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BUSINESS-GOVERNMENT RELATIONSHIP AND FOREIGN MARKET ENTRY IN
TRANSITION ECONOMIES
by
Jinsil Kim
APPROVED BY SUPERVISORY COMMITTEE:
___________________________________________
Seung-Hyun Lee, Chair
___________________________________________
Gregory G. Dess
___________________________________________
Mike W. Peng
___________________________________________
Jun Xia
Copyright 2017
Jinsil Kim
All Rights Reserved
Soli Deo Gloria
To Luke, Priscilla, David, and Ashlee
BUSINESS-GOVERNMENT RELATIONSHIP AND FOREIGN MARKET ENTRY IN
TRANSITION ECONOMIES
by
JINSIL KIM, BA, MA, MBA
DISSERTATION
Presented to the Faculty of
The University of Texas at Dallas
in Partial Fulfillment
of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY IN
INTERNATIONAL MANAGEMENT STUDIES
THE UNIVERSITY OF TEXAS AT DALLAS
August 2017
v
ACKNOWLEDGMENTS
I am most indebted and thankful to all those without whom I would not be where I am today. I
would like to express my deepest gratitude to my adviser, Dr. Seung-Hyun Lee, for being the
greatest teacher, mentor, co-author, and role model from day one, and to my committee members
whose kindness and dedication truly touched me. I am also grateful to my cohort and friends in
the program, as well as my family who made my PhD journey a true blessing. Most of all, thank
you God for carrying me through.
May 2017
vi
BUSINESS-GOVERNMENT RELATIONSHIP AND FOREIGN MARKET ENTRY IN
TRANSITION ECONOMIES
Jinsil Kim, PhD
The University of Texas at Dallas, 2017
ABSTRACT
Supervising Professor: Seung-Hyun Lee
My dissertation encompasses three essays: 1) Political Market Competition in Transition
Economies, 2) How Does Home Country Bribery Change Firms’ Foreign Market Focus? and
3) Divergent Learning Effects from Alliance Experience and Cross-Border M&A Entries. The
first essay hinges on observing how firms deploy corporate political strategies as competitive
reaction when faced with politically active industry competitors, especially in the transition
economy setting. The second essay explores the effect of firm bribes paid in the home countries
on firms’ export tendencies. Using resource dependence theory, we find that bribery improves
firms’ positions in their domestic market then in turn reduce their motivation to seek foreign
markets. The third paper endeavors to fill a gap in the learning literature in terms of gauging the
different effects of within-form and cross-form experience on making subsequent M&A deals,
and how these manifest given a regulatory change. All three essays study how firms strategize
given the institutional forces that they face in transition economies, in terms of their political
actions and entry strategies into foreign markets.
vii
TABLE OF CONTENTS
ACKNOWLEDGMENTS ..............................................................................................................v
ABSTRACT ..................................................................................................................................vi
LIST OF FIGURES .....................................................................................................................viii
LIST OF TABLES ................................................................................................................................ix
CHAPTER 1 POLITICAL MARKET COMPETITION IN TRANSITION ECONOMIES ................1
CHAPTER 2 HOW DOES HOME COUNTRY BRIBERY CHANGE FIRMS’ FOREIGN
MARKET FOCUS? ................................................... ......................................................38
CHAPTER 3 DIVERGENT LEARNING EFFECTS FROM ALLIANCE EXPERIENCE AND
CROSS BORDER M&A ENTRIES ...................................................................................................74
BIBLIOGRAPHY……………………............................................................................................110
BIOGRAPHICAL SKETCH .............................................................................................................134
CURRICULUM VITAE
viii
LIST OF FIGURES
1.1 Interaction of Perceived Competitors’ Political Influence and Regulatory Uncertainty…..…29
1.2 Interaction of Perceived Competitors’ Political Influence and Pressure for New Product
Development...........................................................................................................................29
1.3 Interaction of Perceived Competitors’ Political Influence and Impact of CPA....................... 29
2.1 Interaction Plots……………………………………………………………….........................60
3.1 Hypotheses Setup..........................................................................................................................87
3.2 Number of cross-border M&A deals into China and total M&A amount.................................88
3.3 Moderating effects of economic distance on alliance experience before 2003.........................97
ix
LIST OF TABLES
1.1 Descriptive Statistics and Pearson Correlations..........................................................................25
1.2 Results of Multilevel Mixed Effects Logit Models.....................................................................26
2.1 Descriptive Statistics and Pearson Correlations..........................................................................56
2.2 Results of Fixed-effect Regression Models.................................................................................57
2.3 Robustness Checks........................................................................................................................61
2.4 Reverse Causality..........................................................................................................................64
A.1 Sample Firms, Home Countries, and Bribery Activities........................................................70
3.1 Summary Statistics and Correlation Matrix................................................................................94
3.2 Coefficients of GEE Negative Binomial Estimates Predicting M&A Entry.............................95
3.3 Robustness Checks........................................................................................................................99
B.1 Distribution of Firms in the Sample by Home Country...........................................................105
1
CHAPTER 1
POLITICAL MARKET COMPETITION IN TRANSITION ECONOMIES
Abstract
In countries where institutions are not evenly developed like transition economies, many firms
engage in corporate political action (CPA) to influence policy-making to their advantage at the
expense of their competitors. In this setting, we argue that perceived competitors’ political
influence makes a focal firm feel threatened, which sways the focal firm to also competitively
engage in CPA. We argue that as the focal firm is faced with a higher level of regulatory
uncertainty and experiences a higher level of impact from its CPA, its competitors’ political
influence is more likely to stimulate CPA. On the other hand, if the focal firm feels competitive
pressure for new product development, the effect of perceived competitors’ political influence is
weakened. We largely find support for our arguments using survey data in a multi-country
context: 25 countries in transition economies.
Introduction
In transition economies, institutions are unevenly developed, and the abrupt change from
a planned economy to a market economy naturally provides rooms for firms to manipulate policy
formation processes (Davis and Walters, 2004; Hoskisson, Eden, Lau, and Wright, 2000) and
change the “rules of the game” (North, 1990: 3) to their advantage. It is because in these
economies, government officials often exercise greater discretion over resource distribution and
2
rule-making (Ma and Delios, 2010), creating opportunities to abuse their power for private
benefit and solicit payments from firms in exchange for access (Banfield, 1975; Murphy et al.,
1993).
