pursue their own self-interests, and thereby disregard the interests of the
ﬁrm’s principals (Jensen & Meckling, 1976; Williamson, 1963). Further, slack may lead
these ﬁrms to undertake low beneﬁt or value destroying activities, which harm the
principal’s interests, because slack allows managers to discount the economic
implications of their decisions (Jensen, 1986). Under these conditions, the self-interested
behavior and economic indifference that slack promotes may lead to organizational

complacency, which in turn leads to late market entry. Therefore,
H4: Slack moderates the relationship between relative performance and order of market
entry with a stronger relationship for ﬁrms with high levels of slack than ﬁrms with low

levels of slack. (See Figure 5)

METHODOLOGY

33

I test the hypotheses in this study on a sample of 102 publicly traded, ﬁnancial
institutions that entered the equity mutual fund15 market in the United States between
198016 and 2006. These data include domestic and international mutual funds opened in
the United States. The data include twenty-one mutual fund categories. Appendix A
includes the titles of each fund category. Each category had an average of twenty-six
mutual funds. I obtained these data from Momingstar Principia. Momingstar Principia
has data on over 10,000 mutual funds. For simplicity, I only considered the Class A

shares17 of each mutual fund in this analysis.

Research Setting

The equity mutual fund industry experienced tremendous growth in the past two
decades primarily because of growth in the US. economy and because more Americans
placed their retirement savings into mutual funds. During the 1990s, the US. economy
had strong corporate ﬁnancial performance, low inﬂation and low interest rates-factors
that stimulate growth in equity mutual fund investments. Further, more Americans used
mutual funds to grow their retirement savings. This change reﬂected a switch from
deﬁned beneﬁt plans (i.e., pension plans) to defined contribution plans (i.e., 401(k)s,

403(b)s and IRAs) as the principal vehicle for retirement savings in the United States.

 

‘5 A mutual fund is a company that offers investors an interest in a portfolio of professionally managed
investment assets (Leonard & Caudill, I996). An equity mutual fund is a mutual fund that invests
primarily in the stock or equity securities of publicly traded companies.

6 The year 1980 is the beginning cut-off year to capture the behavior of ﬁrms that recognized the
opportunity in this market earlier than other ﬁrms. The Revenue Act of 197 8 created 401 (k) plans (i.e.,
deﬁned contribution plans) (EBRI, 2005). The government issued regulations for the plans in late 1981
and many organizations started offering 401(k) plans in 1982 (EBRI, 2005). Because many organizations
funded their 401(k) plans with equity investments, the growth in 401(k) plans coincides with the grth in
the equity mutual fund market.

'7 Class A shares are the highest tier of shares available because they grant the owner more voting rights
than any other share type.

34

Between 1986 and 1993, the percentage of 401(k) plans invested in mutual funds
increased from 9% to 23.2%, or $155 billion to $475 billion (Leonard & Caudill, 1996).
Overall, the equity mutual fund market grew from approximately $250 billion to $750
billion between 1986 and 1993 (Leonard & Caudill, 1996). Because of the growth in the
US. economy and changes in retirement funding, the early stages of the equity mutual
ﬁmd market provided an opportunity for ﬁrms to gain advantages by entering the market
early. Speciﬁcally, ﬁrms that entered the market early were more likely to pre-empt
investment assets 18 and establish customer loyalty19 to their equity funds, which in turn
may have enhanced the performance of the fund and the ﬁrm (e.g., Makadok, 1998).
Further, because the early period of the industry was probably marked with uncertainty,
ﬁrms may have been constantly adjusting their aspiration levels, and leveraging their
prior experience and slack to adapt in such an environment. Therefore,the early stages
of the equity mutual fund market provide an appropriate competitive context to study the
relationship between order of market entry, performance, aspirations, prior entry

experience and slack.

Analysis

Given that the dependent variable (order of entry) is left-censored, tobit regression is
the appropriate analytical technique.20 Additionally, given that each fum may have
issued more than one mutual fund, the data are grouped by ﬁrm, which prevents

independence of the observations. To capture the inﬂuence of this group membership, a

 

‘8 Mutual funds earn income based on the percentage of assets under management.

'9 Customers incur fees to switch from one mutual fund to another. Therefore, customers are more likely to
remain committed to their initial fund, particularly if the fund is performing well.

2° Ordinary least squares estimates would have biased because OLS assumes the dependent variable can
take on values above and below zero.

35

ﬁxed or random effect model is appropriate. Tobit ﬁxed effects models yield co-efﬁcient
estimates that are inconsistent for panel data (Maddala, 1987). Therefore, I analyzed the
data using a random-effects tobit model. As is the case in this analysis, random-effects
estimators are recommended when the analysis contains a large number of groups
(greater than 10)21 and when one wishes to generalize to the population of groups

(Snijders & Bosker, 1999).

Tables 1 and 2 contain a correlation table and descriptive statistics. All variance
inﬂation factors were below the 10 point cut-off and condition numbers were below the
30 point cut-off for the fully saturated models (Cohen, Cohen, West, & Aiken, 2003). To
eliminate the inﬂuence of outliers, I removed all observations in the dataset with
externally studentized residuals of plus or minus two (Cohen et al., 2003).22 I centered all
base terms before creating the interaction terms to control for multi-collinearity. Tables 3
and 4 contain the tobit random-effects results that measure relative performance as a
continuous variable for historical and social aspirations, respectively. As a robustness
check, Table 5 contains the tobit random-effects results that measure relative
performance as a spline function (i.e., relative performance is measured as two

independent variables with values above and below zero, respectively).

Dependent Variable
Order of Market Entry. Order of entry is usually measured as the rank order in the

sequence of entry (e.g., Lee et al., 2000). Throughout my dissertation, I measure order of

 

2' I had 42 groups in this study with an average of fourteen observations per group.
22 The inclusion of these observations do not change the results signiﬁcantly.

36

market entry as the elapsed time since the entry of the pioneer, which is an alternative
measure that Lieberman and Montgomery (1988) recommend. The elapsed time since
entry of the pioneer (i.e., temporal order) is a more effective measure than rank order
because the observations are more independent. In this study, I based order of entry on
the date of inception of each equity mutual fund. The order of entry variable reﬂects the
entry of all mutual funds that were opened by a private or publicly traded financial
institution. I coded the ﬁrst entrant (in each fund category) with a “0.” I coded the
remaining entrants based on the number of days since the date of inception of the ﬁrst

entrant in each fund category.

Independent and Moderating Variables

Performance. Following other work (Bromiley, 1991; Chen & Miller, 2007; Iyer &
Miller, 2008), I based ﬁrm performance on the return on assets (ROA) of the ﬁrm one
year prior to the inception of the equity mutual fund (t-l). ROA is the performance
metric that is typically used in studies based in the behavioral theory of the ﬁrm. Further,
in a study of insurance companies, Greve (2008) found that ﬁrms pay more attention to
performance goals (e.g., ROA) rather than growth goals (e.g., ﬁrm size) because
performance goals are more closely tied to ﬁrm survival. Thus, while growth goals are
important for ﬁnancial services ﬁrms, proﬁt performance appears to be a more prominent
goal. In particular, ﬁrms focus on growth goals after performance goals are satisﬁed
(Greve, 2008). Therefore, I used ROA (i.e., a performance metric) rather than total assets
under management (i.e., a size metric) as the measure of ﬁrm performance. I obtained

ROA data from Compustat.

37

Aspirations. Based on behavioral theory of the ﬁrm prescriptions (Cyert & March,
1963; March, 1988), aspirations are a weighted combination of social and historical
aspirations. However, existing research has provided little consistent indication of how
ﬁrms weight their own or other’s performance (Baum et al., 2005; Greve, 2003a). Given
this uncertainty and following prior work (Chen & Miller, 2007), I used two different

models with different aspiration proxies: one for social aspirations and one for historical

aspirations.

Social Aspirations. Following prior work (Audia & Greve, 2006; Baum et al., 2005;
Greve, 1998; Mishina, Dykes, Block, & Pollock, 2008), I deﬁned social aspirations as the
mean ROA23 for all ﬁnancial institutions included in the focal ﬁrm’s two digit SIC code,

excluding the RCA of the focal ﬁrm. Formally,

Social Aspirations,l = (Eij.)/N ,

where j is another ﬁrm and N is the number of other ﬁrms, j, and P is the performance
metric (ROA). I based social aspirations on the mean performance of the firms in the
industry one year prior to the focal ﬁrm’s past performance (t-2). I obtained these data

from Compustat.

 

23 The mean (versus the median) is a more appropriate measure of central tendency if the data is normally
distributed (McClave & Benson, 1985). Based on my analysis of this variable by industry (i.e., 1 evaluated
measures of skewness and kurtosis), I determined that the majority of the industries represented in the
sample (80%) were normally distributed, and thus the mean ROA is an appropriate measure of social
aspirations in this study.

38

Historical Aspirations. Following prior work (Chen & Miller, 2007), I deﬁned
historical aspirations as the focal ﬁrm’s ROA one year prior to past performance (t-2). I

obtained these data from Compustat.

Further, the literature suggests that the slope of the line above and below the aspiration
point may be different (Greve, 1998). Therefore, as a robustness check and following
other work (Baum et al., 2005; Greve, 1998, 2003a), I speciﬁed relative performance as a
spline function. The spline function permits a comparison of the slopes for performance
above or below aspirations (Greene, 1993). To specify the spline function, I created two

separate variables, as described below (Mishina et al., 2008).

Performance Above Aspirations“ = ROAR — Aspirations“ if ROAi. > Aspirations“,

= 0 if ROAi, < Aspirations“

Performance Below Aspirationsi, = Aspirations“ - ROAR if ROAR < Aspirations“,

= 0 if ROAR > Aspirations“

Firm Slack. Researchers have measured slack in multiple ways (Bourgeois, 1981;

Bourgeois & Singh, 1983). 24 I measured slack one-year prior to market entry using two

 

2‘ Existing empirical work suggests that each type of slack has a similar effect on ﬁrm outcomes (Bromiley,
1991; McNamara et al., 2008). However, Singh (I986) found that each type of slack may have a different
effect.

39

indicators”: recoverable slack (SG&A/sales) and potential slack (total debt/total equity).
Recoverable slack represents resources that the ﬁrm has absorbed, but that may be
recovered during adverse times (Bourgeois & Singh, 1983). Potential slack represents
the ﬁrm’s capacity to obtain debt from external sources. I obtained these data from

Compustat.

Prior Entry Experience. I measured prior entry experience based on the number of
other equity funds the ﬁrm operated. I based the number of other equity funds on a count
of other equity funds the ﬁrm opened (in any fund category) prior to the focal equity
fund. I measured prior experience at the ﬁve and ten year time point. I logged the
experience counts to account for any extreme values. I obtained these data from

Morningstar.

Control Variables

Firm Size. I included ﬁrm size as a control variable to assess the competitive strength
of each market entrant (Vanderwerf & Mahon, 1997). I based ﬁrm size on the total assets
of each ﬁrm one-year prior to market entry.26 I logged these values to account for any

extreme values. I obtained these data from Compustat.

 

25 Most of the research based in the behavioral theory of the ﬁrm uses three measures of slack: available,
recoverable and potential slack. I did not use available slack (current assets/current liabilities) because, by
accounting convention, many ﬁnancial institutions (e. g., insurance companies, brokerage ﬁrms, etc.) do not
structure their balance sheets to reﬂect current assets or current liabilities.

2" Firm size can also be measured based on total sales or total employees. However, I used the log of total
assets as the measure of ﬁrm size because, relative to the log of total sales or the log of total employees, it
is the least correlated with the other variables of interest. Further, I ran separate models using the log of
total sales and the log of total employees and achieved a similar pattern of results compared to the models
in which I used the log of total assets as the measure of ﬁrm size.

40

Year Dummies. I included twenty-six year dummies (1981 — 2006) as control
variables to account for any macroeconomic factors in the United States (i.e., changes in
interest rates, inﬂation, etc.) that may have inﬂuenced the level of activity in the equity

mutual fund market.

41

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#
TABLE 3 Random-Effects Tobit Regression Predicting Order of Entry: Historical Aspirations

 

 

Variables Model 1 Model 2 Model 3 Model 4 Model 5
Control Historical Historical Historical Historical
Model Aspirations Aspirations Aspirations Aspirations
Year Dummies a a a a a
Firm Size“ 1102.03** 437.45 410.01 178.32 109.91
(2.89) (1.28) (1.13) (.63) (.39)
Recoverable Slack -768.69*** -1437.88*** 2020.63*** 1825.02*
(4.74) (-3.34) (3.34) (3.03)
Potential Slack 85.41 83.78 99.66 112.191:
(1.20) (1.15) (1.59) (1.82)
Equity Exp-5 yrs " -612.91*** -617.36*** -525.20*** 494.60***
(4.73) (4.71) (4.29) (4.60)
Relative Performance -1966.13*** -1469.06*** -3600. 13* ~6004.65**
(-5.72) (-3.25) (-1.99) (—3. 12)
Relative Performance -7299.64t -16984.28***
)1 Equity Exp-5 yrs (-1.69) (-3.79)
Relative Performance -6341 .08*** -9382.65***
x Recoverable Slack (4.71) (-6.04)
Relative Performance -33.64 -2661.84
x Potential Slack (-.02) (-l.49)
Constant 1014.53 -501.13 403.12 -577.40 -516.33
(1.45) (-.67) (-.53) (-.82) (-.75)
N 600 600 600 600 600
Log Likelihood 4909.36 4866.66 4865.19 4857.97 4850.87
W 2 l958.73*** 2297.81*** 23 l4.28*** 2403.03*** 2475.41***
ald 1
(If 23 27 28 29 30

 

Unstandardized coefﬁcients are reported with t-values in parentheses.

a
All models include year effects, but their coefﬁcients are not reported for simplicity.

Results are similar based on 10 years of experience.

A

Logarithm

'1‘ p< .10 * p<.05

** p<.01

*** p<.001

#
TABLE 4 Random-Effects Tobit Regression Predicting Order of Entry: Social Aspirations

 

 

Variables Model 6 Model 7 Model 8 Model 9 Model 10
Control Social Social Social Social
Model Aspirations Aspirations Aspirations Aspirations
Year Dummies a a a a a
Firm Size“ 1102.03** 202.70 301.10 146.13 666.22*
(2.89) (.55) (.91) (.50) (1.98)
Recoverable Slack -l320.00*** 401.00 2018.77“ 1270.441”
(-7.25) (1.07) (3.17) (1.91)
Potential Slack 84.18 110.72 125.391" 206.54"
(1.14) (1.61) (1.82) (2.90)
Equity Exp-5 yrs A -630.6l*** -797.73*** -570.69*** -942.04***
(4.75) (-6.08) (4.60) (-6.05)
Relative Performance —1706.34*** -585.09 1531.51 5905.02***
(4.71) {-1.45) (1.09) (3.53)
Relative Performance -1198l.94*** -17507.46***
x Equity Exp-5 yrs (-5.02) (4.12)
Relative Performance 4679.41 *** 84.90
x Recoverable Slack (5.31) (.06)
Relative Performance 1575.66 5563.33***
x Potential Slack (1.32) (3.77)
Constant 1014.53 - 142.69 -292.51 -592.98 -900.62
(1 .45) (-. 19) (-.40) (-.82) {-1.24)
N 600 600 600 600 600
Log Likelihood 4909.36 4871.77 4860.13 4860.93 4851.57
W 1958.73*** 2251.22*** 2374.67*** 2376.32*** 2473.11***
ald x
df 23 27 28 29 30

 

Unstandardized coefﬁcients are reported with t-values in parentheses.
a
All models include year effects, but their coefﬁcients are not reported for simplicity.
Results are similar based on 10 years of experience.

A

Logarithm

1' p< .10 * p<.05 ** p<.01 *** p<.001

45

#
TABLE 5 Random-Effects Tobit Regression Predicting Order of Entry: Spline Function

 

 

Variables Model 11 Model 12
Historical Social Aspirations
Aspirations

Year Dummies a a

Firm Size“ 428.11 -672.19
(- l .60) (- 1.37)

Recoverable Slack 1602.62*** -1463.76***
(6.28) (-7.l4)

Potential Slack 1 17. 17* 92.61

A (2.06) (1.21)

. -360.68** -561.l2***
Eq‘my EXP'S yrs (-3.08) (413)
Performance above Aspirations -25957.48*** -8207.34***

(-1 1.24) (-3.92)

Performance below Aspirations 610.94? 1333.19***

(1.80) (3.59)
Constant 3842.83*** 4677.90***

(7.16) (5.91)
N 600 600
Log Likelihood 4820.04 4866.67
Wald x2 2804.66*** 2313.31***
df 28 28

 

Unstandardized coefﬁcients are reported with t-values in parentheses.

a
All models include year effects, but their coefﬁcients are not reported for simplicity.

Results are similar based on 10 years of experience.

A

Logarithm

r p< .10 * p<.05

*** p<.001

46

RESULTS
Model 1 in Table 3 is the control model (model 6 replicates the control model in Table
4). Models 2, 3, 4, 7, 8 and 9 test hypotheses one through four. Models 5 and 10 show
the fully saturated models for historical and social aspirations, respectively. Models 11
and 12 show the robustness check of Hypothesis 1 using a spline function. The Wald x2

statistic is signiﬁcant in all models (p<.001), which indicates good overall model ﬁt.

Modell shows an interesting result related to the control variable. Firm size is
positive and signiﬁcant in the model, which suggests that larger ﬁrms enter markets later
than smaller ﬁrms. Perhaps the bureaucratic and complex structure of large ﬁrms
prevents them from responding to new market opportunities in a timely manner.
However, ﬁrm size fails to reach statistically signiﬁcant levels in most of the remaining
models (except Model 10), which adds to the existing mixed ﬁndings regarding the effect
of ﬁrm size on market entry timing (Fuentelsaz et al., 2002; Haveman, 1993a; Khorana &

Servaes, 1999; Mascarenshas, 1992; Teece, 1986; Wemerfelt & Kamani, 1987).

Models 2 and 7 show the results that predict order of entry based on performance
relative to historical and social aspirations, respectively. Both models indicate a
negative and signiﬁcant relationship between relative performance and order of entry,
which is contrary to the hypothesized direction. Therefore, Hypothesis 1 is not supported
for historical or social aspirations. The negative slope suggests that ﬁrms with
performance levels below historical and social aspirations tend to search narrowly and

thus be late entrants into new markets, whereas ﬁrms with performance levels above

47

historical and social aspirations appear to search broadly for new markets and thus be
early entrants into these markets. Although this ﬁnding is contrary to behavioral theory
of the ﬁrm predictions, it is consistent with other research streams, which I discuss

below.