Given such an institutional setting, it is natural for firms to compete in order to gain and
sustain access to government resources. Studies find that firms use firm-specific resources and
capabilities to shape the nonmarket environment to its advantage through corporate political
actions (CPAs), for example by forestalling or manipulating additional regulation and raising
rivals’ costs (McWilliams, Van Fleet, and Cory, 2002; Mellahi, Frynas, Sun, and Siegel, 2016;
Oliver and Holzinger, 2008). These actions of firms would be threatening in the eyes of their
competitors, and naturally elicit their reactions. Thus, in this study, we ask whether a focal firm’s
perceived level of competitors’ influence on policy-making prompts the focal firm to engage in
the same pursuit as a competitive reaction, given this particular institutional context.
While the literatures on competition and on CPA have developed respectively, we find
that firms’ political actions are a vital but underexplored element of a firm’s competitive
repertoire (Capron and Chatain, 2008), and that the presence of multiple firms attempting to gain
government access breeds intercorporate political market competition (Epstein, 1969). Notably
in transition economies, firms build competitive advantage in the political market through CPA,
by influencing laws to be favorable to themselves at the expense of their competitors’ loss.
3
An example is from Bitonti and Harris (2017): Hungary recently adopted the Tobacco
Retailing Act, a new law that nationalizes the exclusive right to award long-term tobacco kiosk
licenses, under the pretext of curbing underage smoking. Hungarian investigative journalists
found that a preparatory version of the Act had been drafted on a computer that belonged to
Janos Santa, CEO of Continental Tobacco Group. At the end of the process, 500 tobacco kiosk
licenses were awarded to Janos Santa and his affiliates, and the overall number of licensed
tobacco kiosks dropped dramatically in Hungary. This shows that firms can woo those with
legislative prerogatives to earn key market advantage over its competitors (Bitonti and Harris,
2017).
Relying on the literature on competition (i.e., Capron and Chatain, 2008; Chen, Su, and
Tsai, 2007), we postulate that focal firm managers identify competitor firms based on resource
similarity and market commonality and carefully observe them and their actions that can be
harmful for the focal firm (Chen et al.,2007; Peteraf and Bergen, 2003). When managers
perceive that their competitors exerted political influence to manipulate new laws to have an
impact on the focal firms as well, this prompts the focal firms to engage in actions in the political
market to deter this threat and also aim for their rivals’ resources (Capron and Chatain, 2008).
Therefore, we posit that the perceived level of political involvement of competitors will heighten
a focal firm’s engagement in CPA.
4
In addition, we also contribute to the literature by introducing how factors such as market
contingencies and firm resource heterogeneity (Capron and Chatain, 2008) alter firms’
tendencies to engage in CPA when faced with politically active competitors. First, we test
whether a higher level of regulatory uncertainty strengthens the effect of competition on CPA
(Capron and Chatain, 2008). A higher level of regulatory uncertainty means that there is more
room to influence policy making (Frynas, Mellahi, and Pigman, 2006; Hoskisson et al., 2000; Ma
and Delios, 2010) and thus we expect that firms become more sensitive to their competitors’
moves in the political market and are more likely to react to them through engaging in CPA.
Then, we examine if competitive pressure for new product development enhances or
lessens the effect of competitors’ political involvement on the drive for CPA. We expect that
with heightened pressure from the product market to innovate, focal firms’ attention will be
divided (McCann and Bahl, 2015) between two types of competitive pressure they must tend to.
As these firms are pressured to develop better products through engaging in product market
activities, their focus on their competitors’ political influence and CPA will be lessened. We also
anticipate that a high level of impact from CPA (Capron and Chatain, 2008) on firms’ operations
makes firms be more attentive to the political market, which increases the effect of competitors’
political involvement on the focal firm’s CPA.
5
Background
CPA in the Transition Economy Context
Transition economies present certain pre-conditions such as “institutional voids” (Khanna
and Palepu, 1997), as they are in the course of transitioning to a market economy and democratic
governance. In such an environment, there is continued government involvement and high levels
of administrative coordination for economic activity retained from persistent and residual
socialist values (Hoskisson et al., 2000; Makhija, 2003; North, 1990; Peng and Heath, 1996). For
example, in Ukraine, the government has absolute discretion for the distribution of financial
resources to firms (Zvirgzde, Schiller, Revilla, 2013), and Russian firms rely heavily on the
government that controls of the economy’s commanding heights (Aven, 2013). Political
strategies become more useful when the government has a big influence over business activities
(Baron, 1997), and thus government-business relationship has significant impact on the firms in
transition economies (Coen, Grant, and Wilson, 2010; Peng, 2000; Meyer and Peng, 2005).
Firms in transition economies are also often faced with costly bureaucracy or rules.
Under these circumstances, the payoffs from changing the existing rules or writing new ones
would be very high. Corporate actors thus attempt to extract benefits from the state by
influencing the formulation of policies, laws, rules, and regulations in order to elicit a favor
(Hellman and Kaufmann, 2001). Through such activities, firms can garner advantages for
6
themselves into the “legal and regulatory structure of the economy” (Hellman and Kaufmann,
2001:2).
Theory and Hypotheses
CPA as Competitive Action in the Political Market
In the CPA literature, corporate political strategies have been conceptualized as the
primary vehicle for exchange between demanders and suppliers for policies (Hillman and Hitt,
1999; Hillman and Keim, 1995; Schuler et al., 2002; Stigler, 1971). In this context, the
demanders of policies are firms that interact with the suppliers—politicians and bureaucrats
representing the government—for favorable policies (Boddewyn, 1993; Bonardi et al., 2005) by
offering information, money, and/or votes. This creates a political market (Bonardi et al., 2005;
Capron and Chatain, 2008; Kingsley, Bergh, and Bonardi, 2012), a market for political influence.
As the government controls the making and enforcement of regulations which entails
critical resources for the firm (Pfeffer, 1976), access to these regulations has frequently been
sought by those being regulated (Posner, 1974; Stigler, 1971). As firm resources become
integrated into the political market environment (Baysinger, 1984; McWlliams et al., 2002;
Hillman et al., 2004; Bonardi et al., 2006; Frynas et al., 2006; Capron and Chatain, 2008; Oliver
and Holzinger, 2008), firms attempt to shape and control this external resource environment in
order to build an environment that better serves their interests (Capron and Chatain, 2008; Keim
and Baysinger, 1988; Pfeffer and Salancik, 1978).
7
According to past literature, it is suggested that firms endeavor to gain resources and also
attack its competitors’ resources in the political market (Capron and Chatain, 2008). Keim and
Baysinger (1988: 164) state that the “pursuit of political advantage […] is itself a competitive
behavior”. Just as a firm can hinder its competitors in the factor markets through actions such as
preemptive acquisitions of resources, it can engage in CPA such as lobbying to make its rivals’
resources less acceptable and thus attack competitor’s resource position (Capron and Chatain,
2008; McWlliams et al., 2002). Through such action, the firm betters its own competitive position
and creates an opportunity for itself to make the most out of its resources. As such, firms could
succeed in hurting their competitors through actions in the political market. It has been observed
that firms use CPA as a means to stifle competition (Robertson et al., 2008).
In these countries, firms may discreetly and informally influence the making of new
legislations to hold benefits for themselves such as tax breaks, subsidies, capital, free grants of
state property or an “open economic zone” status for its territory (Slinko et al., 2004). Slinko and
colleagues analyzed all newly enacted legislation in Russia from the period of 1992–2000 that
contain preferential treatment for specific firms: for example, in 1999, a law was enacted which
specifically grants the meat-packing plant Li-Chet-Nekul a property-tax break for two years. In
2001, a budget law was enacted with special emphasis on the support of the fishing industry, and
it named only one firm, Akros, as recipient of a large subsidy although there were many other
fishing firms in that area. Through this process, these firms could get ahead of their competitors
8
and establish their position in the market. Besides Russia, other transition economies such as
Romania also abound with many cases of firms illicitly obtaining exceptions from various laws,
reported in the news (Young, 1999).
CPA as Competitive Reaction in the Political Market
We have discussed that proactive political strategies to influence policymaking is critical
for firms in transition economies (Lawton and Rajwani, 2015) and that through the process, firms
aim for benefits that are more firm-specific, or even adversary to competitors. As these firms
may initiate these non-market actions to improve its strategic position or harm the position of a
competitor, for the same reason, they naturally deploy reactive political strategy as response to a
competitor’s hostile political action (Lawton and Rajwani, 2015).
Managers gather information on its competitors and gauge the level of competitive
apprehension (Chen et al., 2007; Smith et al., 2001) and deploy strategies that deter this threat
and aim for their rivals’ resources (Capron and Chatain, 2008). They perceive those firms that
target the same market segment and possess the resource profile most similar to their own as
posing the greatest rivalrous challenge to their firm (Iriyama et al., 2016). So, when managers
sense that their direct competitors—firms that share similar markets and resources—have
successfully influenced policy-making through CPA, which had an impact on their own firm’s
competitive position, they would feel threatened and pressed to react. CPA seen as competitive
9
behavior to a competitor increases the likelihood of reaction (Chen et al., 2007; Yu and Cannella,
2007).
Thus, when focal firms perceive that their competitors exerted political influence on new
regulations, they would feel prompted to utilize more political tactics in order to deter the
negative influence of those regulations and secure policies that confer advantages over rivals
(Leone, 1986).
Hypothesis 1: The extent to which a focal firm perceive that their competitors have
influenced policy-making will increase the focal firm’s own engagement in policy-making.
Regulatory Uncertainty in the Political Market
A firm’s motivation to react to perceived competitive threat in the political market may
be influenced by other factors (Capron and Chatain, 2008). We first argue that the impact of
perceived competitors’ political influence on focal firm’s CPA is contingent on the level of
uncertainty in the legal environment of the transition economy in which the firm operates. Even
though transition economies are moving from a centrally-planned system toward a free-market
economy, there is less progress in the legal and regulatory frameworks, and many laws are still in
the making. For example, about 60 percent Russian firms view that their policy environment is
very uncertain with unpredictable changes in regulations, which is a major concern (Desai and
Goldberg, 2009).
10
This level of regulatory uncertainty is not uniform in each transition country, though,
with seemingly similar countries at different levels (Makhmadshoev et al., 2015). For example,
in Romania, 65% of surveyed entrepreneurs responded that they expect sudden, unexpected
changes to the legal and regulatory system in the short term (OECD, 2003). One businessman
stated: “we, small enterprises, just can’t keep up with the [tax] legislation. Just when you think
you know the law, it is changed” (OECD, 2003: 26). On the other hand, this number is even
higher in Albania: 74% answered that they feel that the amendments to business-related
legislations happen frequently (OECD, 2003).
Environmental context influences the extent to which a firm devotes attention and action
to a given stimulus (McCann and Bahl, 2016). Less developed institutional and legal frameworks
indicate a significantly higher discretion of the government and high uncertainty regarding
regulations (Frynas et al., 2006; Hoskisson et al., 2000; Ma and Delios, 2010), and this means
that government regulators are capable of easily dismissing exclusive rights, set new structures,
change license terms, or offer new benefits to competitors through the issuance of new
regulations (Kingsley et al., 2012). Political resources are thus more important in such a
competitive landscape. This factor is noteworthy since according to Capron and Chatain (2008:
21), “firms have more opportunities to shape policy when their resource environment is in the
process of institutional formation or undergoing some discontinuity”.
11
Studies have also found that with extreme regulatory and political uncertainty, firms need
to woo key decision makers and adjust their political strategy accordingly (Dieleman and
Boddewyn, 2012; Hillman, 2003; Kingsley et al., 2012). Other studies find that when managers
face environmental and regulatory uncertainty, firms are more motivated to campaign for
policies that bring benefits to them and raise competitors’ costs (Fremeth and Richter, 2011;
Martinez and Kang, 2014). A greater level of regulatory uncertainty creates a more attractive,
opportunistic political market (Kingsley et al., 2012).
Thus, naturally, focal firms are likely to notice that their competitors are thinking the
same and focal firms become more sensitive to their competitors’ actions in policy-making. In
such an environment with much room for changes in policies that potentially has large impact on
business operations, firms are more likely to be mindful of how their competitors leverage the
situation to gain competitive advantage. Consequently, when firms perceive their political market
as more unpredictable, they need to be more responsive to competitors’ political moves.
On the other hand, it is more difficult to influence policy-making where the resource
environment is “more mature and structured”, and where the “interests of competitors are
entrenched” (Capron and Chatain, 2008: 21). Where there are less uncertainties regarding
regulations due to more developed institutions and markets, there are less opportunities for firms
to influence policy-making, and thus engaging in CPA as reaction to competitors’ CPA becomes
less viable and relevant. Thus, in contrast, we argue that when firms perceive greater regulatory
12
uncertainty, they are more likely to react to their competitors’ involvement in policy-making by
engaging in CPA.
Hypothesis 2: The relationship between perceived competitors’ political influence and a
firm’s engagement in CPA will be strengthened by higher levels of regulatory uncertainty.
Competitive Pressure in the Product Market
Would the likelihood of engaging in CPA as competitive reaction to perceived
competitors’ political influence increase or decrease when the focal firm faces strong competitive
pressure in the product market for new product development? When faced with competitors that
are not only competitive in the political market but also in the product market, we argue that
focal firms’ focus may be divided over the two different realms of competition that they have to
tend to. When firms compete with the same rivals in more than one market (i.e., political market
and product market), studies find that their competitive behavior may differ from that of single-
market competition (Gimeno and Woo, 1999; Baum and Korn, 1999).