First, ﬁrms with performance below aspirations may be late entrants into new markets
because the threat rigidity perspective suggests that as ﬁrms face a threat, such as
declining performance, they may restrict information ﬂows and rely more on existing
operating procedures and knowledge (Staw, Sanderlands, & Dutton, 1981). In a market
entry context, such behavior may result in reduced search processes, which in turn would
lead to late market entrance for ﬁrms with performance below aspiration levels. Further,
the existing literature suggests that ﬁrms may have two reference points. When faced
with severe ﬁnancial distress, ﬁrms may use bankruptcy as their reference point (Chen &
Miller, 2007; March & Shapira, 1987, 1992), and thus conserve their resources in order to
maintain current operations. In a market entry context, this suggests that ﬁrms may
reduce search and avoid the risks of early entry. When faced with a performance
shortfall, this conservative behavior may be particularly prevalent for the ﬁnancial

services ﬁrms represented in the sample.

For ﬁrms with performance above aspirations, their risk-seeking behavior may be
reﬂective of the conﬁdence and feelings of infallibility they have in their superior market
position. This is consistent with the literature that suggests that the success of

performing above aspirations breeds conﬁdence and a willingness to accept risky

48

alternatives (e.g., Baum, etal., 2005). Current research proposes that this conﬁdence
may be particularly acute in pioneering decision contexts because in such contexts
managers tend to have more salient information about the potential success of the
outcome (i.e., the success of pioneering behaviors tends to yield more dramatic and
publicized results) (Simon & Houghton, 2003). As a result, managers become
overconﬁdent and thus may pursue riskier strategic actions, such as early market entry.
Even further, successful ﬁrms are often under pressure to maintain their superior
performance in order to meet ongoing market expectations for further success (Mishina et
al., 2008). Therefore, successful ﬁrms may engage in risky behavior to order to avoid

disappointing their customers, investors or other constituents.

Models 2 and 7 show a negative and signiﬁcant co-efﬁcient for recoverable slack,
which lends support for Hypothesis 2 for both aspirations. However, neither model
shows a signiﬁcant term for potential slack. Consistent with behavioral theory logic
(Cyert & March, 1963), the ﬁndings related to recoverable slack suggest that ﬁrms
leverage their excess resources to buffer themselves against the risks and uncertainties of
early market entry. In particular, firms may be more likely to use recoverable slack
rather than potential slack because, by deﬁnition, recoverable slack represents resources
ﬁrms may use under challenging and uncertain circumstances (Bourgeois & Singh,
1983), such as early market entrance. Given that potential slack is a debt capacity
measure, and thus less likely to be deployed for current strategic needs, the insigniﬁcant

ﬁndings related to the main effect of potential slack are not surprising.

49

Models 3 and 8 show the results of the interaction between relative performance and
prior entry experience. Model 3 shows a negative interaction term, but the term fails to
reach conventional significance levels (p<.10). Model 8 shows a negative and signiﬁcant
co-efﬁcient for the interaction term between relative performance and prior experience
(See Figure 6). Therefore, Hypothesis 3 is not supported. The negative interaction term
in Model 8 suggests that high performing ﬁrms with prior entry experience enter markets
early, whereas high performing ﬁrms with less experience enter markets late. In other
words, experience causes ﬁrms to react differently to performance feedback, which
points to a potential boundary of the behavioral theory of ﬁrm (Cyert & March, 1963).
Speciﬁcally, this ﬁnding suggests that high performing ﬁrms engage in problemistic
search under certain conditions, which is contrary to the behavioral view of this type of
search behavior. It seems that prior experience encourages the risk-seeking behavior
associated with early market entry for successful ﬁrms. One potential explanation for
this behavior is that rather than creating routinization or insensitivity to performance
cues, prior market entry experience may be transformational in that it encourages entry
into new markets (King & Tucci, 2002). Restated, experience with market entry may
reduce search costs or bolster the value of current routines, and thus encourage earlier
market entry. Further, existing work recognizes that routines may lead to a wide variety
of outcomes, including stability or change (Feldman & Pentland, 2003), which means
that the routines created from prior experience with a certain activity could result in
innovative behavior, such as early market entry. These ﬁndings are consistent with the
arguments related to H1 in that experienced successful ﬁrms appear to act more

conﬁdently in their behavior than successful ﬁrms with less experience. Alternatively,

50

successful ﬁrms with less experience may be complacent (i.e., late market entrants),
which is consistent with the behavioral view of successful ﬁrm behavior. For these
ﬁrms, prior success and inexperience causes them to search narrowly, perceive more risk
in entry and doubt the value of existing routines. Further, the arguments from the
behavioral theory of the firm appear to hold true for unsuccessful ﬁrms with less
experience. These ﬁrms appear to engage in problemistic search in order to overcome
their performance and experience deﬁciencies, and thus are early market entrants.
Collectively, these ﬁndings open the door to future research on order of entry behavior

based in the organizational learning and experience literature.

The interaction terms between relative performance and slack show inconsistent
results. Model 4 shows a negative and signiﬁcant interaction for recoverable slack (See
ﬁgure 7), but this does not support Hypothesis 4 for historical aspirations. However,
Model 9 shows a positive and signiﬁcant interaction term for recoverable slack, which
supports Hypothesis 4 for social aspirations (See figure 8). Neither Model 4 nor 9 shows
a signiﬁcant interaction term for potential slack. As predicted, the results from Model 9
suggest that slack may cause ﬁrms with performance above social aspirations to become
complacent and thus enter markets later than other ﬁrms. Alternatively, slack may
provide ﬁrms with performance below social aspirations with a cushion against market
entry uncertainties, which in turn leads to early market entry. In other words, for
successful ﬁrms, slack discourages early entry. However, slack encourages early entry

for low performing ﬁrms. As ﬁrms refer to the performance of others to determine

51

future action, they draw upon their slack resources to either maintain or improve their

performance as the behavioral theory of the ﬁrm would predict.

The interaction term for recoverable slack in Model 4 has a different interpretation.
The negative co-efﬁcient suggests that slack may provide ﬁrms with performance above
historical aspirations with an incentive to enter markets early rather than a license for
complacency, and thus late market entry. On the other hand, rather than using their slack
resources to correct a performance shortfall, ﬁrms with low relative historical aspirations
appear to conserve their slack resources and enter markets later than other ﬁrms. As
ﬁrms refer to their own prior performance to determine future action, they appear to use

slack resources in ways counter to the behavioral theory of the ﬁrm.

These conﬂicting ﬁndings related to the slack interactions are consistent with current
work. First, recent ﬁndings suggest that slack resources exert different and sometimes
conﬂicting inﬂuences on ﬁrm search processes (Voss, Sirdeshmukh, & Voss, 2008).
Slack may either encourage or discourage exploration (i.e., early market entry) or
exploitation activities (late market entry) depending on the environmental context and the
type of slack deployed (Voss et al., 2008). Second, it is also interesting to note that the
interactive effect of slack differs based whether the reference point is historical or social
aspirations. This result is consistent with current research that ﬁnds that ﬁrms employ

different aspiration points to determine future behavior (Harris & Bromiley, 2007).

52

As a post hoc analysis, I also modeled the slack interaction using Bromiley’s (1991)
single aspirations variable.27 In Bromiley’s model (1991), ﬁrms with performance above
the industry mean have aspirations at 1.05 times their prior performance and ﬁrms with
performance below the mean have aspirations at the industry mean. The results using
Bromiley’s aspiration variable show that the interaction between recoverable slack and
the aspirations variable is positive and signiﬁcant (p<.001) and the interaction between
potential slack and the aspirations variable is negative, but non-signiﬁcant. These
ﬁndings are similar to the results related to the slack interactions using social aspirations
(Model 9 in Table 4) and are consistent with Hypothesis 4. Therefore, these results lend
more credibility to the slack interaction results using social aspirations. Future research
should examine when ﬁrms switch between reference points and the implications of this
behavior. Future research should also examine when and how ﬁrms use different types

of slack resources.

Models 5 and 10 show the fully saturated models. The results from these models
should be interpreted very cautiously because the presence of all main effects (relative
performance, slack and experience) along with their interactions introduces multi-
collinearity into the models. For historical aspirations (Model 5), the main effect of
recoverable slack is positive and signiﬁcant. For social aspirations (Model 10), the main
effect of potential slack is positive and signiﬁcant. The results for recoverable slack
(Model 5) indicate that slack leads to late market entry. This ﬁnding supports the agency

theory perspective of slack as an indication of excess and waste (Jensen & Meckling,

 

27 Compared to Greve’s (1998) weighted average aspiration variable and the separate aspiration variables
used in this study, recent work suggests that Bromiley’s single aspiration variable provides more consistent
results (Bromiley & Harris, 2008).

53

1976). Because potential slack is measured as debt divided by equity, the higher the
potential slack, the less slack the ﬁrm possesses. Therefore, the positive coefﬁcient in
Model 10 for potential slack suggests that more potential slack leads to early entry, which
is consistent with H2. Further, Model 10 shows a positive and signiﬁcant co-efﬁcient for
relative social aspirations, whereas Model 5 still shows a negative and signiﬁcant term
for relative historical aspirations. The positive co-efﬁcient for relative social aspirations
in Model 10 supports Hypothesis 1 and is consistent with the behavioral theory of the
ﬁrm arguments (i.e., performance above aspirations leads to late market entry, but

performance below aspirations leads to early market entry).

As a robustness check, Models 11 and 12 (Table 5) predict order of entry measuring
relative performance as a spline function for historical and social aspirations. Both
models indicate a‘negative and signiﬁcant relationship between performance above
aspirations and order of entry. Performance below aspirations shows a positive and
signiﬁcant result for social aspirations, but a positive and non-signiﬁcant result for
historical aspirations. Both models support the results of Models 2 and 7, which show a
negative relationship between relative performance and order of market entry,

particularly for social aspirations.28

Further, it is worthwhile to note the effect of prior experience as a main effect. In all
models (historical and social aspirations), prior experience has a negative and signiﬁcant

relationship with entry timing. That is, more experience with entry promotes early

 

28 I coded performance below aspirations in a manner such that its values are always positive. Therefore.
the positive co-efﬁcient for this variable indicates that its values increase as entry timing increases.

54

market entry. This ﬁnding is similar to other work that suggests that prior experience
leads to innovative behavior in a market entry context (King & Tucci, 2002). Future
research should seek to tease apart the effects of prior entry experience versus prior early

entry experience on future ﬁrm behavior.

Finally, my dataset includes domestic (84%) and international (16%) equity mutual
funds. Iran the main models using only the domestic mutual funds in the analysis.29 In
this analysis, I achieved the same pattern of results as indicated in Models 1 through 10.
This suggests that entry timing is conditioned on the same set of variables even in

exclusively domestic markets.

DISCUSSION

The purpose of this study was to apply the behavioral theory of the fum (Cyert &
March, 1963) to predict the order in which ﬁrms will enter a new market. I also sought to
reconcile the mixed ﬁndings regarding the relationship between prior performance and
the tendency of ﬁrms to engage in innovative or imitative behavior. Contrary to
behavioral theory of the ﬁrm predictions, ﬁrms with performance above historical and
social aspirations appear to search broadly and thus engage in the innovative and risky
behavior of early market entry. Alternatively, ﬁrms with performance below these same
aspirations appear to search more narrowly and thus engage in the imitative and more
conservative behavior of late market entry. Prior entry experience encourages more

innovative behavior, especially for successful ﬁrms. The effect of slack depends upon

 

29 The number of international equity mutual funds in the dataset is less than 100, which would have made
the results difﬁcult to interpret if I had run the models with only international mutual funds.

55

the ﬁrm’s reference point. Ironically, these ﬁndings add to rather than reconcile the
mixed results regarding the relationship between prior performance and innovative or

imitative ﬁrm behavior.

The behavioral theory of the ﬁrm conceptualizes organizations as goal-oriented and
routine-based entities. More importantly, the theory suggests that performance feedback
based on certain reference points can predict future outcomes. Much of the current
research using the behavioral theory of the ﬁrm has predicted various forms of ﬁrm
behavior (Baum et al., 2005; Harris & Bromiley, 2007). I add to the body of research
that applies the behavioral theory of the ﬁrm to analyze corporate level ﬁrm decisions
(e.g., Iyer & Miller, 2008). Speciﬁcally, I found that prior relative performance
motivates ﬁrms to consider their market entry decisions based on their search processes
and risk preferences. Quoting Greve (2003b), performance relative to aspiration levels
appears to serve as a “master switch” for a wide range of organizational behaviors.
However, my ﬁndings associated with relative performance (i.e., the results from H1) are
contrary to the predictions of the behavioral theory of the ﬁrm and may suggest that the
theory has limited ability to predict market entry timing. Indeed, other perspectives may
provide a better explanation for the ﬁndings. For example, the entrepreneurship literature
suggests that successful ﬁrms engage in innovative behavior, such as early market entry,
and do so more frequently than unsuccessful ﬁrms (Mansﬁeld, 1963). Further, the degree
of innovation and exploration that determines market entry timing may not depend on
prior performance, but rather on a ﬁrm structure and explicit organizational culture that

values pro-activeness and on-going learning (Slater & Narver, 1995).

56

I also add to the body of literature related to the effect of ﬁrm slack and prior
experience. Speciﬁcally, the mixed ﬁndings related to slack resources highlight the need
for scholars to develop a better understanding of how organizations may deploy different
types of slack resources in their strategic decision-making. Further, the ﬁndings related
to prior experience challenge the traditional notion that prior experience thwarts
organizational change. Indeed, experience may lead to change or stability depending
upon the ﬁrm’s prior ﬁnancial performance. Taken together, the ﬁndings related to slack
and prior experience demonstrate that ﬁrms draw upon multiple factors, in sometimes

unpredictable ways, to make entry timing decisions.

This study is not without limitations. First, Lieberman & Montgomery (1988)
identiﬁed sample selection bias as one of the major methodological threats in the market
entry literature. In particular, the exclusion of non-survivors (i.e., ﬁrms that entered
early, but then quickly failed) is a typical issue in most market entry datasets. The
exclusion of non-survivors may bias the empirical results (Golder & Tellis, 1993). My
dataset excludes non-survivors because information on non-surviving mutual funds is
scarce to non—existent. Because non-survivors are excluded, my order of entry variable
may be over-estimated (i.e., ﬁrms may appear to have entered earlier than they actually
did so). However, current work proposes that less than 1% of mutual funds close in any
given year (Haslem, 2003). Furthermore, in their meta-analysis of the research on ﬁrst
mover advantages, Vanderwerf and Mahon (1997) found that the exclusion of non-

surviving entrants had little effect on the collective empirical ﬁndings.

57

Second, I essentially examined a single industry, which limits the generalizability of
the ﬁndings to other industries. Nevertheless, to examine the generalizability of these
ﬁndings to the larger population of ﬁnancial services ﬁrms, I conducted a post-hoe
analysis that compared the entering ﬁrms represented in my sample to those ﬁrms that
chose not to enter the mutual fund industry during my sampling period. The non-entering
ﬁrms represent those ﬁrms in the same fourteen SIC codes as those ﬁrms in my dataset.
In this analysis, I compared entrants versus non-entrants on ﬁrm size, performance
(RCA) and slack (recoverable and potential slack) by industry. The results indicate that
the entering ﬁrms are slightly larger than non-entrants,30 but that both sets of ﬁrms appear
to be comparable on performance and slack measures.31 These results suggest that the
hypothesized relationships in this study may be robust to a large segment of the ﬁnancial

services industry.

Third, many of the assets that were invested in mutual funds during the period of
study were done so through an individual’s employer. This means that mutual fund
companies that had signiﬁcant corporate relationships may have been being able to gain
access to a large number of customer accounts more readily. Therefore, these corporate
relationships may have inﬂuenced the ﬁrm’s entry timing decisions. This suggests that

social network factors may also be affecting these results.

 

3° Entrants may be larger than non-entrants because entrants may have more resources to leverage in an
entry context.

3‘ A better approach to this analysis would have been to examine the non-entrants that were registered
broker-dealers versus the ﬁrms in my sample, which presumably had a broker-dealer license. However, the
SEC does not provide historical data on broker-dealer licenses.

58

Despite these limitations, by applying the behavioral theory of the ﬁrm, I examined
the internal processes ﬁrms use when making entry-timing decisions. Speciﬁcally, ﬁrm
search processes and risk preferences shape entry decisions. These decisions are further
shaped by the availability of slack resources and prior entry experience. Moreover, by
applying the behavioral theory of the ﬁrm, I was also able suggest, with some
reservations, which ﬁrms may be early and late entrants into new markets. Collectively,
these ﬁndings suggest that market entry timing is a complicated undertaking that warrants

further investigation.

59

THE CULTURAL DETERMINANTS OF THE ORDER OF MARKET ENTRY

BACKGROUND AND HYPOTHESES

In addition to a behavioral perspective, a perspective that focuses on how national
culture may inﬂuence order of market entry behavior may enrich the order of entry
literature. In an international business setting, order of entry dynamics occur as
multinational organizations compete for the opportunity to enter a market through diverse
forms of foreign direct investment. In such a global setting, the national environment
inﬂuences the competitive behavior and success of ﬁrms and industries because it
supplies them with the resources and tools they need to identify and seize opportunities in

international competition.

More importantly, the national environment reﬂects a society’s national culture
(Hofstede, 1980, 2001). Therefore, because national culture is an element of the national
environment, national culture could affect ﬁrm behavior, such as order of entry behavior.
Speciﬁcally, national culture is an important aspect of organizational behavior because it
affects how organizations make strategic decisions (e. g., Kogut & Singh, 1988). Finally,
given that multinational organizations face a wide variety of business risks, they may be
prone to manage such risks by collaborating with other multinational organizations.
Therefore, market entry, which is a strategic option laden with risks and uncertainties, is
sometimes made in the context of a team of ﬁrms, which represent diverse national
cultures, rather than in the context of a single ﬁrm. Consequently, in an international

business setting, the multi-cultural aspects of strategic decision-making for multinational

60

organizations complicate market entry decisions. Therefore, in this study, I investigate
whether national culture can predict order of market entry decisions across multiple

international markets for individual ﬁrms and teams of ﬁrms.