Past studies that look into both nonmarket and market strategies and competitive actions
simultaneously are few, which leave us with a meager understanding of the role of
complementarity and tension between market and nonmarket strategies (Mellahi et al., 2016).
Here we gauge the interaction between competitive pressures in the nonmarket (political market)
and product market, to find out how the pressure for new product development may shape the
relationship between perceived competitor’s political influence and a firm’s likelihood of CPA.
13
Literature notes that competitive pressure to develop new products results from an
increase in the degree of product substitutability, or in the number of competitors, or in the ease
of entry (Vives, 2008). In addition, the pressure for new product development means that the
firm is faced with competitors in the market that have higher technological capability to produce
more advanced goods, and that it feels the need to catch up with its competitors and surpass them
by offering innovative products.
Furthermore, when managers perceive that their firm is pressured to innovate from
increased product market competition, they also expect to capture incremental profits by the
introduction of a product, and thus are encouraged to make investments accordingly (Wang and
Carayannis, 2011). The competitive pressure for new product development prompts firms to
attend to the product market and invest in this endeavor. Wang and Carayannis (2011) find that
domestic competition has a high impact on a firm’s decision on developing new product or
service and that this serves as the predictor for firms’ commitments to focus on R&D.
McCann and Bahl (2015) state that managers display selective allocation of attention, meaning
they cannot attend to all alternative strategies and have to split their focus among competing
possible options. Thus, when managers are more focused on pursuing nonmarket responses such
as CPA, they are less likely to develop market responses, and vice versa. According to Ahuja and
Yayavaram (2011: 1648–1649), rent-seeking nonmarket strategies “may well imply a weakening
in the development of some other productive capabilities and thus weaken firms’ ability to earn
14
other forms of rents such as efficiency and innovation rents.” Sun and colleagues (2010) also
find that nonmarket activities may generate negative impact on market-based competitive
capabilities.
On the flip side, for those firms that feel pressured in the product market to develop better
products and are naturally more focused on engaging in product market activities such as R&D
and advertising, their focus on their competitors’ political influence and CPA will drop.
Accordingly, despite the fact that firms perceive their competitors influenced policy-making,
when they are also pressured in the product market, they are less likely to react to their
competitors’ actions in the political market through CPA.
Hypothesis 3: The relationship between perceived competitors’ political influence and a
firm’s engagement in CPA will be weakened by the competitive pressure for new product
development.
Level of Impact from CPA
The implications of lobbying and other political activities on organizational performance
have been studied widely, providing inconclusive results at best (Hadani and Schuler, 2013):
Hadani and Schuler (2013) finds that CPA can yield positive market performance for firms
especially when there are more rooms for government to intervene such as in regulated industries.
In a more recent study, Chen and colleagues (2015) find that lobbying expenditures can indeed
reward firms with significant improvements in operating income and market valuation.
15
In their endeavors, naturally, only those firms that find their CPA as having the intended
impact on their business will continue to be attentive to their political market and incur the costs
of engaging in CPA. When firms find that their endeavors to influence policy-making have
enough impact, they are more likely to invest in this activity when faced with competitors that
are influential in the political market. Those firms that benefited more from their past CPAs
would be more attentive to the happenings in the political market and prone to engage in CPAs
as reaction to their competitors’ political moves. Capron and Chatain (2008) state that those
firms that benefit most from changes in regulations influenced by their political market activity
are more likely to continue to interfere in political markets.
On the other hand, some firms may find that engaging in CPA and influencing
regulations has less bearing on their overall business activities. Firms develop rational
estimations and calculations about the future costs and benefits of their political actions
according to the information they have (Capron and Chatain, 2008). Firms are also able to factor
in potential costly competitive escalation, and recognize how the outcome from changes in
policies will influence the focal firm and its competitors (Capron and Chatain, 2008).
Thus, while perceived competitive tension in the political market influences managerial
actions (Chen et al., 2007; Dutton and Jackson, 1987; Reger and Huff, 1993), this is further
intensified through the impact of the policy change resulting from CPA on the firm. Managers
are more motivated to engage in CPA as competitive reaction when they perceive that their CPA
16
in the past had directly impacted their businesses. On the other hand, if managers feel that their
firms don’t gain as much from this reaction, they are less likely to engage in CPA despite
perceived competitors’ political influence.
Hypothesis 4: The relationship between perceived competitors’ political influence and a
firm’s engagement in CPA will be strengthened by level of impact that past CPA has.
Data and Method
As CPA such as lobbying is often a sensitive and discreet activity especially in transition
economies, it is hard to gather information on such firm endeavors. Thus, empirically, there are
limitations to data acquisition and many studies have used proxy data (Rehbein and Schuler,
1998; McWilliams et al., 2006) and survey responses (Iriyama et al., 2016; Macher and Mayo,
2015). Our variables are thus extracted from the Business Environment and Enterprise
Performance Survey (BEEPS). This survey gathers data on the quality of the business
environment as measured by a wide variety of interactions between firms and the state.
The BEEPS is a well-established database used for numerous studies in the management
field (Lee and Weng, 2013; Zheng et al., 2013; Zhou and Peng, 2010). Survey data is collected
via face-to-face interviews at either the firm or establishment levels. The method through which
the survey was conducted adds credibility to its validity: each firm was typically visited at least
twice by one or two enumerators in order to accommodate the manager’s time schedule. The
17
sample design was random in order to represent the population of firms within each country,
with quotas placed on firm size and ownership (see Hellman et al., 2003).
The transition economies for our study are countries located in Eastern Europe and
Central Asia that used a central planning regime through 1990 but have since been moving
toward a market-based system. These economies provide a setting for unique societal quasi-
experiments with opportunities to test the applicability of existing theories in management
studies as well as develop new ones (Meyer and Peng, 2005). We gather data for two different
years (2002 to 2005) for analyses because important information on lobby and political influence
were recorded only for these two rounds.
We alleviate the simultaneous bias concern by lagging all independent and control
variables for one period in our models. Our independent variables come from the first wave of
survey (2002) and the dependent variables is drawn from the second wave (2005). Since our
dataset included two-period observations for the same firm, we were left at the end with only a
one-period dataset, obviating the need to control for the year or to use over-time variations within
firms for identification.
Our sample includes 1,453 business enterprises across 26 economies: Albania, Armenia,
Azerbaijan, Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan,
Kyrgyz Republic, Latvia, Lithuania, Macedonia, Moldova, Poland, Romania, Russia, Slovak
Republic, Slovenia, Tajikistan, Ukraine, Uzbekistan, and Yugoslavia. These 26 countries are
18
specifically those in which the European Bank for Reconstruction and Development (EBRD)
was officially promoting transition to market-oriented economies after the soviet era (CEE
Bankwatch Network) and thus are good representations of transition economies.