The Organization and its Environment

The relationship between the organization and its environment is a traditional area of
study in organization theory. Much of the early theorizing took an open systems
perspective, which proposes that the interaction between the organization and its
environment is essential to ﬁrm survival, and that the characteristics of the environment
inﬂuence the characteristics of the organization (Scott, 2003). The open systems
perspective suggests that the environment permeates the organization and shapes
organizational behavior. Therefore, because the environments in which organizations
operate vary (Dess & Beard, 1984), organizational behaviors vary also. Traditionally,
research in organization theory considered the relationship between the organization and
its environment irrespective of the implications associated with any particular geographic

location Mestney, 1997).

However, in the current business environment, the speciﬁc national environment in
which a ﬁrm operates is an important context of study. Organizations are increasingly
entering (and operating in) multiple national environments to gain access to alternative
sources of knowledge, skills and technologies. Entry can take the form of a subsidiary,
joint venture or other form of direct investment depending on the organization’s desired

level of resource commitment and managerial control (Anderson & Gatignon, 1986).

61

However, organizations enter markets at different times (i.e., early or late) based on their

characteristics (Schoenecker & Cooper, 1998) and skills (Robinson et al., 1992).

Because of their movement into different national markets, organizations often
compete with organizations that exist not only in their own national environment, but also
with ﬁrms from foreign national environments. Porter (1990) argues that each national
environment provides its organizations with a distinctive set of factors that may help or
hinder their international competitiveness. Therefore, in international competition,
organizations bring to bear their internal strengths and weaknesses as well as the
strengths and weaknesses inherent in their national environment. Indeed, the national
environment has become such an important factor in the current business environment
that Porter (1990) proposes that the national environment is a source of competitive

advantage for ﬁrms, industries and nations.

“Cultural factors are important as they shape the environment facing ﬁrms... such
inﬂuences are important ones to competitive advantage because they change slowly

and are diﬂicult for outsiders to tap or emulate” (Porter, 1990: 129).

One of the chief elements of a national environment is the national culture of the
environment. National culture reﬂects the characteristics (i.e., the values, norms and
attitudes) of the country (Hofstede, 1980, 2001). This culture develops over time and is a
result of the nation’s history, geography, natural resources, etc. Current research suggests

that cultural differences impact various management phenomenon, including the

62

diffusion of innovations (Gatignon, Eliashberg, & Robertson, 1989) and corporate capital

structures (Chui, Lloyd, & Kwok, 2002).

The multinational organization has a complex relationship with national culture.
Multinational organizations often engage in strategic decisions that involve multiple
parties across the globe, such as joint ventures or strategic alliances, in order to spread
their risk. Put differently, multinational organizations sometimes make strategic
decisions and foreign investments jointly with other multinational ﬁrms. Consequently,
multinational organizations are likely to be subject to the impact of multiple national
cultures concurrently in their strategic decision-making. Therefore, given the
pervasiveness of market entry (Geroski, 1995) and the importance of the national
environment (Porter, 1990), a study that explores the inﬂuence of national culture on
order of market entry (i.e., whether ﬁrms will enter markets earlier or later than other
ﬁrms) across multiple international markets for individual and teams of ﬁrms is a

relevant area of inquiry.

Conceptual Framework

The conceptual framework I use to develop the hypothesized relationships will
primarily be based the proposed relationship between national culture and firm strategy,
structure and rivalry (Porter, 1990).32 Firm strategy, structure and rivalry represent the
conditions in a nation that govern how ﬁrms are created, organized and managed, and the

nature of domestic rivalry. Firm strategy, structure and rivalry contribute to the

 

32 Porter (1990) discusses four factors that shape the competitiveness of ﬁrms and industries in a particular
nation. Among the potential factors, ﬁrm strategy, structure and rivalry have the most salient implications
for ﬁrm decisions and outcomes.

63

competitiveness of a nation’s industries in international markets. More explicitly, ﬁrm
strategy, structure and rivalry shape the capabilities that ﬁrms possess as well as the
behaviors and decisions that ﬁrms make that contribute to their success or failure against

rivals in international competition.

Porter (1990) implies that a country’s national culture has an impact on ﬁrm strategy,
structure and rivalry, which ultimately may have an effect on market entry decisions.
Speciﬁcally, Porter (1990) proposes that the national environment determines
management practices, organizational structures and strategic approaches towards
decision-making, training, hiring, leadership, customer relationships, labor relations, etc.
Porter (1990) suggests that this is because the national environment reﬂects the national
culture of the society. National culture shapes the norms, structures, practices and
approaches of the organization (Hofstede, 1983), which in turn inﬂuences organizational

behavior.

Existing empirical work supports Porter’s claim.33 First, differences in cultural values
appear to affect ﬁrm strategy. For example, national culture affects intemational entry
strategies (Hennart & Larimo, 1998; Kogut & Singh, 1988), foreign direct investment
location decisions (J ohanson & Vahlne, 1977; Li & Guisinger, 1992) and the formation
of cross-country technology alliances (Steensma, Marino, Weaver, & Dickson, 2000).

Further, at the individual level of analysis, cross-cultural differences explain managerial

 

33 General support has been found for Porter’s framework in that country level effects appear to explain a
signiﬁcant portion of the variance in ﬁrm performance (Makino, Isobe, & Chan, 2004).

64

attitudes towards strategic issues34 (Schneider & De Meyer, 1991), changes in
organizational leadership and strategic orientation (Geletkanycz, 1997), potential
acquisition targets (Hitt, Dacin, Tyler, & Park, 1997) and international marketing
decisions (Tse, Lee, Vertinsky, & Wehrung, 1988). For example, because they prefer to
avoid uncertainty, Latin European executives tend to perceive strategic issues as threats,
which ultimately effects their behavior when faced with a strategic decision (Schneider &

De Meyer, 1991).

Second, national culture appears to affect organizational structures. For example,
differences between the structure of Japanese and American manufacturing ﬁrms is due
to cross-cultural differences in organizing (Lincoln, Hanada, & McBride, 1986). Relative
to the United States, Japanese ﬁrms have less functional specialization and less
delegation of authority (Lincoln et al., 1986). In general, these differences are due to the
Japanese cultural emphasis on the group, rather than the individual, as the basic building
block of the organization (Lincoln et al., 1986). Further, in a decision context, certain
organizational structures and practices facilitate efﬁcient strategic decision-making
(Baum & Wally, 2003). In particular, decentralization of operations management,
centralization of strategic management and formalization of routines allow organizations
to make faster strategic decisions (Baum & Wally, 2003). In other words, these
organizational structures are likely to result in faster market entry decisions when
compared to other organizational structures. Therefore, given that cross-cultural

differences exist among organizational structures and that certain structures are more

 

3" A strategic issue is a development that is likely to have an important impact on the ability of the
organization to meet its objectives (Ansoff, 1980). Strategic issues may be internal or external to the ﬁrm,
and may involve a threat or opportunity (Ansoff, 1980).

65

conducive to efﬁcient decision-making, national culture could affect market entry

decisions.

Finally, national culture may have an affect on the values and attitudes towards
rivalry. Rivalry is closely associated with the concept of competition. Competition is a
process in which market participants engage each other through a series of moves and
countermoves (Chen & Hambrick, 1995). In general, competition results in the
expansion of one ﬁrm and a reduction in the relative growth or size of other ﬁrms
(Carroll & Harman, 1989). Competition is a fundamental concept in order of entry
dynamics because competition forces ﬁrms to improve or innovate in order to gain the
advantages associated with entering a new market. Rivalry is overtly conﬂictive
competitive behavior and occurs when ﬁrms blatantly attempt to challenge or overthrow
the position of other ﬁrms. Rivalry rewards only the most-ﬁt actors (Park, 1998). Given
its antagonistic orientation, aggressive, goal-directed and self-centered behaviors lie at

the core of rivalry.

Cross-cultural differences exist regarding attitudes towards rivalry, which ultimately
has an affect on ﬁrm behaviors in competitive settings. For example, managerial
perceptions of pioneer advantages differ across national cultures because of inherent
differences in attitudes towards rivalry, competition and business-to-business
relationships (Song, Di Benedetto, & Zhao, 1999). More speciﬁcally, because of their
collectivist orientation, Asian Paciﬁc businesses are encouraged not to undercut

competitors, but rather build long-term relationships. On the other hand, because of their

66

individualistic orientation, Western businesses are encouraged to adopt competitive
strategies and accept adversarial relationships (Song et al., 1999). Therefore, given the
existence of differences in cross-cultural values and attitudes toward rivalry, these
attitudes and values could create diverse behavioral tendencies of organizations in a

competitive decision-making context.

Taken together, these arguments suggest that through its inﬂuence on ﬁrm strategy,
structure and rivalry, the national culture of an environment has implications for the
market entry decisions that ﬁrms make. That is, national culture may predict whether
ﬁrms will enter markets earlier or later than other ﬁrms. In that such decisions also affect
ﬁrm performance (Lieberman & Montgomery, 1988), national culture eventually has
implications for the proﬁtability of the industries in a nation and for the nation as a
whole. In the next sections, I specify how national culture might affect market entry

behaviors.

National Culture and its Dimensions

The international business literature deﬁnes national culture in several ways. I adopt
the traditional deﬁnition of national culture developed by Geert Hofstede whereby
national culture is deﬁned as “collective mental programming”: it is that part of our
conditioning that we share with other members of our nation, region or group, but not
with members of other nations, regions or groups (Hofstede, 1983). Culture is a latent
construct knowable through patterns in its manifestations, such as symbols, artifacts,
modes of communication, values, behaviors, institutions and social systems shared

among group members (Brett, Tinsley, J anssens, Barsness, & Lytle, 1997). Culture

67

controls behaviors in deep and lasting ways, many of which are outside of human
awareness or consciousness (Hall, 1959). In general, culture is difﬁcult to change, but if

it does change, the change is gradual (Hofstede, 1980).

Hofstede (1980a) developed the most popular framework to study national culture. In
his original work, Hofstede surveyed employees from IBM across forty countries during
the 19703. From this work, Hofstede identiﬁed four dimensions of national culture:
uncertainty avoidance, power distance, individualism-collectivism and femininity-
masculinity (Hofstede, 1980, 2001). Hofstede argues that these dimensions can be used
to describe the values and attitudes of a speciﬁc society. He further argues that these
dimensions have repercussions for the study of management and organizations (Hofstede,

1983).

Scholars have used Hofstede’s dimensions of national culture to make predictions
across multiple areas of interest to organizations.35 For example, national culture, based
on Hoftstede’s dimensions, has been shown to affect compensation practices (Schuler &
Rogovsky, 1998), corporate governance structures (Davis, Schoonnan, & Donaldson,
1997), executive compensation (Tosi & Greckhamer, 2004), innovation championing
strategies (Shane, Venkataraman, & MacMillan, 1995) and post-merger integration
processes (Weber, Shenkar, & Raveh, 1996). Further, in the international business
literature, Hofstede’s dimensions have been used to predict economic performance

(Franke, Hofstede, & Bond, 1991) as well as entry mode choice decisions (Hennart &

 

35 For a review of the literature based on Hofstede’s dimensions of national culture, see Kirkman, Lowe &
Gibson (2006).

68

Larimo, 1998; Kogut & Singh, 1988). Combined, these studies suggest that Hofstede’s
dimensions may have direct implications for the firm across multiple strategic issues and

decisions.

The Multinational Organization and National Culture

Because the multinational organization operates in multiple countries, it faces multiple
risks. These risks include the political, economic and social risks associated with doing
business or investing in a foreign market (Miller, 1992). Multinational organizations may
respond to these risks through different approaches (Miller, 1992). Among the many
alternatives, one approach is to collaborate with other multinational organizations, which
allows the multinational organization to manage its environmental dependencies (Pfeffer
& Salancik, 1978) and its risk exposure. As discussed in Study I, market entry contains
risks and uncertainties, and therefore, multinational organizations may be inclined to
partner with other multinational organizations to enter a new market. In such
collaborative settings, this implies that the multinational organization is susceptible to
multiple cultural inﬂuences as it works with other multinational organizations from

diverse national cultures to make strategic decisions.

To examine the complex inﬂuence of national culture on multinational organizations
more fully, I examine the hypothesized relationships for individual fums and teams of
ﬁrms that made market entry decisions. To do so, I adopt the position that the national
culture of the ﬁrm’s home country governs the strategic decisions of the multinational

organization. A ﬁrm’s home country is likely to be the center that directs and controls its

69

competitive strategy (Porter, 1990). Put differently, the ﬁrm’s home country is more
likely to be the location where the organization devises and directs its strategic actions36,
such as market entry decisions. Further, although a multinational organization may have
a physical presence in multiple geographic locations, the ﬁrm’s home country should
have the most inﬂuence on the ﬁrm’s behavior because the national culture of the home
country is more proximal, and thus the ﬁrm is more likely to be predisposed to the values

of the home country.

In the context of teams of multinational organizations, national culture has two
implications. First, in a team setting, the national culture of the dominant ﬁrm is likely to
drive market entry decisions. The dominant ﬁrm is the ﬁrm that controls the decisions
and operations of the team for a prolonged period of time (Shamsie, 2003). Dominance
results from the ability of the ﬁrm to maintain its leadership position. By virtue of its
dominance, the dominant ﬁrm should have greater leverage in its exchange relationships
with other team members (Pfeffer & Salancik, 1978). Most importantly, by virtue of its
dominance, the national culture of the dominant ﬁrm should determine the values and
approaches that the team would adopt in a market entry decision context. Second, in a
team setting, the degree of cultural diversity on the team of firms is likely to drive market
entry decisions. In this study, cultural diversity refers to the degree of national cultural
differences that exists on the team of ﬁrms. The cultural diversity of the team of ﬁrms is

important because the organizational behavior literature suggests that cultural diversity

 

36 Strategic actions are those actions that involve material commitments of resources and are difﬁcult to
implement or reverse (Egelhoff, 1982).

70

has implications for how effectively and efﬁciently the team of ﬁrms may develop and

implement its entry decisions.

National Culture and Market Entry Decisions for Single & Dominant Firms

I base the following hypotheses regarding the relationship between national culture
and order of entry into a market on the four dimensions of uncertainty-avoidance, power
distance, individualism-collectivism and masculinity-femininity in a single and dominant
multinational ﬁrm decision context. Each dimension of national culture is likely to have
an affect on either the willingness or ability of the ﬁrms in a country to enter a market

earlier or later than other ﬁrms.

Uncertainty-Avoidance. Uncertainty avoidance refers to the degree to which a
society is comfortable with ambiguous situations and the inability to predict future events
with certainty. High uncertainty avoidance cultures avoid ambiguity and prefer to
construct institutions that create security, whereas low uncertainty avoidance cultures
more readily accept the uncertainty of the future. High uncertainty avoidance cultures
prefer rules and are risk averse (Hofstede, 1980, 2001; Triandis, 1990), whereas low

uncertainty avoidance cultures are more willing to accept risks and nonconformity.

Empirically, existing research supports the predictions of uncertainty avoidance. For
example, ﬁrms in high uncertainty avoidance cultures prefer low risk forms of entry
mode (e.g. joint venture versus acquisition) compared to ﬁrms in low uncertainty
avoidance cultures (Kogut & Singh, 1988). Low uncertainty avoidance cultures prefer

that innovations be developed by challenging existing organizational norms, rules and

71

proCedures rather than relying on existing routines, rules and procedure (Shane et al.,
1995). Further, high uncertainty avoidance cultures prefer bank-based ﬁnancial systems
because such systems offer high levels of monitoring and return predictability (Kwok &

Tadesse, 2006).

In an order of market entry setting, the cultural dimension of uncertainty-avoidance is
likely to affect the willingness of ﬁrms to enter new markets earlier or later than other
ﬁrms. Speciﬁcally, because of their aversion for ambiguity, ﬁrms in high uncertainty
avoidance cultures are less likely to accept the risks of moving early into a market in
order to minimize or postpone the costs of entry, such as liability of foreignness costs.37
On the other hand, low uncertainty avoidance cultures are more likely to accept the
ambiguity associated with moving early into a foreign market. This is due to the fact that
ﬁrms in low uncertainty avoidance cultures are more tolerant of behaviors and modes of
operation that are different from their own (Hofstede, 1983) and thus are more likely to

explore opportunities embedded within different cultural environments. Therefore,

H1: The lower a country’s level of uncertainty avoidance, the earlier its ﬁrms will enter a

market.

 

37 The liability of foreignness costs are the costs of doing business abroad that may result in a competitive
disadvantage for the ﬁrm (Hymer, 1976). These costs arise due to country differences in social, economic
and institutional structures that create barriers to doing business abroad. These costs are generally not
incurred by local firms and can result from a general unfamiliarity with the local environment (Kostova &
Zaheer, 1999; Zaheer, 1995). Empirical evidence supports the existence of the liability of foreignness and
its detrimental effect on organizational performance (Miller & Parkhe, 2002; Zaheer, 1995).

72

Power Distance. Power distance describes the extent to which less powerful
members of a society expect and accept that power is unequally distributed. High power
distance cultures emphasize differences in status, wealth and abilities, whereas low power
distance cultures try to minimize these differences. Further, power distance refers to the
degree of hierarchy, inequality and autocratic leadership in organizations (Hofstede,
1983). High power distance cultures are likely to have more hierarchal decision styles in
order to differentiate between bases of power and status within the organization, whereas
low power distance cultures are likely to have decision styles that minimize hierarchy and

differences between occupational roles.

Empirically, existing research also supports the predictions of power distance. For
example, high power distance cultures tend to offer higher levels of CEO compensation
because the increased organizational layering in such cultures creates greater wage
disparities (Tosi & Greckhamer, 2004). Managers in high power distance countries are
more likely to favor direct foreign investment over licensing as an entry mode because
direct investment involves more monitoring and control over the investment activities
(Shane, 1994). Further, high power distance cultures are more likely to develop
principal-agent forms of corporate governance, whereas principal-steward forms of
corporate governance are more likely to develop in low power distance cultures (Davis et

al., 1997).38

 

38 Principal-agent forms of corporate governance are comprised of systems and structures in which the
principal and agent seek to maximize their own self-interests. In principal-steward forms of corporate
governance, the agent seeks to act in the best interest of the principal.

73

In an order of market entry setting, the cultural dimension of power distance is likely
to affect the ability of ﬁrms to enter new markets earlier or later than other ﬁrms.
Speciﬁcally, ﬁrms in high power distance cultures are more likely to have mechanistic
(i.e. rigid) approaches to decision-making. Mechanistic decision-making approaches are
less responsive to environmental uncertainty because they involve reduced information
processing capabilities (Tushman & Nadler, 1978), which in turn create lengthier
decision-making processes. Thus, ﬁrms in high power distance cultures are more likely
to respond to competitive interactions in a delayed fashion. Alternatively, low power
distance cultures are likely to have more organic decision-making approaches whereby
information processing capabilities are high (Tushman & Nadler, 1978) and strategic
decision-making is adaptive and ﬂexible (Donaldson, 2001). Therefore, ﬁrms in low
power distance cultures are more likely to respond to competitive interactions quickly.
These arguments suggest that ﬁrms in low power distance cultures may have more

expedient market entry processes. Therefore,

H2: The lower a country’s level of power distance, the earlier its ﬁrms will enter a

market.

Individualism-Collectivism. Individualism-collectivism describes the extent to
which the culture emphasizes personal goals over group goals (Triandis, 1990).
Individualist cultures are characterized by loose social ties and self-interested bases of
motivation, whereas collectivist cultures are characterized by tight social ties and a duty

to look after the needs of the other group members. Individualist cultures focus on

74

freedom of choice, and reward, recognize and emphasize individual efforts and
achievements. Achievements and success also serve to determine roles and status.
Collectivist cultures deﬁne themselves in terms of their relation to others. Therefore,
collectivist cultures are more likely to refer to social referents to determine future
behavior and thus more likely seek compliance with societal expectations. Collectivist

cultures suppress individualistic desires for the sake of group harmony.

Current research supports the predictions of individualism-collectivism. For example,
collectivist cultures are more likely to engage in cronyism than individualistic cultures
because cronyism involves fulﬁlling mutually reinforcing exchange obligations, which
often arise in situations with strong relationship ties (Khatri, Tsang, & Begley, 2006).
Individualist cultures are more likely to develop principal-agent forms of corporate
governance, whereas principal-steward forms of corporate governance are more likely to
develop in collectivist cultures (Davis et al., 1997). Groups comprised of individuals
from individualistic cultural traditions tend to display more competitive behaviors than
groups comprised of individuals from collectivist cultures (Cox, Lobel, & McLeod,
1991). Further, banks in individualistic countries exhibit lower levels of information
seeking because they have a higher tendency for independence in thought and action

(Zaheer & Zaheer, 1997).

In an order of market entry setting, the cultural dimension of individualism-
collectivism is likely to affect the willingness of ﬁrms to enter new markets earlier or

later than other ﬁrms. Speciﬁcally, ﬁrms from individualistic cultures are more likely to

75

move early because they place value on task achievement, and thinking and acting
autonomously in goal-oriented activities. On the other hand, ﬁrms from collectivist
cultures are more likely to enter later because they place value on gaining approval from
the other ﬁrms in the group or industry at the expense of task completion or achievement.

Hence,

H3: The higher a country’s level of collectivism, the later its ﬁrms will enter a market.

Masculinity-Femininity. Masculinity-femininity describes the extent to which a
society distinguishes between the division of roles between men and women. Masculine
societies emphasize the values of dominance, achievement and aggressiveness, whereas
feminine societies emphasize the values of compassion, empathy and equality (Hofstede,
1980, 2001, 1998). In masculine societies, money and things are important, but in
feminine societies, relationships are important (Hofstede, 1983, 1998). In masculine
society’s, one person’s gain is another person’s loss. Further, in masculine cultures,
ﬁrms act aggressively to pursue clearly deﬁned purposes, and are rewarded and praised
for such behavior. On the other hand, in feminine cultures, ﬁrms are expected to take a
more participatory approach in a team setting and to resolve conﬂict through compromise

and negotiation.
Empirical support also exists for this dimension of national culture. For example,

entrepreneurial ﬁrms in feminine cultures are more likely to pursue technological

alliances than similar ﬁrms in masculine cultures (Steensma et al., 2000). Further, by

76

creating an environment of trust and support, feminine cultures encourage more idea
generation than masculine cultures during the initiation phase of new product

development (Nakata & Sivakumar, 1996).

In an order of market entry setting, the cultural dimension of masculinity-femininity is
likely to affect the willingness of ﬁrms to enter new markets earlier or later than other
ﬁrms. In particular, the assertive and aggressive behavior of ﬁrms in masculine cultures
suggests that these ﬁrms may encourage and reward faster and more decisive decision-
making. On the other hand, the more participatory decision approach of ﬁrms in
feminine cultures suggests that these ﬁrms seek and promote inclusion in entry decisions,
which may lengthen the decision timeframe. Thus, ﬁrms in masculine cultures are more

likely to enter markets earlier than ﬁrms in feminine cultures. Therefore,

H4: The higher a country’s level of masculinity, the earlier its ﬁrms will enter a market.

Cultural Diversity and Market Entry Decisions for Teams of Firms

International business scholars have conducted limited research in the area of cultural
diversity among firms in a strategic-decision making context (Kirkman, Lowe, & Gibson,
2006). However, research on teams in the organizational behavior literature may serve as
a guide to make predictions for the behavior of teams of ﬁrms. Speciﬁcally, the
organizational behavior literature suggests that, in terms of task performance, culturally
diverse teams tend to consider a wider range of perspectives and generate a broader range

of alternatives compared to homogeneous teams (Watson, Kumar, & Michaelsen, 1993).

77

However, in terms of the timing of decisions, culturally diverse teams may have longer
decision-making processes because their diverse perspectives create conﬂict and
impediments to task completion, and reduce commitment to the team (J ehn, Northcraft,
& Neale, 1999; Pelled, Eisenhardt, & Xin, 1999). Alternatively, homogenous teams
appear to be better able to adhere to time constraints because they are better able to
develop work plans, priorities and goals seamlessly (Ancona & Caldwell, 1992).
Consequently, culturally homogeneous teams may make decisions more quickly

compared to culturally heterogeneous teams.

Given these arguments, teams of ﬁrms comprised of similar national cultures are
likely to work together more efﬁciently and move quickly to leverage or gain economies
from their equivalencies. Conversely, teams of ﬁrms from diverse national cultures may
act in a delayed fashion as they experience heightened transaction costs associated with

managing the issues caused by their cultural differences. Therefore,

H5: The lower the national cultural diversity of a team of ﬁrms, the earlier the team will

enter a market.

METHODOLOGY
1 based the hypotheses in this study on a sample of market entry decisions in the form
of project-ﬁnance investments across forty-eight countries for single multinational

organizations and teams of multinational organizations.

78

Research Setting

Project-based investments are a form of foreign direct investment used to ﬁnance
capital investment projects. The capital providers, which are usually large multinational
ﬁrms and international institutions, look primarily to the cash ﬂow from the project as the
source of funds to service the loan and provide the return on the equity invested in the
project (Esty, 2004; Finnerty, 1996). In other words, project ﬁnance is used to ﬁnance
“tangible-asset-rich and capital intensive projects with transparent cash ﬂows” (Kleirneier
& Megginson, 2000) with non-recourse debt. The most common applications for project
ﬁnance include investments in natural resources (e. g. mines, pipelines, etc.) and
infrastructure (e. g. toll roads, bridges, power plants, etc.). Some of the most prominent
applications of project ﬁnance include Disney’s Hong Kong theme park, Enron’s Dabhol
power plant in India and Europe’s Channel Tunnel rail link. In 2004, project-fmanced
investments reached $234 billion worldwide, an increase of 36% over 2003 (Esty &

Sesia, 2005).

To establish a project ﬁnance investment in a particular country, a single ﬁrm or a
team of ﬁrms will create a project company and take an equity stake in the company.
The sponsor(s) is generally involved in the construction and management of the project
and bears the risk of completion, operation and maintenance of the project facility.
Project sponsors are usually well-known multinational organizations from diverse
industries, including oil and gas, utilities and construction. The project company is a
standalone entity. Multiple lenders, including private ﬁnancial institutions, afﬁliates of

the World Bank (i.e., International Finance Corporation-IFC) or export-credit agencies,

79

fund the entity with debt. The project company uses the proceeds from the sale of its
output to service any debt. A contractor is responsible for building the project facility
(i.e., toll road, bridge, etc) within cost and on schedule, and may hold an equity position
in the project company. Suppliers provide the project company with the resources it
needs to build and operate the facility. Finally, the project company obtains advisory
services from law and consulting ﬁrms to assist the company in preparing and securing
its business arrangements. In general, project-fmanced investments are smaller, have
longer maturities, involve more participating banks and are more likely to be extended to
non-US borrowers than other types of syndicated ﬁnancing (Kleirneier & Megginson,

2000).

Although the modern history of project-based investments began in the early 19805,
the 19903 marked an increased interest in project-fmanced investments. Economic
growth in the United States and emerging economies stimulated this increase. More
importantly, this increase was stimulated by increasing levels of privatization and
deregulation in developing counties, which provided an opportunity for investment by
private sector ﬁrms (Esty & Sesia, 2005).39 In particular, private sector ﬁrms may have
been attracted to project-based ﬁnancing as a means to invest in foreign markets for
several reasons. First, project-ﬁnanced investments allow investors to share the risks and
uncertainty associated with a foreign investment given that project companies involve
arrangements with multiple parties (Finnerty, 1996; Kensinger & Martin, 1988). Second,

project-based investments reduce the costs of resolving issues of ﬁnancial distress for

 

39 Many of the organizations that were privatized had used project-based investing to fuel their growth.
Also, many of the industries that were deregulated (i.e., power and telecommunications) were industries
that had historically relied heavily on project-based investments (Esty & Sesia, 2005).

80

investors (Finnerty, 1996). Unlike a corporation, project companies have a limited
number of lenders and classes of debt. As a result, resolving any issues related to
repayment or restructuring the debt is a less complex undertaking. Third, project—based
ﬁnancing reduces corporate income tax. If the project company is incorporated as a
limited partnership, it avoids the double taxation levied against corporations based in the
United States (Kensinger & Martin, 1988). Fourth, project-based investments expand the
debt capacity of the sponsoring corporation because the project company obtains debt
ﬁnancing based on its own creditworthiness (Finnerty, 1996). Most importantly, the
ﬁrms that entered foreign markets using project-based investments earlier than other
ﬁrms were probably more likely to select the most attractive locations (e.g., countries
with growing economies), gain access to the most high proﬁle projects and secure the
most beneﬁcial contractual relationships. These advantages may have enhanced the

performance of the project and the sponsoring ﬁrm(s). 40

The project-fmance investments are a promising setting to study national culture and
its effect on order of entry decisions for multinational organizations. First, the market for
foreign direct investment and the potential beneﬁts of project-based investments provided
a setting in which ﬁrms were likely to compete for the advantages of entering the market
early. As discussed above, project-based investments presented investors with several

advantages to capitalize on the growing market for foreign direct investment, particularly

 

4° Project-based investments involve projects that have long pay-off horizons (Esty, 2002). As a result, the
performance of these projects is difﬁcult to assess in the short-term term. Therefore, during the early stages
of the increase in project-based investments, investors were probably attracted to the perceived
performance outcomes of these investments.

81

during the 19903.41 Further, the public procurement process in which project-ﬁnance
investments are awarded creates a market in which multinational ﬁrms are likely to
exhibit competitive behavior in order to win a contract. Through this competitive bidding
process“, host governments grant project investments to a winning sponsor and project
company to build and operate the facility within its sovereign borders (Yescombe,
2002).43 Second, project-fmanced investments are a form of investment that ﬁrms
undertake across a wide range of countries. Although project-fmanced investments got
their early start in the power plant industry in the United States (Finnerty, 1996), ﬁrms in
multiple countries are currently involved in project-based investments. As a result, the
organizations involved in project-fmanced investments represent a broad spectrum of
national cultures. Third, project-based investments involve single or multiple ﬁrms,
which makes such projects subject to singular and multiple inﬂuences of diverse societal
cultures. Finally, project-based investments are discrete single events, which make it

easy to identify the timing of entry decisions.

Data
I based the hypotheses on two samples of project ﬁnance investments. The ﬁrst
sample includes approximately 600 project ﬁnance investments undertaken by a single or

dominant sponsoring ﬁrm. Dominant ﬁrms are those ﬁrms on a team of ﬁrms with a

 

4' Although the Asian ﬁnancial crisis in the late 19905 started a decline in worldwide foreign directed
investment (FDI), worldwide FDI was strong until that time.

42 A competitive bidding process is required in most countries involving public funding or where services
will be provided to the public (Yescombe, 2002). Additionally, a competitive bidding process is usually
required if the World Bank or other multilateral development bank is involved in the project ﬁnance
funding (Yescombe, 2002).

’3 The order of entry arguments still hold in the project-based investment context because the bidding
process indicates how aggressive ﬁrms might be in terms of preparing and negotiating a bid for the project.
However, the number of bidders per project is not available, which is a limitation of this study.

82

greater than 50% stake in the project company. I used this sample to test the ﬁrst four
hypotheses. The second sample consists of approximately 400 project ﬁnance
investments undertaken by a team of ﬁrms, where no one ﬁrm has more than a 50% stake
in the project company. I used this second sample to test Hypotheses 5. The project-
ﬁnanced investments included in this study range from 1980 to 2003 (for both samples)
across a variety of industry segments. I collected these data from the Thomson-SDC
project ﬁnance database, which has data on over 4,000 project-based investments. The
year 1980 is the beginning cut-off year in order to mark the beginning of the
contemporary history of project-based investments (Esty & Sesia, 2005). See Appendix

B for the list of countries involved in the projects for both samples.

Analysis

Given that the dependent variable (order of entry) is left-censored, tobit regression is
the appropriate analytical technique.44 Additionally, each country may have been
involved in more than one project, which prevents independence of the observations. To
capture the inﬂuence of this group membership, a ﬁxed or random effect model is
appropriate. Tobit ﬁxed effects models yield co-efﬁcient estimates that are inconsistent
for panel data (Maddala, 1987). Therefore, I base the discussion on the results using a
random-effects tobit model. As is the case in this analysis, random-effects estimators are

recommended when the analysis contains a large number of groups (greater than 10)45

 

4" Ordinary least squares estimates would have biased because OLS assumes the dependent variable can
take on values above and below zero.

”’5 In this study, the ﬁrst sample has 48 groups with an average of twelve observations per group. The
second sample has 46 groups with an average of nine observations per group.

83

and when one wishes to generalize to the population of groups (Snijders & Bosker,

1999).

I used the set of variables described below for the analysis. All observations are at the
project level. Tables 6, 7 and 8 present the correlation tables and descriptive statistics of
the ﬁrst sample. Table 9 presents the correlation table and descriptive statistics for the
second sample. All variance inﬂation factors were below the 10 point cut-off (Cohen et
al., 2003). To eliminate the inﬂuence of outliers, I removed all observations in the
dataset with externally studentized residuals of plus or minus two (Cohen et al., 2003).
Tables 10, 11 and 12 show the results predicting order of entry using tobit regression with

random effects.

Dependent Variable

Order of Market Entry. Order of entry is usually measured as the rank order in the
sequence of entry (e.g., Lee et al., 2000). Throughout my dissertation, I measure order of
entry as the elapsed time since the entry of the pioneer, which is an alternative measure
that Lieberman and Montgomery (1988) recommend. The elapsed time since entry of the
pioneer (i.e., temporal order) is a more effective measure than rank order because the
observations are more independent. Therefore, in this study, I based order of entry on the
chronological announcement date of each project in each country. I obtained these data
from the Thomson-SDC project ﬁnance database. I coded the ﬁrst entrant (in each

country) with a “0.” I coded the remaining entrants based on the number of days since

84

the date of announcement of the ﬁrst entrant. The dataset includes all countries with at

least three project-fmanced investments from 1980 to 2003.

Independent Variables

National Culture. The independent variables represent the Hofstede scores (Hofstede,
1980, 2001) on the four dimensions of national culture of the home country for each
sponsoring ﬁrm. Many have criticized Hofstede’s work for his reliance on a single
company (IBM) and a single survey measure at one point in time46 (Roberts &
Boyacigiller, 1984; Smith, 2002; Spector, Cooper, Sparks, Bemin, & Bussing, 2001).
However, given its parsimony, widely—held use and predictive validity, I used Hofstede’s
dimensions in the primary analysis. Further, although other frameworks of national
culture exist (House, Hanges, Javidan, Dorfman, & Gupta, 2004; Schwartz, 1994)“,
Hofstede’s dimensions ﬁt more appropriately with the relationships of interest in this

study. I obtained the Hofstede values from his published work (Hofstede, 1980, 2001). i

I applied the Hofstede scores differently for each set of hypotheses. For the ﬁrst four
hypotheses, I applied the Hofstede scores to the single sponsoring ﬁrm (i.e. the sponsor
company with a 100% equity stake in the project company) or the dominant sponsor (i.e.,
the sponsor company with a greater than 50% equity stake on the team of ﬁrms) of each
project company. For Hypothesis 5, I applied the Hofstede scores to the top three
sponsoring ﬁrms in terms of project-company ownership, where no one ﬁrm had a

greater than 50% ownership.

 

4" Recent work has updated Hofstede’s dimensions based on changing economic conditions across
countries (Tang & Koveos, 2008).
47 See (Nardon & Steers, In Press) for a comparison of the existing models of national culture.

85

As a robustness check, I used the House scales (House et al., 2004; House et al., 1999)
in the analysis. I used those House dimensions (i.e., power distance, uncertainty
avoidance, institutional collectivism and assertiveness) that corresponded to the Hofstede

dimensions and arguments I used in this study.

Cultural Diversity. In almost all cases in the international business literature,
researchers use the construct of cultural distance to assess the cultural diversity between
the ﬁrm in the investing country and the country of entry in the context of an entry mode
choice or foreign direct investment decision.48 Developed by Kogut and Singh (1988),
cultural distance (or “CD”) measures the distance between two cultures using a Euclidian

distance measure based on Hofstede’s four dimensions of national culture. Formally,

4
CDjk=2 {(Dij - D ik)2Ni}/4 .
i =1
where CDjk = the cultural distance between countries j and k, Dij = the score for country j
on cultural dimension 1, Duc = the score for country k on cultural dimension i and V- = the

variance of the index for cultural dimension i. This formula corrects for the variance of

each cultural dimension and averages across the four dimensions.