As appropriate for studies with survey data variables, we address the flaws of firm-level
survey data such as potential response bias and common method variance (CMV) (Chang et al.,
2010; Podsakoff et al., 2003) as suggested in Conway and Lance (2010). To avoid potential
CMV, our models comprise of non-linear interaction terms, which is effective in reducing the
likelihood CMV as “respondents are unlikely to be guided by a cognitive map that includes
difficult-to-visualize interaction and non-linear effects” (Chang et al., 2010: 179). We also use a
key variable in our study—the level of regulatory uncertainty—from a different source that the
rest of the key variables from the same survey (Chang et al., 2010) as robustness checks.
Dependent Variable
The dependent variable is a measure of firm engagement in lobbying activities in the
BEEPS survey. We proxy this variable using firms’ top executives’ responses regarding the
question: Did your firm seek to lobby government or otherwise influence the content of laws or
regulations affecting it? This binary variable takes the value of 1 if a firm’s response to the
question is “yes”, and 0 otherwise. This measure has been well established in previous studies
(Choi and Lu, 2013; Weymouth, 2012). In our sample, 380 out of 1,453 firms (27%) responded
that they have sought to lobby government or influence the content of laws or regulations.
19
Independent Variable
The independent variable is a measure of firm perceptions regarding the effectiveness of
industry competitors’ efforts toward achieving political influence over new regulations. The
survey includes a set of questions asking firms to indicate specific entities that had influence on
the rule-making process. We use the answer to the question: “How much influence do you think
your competitors actually had on recently enacted national laws and regulations that have a
substantial impact on your business?” The answers were given on a scale: no impact (0), minor
(1), moderate (2), major (3), and decisive influence (4), and we exclude those firms that have
answered that that don’t know. We also try to represent it as a dummy variable (0 for having no
impact and 1 as having any impact) and estimate the effects on the dependent variable for
robustness, and find that the results are similar.
Moderating Variables
Regulatory Uncertainty is measured at the firm-level based on the survey question: “How
likely do you think it is that an unforeseen change in laws or regulations will occur in 2003 and
have a significant impact on your business?” The answers were given on a scale: Extremely
unlikely (1), highly unlikely (2), fairly unlikely (3), fairly likely (4), highly likely (5), extremely
likely (6). We exclude those firms that have answered that they don’t know.
Competitive Pressure in the Product Market is measured with the answer to the survey
question: “How would you rate the importance of each of the following factors on key decisions
20
about your business with respect to developing new products or services and markets: Pressure
from domestic competitors.” Answers range from: Not at all important (1), slightly important (2),
fairly important (3), very important (4). Again, we exclude those firms that have answered that
they don’t know.
Level of Impact from CPA is extracted from the question: “It is often said that firms make
unofficial payments/gifts, private payments or other benefits to public officials to gain
advantages in the drafting of laws, decrees, regulations, and other binding government decisions.
To what extent have the following practices had a direct impact on your business: Private
payments/gifts or other benefits to government officials to affect the content of government
decrees?” Firms could answer on a scale from no impact (0), minor (1), moderate (2), major (3),
and decisive impact (4), and we exclude those firms that have answered that that don’t know.
Control Variables
Studies on the determinants of CPA have found various environmental and organizational
factors (Luo and Zhao, 2013). Particularly, those examining CPA in transition economies (i.e.,
Hellman and Kaufmann, 2001) argue that large, private, new venture firms competing against
influential incumbents are most likely to engage in it. Accordingly, we include firm-specific
controls such as the firm’s size as proxied by the number of employees (managers checked 1 if
there are 2–10 employees, 2 if 11–49, 3 if 50–99, 4 if 100–249, 5 if 250–499, 6 if 500–999, 7 if
1000–9999), firm age, and ownership structure such as foreign ownership (%) and government
21
ownership (%). We also controlled for sales in export (%) and the amount of firm bribe,
obtaining the bribe measure from the answer to the question: “What percent of total annual sales
do firms like yours typically pay in unofficial payments?”
The wording of the question was devised in a manner that would encourage interviewees
to respond and do so in an honest manner. Given to the sensitive nature of the subject, rather than
directly asking about the interviewed firm itself, asking about the behavior of a “firm like yours”
is an accepted surrogate in the literature regarding firm’s illicit behavior (Joulfaian, 2009; Nur-
Tegin, 2008; Tedds, 2010). Johnson and colleagues (2000) find this strategy has been employed
in self-administered surveys by political scientists and economists. Indirect questions were found
to mitigate the social desirability bias of over-reporting own good behavior and underreporting
bad behavior, which overflows from the desire of respondents to project a favorable image to the
interviewer (Fisher, 1993). Also, indirect questioning methods provide respondents with the
feeling that they are not being directly scrutinized, which leads to a better image of what people
actually did (Lusk and Norwood, 2009) and thus mitigate social desirability bias of over-
reporting their own good behavior and underreporting bad behavior (Fisher, 1993).
Following Masters and Baysinger (1985), we control for sales to government. Studies
find that firms relying heavily on government contracts and sales for economic survival are more
active political players than those that do not (Becker, 1983) and that a “high dependence on the
government via financial relationships would motivate firms to seek private benefits” (Ozer and
22
Lee, 2009: 6). The most government-reliant companies are the most vigilant pursuers of political
solutions (Becker, 1983).
We also control for the number of industry competitors using the variable which is the
answer to the survey question: “Thinking of your firm’s major product line or main line of
services in the domestic market, how many competitors do you face? Please give me the exact
number of your competitors.” In the presence of many competitors engaging in CPA, and thus
generating more competition and saturation in the political market, a firm may seek other ways
to create value. We also include industry dummies in order to control for other industrial features,
and find that none of them proved to be significant in the models.
Additionally, the tendency to pursue political strategies is related to how much firms can
overcome free-rider problems and benefit from collective political action (Olson, 1965). Studies
consider that firms may use both individual and collective political strategies simultaneously as
multiple tactics in advancing their political interests (Jia, 2014; Masters and Keim, 1985).
Despite the costs associated with the collective action problem, business associations provide
centralized information and coordination with the major purpose of exchanging information and
exerting political influence for member benefit (Pfeffer and Salancik, 1978).
We thus control for whether the firm could collectively influence policy-making through
business association membership. We use the survey question: “how much influence do you
think the business association to which you belong actually had on recently enacted national laws
23
and regulations that have a substantial impact on your business?” Firms could answer on a scale
from no impact to decisive influence (0–4) which we convert to a dummy variable of whether
their business associations had impact.