For Hypothesis 5, I used a modiﬁed version of Kogut and Singh’s (1988) cultural

distance index. This modiﬁed cultural diversity measure captures the difference between

 

"’8 See Kirkman, Lowe & Gibson (2006) for a review of the literature on cultural distance.

86

multiple cultures based on the four Hofstede dimensions. Specifically, this measure
assesses the cultural distance between each of the top three sponsoring ﬁrms in terms of
project company ownership. Formally, I calculated this modiﬁed version of the

traditional cultural distance index as follows:

4 4 4
CID-11:2 {(Dij - D ik)2/Vi}/4 + 2 {(Dij - Dil)2Ni}/4 + 2 {(13 ik - Dil)2Ni}/4

[=1 1:] i=1

where CDm = the cultural distance between countries j, k and l, Dij = the score for country
j on cultural dimension i, Dik = the score for country k on cultural dimension i, Du = the
score for country 1 on cultural dimension 1 and V, = the variance of the index for cultural
dimension 1. This formula also corrects for the variance of each cultural dimension and

averages across the four dimensions.

Control Variables"9

Firm Dominance Dummy. In the ﬁrst sample, I coded all projects with a single
sponsor with a “1” and projects with a dominant ﬁrm on the team with a “O” in order to
capture any effects that may differ based on whether the project was sponsored by a
single ﬁrm or a team of ﬁrms dominated by one ﬁrm. The effects may differ for projects

sponsored by a team of ﬁrms dominated by one ﬁrm because the national culture of the

 

49 I also tested cultural distance (Kogut & Singh, 1988) as a control variable in the models because the
cultural distance between the host country and entering country may effect entry timing. Cultural distance
was signiﬁcantly correlated with the individual dimensions of national culture (sample 1) and with cultural
diversity (sample 2). Cultural distance was not signiﬁcant in any of the models in which it was included as
a control variable. Further, when cultural distance was included in the models, I achieved the same pattern
of results for the other variables of interest. Therefore, I did not provide the results of the models that
included cultural distance as a control variable.

87

other ﬁrms on the team may inﬂuence the team’s decisions, whereas the projects
sponsored by a single ﬁrm do not have such inﬂuences. I obtained these data from the

Thomson SDC project ﬁnance database.

Prior experience of the sponsoring ﬁrm. To control for the advantages earned from
prior experience, I used the prior experience of the single or dominant ﬁrm as a control
variable. Each ﬁrm investing in a project company may have had prior experience with a
project-based investment in another country prior to the focal project. This experience
may have provided the ﬁrm with the knowledge and skills necessary to operate a project
investment in the focal country (Davidson, 1980). Consequently, if the ﬁrm has had prior
experience with project-based investments, the ﬁrm would more likely feel comfortable
with starting a project investment in another country sooner than other ﬁrms. The prior
experience of the single or dominant sponsoring ﬁrm is the number of proj ect-based
investments the firm had undertaken in any part of the world prior to the focal project.
For Hypothesis 5, I based the prior experience of the team on the average prior
experience of the top three sponsoring ﬁrms in terms of project company ownership. I
measured prior experience as the number of project-based investments the sponsor ﬁrm
(or team of ﬁrms) undertook within a ﬁve and ten-year period prior to the focal project. I
logged the experience counts to account for any extreme values. I obtained these data

from the Thomson SDC project ﬁnance database.

Openness to Foreign Direct Investment. To control for the fact that some countries

may be more open to foreign trade and investment than other countries, I coded countries

88

that are members of the World Trade Organization (WTO) with a “1” and all others with
a “0.” The WTO is the successor to GATT (the General Agreement on Trade and
Tariffs), which was founded in 1947. The WTO is responsible for negotiating,
implementing and monitoring the trade agreements between its member countries. At its
founding in 1995, the WTO started with seventy-ﬁve GATT member countries plus
members of the European Community. Fifty—two other GATT members joined the WTO
during the following two years. I based the coding for this dunnny variable on whether
the host country was a member of either GATT or WTO at the time of the project-based

investment. I obtained these data from the WTO website.

Macroeconomic Condition Controls

The strength of the economic market in which the project-investment is based may
inﬂuence the order in which ﬁrms may decide to enter the market. Countries with
favorable economic trends or conditions are likely to attract market entrants sooner than
other countries. To control for economic conditions that may effect ﬁrm decision-
making (McNamara & Vaaler, 2000), I used the following set of macroeconomic
variables as control variables. I measured each variable one year prior to the
announcement of the focal project (t-l). To create a more parsimonious model, I used
the principal components method to condense the economic variables into one economic
component, which I called economic condition. See Appendix C for further explanation

of the factor analysis.

89

Gross Domestic Product (GDP) per capita. GDP per capita is the total value of
goods and services (produced annually) per capita measured in constant 2000 US. dollars
for each country in which a project was established. 1 obtained these data from the World

Development Indicators (WDI) database.

Gross Domestic Product (GDP) Growth. GDP growth is the average real annual
percentage growth in GDP for each country in which a project was established. 1

obtained these data from the World Development Indicators (WDI) database.

Inﬂation. Inﬂation is the annual consumer price percentage for each country in which
a project was established. I obtained these data from the World Development Indicators

(WDI) database.

Exchange Rate. The exchange rate refers to the average yearly rate of exchange for
the local currency unit relative to the US. dollar. 1 obtained these data from the World

Development Indicators (WDI) database.

External Debt. External debt is the present value of debt repayable in foreign

currency for goods or services for each country in which a project was established. I

obtained these data from the World Development Indicators (WDI) database.

Political Condition Control

90

The political condition in the country in which the project-investment is based may
also inﬂuence the order in which ﬁrms may decide to enter the market. Countries that
support human and civil rights are more likely to have stable political environments and
thus pose less risk for market entrants (Vaaler & McNamara, 2004). To account for the
political condition of each country, I used data that assesses the political and civil rights
of each country as a control variable. I collected these data from Freedom House, a
nonproﬁt organization that provides rankings on political and human rights on over 200
countries. Freedom House measures political and civil rights on a one-to-seven scale,
with one being the highest degree of freedom and seven the lowest degree of freedom.50
For each country, I took an average of the country’s political and civil liberties score one

year prior to the announcement date of the focal project.

 

50 All of the countries represented in the ﬁnal sample had ratings from Freedom House.

91

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wvr cm. vw. :<O\Oa.>> .N
am; mad 95650 EoE—oa ._
n v m N _ .Q.m 502 033:;

 

2352 55830 35:50 ”mocmzﬁm guatomom a vina-

95

Table 10 Random Effects Tobit Regression Results Predicting Order of Entry:

Hofstede Dimensions

 

 

Variables Model 13 Model 14
Political Condition -l98.67** ~196.95**
(-3.09) (-3. 13)
WTO/GATT 1075.64** 1063.20“
(3.21) (3.26)
Economic Condition 129.24*** 135.40***
(4.28) (4.54)
Experience “ 378.80** 478.77**
(2.53) (3.17)
Dominance Dummy 420.58*** 386.07 ***
(4.96) (4.56)
Uncertainty Avoidance .19
(.07)
Power Distance -.54
(-.13)
Collectivism~ 722*
(2.37)
Masculinity 1.61
(.47)
Constant 1546.92*** 1891.81 **
(3.66) (3.13)
N 579 579
Log Likelihood -4694.28 -4686.47
Wald x2 83.18*** 102.45***
df 5 9

 

# . . . .
Unstandardized coefﬁcrents are reported w1th t-values 1n parentheses.

@Results are similar based on 5 or 10 years of prior experience.

Logarithm

Individualism was reverse-coded.

T p< .10 * p<.05

** p<.01

*** p<.001

96

Table 11 Random Effects Tobit Regression Results Predicting Order of Entry: House

 

 

. . #
D1mensrons
Variables Model 15
Political Condition -208.74**
(-3.13)
WTO/GATT 1 1 19.03**
(3.34)
Economic Condition 123.16***
A (4.16)
Experience 454-29”
(3.01)
Dominance Dummy 395.06***
(4.53)
Uncertainty Avoidance 291 .06**
(2.72)
Power Distance -503.42**
(-2.35)
Institutional Collectivism 53.87
(.34)
Assertiveness 38.88
(.41)
Constant 1280.61
(1.05)
N~ 536
Log Likelihood -4337.34
Wald x2 110.44***
df 9

 

#Unstandardized coefﬁcients are reported with t-values in parentheses.
Results are similar based on 5 or 10 years of prior experience.
A Logarithm
~ Table 11 has only 536 observations (compared to 579 in Table 10) because the House

dimensions were available for a fewer number of observations in the sample.

* p<.05 ** p<.01 *** p<.001

Tp<.10

97

Table 12 Random Effects Tobit Regression results Predicting Order of Entry: Cultural

 

 

Diversity#
Variables Model 16 Model 17 Model 18
Political Condition 46.86 55.52 55.53
(.68) (.81) (.81)
WTO/GATT 618.15* 638.09* 659.30*
(1.99) (2.07) (2.12)
Economic Condition 157.68*** 154.01*** 151.17***
(4.64) (4.55) (4.46)
Experience@ A 74o.32*** 746.74*** 759.90***
(3.91) (3.97) (4.05)
Cultural Diversitya 1372*
(2.00)
Cultural Diversityb 1-85**
(2.56)
Constant 1 159.70** 961.07* 934.84*
(2.76) (2.24) (2.18)
N 433 433 433
Log Likelihood -3487.86 -3485 .87 -3484.61
Wald x2 38.79*** 43.30*** 46.02***
(If 4 5 5

 

# . . . .
Unstandardized coefﬁcrents are reported Wlth t-values in parentheses.

@Results are similar based on 5 or 10 years of prior experience for average and
variability of experience.

a . .
Based on Hofstede’s cultural d1mensrons

b , . .
Based on House et a1. 5 cultural d1mensrons

A t
Loganthm

t p< .10 * p<.05 ** p<.01 *** p<.001

98

RESULTS
Table 10 presents the results for the ﬁrst sample (Hypotheses 1 through 4). As a
robustness check, Table 11 contains the control variables and the main effects using the
House cultural dimensions. Table 12 shows the results for the second sample
(Hypotheses 5) with calculations for cultural diversity based on the Hofstede and House
dimensions of national culture, respectively. The Wald x2 statistic is signiﬁcant in all

models (p<.001), which indicates good overall model ﬁt.

Model 13 in Table 10 shows the results regarding the control variables. First, the
political condition term is negative and signiﬁcant, which suggests that ﬁrms entered
countries with poor political conditions (i.e., less civil and political rights) earlier than
countries with strong political conditions (i.e., more civil and political rights). This
ﬁnding reﬂects the fact that many organizations undertake project ﬁnance investments in
developing and transition countries, which tend to have weaker political structures and
systems. Similarly, the WTO/GATT dummy and economic condition variables have
positive and signiﬁcant terms, which suggest that ﬁrms entered countries that were
members of the WTO and those with better economic conditions later than other
countries. Again, these results point to the bias in the project-fmance investments
towards developing or transition economies.5 ' Contrary to the results in Study 1, Model

13 indicates that more experience leads to later market entry. The complexities and risks

 

5 ' I conducted a test to determine if the results related to the control variables were an artifact of the data.

In this analysis. I analyzed the correlation between the timing of the ﬁrst entrant in each country with the
economic condition, political condition and WTO/GATT membership of the country. This analysis
showed a negative correlation for the economic condition variable, a positive correlation for the political
condition variable and a negative correlation for the WTO/GATT variable. These results, which are
contrary to the results for these control variables when all entrants are considered, indicate that the ﬁrst
project ﬁnance investments tended to be started in countries with good economic and political conditions as
well as countries that were members of WTO/GATT.

99

 

of doing business in international markets may cause more experienced ﬁrms to become
cautious in their entry decisions over time. Finally, the positive and signiﬁcant co-
efﬁcient on the dominance dummy code indicates that projects comprised of single ﬁrms
entered countries later than project investments comprised of teams dominated by one
ﬁrm. It could be that the projects comprised of teams had more or better information

regarding investment options across countries, which allowed for earlier market entry.

The results regarding the main effects of Hofstede’s dimensions of national culture
provide mixed results regarding the hypothesized relationships (Model 14). Hypothesis I
predicted a positive relationship between uncertainty avoidance and order of entry.

Model 14 shows a positive and non-signiﬁcant relationship for this dimension.

Therefore, H1 is not supported. Hofstede warned that the dimension of uncertainty
avoidance may not necessarily equate to risk avoidance (Hofstede, 1980, 2001), which
may explain the lack of support for this hypothesis. Further, this non-ﬁnding is
consistent with existing empirical work that ﬁnds that uncertainty avoidance cultures may

not necessarily engage in risk-avoidant behavior (Chui & Kwok, 2008).

Hypothesis 2 predicted a positive relationship between power distance and order of
entry. Model 14 shows a negative and non-signiﬁcant coefﬁcient for power distance.
Therefore, H2 is not supported. One possible explanation for this null ﬁnding is that
organizations with hierarchical decision-making styles may prefer to centralize entry
decisions. Centralizing entry decisions places the decision in the hands of a just a few

organizational participants, which could ultimately lead to faster decision-making.

100

Hypothesis 3 predicted a positive relationship between collectivism and order of entry.
Model 14 shows a positive and signiﬁcant coefﬁcient for collectivism. Therefore, H3 is
supported. Hypothesis 3 infers that ﬁrms from collectivist cultures tend to enter markets
later than other ﬁrms. The group-focused orientation of these ﬁrms leads them to forego
task achievement in order to promote the interests of the whole collective. Therefore,

ﬁrms from collectivist cultures tend to react more slowly to new market opportunities.

Hypothesis 4 predicted a negative relationship between masculinity and order of entry.
Model 14 shows a positive, but non-signiﬁcant relationship for this dimension.
Therefore, H4 is not supported. At its core, market entry involves the relationship
between the entering ﬁrm and the host country. Therefore, market entry involves
cooperation between the parties involved. The aggressive behavior of ﬁrms from highly
masculine cultures may not be conducive to the collaboration needed for a quick entry

into a new market.

Finally, Hypothesis 5 predicted a positive relationship between cultural diversity and
order of entry. Model 17 shows a positive and signiﬁcant co-efﬁcient for cultural
diversity. Therefore, H5 is supported. The results from Hypothesis 5 suggest that more
culturally diverse teams tend to enter markets later than less culturally diverse teams. As
predicted, this ﬁnding contradicts the arguments regarding the value of team diversity

and points to the value of team homogeneity in a decision setting. Culturally

101

homogeneous teams appear to work together more effectively and efﬁciently to make

foreign direct investment decisions across international markets.

As a robustness check, Table 11 and Model 18 in Table 12 provide results of the same
hypotheses using the cultural dimensions of House et a1. (2004). In Table 11, the positive
and signiﬁcant co-efﬁcient for uncertainty avoidance suggests that ﬁrms from cultures
that avoid ambiguity tend to be late market entrants. This ﬁnding, which is consistent
with the arguments related to Hypothesis 1, makes sense given that late market entry
allows ﬁrms to avoid the uncertainties of early market entry. The negative and
signiﬁcant co-efﬁcient for power distance suggests that ﬁrms from cultures that may have
more hierarchical decision styles tend to be early market entrants. This ﬁnding, which is
contrary to the arguments related to Hypothesis 2, infers that ﬁrms with more hierarchical
decision styles may be able to make more timely entry decisions. Again, organizations
with hierarchical decision-making styles may prefer to centralize entry decisions, which
may speed entry timing decision-making. Finally, the cultural diversity variable based on
the House dimensions (Model 18) shows a positive and signiﬁcant co-efﬁcient. Again,

culturally diverse teams of ﬁrms tend to be late market entrants.

A comparison of the results between the Hofstede and House individual dimensions of
national culture indicates that both sets of dimensions have similar effects on order of
entry decisions for all ﬁve hypotheses. I found signiﬁcant results for three out of ﬁve
hypotheses using the House dimensions versus two out of ﬁve using the Hofstede

dimensions. These results are particularly surprising given the admitted dissimilarities

102

between the two sets of dimensions (Hofstede, 2006; Javidan, House, Dorfman, Hanges,
& de Luque, 2006). These ﬁndings highlight the need for further comparison and

examination of the national culture models.

DISCUSSION
The purpose of this study was to use dimensions of national culture to make
predictions related to the entry timing of ﬁrms into new geographic markets.
Secondarily, this study sought to understand the inﬂuence of national culture for single
ﬁrms and teams of ﬁrms making entry timing decisions across countries. I used the

national culture dimensions of Hofstede (1980) to develop and test the hypotheses.

The national culture of an organization is a key factor in the ﬁrm’s environment,
especially for multinational ﬁrms. The national culture of the environment inﬂuences the
norms of ﬁrm behavior, including the structures and strategies the ﬁrm may employ.
Because of its inﬂuence, national culture may shape the entry timing choice of the ﬁrm.
Further, in a team context, national culture may inﬂuence the collective decision-making
processes of the group. However, the results of this study fail to provide strong support

for many of these arguments.

First, some dimensions of national culture predict ﬁrm behavior consistent with the
logic of the dimension, while others suggest behavior that seems contrary to the
dimension. Firms in collectivist cultures are late entrants into new geographic markets,
whereas the entry timing tendencies of ﬁrms from uncertainty-avoidant, power distant or

masculine cultures is unclear. Nonetheless, these ﬁndings should be considered in light

103

of the vast literature that has examined and found support for national culture using

Hoftstede’s dimensions across multiple contexts (Kirkman et al., 2006).

However, the ﬁndings related to cultural diversity in teams is consistent with the
ﬁndings on team diversity in the organizational behavior literature (Ancona & Caldwell,
1992). Culturally homogeneous teams of ﬁrms have the opportunity to gain an advantage
in international market entry. These teams are able to pool their knowledge and
resources efﬁciently so that they maximize their market entry decision—making processes.
Alternatively, culturally diverse teams of ﬁrms appear to be hampered by their
differences and thus lose the opportunity to gain early mover advantages in these same
markets. These ﬁndings contribute to the debate regarding the advantages and
disadvantages of diversity in teams (Horwitz & Horwitz, 2007; Williams & O'Reilly,

1998).