We also tried country-specific controls such as the GDP per capita (PPP), and the
measure commonly used in literature for institutional development: World Governance
Indicators (WGI) (Abdi and Aulakh, 2012). We use the annual aggregate institutional index as
the unweighted average for the six dimensions of governance: Voice and Accountability,
Political Stability and Absence of Violence, Government Effectiveness, Regulatory Quality, Rule
of Law, and Control of Corruption. In addition, we also try the measure as seen in Meyer (2001),
a composite index based on the EBRD transition indices reflecting an expert evaluation of the
institutional reforms, and confirm that our findings persist.
Statistical Model
For the main analysis, we use the logit model because our dependent variable is binary,
following other studies that use similar survey data (Macher and Mayo, 2015). We also take into
account the cross-national nature of the survey data for our research design. Our sample firms in
the same country are clustered within a common institutional and economic environment with
firms nested in 25 countries, and thus the estimation of a multilevel (or hierarchical) model
(Rabe-Hesketh and Skrondal, 2008) is needed. We use a three-level model where the
observations (firms) comprise the first level, industries comprise the second level, and countries
24
comprise the third level (StataCorp, 2013). We ran all of our models and robustness checks using
the statistical software package STATA version 14.1.
Results
Table 1.1 summarizes the descriptive statistics and correlations for the variables used in
this study. We can find a reasonable variation in the dependent variable (S.D. = 0.41); its mean is
0.21, indicating that an average firm is engaged in CPA to a minor extent. None of the
correlations reported in Table 1.1 are particularly high. We also check the variance inflation
factor (VIF) since our models had interaction variables. The highest VIF value is 3.31, well
below the recommended threshold of 10 and suggesting multicollinearity is not a major concern.
The results of the multilevel mixed effect model are provided in Table 1.2. Model 1
includes all the controls and moderators. In Model 2, we find our independent variable to be
positive and significant (β = 0.29, p = 0.00), showing that competitors’ political influence
increases firms’ likelihood to engage in CPA, supporting our H1. We can be sure a positive
coefficient translates to a positive marginal impact, and gauge the economic magnitude of the
25
Table 1.1. Descriptive Statistics and Pearson Correlations
Obs. Mean S.D. Min Max 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
CPA 1,453 0.21 0.41 0 1 1
Competitors’ Political Influence 1,315 0.66 0.93 0 4 0.15* 1
Regulatory Uncertainty 1,387 3.70 1.32 1 6 0.07* 0.07* 1
Product Market Competition 1,432 2.72 1.01 1 4 0.04 0.16* 0.08* 1
Impact of CPA 1,244 0.42 0.86 0 4 0.07* 0.05 0.14* 0.09* 1
Firm Sizea 1,453 2.34 1.54 1 7 0.20* 0.08* -0.05 -0.09* 0.00 1
Firm Age 1,453 2.29 0.88 1 5 0.16* 0.07* 0.02 -0.05* 0.00 0.49 1
Foreign Ownership 1,453 0.12 0.31 0 1 0.09* 0.03* -0.05 -0.03 0.01 0.11 -0.10* 1
Government Ownership 1,453 0.15 0.35 0 1 0.12* -0.03 -0.01 -0.14* 0.00 0.37* 0.44* -0.17* 1
Export 1,435 0.11 0.25 0 1 0.09* 0.03 -0.02 -0.12* -0.01 0.27 0.14* 0.25* 0.00 1
Bribe 1,338 0.02 0.03 0 0 0.00 0.01 0.04 0.08* 0.13* -0.12* -0.16* -0.06* -0.13* -0.08* 1
Governmnet Sales 1,404 0.11 0.24 0 1 0.01 0.01 -0.03 -0.02 -0.04 0.11* 0.11* -0.10 0.24* -0.09* 0.04* 1
Number of Competitors 1,428 2.81 0.42 1 3 -0.06* 0.07* 0.05* 0.18* 0.07* -0.14* -0.05 -0.05 -0.16* -0.11* 0.06* -0.04 1
Business Association Lobby 1,453 0.45 0.50 0 1 0.20* 0.24* 0.07* 0.11* 0.08* 0.22* 0.15 0.06* 0.00 0.12* -0.04 0.01 0.00 1
Level of Institutional Development 1,453 -0.11 0.71 -1.35 1.02 0.03 0.14* 0.00 0.20* 0.01 0.01 0.11* 0.06* 0.00 0.12* -0.18* -0.16 0.07* 0.18* 1
GDP per capita 1,453 23.63 1.39 20.92 26.57 -0.07 -0.01* 0.06 0.11* -0.05 0.04 0.13* 0.01* 0.04 0.02* -0.11* -0.12 0.08* 0.19* 0.34*
aLogarithm.
Correlations greater than 0.05 are significant at p < 0.05.