This study has its limitations. First, I applied that dimensions of national culture to the
ﬁrms in this study based on the ﬁrrn’s headquarters. A more precise approach may have
been to measure the national culture of the fum’s CEO or top management team. A
ﬁrm’s CEO or top management team is typically involved in the market entry decision of
most major corporations. Existing research points to the large body of work that tests and
substantiates the relationship between the characteristics of the top management team and
strategic choices (Carpenter, Geletkanycz, & Sanders, 2004). However, Hofstede argues
that his dimensions are more appropriately applied at the country level (Hofstede, 1980,

2001). I applied his dimensions at the ﬁrm level of analysis. Nevertheless, existing

104

research has applied the Hofstede dimensions across multiple levels of analysis and found
signiﬁcant results (Kirkman et al., 2006). Finally, given the ties between host
governments and project-ﬁnance investments, 1 cannot rule out the possibility that the
government may have played an inﬂuential role in the competitive dynamics of the
project bidding process. If so, then other unobserved and unmeasured factors may be

inﬂuencing the model results.

Notwithstanding these limitations, this study offers several areas for future research.
First, given the multinational and multi-party nature of project-based investments, they
are rife with the opportunity to examine how governance mechanisms across each project
may interact in foreign direct investment decisions. For example, whose interests are
met when several principals are involved in the decision-making of a project company?
Similarly, given the multinational and multi-party nature of project-based investments,
future research may also want to examine these investments from a social network
perspective. For example, how might the structure of the social network between
sponsoring ﬁrms determine the location of the project-based investment? Finally, future
research may also want to examine the behavior of teams of ﬁrms in international
business settings. What predicts how many ﬁrms participate in the project investment?
What predicts whether or not a ﬁrm decides to sponsor a project by itself or with other
ﬁrms? These questions point to the rich possibilities for future research based on project-

ﬁnance investments.

105

THE CONSEQUENCES OF IMITATION
ON
EARLY ENTRANT PERFORMANCE

THEORY AND HYPOTHESES

One of the most practical aspects of order of entry behavior is not its behavioral or
cultural determinants, but its relationship to ﬁrm performance. Lieberman and
Montgomery (1988) ﬁrst theorized that imitation diminishes the performance advantages
that early moving ﬁrms earn.52 However, to date, the literature on order of entry has
ignored the multi-dimensional nature of ﬁrm imitation. In this study, I examine the
inﬂuence of the three modes of imitation, frequency-based, trait-based and outcome-
based imitation (Haunschild & Miner, 1997), with a particular emphasis on
understanding the consequences of ﬁrm imitation on the performance of early moving

ﬁrms. I also test the relative strength of each mode of imitation.

The Basics of Firm Imitation

Organizational imitation occurs when the execution of a practice or behavior by one or
more organizations increases the likelihood of that practice or behavior being used by
other organizations (Haunschild & Miner, 1997).53 In general, imitation is the result of a
social comparison process. Actors have a natural tendency to compare themselves to
others (i.e., a reference group), especially in the absence of other objective measures of

evaluation (Festinger, 1954). Imitation occurs as actors attempt to reduce the perceived

 

57' Lieberman and Montgomery (1988) do not refer to any speciﬁc type of imitation, but rather to the
general imitation of ﬁrm strategies and tactics.
3 See Lieberman and Asaba (2006) for a review of the literature on ﬁrm imitation.

106

discrepancy between their abilities or outcomes and those of similar others.54
Communication or social inﬂuence facilitates imitative behaviors. Communication may
often come from sources outside of the organization (i.e., the media or change agents),
whereas social inﬂuence mechanisms are often based on network ties or spatial proximity

(Strang & Soule, 1998).

Empirical work on organizational imitation suggests that different strategies, products
and practices are subject to imitative behavior. For example, radio stations imitate the
format of other stations in the same organization or proximal regions (Greve, 1996).
Television networks imitate other networks in terms of program introductions (Kennedy,
2002). Savings and loan organizations mimic the entry into new markets of other thrifts
(Haveman, 1993a). Firms imitate the acquisition activities of other ﬁrms to which they
have a board tie (Haunschild, 1993). Further, Fortune 500 ﬁrms imitate the corporate
governance practices of other ﬁrms of similar size and geographic proxirrrity (Davis &

Greve, 1997).

In addition, imitation occurs in diverse contexts, including international settings. For
example, multinational ﬁrms follow the plant location patterns of other multinational
ﬁrms (Henisz & Delios, 2001). Multinational ﬁrms imitate the foreign entry decisions of
other ﬁrms in the same industry (Guillen, 2002). Among other things, this clustering
behavior or geographic agglomeration may be the result of decision-making based on the

action of a referent ﬁrm operating in the same domestic market (Gimeno, Hoskisson,

 

54 More speciﬁcally, equity theory (Adams, 1965) suggests that actors are inclined to imitate others if their
inputs are greater, but outcomes lesser than those in their comparison group.

107

Beal, & Wan, 2005). Finally, multinational ﬁrms mimic the entry mode choice (i.e.,
wholly-owned subsidiary vs. joint venture vs. licensing) of other multinational ﬁrms (Lu,
2002). Imitation in entry mode choice may be based on similarities in national culture
(Kogut & Singh, 1988), preferences for managerial control and resource commitment
(Anderson & Gatignon, 1986), ownership advantages (Agarwal & Ramasswami, 1992) or

tolerance for host government corruption (Rodriquez, Uhlenbruck, & Eden, 2005).

Finally, certain conditions appear to diminish or enhance the possibility of imitation.
For example, ﬁrm imitation is least likely when the costs of imitation (i.e., the cost of
developing and introducing the new product, service or practice) are large (Mansﬁeld,
Schwartz, & Wagner, 1981). On the other hand, ﬁrms are more likely to imitate the
behavior of those in the reference group when the reference group is large and
homogeneous in terms of practices (Rhee, Kim, & Han, 2006). Nevertheless, existing
ﬁndings suggest that decision makers sometimes make mistakes or experience regret in
decision outcomes when using social proof (i.e., looking to the actions of others for clues
to what constitutes appropriate action) as a heuristic for future behavior (Rao, Greve, &
Davis, 2001). This due to the fact that imitation sometimes leads to increased

expectations and over-valuation of the adoption or behavior (Rao et al., 2001).

Imitation in an Order of Entry Context
In an order of entry setting, late moving firms imitate the behavior of pioneer ﬁrms in
order to gain competitive parity. From an industrial/organizational economics

perspective, pioneer ﬁrms erect barriers to entry (e. g., advertising costs, economies of

108

scale, etc.) that make subsequent entry or imitation by other ﬁrms difﬁcult. In essence,
these barriers preclude late moving ﬁrms from gaining access to the advantages acquired
by the pioneer ﬁrm. The most effective barriers to imitation are those that are causally
ambiguous (i.e., those barriers where the causal linkages between actions and results are
unclear) (Lippman & Rumelt, 1982). However, by leveraging their existing
competencies, late entrant or copy-cat ﬁrms may be able to catapult the barriers to entry
(Reed & DeFillippi, 1990) and thus gain the Opportunity to compete and earn the returns
similar to those of the pioneer ﬁrms. The competencies that imitators may possess are
internal to the ﬁrm and may serve as barriers to entry to subsequent imitators as ﬁrms
further reﬁne their competencies through continuous reinvestment (Reed & DeFillippi,
1990). In addition, late moving ﬁrms may gain competitive parity by free-riding on the
beneﬁts earned by the early moving ﬁrms, capitalizing on incumbent inertia, or
exploiting the resolution of technological or market uncertainties (Lieberman &
Montgomery, 1988). Finally, the success of a late moving or imitating ﬁrm may depend
upon its existing resource pool and the quality, price and innovativeness of its product

offering (Shamsie et al., 2004).

Similarly, late moving ﬁrms may imitate the behavior of pioneer firms in order to gain
legitimacy. From an institutional theory perspective, organizations operate beyond a
rational and economic context. Instead, organizations exist within a social framework
that deﬁnes the expectations and norms of economic behavior. Under the assumption
that they seek social approval (Zucker, 1987), organizations conform to the norms and

expectations of their environment and thereby gain legitimacy. Legitimacy is a

109

generalized perception or assumption that the actions of an entity are desirable, proper or
appropriate within some socially constructed system of norms, values, beliefs and
deﬁnitions (Suchman, 1995). Legitimacy grants organizations access to critical
resources, and thus they are “socially ﬁt” to survive (DiMaggio & Powell, 1983; Pfeffer
& Salancik, 1978) and operate more effectively in competitive situations. Thus,
organizations seek to gain their legitimacy by following the behaviors that are prescribed
or expected within their social environment so that they may reap the beneﬁts obtained

by similar others.

The Effects of Imitation

Existing research suggests that as they enter a market, imitative or late moving ﬁrms
erode the performance advantages that early moving ﬁrms earn (De Carolis, 2003; Lee et
al., 2000; Lieberman & Montgomery, 1988). However, what remains unclear from an
empirical perspective is how the performance of early moving ﬁrms declines based on
the diverse modes of imitation (Haunschild & Miner, 1997) enacted by rival ﬁrms. This
shift in research attention from the sources of early moving advantages to the sources of
early moving advantage erosion helps to extend this line of research. Further, by testing
performance outcomes under diverse modes of imitation, I move beyond examining the
effects of a generic form of imitation (Lee et al., 2000; Sinha & Noble, 1997) to

examining a multi-faceted form of imitative behavior.

To analyze this research question, I examined the imitative behavior of a sample of

international equity mutual funds (IEMFs) as these funds entered the market between

110

1985 and 2006. Because of the potential beneﬁts that mutual funds could gain from
entering the market early, the [EMF market supplies a good setting to study the

competitive and imitative behavior of organizations.

Research Setting

International equity mutual funds (IEMFs) based in the United States are mutual funds
that offer investors an interest in a portfolio of equity securities of non-US. companies.
International equity funds include mutual funds that invest in small, large or medium-
sized companies in emerging or developed markets. The funds may focus on a speciﬁc
region of the global, such as China or Canada, or they may invest in a diversiﬁed mix of
investments across diverse markets. Like other types of mutual funds, international
equity funds have investment objectives that include growth, growth and income,
balance, liquidity or long-term capital appreciation. Between 1984 and 2004, the number
of share classes in the United States that had an international equity focus grew from
twenty-nine to 2,172, while the number of shareholder accounts in this class of funds

grew from 713,000 to 28.9 million for the same time period (ICI, 2005).

The extensive growth of international equity funds began in the 1990s. A general
increase in the number of mutual funds in the United States fueled this growth,
particularly as more Americans placed their retirement savings in equity securities. The
globalization of ﬁnancial markets and economic growth in emerging economies also
stimulated this growth. First, during the 1990s, strong corporate ﬁnancial performance,

low inﬂation and low interest rates-factors that are ideal for equity mutual fund

111

investments-characterized the economic environment in the United States. Further, a
growing percentage of American households placed their retirement savings in mutual
funds. Assets in retirement savings plans invested in mutual funds rose from one-ﬁfth of
all fund assets in 1990 to more than one-third by the end of the decade (ICI, 2000). In
response, the mutual fund industry enlarged the number and variety of mutual fund
offerings while reducing the cost of acquiring and holding the funds (Fernando, Klapper,

Sulla, & Vittas, 2003; ICI, 2000).

Second, advances in electronic technology stimulated the globalization of ﬁnancial
markets. Globalization involves increases in the ﬂow of trade, information, assets and
human capital across national borders (Masson, 2001). Advances in telecommunications,
computers and the Internet over the past two decades signiﬁcantly decreased
communication and transportation costs thereby making it less expensive to conduct
business abroad. More importantly, these technological advances created greater
interdependencies between organizations across countries thereby increasing the ﬂow of
information and goods. As it relates to the mutual fund industry, these advances
facilitated the ﬂow of ﬁnancial capital across markets, thus making it more efﬁcient for

U.S.-based mutual funds to manage large volumes of transactions across the globe.

Third, as emerging economies grew in economic strength, so did the number of
American mutual funds offering investment opportunities in these countries. Emerging
economies are low-income, rapid growth countries using economic liberalization as their

primary engine of grth (Hoskisson, Eden, Lau, & Wright, 2000). During the late

112

19803 and early 19905, emerging economies around the globe experienced tremendous
economic growth. 55 Countries in Asia and Latin America adopted economic policies that
favored privatization and free-market systems, and thus investor confidence increased.
The fall of Communism in 1989 created a group of transition economies in Central and
Eastern Europe that was eager for foreign direct investment to fund its newly found
freedom. Further, the collapse of apartheid in South Africa stimulated much-needed
investment in South Africa and surrounding countries. In 1996, over sixty countries in
emerging markets had stock markets compared to half that number in 1985 (IFC, 1996).
Emerging economies grew by four percent in the 1990s, compared to three percent in
developed countries (WB, 2000). Mutual funds dedicated to emerging markets held $35
billion in assets in 1996, up from $1 billion in 1991 (Kaminsky, Lyons, & Schmukler,
2001). Although the currency crisis in Mexico in 1994 and Thailand in 1997 lead to a
contagion of ﬁnancial strife across the globe in the late 19903 (Vaaler & McNamara,
2004), prospects for future growth in emerging economies continued to remain strong in
the early 21St century. Indeed, by 2025, the economies of Brazil, India, China and Russia
may account for over half of the size of the G6 nations (i.e., the United States, Italy,
Germany, France, Japan and Great Britain) (Wilson & Purushothaman, 2003). Currently,

they are worth less than fifteen percent (Wilson & Purushothaman, 2003).

The Opportunity for a Competitive Advantage

 

55 Despite the growth, doing business in emerging markets involves managing difﬁcult issues. These
issues, such as foreign exchange exposure and complex business-government relationships (Austin, 1990),
often make investing in these economies unattractive. However, until the mid 19905, investments in
emerging markets offered a bundle of assets that had a low correlation with US. stocks (Kim & Singal,
1997; R. Smith & Walter, 1997). Therefore, investments in emerging market economies provided mutual
fund companies with the opportunity to offer their customers a diversiﬁed portfolio.

113

The growth in the mutual fund market, particularly in the international equity market,
created an opportunity for early moving IEMFs to gain a competitive advantage from
various sources. First, by preempting the assets of investors, early moving IEMFs were
able to earn income more quickly relative to late moving funds. Mutual funds do not
earn their income based on fund performance, but rather by assessing fees on the
percentage of total assets under management. The total fee of a fund (usually called the
expense ratio) covers expenses for management and administrative services, marketing
and sales support, and trading and execution services. Typically, fees range from .5% to
3% of annual net assets depending on the transaction costs, administrative expenses,
director compensation, accounting and tax fees, etc. of the fund (Leonard & Caudill,
1996). Therefore, by preempting investment assets, early moving IEMFs were able to
earn the income associated with managing the portfolio of assets faster than later moving

funds. The funds could then reinvest this income to fuel future growth and development.

Second, early moving IEMFs could gain a competitive advantage by inﬂuencing
investor switching costs. Mutual fund investors incur expenses to switch from one
mutual fund to another. These expenses include the cost of transacting the sale through a
clearinghouse, any back-end sales charges associated with the class of shares in the fund
(i.e., back-end sales load) and, for non-retirement funds, the potential tax consequences
of a gain or loss on the sale of the mutual fund shares. These expenses act as barriers that
prevent the investor from switching between mutual funds. In addition, investors face the
opportunity costs of searching through the thousands of other existing mutual funds if

they desire to make a fund change. Between 1990 and 1999, the number of mutual funds

114

in the United States increased from 2,900 to 7,800 for all fund categories (ICI, 2000).

For the international category, the number of funds increased from 129 to 950 for the
same time period (ICI, 2000). At the same time, the number of potential outlets where
investors could purchase mutual funds shares increased and diversiﬁed with the advent of
electronic do-it-yourself clearinghouses (i.e., E-trade and Charles Schwab). The growth
in the number of mutual funds and the number of sales outlets increased search costs and
intensiﬁed cognitive processing requirements for investors who wished to switch from
one fund to another. Taken as a whole, these arguments suggest that because of the high
switching costs, IEMFs could gain the commitment of investors by entering the market

early.

Similarly, early moving IEMFs could gain a competitive advantage through customer
commitment given the imperfect information regarding the quality of mutual funds.
Among other things, analysts evaluate the quality of a mutual fund by its historical
performance. Companies such as Momingstar and Value Line are the most popular
companies that provide ratings of mutual funds to the public based on the fund’s
performance. Investors often use mutual frmd ratings to make purchase decisions.
However, critics in academia and industry contend that mutual fund ratings offer
investors questionable information about a frmd’s potential performance. This is due to
the fact that, unlike the performance of a durable good, the performance of a mutual fund
changes based on economic conditions (Edelman, 2006). These changing economic
conditions make ratings of future performance based on past performance unreliable

(Edelman, 2006). Further, Momingstar ratings appear to be reasonable predictors of poor

115

future fund performance, but only marginally effective at predicting good future fund
performance (Blake & Morey, 2000). As a result, investors who use mutual fund ratings
face making mutual fund investment decisions with less than ideal information. In this
setting, investors are more likely to remain invested in their current mutual fund if it
provides satisfactory performance. Therefore, early moving IEMFs with solid

performance may also gain loyalty to their product offering relative to late moving

IEMFs.

Finally, by moving early, IEMFs could also gain as advantage through learning
experiences in international markets. The cost of operating a mutual fund differs across
markets. International markets have diverse legal, accounting, reporting and institutional
requirements for ﬁnancial investment. Further, the availability of information regarding
these requirements is often limited, particularly in emerging economies. As a result, the
cost of operating international mutual funds is higher than domestic funds (Leonard &
Caudill, 1996). However, by moving early into international markets, mutual funds could
learn the methods and practices needed to operate successfully. This learning advantage
can be particularly effective if the fund company can keep the learning proprietary
(Lieberman & Montgomery, 1988) or if the learning can be used to operate in other
international markets that may have similar operational requirements or institutional

constraints.

The Erosion of Early Moving Advantages

116

Although early moving IEMFs may experience several advantages, they may also
experience an erosion of their advantages as later moving IEMFs imitate their behavior.
The traditional premise in the literature on ﬁrst-mover advantages is that ﬁrms that
imitate the early moving ﬁrms negate the performance of the early movers. The
assumption is that late movers capture some of the earnings of the early mover by “free
riding” on the practices or strategies of the pioneer. In the context of this study, these
arguments imply that IEMFs that imitated the pioneer IEMFs decreased the proﬁtability
of the early moving funds by reducing (l) the potential new assets available for
management by the pioneer and/or (2) reducing the pioneer’s current assets under
management. In this vein, I examine the effects and the relative strength of the diverse

modes of imitation on the performance of early moving ﬁrms.

The Three Modes of Imitation

Frequency-Based Imitation of a Fund

Frequency-based imitation occurs when a large number of other organizations follows
the behavior of the early moving ﬁrm (Haunschild & Miner, 1997). Frequency-based
imitation resembles the “taken-for-granted” status that certain practices achieve because
they are unconsciously mimicked by a large number of organizations (DiMaggio &
Powell, 1983; Meyer & Rowan, 1977). That is, frequency-based imitation occurs
because organizations simply do what other ﬁrms are doing without any consideration of
the costs or beneﬁts of the behavior. Because frequency-based imitation results from

observing cues from referent others, it is a basic form of social inﬂuence. An underlying

117

assumption of frequency-based imitation is that the behavior of all other actors equally

inﬂuences each actor.

Frequency-based imitation also closely resembles bandwagons, information cascades
or herd behavior.56 Bandwagons are diffusion processes whereby organizations adopt a
practice, not because of their individual assessment of the efﬁciency or returns to be
gained from the adoption, but because of pressures caused by the sheer number of
organizations that have already adopted the practice (Abrahamson & Rosenkopf, 1993).
Increases in the number of adopters increase bandwagon pressures, which in turn, cause
the number of adopters to grow and the imitative behavior to continue (Abrahamson &
Rosenkopf, 1993). This form of imitative behavior is also similar to information
cascades or herd behavior (Banerjee, 1992; Bikchandani et al., 1992; Denevow & Welch,
1996; Palley, 1995; Scharfstein & Stein, 1990), which occurs when agents follow the
behavior of other agents by ignoring their own information and rely on the information or
behavior of others to determine their own course of action. Signals, cues or information
from reputable actors maximize the inferences that others may make regarding the
underlying value of the behavior (Fombrrm & Shanley, 1990).57 Even so, some argue
that decision-makers may not necessarily ignore the information of others, but rather lack
the mindful attention needed to make the information meaningful (Fiol & O'Connor,

2003). Nevertheless, herding provides “safety in numbers” (Palley, 1995), and allows

 

56 Bandwagons, herd behavior and information cascades have similar outcomes (i.e., imitative behavior),
but the causal explanation for each type of behavior is different. A detailed explanation of these
differences is beyond the scope of this study.

57 Signals or cues may be explicit or suggested by the behavior of others (Paris, 1926).

118

actors to hide their imperfections (Denevow & Welch, 1996) and share the blame for

erroneous decisions (Scharfstein & Stein, 1990).

Several studies support frequency-based imitation. Firms adopted the multidivisional
form of organization based on the number of other ﬁrms that had adopted such a
organizational structure within the same industry (Fligstein, 1985). Hospitals adopted the
matrix management form of organization based on the number of other hospitals in the
same geographic region that had done the same (Burns & Wholey, 1993). Firms
followed the plant location patterns of other ﬁrms based on the number of prior plant
locations of other ﬁnns in the same industry (Henisz & Delios, 2001). Further, ﬁrms
used the same investment-banking ﬁrm in an acquisition based on the number of other
ﬁrms using the same investment bank (Haunschild & Miner, 1997). However, the effects
of frequency-based imitation do not appear to hold in the spread of poison pills (Davis,

1991).

In terms the international equity mutual fund market, more international equity funds
opened as conditions inside and outside of the United States strengthened for this type of
investment. Based on arguments in the literature on order of entry advantages
(Lieberman & Montgomery, 1988, 1998), early moving IEMFs likely gained a
competitive advantage by preempting investment assets under management, increasing
investor switching costs, gaining customer loyalty or learning from experience.
However, in order to capitalize on the opportunity also, other funds entered the [EMF

market. This imitative behavior may have resulted from institutional or competitive

119

bandwagon pressures as other funds followed the behavior of the early moving funds in

an unsystematic manner.

Trait-Based Imitation of a Fund Family

Trait-based imitation occurs when organizations mimic the behavior of some subset of
other organizations (Haunschild & Miner, 1997). Speciﬁcally, trait based imitation
occurs when the characteristics of the early moving ﬁrm enhances the legitimacy of the
action and triggers imitative behavior, regardless of the whether the practices or strategies
used by the early mover are beneﬁcial. That is, ﬁrms make certain attributions about the
characteristics of the early moving ﬁrm and then use these attributions to determine if
they should imitate the early mover’s behavior. Institutional theory argues that
organizations imitate the behavior of “legitimate” ﬁrms, which are usually large,
reputable or successful organizations (DiMaggio & Powell, 1983). This mimetic
isomorphism is particularly advantageous to the organization when it faces an ambiguous
situation (DiMaggio & Powell, 1983) because when faced with ambiguity, ﬁrms may rely
on cues from others to reinforce their own actions. Further, because decision-makers
selectively attend and interpret certain cues from the environment (Weick, 1979),
imitating ﬁrms are more likely to recognize and mimic more visible ﬁrms, such as those
that are legitimate, successful or reputable, and thus reduce the perceived uncertainty

from adopting the practice or behavior.

Empirical support for trait-based imitation also exists. In general, existing studies

indicate that the size and/or success (i.e., proﬁtability) of the early moving ﬁrm are two

120

particularly salient characteristics that trigger trait-based imitation (Haunschild & Miner,
1997; Henisz & Delios, 2001). For example, savings and loans associations (i.e., thrifts)
followed large and proﬁtable associations into new markets (Haveman, 1993a).

Hospitals adopted the matrix management form of organization of other hospitals that are
more prestigious than less prestigious hospitals (Burns & Wholey, 1993). The size and
performance of other ﬁrms using a particular investment bank in the past was found to be
closely related to whether an acquiring ﬁrm would use the same investment bank in its
current acquisition (Haunschild & Miner, 1997). Further, nursing home chains were
more likely to acquire targets located near the recent acquisitions of other large chains

(Baum, Li, & Usher, 2000).

In the context of international equity mutual funds, the size of the early moving fund
family is an important trait that would likely stimulate imitative behaviors. The size of an
early moving fund family is a possible reﬂection of an organization’s capabilities,
visibility, prestige and competitive strength. In particular, large early moving fund
families are likely to be more visible and have greater market power than other ﬁrms
(Haveman, 1993b). Further, large early moving fund families are more likely to possess
the capabilities to integrate and leverage their core competencies to address
environmental issues effectively. Thus, ﬁrms are likely to follow large fund families
because, in so doing, they are able to economize on search and decision-making
processes because the actions of the large fund family are likely to be perceived as
legitimate (DiMaggio & Powell, 1983) and advantageous. Restated, follower ﬁrms are

more likely to imitate large early moving ﬁrms.

121

The success (or proﬁtability) of the early moving fund family is another trait that may
also stimulate imitative behavior. Successful, early moving fund families are those ﬁrms
that have excelled at fulﬁlling the economic and social expectations of investors,
ﬁnancial markets and other stakeholders (DiMaggio & Powell, 1983). Successful, early
moving fund families are also ﬁrms that have been able to deploy their capabilities to
sustain a competitive advantage (Teece, Pisano, & Shuen, 1997). More importantly, the
presence of successful, early moving fund families in an industry makes the industry
more attractive to other ﬁrms because proﬁtability is an attribute that gains the attention
of other potential entrants. Therefore, because of the perceived accomplishments and
capabilities of successful ﬁrms, follower fund families are more likely to imitate

proﬁtable early moving fund families.

Outcome-Based Imitation of a Fund

In outcome-based imitation, ﬁrms mirror the behavior of early moving firms based on
the outcomes that the early moving ﬁrms receive from employing certain practices or
structures (Haunschild & Miner, 1997). Firms imitate practices g structures that appear
to receive positive outcomes, but ignore those practices or structures that appear to
receive negative outcomes. That is, neither the number nor trait of the early moving
ﬁrms that engage in a practice is the focus of attention, but rather late movers focus their

attention on the outcomes that are associated with a particular practice.

122

Outcome-based imitation is often associated with organizational learning theories.
Learning theories suggest that organizations learn about the outcomes associated with an
innovation or practice by observing the outcomes that others receive from adopting the
innovation or practice (Levitt & March, 1988). Theorists in organizational learning have
long argued that ﬁrms learn vicariously by imitating or avoiding speciﬁc actions and
behaviors based on their perceived outcomes (Cyert & March, 1963; Levitt & March,
1988). Vicarious learning is particularly important under conditions where
organizational decision-makers face uncertainty, because under uncertain conditions,

ﬁrms may be inclined to refer to others for direction in terms of which actions to take.

Empirical support for outcome-based imitation also exists throughout the literature.
For example, ﬁrms copied the acquisition premium paid, which reﬂects the practices
employed during the acquisition negotiation process, by other acquiring ﬁrms
(Haunschild & Miner, 1997 ). Further, outcome-based imitation is implicit in the order
of entry literature. Firms imitate the practice of early market entry in order to receive the
same or similar ﬁnancial and social rewards as the pioneer ﬁrm (Lieberman &

Montgomery, 1988, 1998).

In terms of international equity mutual funds, one important and visible structure for
mutual funds is the expense ratio of the fund. The expense ratio is a fee that mutual
funds assess against all mutual fund shareowners. Mutual funds set their expense ratio to
cover the on-going costs of managing the fund. The higher the expense ratio, the more

fee income the fund generates. For the investor, the expense ratio represents the price

123

that the fund charges to invest in the fund and to receive the services the fund provides.
Typically, a portfolio with a higher turnover rate will have a higher expense ratio due to
higher transaction costs (Leonard & Caudill, 1996). Therefore, a fund with an active
management style will have higher expenses than a fund with a passive management

approach (Leonard & Caudill, 1996).

The proliferation of mutual funds has placed increasing pressure on mutual funds to
lower their fees. Between 1990 and 1999, the number of equity mutual funds (across all
categories) increased from 1,051 to 3,952, an increase of almost 400% (ICI, 2000).
Similarly, the total value of the assets in these funds grew from $229 billion to over $4
trillion for the same time period (ICI, 2000). As the account values in these funds grew,
fund companies faced pressure to reduce their expense ratios. Between 1980 and 1998,
total average distribution fees (i.e., the 12b-1 fee)58 for equity funds declined from 227
basis points to 115 basis points (ICI, 1999). Further, the grth in index funds and
institutional funds placed even further pressure on retail mutual funds to decrease their
fees. Institutional funds have much larger account balances than retail funds, and thus
institutional funds usually have much lower expense ratios than retail mutual funds.
Additionally, index funds typically have lower expense ratios than other types of funds

because they take a more passive approach to investment decisions.

Combined, these arguments suggest that the expense ratio is important for a mutual

fund because it reﬂects that fund’s fee structure, and thus is a potential source of

 

58 Distribution fees are part of the expense ratio.

124

outcome-based imitation. In spite of the downward pressures, mutual funds prefer a high
expense because a high expense ratio means that more fee income is available to pay the
expenses associated with managing the fund. More importantly, in a competitive context,
a high expense ratio indicates that more opportunity exists for follower funds because a
high expense ratio suggests that funds can earn higher margins from fund management.
Therefore, follower funds are more likely to imitate early moving funds with high

expense ratios.

The Effect of the Three Modes of Imitation

Theoretically, frequency, trait and outcome—based imitation are grounded in
institutional theory (DiMaggio & Powell, 1983; Meyer & Rowan, 1977). That is,
frequency, trait and outcome-based forms of imitation imply that ﬁrm imitation is the
result of attempts to gain legitimacy in the eyes of other organizations. In frequency-
based imitation, the larger the group of “other” organizations, the more inclined the focal
organization is to imitate the other group members because the size of the group signals
the legitimacy of its behavior. In trait-based imitation, ﬁrms mirror the behavior of large
or successful “other” organizations, for the characteristics of such organizations lend
legitimacy to a particular action, especially in the presence of environmental uncertainty
(DiMaggio & Powell, 1983). In outcome-based imitation, organizations learn which
behaviors to imitate by observing the outcomes that others receive. According to
learning theory, organizations select which behaviors to learn based on the

appropriateness or legitimacy of the behavior (Levitt & March, 1988).

125

Although each mode of imitation is based in institutional theory, early moving ﬁrms
may experience different performance outcomes with each mode. Imitation plays a key
role in the ability of ﬁrms to sustain a competitive advantage. The general argument is
that once early moving firms earn superior returns from entering a new market, imitating
ﬁrms are likely to enter the market in order to gain these same returns. However, early
moving ﬁrms are able to sustain their competitive advantage because certain “ex post”
factors limit the competition for these advantages (Peteraf, 1993). Among other things,
these factors include casual ambiguity (Lippman & Rumelt, 1982), scale economies,
information asymmetries, rare resources (Barney, 1991), advertising costs and fum
reputation. In the face of these barriers to entry (Demsetz, 1982), imitating ﬁrms are less
likely to duplicate the strategies and tactics of the pioneer ﬁrms and thus challenge the
pioneer’s performance. However, because different modes of imitation exist, each mode

may have a different effect on the performance of early moving ﬁrms.

For the mutual fund industry in particular, Makadok (1998) proposes that ‘resource
position barriers’ prevent imitating funds from gaining the advantages of pioneer funds.”
Resource position barriers exist when one ﬁrm possesses a resource that affects the costs
and/or revenues of later entrants (Wemerfelt, 1984). For the mutual fund industry,
Makadok (1998) proposes that this resource is access to existing customers. Speciﬁcally,
pioneer funds are able to gain superior returns by locking in customers who are unlikely
to switch to another fund because the costs and search time necessary to do so are

excessive (Makadok, 1998). Therefore, given that the international equity mutual fund

 

59 Makadok’s work (1998) focuses exclusively on the money market mutual fund industry.

126

industry is a growing industry 60 and that it likely has similar resource position barriers, it
is an ideal setting to examine the sustainability of early mover advantages from imitative

behaviors.

First, for frequency-based imitation, early moving ﬁrms are unlikely to be able to
defend their competitive advantage because of the general increase of competitors in the
market. Population ecologists assert that as the number of organizations in a population
grows, competition for scare resources intensiﬁes (Carroll & Harman, 2000; Harman &
Freeman, 1977).61 As competition intensiﬁes, ﬁrms ﬁnd it more difﬁcult to develop or
acquire unique and valuable products, services, resources and relationships (Barney,
1991; Penrose, 1959; Wemerfelt, 1984), which can then be used to develop superior
strategies. Consequently, under these conditions, performance declines. In the context of
this study, the increasing supply of IEMFs probably placed downward pressure on the
performance of early moving funds because as more frmds entered the market, the early
moving funds were less able to distinguish their investment strategies and services from

other funds, gain scale econorrries62

or secure customer loyalty. Consequently, rival
funds were able to scale the baniers to entry, and thus the early moving funds were less

able to sustain their competitive advantage.

 

6° Young and growing mutual fund industries are settings in which imitation is more likely to occur and
where fund categories proliferate (Makadok, 1998). Therefore, multiple opportunities exist to measure the
sustainability of competitive advantage in such industries (Makadok, 1998).
6' Early research in industrial organizational economics viewed industry concentration (i.e., industry
structure) as a factor that inﬂuenced ﬁrm strategy (i.e., ﬁrm conduct), which in turn inﬂuenced industry
performance (Porter, 1981).

2 Mutual funds gain scale economies through back ofﬁce processing functions (i.e., investment
management, distribution, custody, transfer agency activities) (Levinthal & Myatt, 1994).

127

However, for trait-based imitation, the large size and success of the early moving ﬁrms
suggests that they have the resources and capabilities to defend their competitive position
even as competition intensiﬁes. Speciﬁcally, large organizations are more likely to have
the market power to enact the changes necessary to respond to rivals (Haveman, 1993b).
Further, large and successful organizations are more likely to have the slack resources to
undertake competitive actions. These slack resources allow the ﬁrm to sustain longer
competitive attacks against rivals (Ferrier, 2001). Put differently, large and successful
ﬁrms are more likely to have the resources and capabilities to erect barriers to entry that
would limit the ability of rivals to erode their performance. For the [EMF industry, the
large and successful early moving fund families were more likely to have had the
resources and capabilities to gain customer awareness and commitment before other
funds families, and thus secure the investment assets needed to sustain their competitive
advantage. Speciﬁcally, large and successful fund families are more likely to have
preferred relationships with retail financial planners, brokers and retirement plan

administrators, which provides them with access to investment customers.

Finally, for outcome-based imitation, the beneﬁcial strategies and tactics undertaken
by the early moving ﬁrms suggest that these ﬁrms have superior knowledge or
information relative to late moving or imitating ﬁrms. Firms gain knowledge or
information about how to compete effectively in markets through their own experience or
the experience of others. Such knowledge is a source of competitive advantage (Grant,
1996) and if combined properly (Kogut & Zander, 1992) and kept proprietary, superior

knowledge can be used to develop the most effective and appropriate responses to the

128

actions of rivals. Further, pioneer ﬁrms with superior or asymmetrical information can
create competitive “blind spots” for imitating ﬁrms, which can often lead rival ﬁrms to
make mistakes in strategic decision-making (Zajac & Bazerman, 1991). Therefore, even
as competition intensiﬁes from increasing levels of new entrants, ﬁrms with superior
knowledge or information can enact the necessary strategies and tactics to sustain their
competitive advantage. For the [EMF industry, early moving funds that had superior
knowledge or information were able to enact strategies to defend their competitive

position and thus gain and sustain higher returns.

Given these arguments, the performance of the early mover should diminish from all
three modes of imitation. However, these arguments suggest that trait and outcome-
based imitation should have similar effects on the performance of early movers, and that
either of their effects will be weaker than frequency-based imitation. This is because
early movers are better able to sustain their advantage when faced with trait and outcome
based imitation than frequency-based imitation. Therefore, for example, if a fund
category is high on trait and frequency-based imitation, frequency-based imitation would

have a stronger negative effect on the performance of the early mover. Therefore,

H1: Imitation diminishes the performance of early moving ﬁrms with a weaker negative

effect on the performance of early movers from trait and outcome-based inritation than

frequency-based imitation.

129

METHODOLOGY

Data

I obtained the sample of international equity mutual funds from Momingstar Principia
Mutual Funds Advanced. Momingstar Principia provides historical data and analysis for
over 10,000 mutual funds. I also cross-referenced these data to the listing of international
equity mutual funds listed by Value Line to ensure the completeness of the dataset.
Value Line is one of the largest and oldest investment research ﬁrms in the United States
for mutual funds, stocks and other types of investments. This cross-referencing process
yielded a ﬁnal set of approximately 132 mutual funds, representing 10 fund categories,
for which complete data was available. The ﬁnal dataset includes the Class A and
institutional shares“ of each mutual fund. These data include international equity mutual
funds that were started in the United States between 1985 and 2006. The year 1985 is the
beginning cut-off year in order to capture beginning of the growth in foreign markets,

particularly the growth in emerging economies.