26
Table 1.2. Results of Multilevel Mixed Effects Logit Models
Independent Variables (Hypothesis, Prediction) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Firms Size 0.23*** 0.23*** 0.23*** 0.24*** 0.23*** 0.24*** 0.24*** 0.25***
(0.06) (0.06) (0.06) (0.06) (0.06) (0.06) (0.07) (0.07)
Firm Age 0.32** 0.34** 0.34** 0.34** 0.34** 0.33** 0.38** 0.38**
(0.10) (0.11) (0.11) (0.11) (0.11) (0.11) (0.12) (0.12)
Foreign Ownership 0.28 0.32 0.34 0.34 0.33 0.37 0.27 0.30
(0.27) (0.28) (0.28) (0.28) (0.28) (0.28) (0.30) (0.30)
Government Ownership 0.48+ 0.47+ 0.48+ 0.49+ 0.47+ 0.50+ 0.36 0.38
(0.27) (0.28) (0.28) (0.28) (0.28) (0.28) (0.31) (0.31)
Export 0.05 0.05 0.04 0.06 0.05 0.04 0.21 0.21
(0.38) (0.39) (0.39) (0.40) (0.39) (0.40) (0.43) (0.43)
Bribe -0.20 0.23 0.22 0.06 0.22 0.02 2.31 2.13
(2.47) (2.62) (2.62) (2.64) (2.62) (2.65) (2.94) (3.00)
Government Sales 0.03 0.02 0.01 0.02 0.02 0.00 0.05 0.07
(0.35) (0.37) (0.37) (0.37) (0.37) (0.37) (0.43) (0.43)
Number of Competitors -0.17 -0.21 -0.21 -0.23 -0.21 -0.23 -0.32 -0.34
(0.18) (0.19) (0.19) (0.20) (0.19) (0.201) (0.21) (0.22)
Collective Action via Business Association 1.25*** 1.22*** 1.23*** 1.21*** 1.22*** 1.23*** 0.78*** 0.76***
(0.18) (0.19) (0.19) (0.19) (0.19) (0.20) (0.20) (0.20)
Level of Institutional Development -0.39 -0.51+ -0.50 -0.51+ -0.50+ -0.49 -0.42 -0.42
(0.29) (0.30) (0.30) (0.30) (0.30) (0.30) (0.37) (0.37)
GDP per capita 0.03 0.10 0.10 0.11 0.10 0.10 0.20 0.19
(0.21) (0.22) (0.22) (0.22) (0.22) (0.22) (0.26) (0.26)
Regulatory Uncertainty 0.19** 0.18** 0.14 0.17* 0.18** 0.13 0.17 0.12
(0.06) (0.06) (0.08) (0.06) (0.06) (0.08) (1.87) (2.09)
Product Market Competition 0.10 0.02 0.02 0.12 0.02 0.13 0.03 0.15
(0.08) (0.09) (0.09) (0.11) (0.09) (0.11) (0.10) (0.12)
Impact of CPA 0.15 0.13 0.13 0.13 0.10 0.10 0.20* 0.17
(0.09) (0.09) (0.09) (0.09) (0.12) (0.12) (0.10) (0.14)
Competitors’ Political Influence (H1, +) 0.45*** 0.27 0.90** 0.44*** 0.71+ 0.32*** 0.94
(0.08) (0.25) (0.30) (0.09) (0.37) (0.09) (0.70)
Competitors’ Political Influence*Regulatory Uncertainty (H2, +) 0.05 0.05 -0.14
(0.06) (0.06) (0.98)
Competitors’ Political Influence*Product Market Competition (H3, -) -0.15 -0.16+ -0.18+
(0.09) (0.09) (0.10)
Competitors’ Political Influence*Impact of CPA (H4, +) 0.03 0.04 0.04
(0.09) (0.09) (0.10)
Constant -3.88* -4.51* -4.33* -4.77* -4.49* -4.55* -4.11 -4.34
(1.80) (1.86) (1.88) (1.88) (1.87) (1.91) (2.59) (2.68)
Log Likelihood -537.28 -485.91 -485.62 -484.68 -485.86 -484.14 -410.06 -408.52
LR Chi-square 122.31*** 129.98*** 130.27*** 131.01*** 129.82*** 131.24*** 83.31*** 84.18***
N 960 960 960 960 960 960 903 903
Standard errors in parentheses
† p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001
27
independent variable that the coefficient (or parameter estimate) of the independent variable
means that for a one-unit increase in the perceived level of competitors’ influence in policy
making, there is a 0.29 increase in the log-odds of the dependent variable—the probability that a
firm will engage in CPA, holding all other independent variables constant.
To facilitate interpretation, we calculate the odds ratio—the exponentiated version of the
coefficient—: 1.34 for a unit change in the independent variable. We thus could say that the odds
of a firm engaging in CPA are increasing as perceived competitors’ political influence margin
increases. The odds ratio when firms perceive their competitors had decisive influence on policy-
making (4 on the scale) is 5.36, which means that the chances of a firm engaging in CPA
increase 5.36 times when it thinks that its competitors have decisive political influence versus
when it thinks that its competitors had no influence.
We are mindful of the fact that while the interaction effect is equivalent to the marginal
effect in a linear model, this idea does not work in nonlinear models. Also, one cannot rely on
the sign of coefficients to infer a positive or negative moderation (Hoetker, 2007; Zelner, 2009).
Furthermore, reported standard errors do not infer whether the interaction effects have statistical
significance (Ai et al., 2003; Huang et al., 2000) due to nonlinearity. Thus, in nonlinear models
with interaction terms, we cannot verify the direction, statistical significance, or economic
significance of the joint effects from the magnitude and standard error of a single coefficient
(Macher and Mayo, 2015).
We thus follow the guidelines in literature regarding how interactions could be
interpreted in logit models and offer graphical presentations to achieve the most complete
understanding of interaction’s effect over the sample of observations (Hoetker, 2007; Leiblein
28
and Miller, 2003; Zelner, 2009). We use the simulation approach recommended by Zelner (2009):
by using the CLARIFY suite of Stata commands, a distribution of coefficient estimates is
simulated by drawing new estimate values from a multivariate normal distribution repeatedly,
with other variables are held each at their means (King et al., 2000; Macher and Mayo, 2015;
Zelner, 2009).
In Figure 1.1, we graph the effect of perceived competitor’s political engagement across
all levels on the focal firm’s likelihood of engaging in CPA. We see that the probability of the
focal firm’s CPA increases with the level of perceived competitor’s influence, and that those
firms facing the highest level of regulatory uncertainty (4) are even more likely to engage in
CPA, as we had predicted in H2. In Figure 1.2, we see that in contrast, firms facing the highest
level of competitive pressure for new product development (6) are less likely to engage in CPA
despite their competitors’ political influence (H3).
Figure 1.3 maps out the effect of the interaction term associated with competitors’ political
influence and the level of impact from CPA. The graph shows that while firms engage in CPA
relatively more as they perceive their competitors to have successfully meddled in the policy-
making process, they are more likely to do so if they view their CPA had a decisive impact
(maximum value of 4) on their business.
29
Figure 1.1. Interaction of Perceived Competitors’ Political Influence and Regulatory Uncertainty
Figure 1.2. Interaction of Perceived Competitors’ Political Influence and Pressure for New
Product Development
Figure 1.3. Interaction of Perceived Competitors’ Political Influence and Impact of CPA
0.2
.4.6
.8
Pr(
CP
A=
1)
0 1 2 3 4competitors' political influence
Predicted probability when regulatory uncertainty takes minimum value
Predicted probability when regulatory uncertainty takes maximum value
.2.4
.6.8
Pr(
CP
A=
1)
0 1 2 3 4competitors' political influence
Predicted probability when product market competition takes minimum value
Predicted probability when product market competition takes maximum value
0.2
.4.6
.8
Pr(
CP
A=
1)
0 1 2 3 4competitors' political influence
Predicted probability when impact of CPA takes minimum value
Predicted probability when impact of CPA takes maximum value
30
Robustness Checks
Alternative specifications and operationalization of the variables were tried for robustness.
We use the measures for regulatory quality from the World Economic Forum Global
Competitiveness Survey (GCS) and subtract the values from 1 so that higher values for
regulatory quality indicate lower regulatory uncertainty and vice versa. In Models 7 and 8, we
find that the results are robust to the use of an alternative measure for regulatory uncertainty.