Analysis

I used the set of variables described below for the analysis. Because the arguments
related to trait-based imitation imply cross-level effects, I estimated the models using 2-
level hierarchical linear modeling (Hofmann, 1997). Level 1 represents the variance at
the fund level and Level 2 represents the variance at the fund family level. I have

specified the HLM equations below.

 

63 Institutional shares are shares of a mutual fund that are sold to banks, insurance companies, hedge funds,
pension funds and other large institutions.

130

Level 12 Yij = Boj + Bljwij + BZjVij + I'ij

where Yij is the performance of early moving fund i in category j, 130,31 and 32,
are the intercept and slopes on the predictors, Wij and Vij represent the values of
frequency and outcome-based imitation for each early moving fund i in category

j, and rij is the residual.

Level 2: Boj = 700 + 'YOlGj + 70ij + U0}
Bu: Y10+ Ulj

sz= 720 + U2j

where Gj and Hj are the size and profitability of the early moving fund family
(i.e., trait-based imitation), 700, 710 and 720 are the second stage intercept terms, 701
is the slope relating Gj to the intercept term in the Level 1 equation, yoz is the
slope relating Hj to the intercept term in the Level 1 equation and UOj, U; j and U2,-

are the level 2 residuals.
Table 13 represents the descriptive statistics and correlation table. All variance
inﬂation factors were below the 10 point cut-off (Cohen et al., 2003). To eliminate the
inﬂuence of outliers, I removed all observations in the dataset with externally studentized

residuals of plus or minus two (Cohen et al., 2003). Table 14 shows the HLM results.

Definition of Early Moving Fund

131

Research based on subjective evaluations of pioneering activities suggests that
approximately ﬁfty percent of ﬁrms consider themselves early entrants (Robinson &
Chiang, 2002; Robinson & Fomell, 1985)."4 Other studies in this research stream employ
more conservative deﬁnitions of “early moving” ﬁrms (Carow et al., 2004; Coeurderoy &
Durand, 2004; Durand & Coeurderoy, 2001; Sinha & Noble, 1997). Due to sample size
limitations, I employed the more liberal deﬁnition of “early movers” (i.e., the ﬁrst 50% in

each fund category).

Dependent Variable
Performance of the Early Movers. I measured performance based on the total net
assets under management in the early moving fund one, two and three years after the

inception of the fund. I obtained these data from the CRSP mutual fund database.

Independent Variables“

F requency-Based Imitation. Frequency-based imitation is the cumulative number of
international equity mutual funds in each of the three years following the inception of the
early moving fund category entrants (i.e., following the inception of the ﬁrst 50% of the
category entrants). Following Haunschild and Miner (1997), I assume that any inﬂuence
on an organization beyond three years is too old to inﬂuence the organization’s behavior.

I obtained these data from Morningstar.

 

64 See the PIMS (Proﬁt Impact of Market Strategies) Data Manual, 1978.
65 The imitation measures are invariant at the fund level because the argument is that the behavior and
characteristics of the early movers trigger the imitative behavior of the follower ﬁrms.

132

Trait-Based Imitation. Trait-based imitation is the average size and success (or
proﬁtability) of the early moving fund family category entrants. I based the size of a
focal fund family on the average total assets of the fund family one year prior to the
inception of the focal fund. I logged these values to account for any extreme ﬁrm sizes. I
based the success of a focal fund family on the return on assets (ROA) of the fund- family

one year prior to the inception of the focal fund. I obtained these data from Compustat.

Outcome-Based Imitation. Outcome-based imitation is the average expense ratio (on
the fund inception date) of the early moving fund category entrants. I scaled the expense
ratios in each fund category by dividing the expense ratio of each fund by the average
expense ratio of the fund category. I obtained these data from the CRSP mutual fund

database.

Control Variables

Prior Experience. Mutual fund companies may have opened more than one
international equity fund prior to opening of the focal fund. Because of this prior
experience, [EMFs within the same fund family are likely to have learned from each
other. This learning may have created positive or negative operational synergies and thus
inﬂuenced each fund’s performance. Therefore, I included prior experience as a control
variable. I measured prior experience as a count of other international equity funds the
ﬁrm opened (in any fund category) prior to the focal equity frmd. I measured prior
experience at the ﬁve and ten year time point. I logged the experience counts to account

for any extreme values. I obtained these data from Morningstar.

133

Total Firm Fund Assets. I included this variable as a control variable to assess the
competitive strength of each market entrant (Vanderwerf & Mahon, 1997). I based ﬁrm
fund assets on the total assets of each ﬁrm one-year prior to market entry excluding the

focal fund. I obtained these data from Morningstar.

Type of Share Dummy. The sample in this study includes Class A shares and
institutional shares. To capture any idiosyncratic effects related to the type of shares, I
coded Class A shares with a “0” and institutional shares with a “1.” I obtained these data

from Morningstar.

134

 

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a w b c w v m N _ dd 532 032:3

 

E 852% 93:88 2 mam-E

135

#
TABLE 14 HLM Results Predicting Total Net Assets

 

 

Variables Model 19 Model 20 Model 21 Model 22
Control
Model TNAt+l TNAt+2 TNAt+3
A
. . 13.59 18.45 -37.52 -35.04
Prror Expenence (5 years) (.65) (.86) (41) (-. 66)
Type of Share dummy - 14.59 -21.31 -39.24 -39.1 1
(-.86) (- l .22) (-.91) (-.90)
Firm Fund Assets (excluding focal fund) .06* .06* .03 .01
(2.22) (2.34) (.85) (.49)
. . 6.51 8.84 8.21
Frequency-Based Imrtatront+1 (1.60) (.87) (.81 )
. . -110.40 -8.91 —7.31
Outcome-Based Irmtatron t (_ l .05) (-03) (-.02)

. . . . . 2.18 -2.32 -2.13
Tran-Based Irmtatron (Firm Srze t- 1) (.55) (-.24) (-.21)

. . . . . . 616.96 1648.92‘1' 1547.92
Tran-Based Irnrtatron (Firm Proﬁtability t- 1) (1.52) (1.65) (1.56)
Constant 51.59*** .83 99.49*** 99.49***

(5.06) (.02) (6.57) (6.56)
N 132 132 132 132

 

Unstandardized coefﬁcients are reported with t-values in parentheses.

A

Logarithm

'1' p< .10 * p<.05 ** p<.01

136

*** p<.001

RESULTS
Because HLM models do not provide an overall model ﬁt statistic (Raudenbush &
Bryk, 2002), I relied upon the statistical signiﬁcance of the individual co-efﬁcients to
determine the support for the proposed hypothesis. Model 19 (Table 14) shows the
results from the control model. Prior experience is positive, but non-signiﬁcant. The co-
efﬁcient for the share type dummy is negative and non-signiﬁcant. Firm fund assets has

a positive and signiﬁcant term, which suggests that larger ﬁrms tend to have larger funds.

Models 20, 21 and 22 show the HLM results that predict total net assets (TNA) one,
two and three years after inception of the focal fund. Neither of the models shows a
signiﬁcant term (at conventional levels) in the predicted direction for the three types of
imitation in years one, two or three. Therefore, Hypothesis 1 is not supported. Given
these null results, I conducted a supplemental analysis to assess if the null results were an
artifact of an inefﬁcient model design. In this supplemental analysis, I tested whether the
three types of imitation mediate the relationship between current performance and
subsequent performance (Baron & Kenny, 1986). The conceptual argument is that
imitation should erode the performance of early entrants and thus subsequent
performance declines. The mediation results indicated that neither type of imitation

mediated the relationship between current performance and subsequent performance.

DISCUSSION
The purpose of this study was to investigate the effects of the three modes of imitation

on the performance of early moving funds. Using the international equity mutual fund

137

industry as the research context, the theoretical arguments presented suggested that
frequency-based imitation should have had more of a negative effect on the performance
of early moving ﬁrms than trait or outcome-based imitation. However, the empirical

evidence does not support the theoretical arguments.

The null results in this study are primarily the result of an ineffective use of the
imitation proxies and low sample size. I used the same imitation measures to reﬂect the
three types of imitative behavior as they were first theorized and developed in Haunschild
and Miner (1997) and subsequently used in other work (Henisz & Delios, 2001; Korn &
Baum, 1999; Williamson & Cable, 2003). Given that other studies used the same
measures of imitation and found signiﬁcant results, I had no reason to believe ex ante that
these measures would not be effective in the context of this study. Nonetheless, the
existing literature on imitation suggests that the three types of imitation are best tested at
the dyad level of analysis (Haunschild & Miner, 1997; Williamson & Cable, 2003). For
example, if Firm A does X because Firm B is large or proﬁtable, then this would be trait-
based imitation. To test if this type of imitation is occurring, researchers would regress
the likelihood that Firm A does X on the size and proﬁtability of Firm B. Unfortunately,

the hypothesis in this study does not imply a dyadic relationship.

However, existing research suggests that an alternative approach to test the
hypothesized relationship is to use a matched sample design. In a matched sample
design, I would match each early moving ﬁrm with its respective follower ﬁrm based on

a content analysis of industry activity. For example, if Mutual Fund A entered the large

138

blend category ﬁrst, I would have coded the next fund that entered as the ﬁrst irnitator
(i.e., second mover) and so on. Then, I would have measured the performance of the
early movers over a short time frame (i.e., within 3 to 5 days of imitation) using event
history methodology (Lee et al., 2000). However, even with a matched sample design, I
would have needed to make signiﬁcant assumptions about whether or not the second
mover entered the fund category based on frequency, trait or outcome-based factors. As

a result, the results from a matched sample design would have been difﬁcult to interpret.

In retrospect, I should have done two things differently: (1) I should have used a
larger segment of the mutual fund industry to test the hypothesis in this study and (2) I
should have used the imitation measures at the dyad level of analysis. With a larger
sample, I could have measured the “early” entrants as the initial two or three market
entrants in each fund category (instead of the ﬁrst ﬁfty percent in each fund category). In
so doing, I would have had a more precise operationalization of early market entrants,
which could have improved the predictive validity of the model. Further, by employing
the measures of imitation at the dyad level of analysis, I would have avoided the
confounding issues associated with the current model design. Unlike the existing work
on imitation (Haunschild & Miner, 1997; Henisz & Delios, 2001; Korn & Baum, 1999;
Williamson & Cable, 2003), my model uses the characteristics of the focal ﬁrm to predict
its own subsequent performance rather than using the characteristics of the focal fum to
predict the behavior of ﬂothel ﬁrm. With my model, it is difﬁcult to determine whether
the three types of imitation are actually occurring and inﬂuencing the performance of the

early moving ﬁrm despite the fact that the measures of imitation are valid. In other

139

words, although the measures imitation are appropriate, their application in the context of
this study is less than ideal. Hence, the results of the model are insigniﬁcant and warrant

more extensive methodological investigation.

Finally, in addition to the methodological issues discussed above, the ﬁndings in this
study are further limited by the operationalization of the dependent variable and outcome-
based imitation. The dependent variable (i.e., total net assets) and outcome-based
imitation (i.e, the expense ratio) reﬂect measures of fund performance. However, I argue
that one measure of fund performance (the expense ratio) determines imitation, but the
other measure of fund performance (total net assets) indicates the fund’s success.

Ideally, there should be a better matching of the construct of the performance in the

model to eliminate any confounding issues in the results.

140

DISSERTATION CONCLUSION

Collectively, the three essays point to the complexities of market entry timing. Study
I shows how ﬁrm level factors, such as prior performance, slack resources and prior
experience can inﬂuence a timing decisions as ﬁrms consider entry into a new product
market. Study 11 demonstrates that when ﬁrms consider entry into a country, cultural
attitudes and values can also shape timing decisions for single ﬁrms and teams of fums.
Moreover, although Study [[1 yielded null results, the theoretical arguments presented in
the study reﬂect an aspect of the relationship between early market entry and ﬁrm
performance that may be worthy of further consideration with more sophisticated

methodological approaches.

All three studies demonstrate the ﬂexibility of market entry as an area of study.
Because market entry is a phenomenon that occurs in diverse marketplaces and at
multiple levels of analysis, it is an area‘of research that can be examined through
numerous theoretical perspectives. I use the behavioral theory of the ﬁrm (Cyert &
March, 1963), national culture (Hofstede, 1980, 2001; House et al., 2004) and arguments
from institutional theory (DiMaggio & Powell, 1983) to explain and predict different
aspects of market entry. All three perspectives yielded a set of arguments that brought
further insights into market entry behavior. Other theories and perspectives from the
management literature, such as the resource-based view, corporate governance or social
networks, could also be used to test various facets of market entry, and thus provide a

different understanding of this phenomenon.

141

Finally, the three studies underscore the contribution of Austrian economics and the
work of Joseph Schumpeter to the study of organizations. Austrian economics and the
concept of “creative destruction” propose that innovative ﬁrm behaviors are the basis of
superior ﬁrm proﬁts (Jacobson, 1992; Schumpeter, 1934). Over the past several decades,
this perspective has yielded a rich ﬁeld of study in strategic management and marketing
that has added to the understanding of what drives certain ﬁrm actions and the
consequences of those actions for the ﬁrm and its stakeholders. The studies in this
dissertation add to this body of work and suggest that even more knowledge about

organizations can be gained from this important research stream.

142

APPENDICES

143

Appendix A: Mutual Fund Category Titles

Asset allocation
Convertibles

Emerging markets

Large blend

Large growth

Large value

Long short

Mid-cap blend

Mid-cap growth

Mid-cap value

Small blend

Small growth

Small value
Specialty-ﬁnancial
Specialty-health
Specialty-natural resources
Specialty-precious metals
Specialty-real estate
Specialty-technology
Specialty-utilities

Target date

144

Appendix B
Algeria
Argentina
Bangladesh
Brazil
Bulgaria
Chile
China
Colombia
Costa Rica
Croatia
Czech Republic
Dominican Republic
Ecuador
Egypt
El Salvador
Ghana
Guatemala
Hungary
India

Indonesia

: Project Finance Countries

145

Appendix B
Iran
Jamaica
Lithuania
Malaysia
Mexico
Morocco
Mozambique
Nigeria
Pakistan
Panama
Peru
Philippines
Poland
Romania
Russian Federation
Slovak Rep
South Africa
Sri Lanka
Taiwan

Tanzania

: Project Finance Countries (cont’d)

146

Appendix B: Project Finance Countries (cont’d)
Thailand
Trinidad & Tobago
Turkey
Uganda
Venezuela
Vietnam
Zambia

Zimbabwe

147

Appendix C: Factor Analysis Details
A principal components factor analysis of the ﬁve economic variables revealed the

following results.

Factor Eigenvalue Difference Proportion Cumulative
Factor 1 1.43 0.41 0.28 0.28
Factor 2 1.02 0.03 0.20 0.49
Factor 3 0.99 0.15 0.19 0.69
Factor 4 0.83 0.13 0.16 0.85
Factor 5 0.70 . 0.14 1.00

Traditionally, management scholars would retain factors with eigenvalues greater than
one for further analysis. Based on the results above, this would mean that I would reduce
the ﬁve economic variables to two factors. However, recent work suggests that this
cutoff rule, which is also know as the Kaiser’s Criteria or the K1 rule (Kaiser, 1960),
severely overestimates the number of factors to be retained (Lance, Butts, & Michels,
2006). Instead, organizational research methodologists recommend parallel analysis.
Parallel analysis (Horn, 1965) creates a random dataset with the same number of
observations and variables as the original data. A correlation matrix is computed from
the randomly generated dataset and then eigenvalues of the correlation matrix are
computed. The parallel analysis of the ﬁve economic variables (based on 100

replications) is provided below.

148

Appendix C: Factor Analysis Details (cont’d)

Factor Principal Components Parallel Analysis
Factor 1 1.43 1.08
Factor 2 1.02 1.04
Factor 3 0.99 1.00
Factor 4 0.83 0.95
Factor 5 0.70 0.91

Based on the parallel analysis, I retained only one factor because only one factor in the
principal components analysis has an eigenvalue greater than any of the eigenvalues from
the parallel analysis (Hayton, Allen, & Scarpello, 2004). Therefore, I standardized the

ﬁve economic values and summed them to create a single factor.

149

Figure 1

 

 

 

 

 

 

 

 

 

 

 

 

 

Prior Entry Firm
Experience Slack
Performance
Relative to v
Aspirations
Order of
Market ‘——’
Entry
4
National
Culture
Frequency, Trait &
Outcome Based
Imitation
Full Model

 

 

 

 

 

 

 

 

 

 

 

 

Performance

 

 

 

 

 

150

 

Figure 2

Late Entry

    
   

Early Entry

 

DI-O-O-o--O-I-I-o-I-u

 

0

Performance Relative to Aspirations

Hypothesis 1

151

Figure 3

H2
Late Entry

Early Entry

 

 

Firm Slack

Hypothesis 2

152

Figure 4

Less Prior Experience

Late Entry H1

More Prior Experience

Early Entry

 

 

Performance Relative to Aspirations

Hypothesis 3

153

Figure 5

More Slack
Late Entry H1

Less Slack

Early Entry

 

 

Performance Relative to Aspirations

Hypothesis 4

154

Figure 6

Entry
Timing
(in days)

It
15000 ----- Low Exp
\
1 0000 ° \ .
s ”.1
° \ , .......
\ O .........
5000 x. .......
\ ......
° \ ........
° \ ........
...... '\
0 A ....... . \A

 

 

V

Relative Social Performance

Interaction between experience and social relative performance

155

- - High Exp

Figure 7

Eritry
Timing Al
(in daYS)
12000 ‘. H.
X """ LOW Slack
10 \\
.\
.\
6 .\ 00'
.\ OOOOOOOOOOOOOOOOOOOO
.\ IIIIIIIIIIIIIIIIIII
IIIIIIIIIIIIIIIIIII .\
0 A oooooooooooooooo .1 ‘

 

 

Relative Historical Performance

Interaction between slack and historical relative performance

156

Figure 8

Entry
Timing A
(in days)
8000 ‘ - - High Slack
’.’ ----- Low Slack
4000 ,°
0’.
l
2000 .’
v .......... .l
I ...............
.10 ...............
0 L ........... 5 >

 

 

Relative Social Performance

Interaction between slack and social relative performance

157

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