Additionally, we address potential endogeneity issues inherent to our research question of
whether perceptions of peers’ political influence actually drive focal firms’ CPA, and the
ambiguous direction of causality between perceptions and behaviors. Perceived risk is
endogenous since it depends on attitudes, and hence on the actions (Bontemps and Nauges,
2015). Thus, following Fonti and colleagues (2017), we use the two-stage least-square (2SLS)
regression: as instrument—which should be correlated with the dependent variable only through
the endogenous variable (Bettis, Gambardella, Helfat, Mitchell, 2014)—, we use the average of
firms’ answers on how they perceive their competitors’ political influence in the same region and
industry as the focal firm. Borrowing from social contagion theories (Monge and Contractor,
2003), our reasoning is that due to social influence, focal firms’ beliefs and perceptions are likely
to be correlated with their peers’ (Fonti et al., 2017).
While we run our two-stage instrumental variable estimations where perceived
competitors’ political influence was treated as endogenous, the Wald test of the exogeneity of the
instrumented variables from the two-stage least-square (2SLS) regression is not significant (p =
0.49). This means there is not sufficient information in the sample to reject the null hypothesis of
no endogeneity.
31
Discussion and Contributions
We make our contributions by exploring the gap of how competition in the political
market, more specifically the perceived level of competitors’ political influence, determines a
firm’s level of engagement in CPA in transition economies. We endeavor to extend the literature
on competition into political markets. Management studies traditionally focus more on
competition in product markets, and we find that while firms deploy political means to shape
government regulations to be self-serving, the use of political means is not often investigated
empirically. We mitigate the dearth of such studies and respond to consider the “competitive
nature of the influence process” (Keim and Baysinger, 1988: 164). We highlight that there is
competition between organizations to obtain political resources such as favorable regulations,
especially in transition economies. We find that in response to a situation in which industry
cohorts are exerting political influence on rule-making, focal firms are motivated to engage more
in CPA as a competitive reaction.
This study also contributes to prior research in nonmarket strategy that has shown that
industry affiliation and structure are major antecedents of firms’ corporate political activities
(Hillman et al. 2004). While prior literature may have paid more attention on industry attributes
to gauge the competitive aspect, we explore how each firm’s perception of its competitors
matters. At the same time, we examine how the dynamics of competition for political influence
are played out particularly in transition economies. Literature finds that institutional constraints
and specific exogenous policy contexts (Lawton and Rajwani, 2011) affect the choice of political
action (Hillman and Keim, 1995; Murtha and Lenway, 1994). We point out that the institutional
context—such as the level of regulatory uncertainty—matters in the discussion of CPA and
32
choose to examine this distinct backdrop of transition economies marked by conditions favorable
for firms to use CPA strategically.
In extension, we measure the degree of regulatory uncertainty perceived by firms in
transition economies and find that, where firms are more uncertain about potential policy
changes in their environment, they are more likely to perceive the political market to be more
attractive and opportunistic (Kingsley et al., 2012), with much room to manipulate policy-
making and attack competitors (Fremeth and Richter, 2011; Martinez and Kang, 2014). In such
situation, firms are more likely to be mindful of how their competitors leverage the situation to
gain competitive advantage, and thus firms’ perceptions regarding competitors’ political
influence has stronger impact on the focal firms’ own tendency to engage in CPA as competitive
reaction.
We also discuss the joint effect of focal firms’ perception of its competitors and the
competitive pressure they face for new product development. Our analyses provide answers to
the question regarding whether firms facing competitors’ political influence would engage more
or less in CPA if they also face high competitive pressure in the product market to innovate. In
this endeavor, we add to the literature exploring the interactions between nonmarket and market
strategies (Mellahi et al., 2016). We find that when firms feel pressured in the product market to
innovate, their focus may be diverted to the product market realm of competition that they have
to tend to, rather than the political market as managers display selective allocation of attention
(McCann and Bahl, 2015), thus weakening the main relationship.
In addition, we find that given firms’ resource heterogeneity, they are impacted
differently by new regulations as a result of their past engagement in CPA and that this alters the
33
main relationship. Firms that perceive their past CPA to have a large impact on their operations
are more likely to engage in CPA as reaction to perceived competitors’ political influence. Firms
whose business are greatly affected by the outcome of their CPA are more likely to be mindful of
their competitors’ political influence and react accordingly through CPA to deter competition,
more so than those firms that are not as much affected by the outcome of their past CPA.
Overall, our findings highlight the existence of a race for competitive favor-seeking in
order to change the rules of the game, prevalent in transition economies. In the process of
institutional development and transition, this race for competitive favor-seeking plays a key role
in shaping the processes and outcomes of economic institutional changes. This makes the
phenomenon discussed in this paper worthy of more scholarly observation. Also, such findings
have important managerial implications, particularly for foreign firms interested in investing in
transition economies. Investing firms should understand that strategies for competition in the
political market for influence should be considered as much as those in the product market.
This study is not without limitations. Notably, one of the greatest challenges we face
arises from the difficulties associated with using survey data. Also, the data availability limits us
to a particular set of 25 countries in central Asia and Eastern Europe. While they are undoubtedly
transition economies, they represent a special case characterized by an abrupt transition from
communist dictatorship, to authoritarian governments, to crony capitalism. Because these
countries only represent a subset of transition economies and share geographic proximity and
many other common characteristics, the generalizability of our findings is limited.
With this disclaimer, we make suggestions for future directions: while our study only
considers competition on the demand side of policies and deems the government as a supplier
34
and a single entity, there are other studies observing how institutional suppliers of public policy
interact with each other in order to generate public policy (Bonardi et al., 2005; Bonardi et al.,
2006; Hillman and Keim, 1995; Schuler et al., 2002). Further study could factor in competition
on the supply side as well. For example, there could be situations of political pluralism wherein
the political orientations of national and subnational policy makers differ (Kozhikode and Li,
2012). In such case, disagreements between policy-makers at different levels may occur from
their competing policy preferences (Lijphart, 1996; Ring et al., 2005).
Conclusion
By applying wisdom from the competition literature to our story of political market
competition in transition economies, we find that when a focal firm perceives that its competitors
are exerting politically influence on regulations that affect the focal firm, a firm tends to employ
more political tactics as competitive reaction, especially in transition economies. While the focal
firm may deter competitors’ political influence and its negative effect via CPA, they are more
likely to do so when they perceive that they face unpredictable policy changes. Also, the focal
firm is more likely to competitively react when it perceives a greater impact of its past CPA on
business operations. On the other hand, when the focal firm faces greater pressure for new
product development, competitors’ political influence is less likely to stimulate CPA by the focal
firm.
35
CHAPTER 2
HOW DOES HOME COUNTRY BRIBERY CHANGE FIRMS’ FOREIGN MARKET
FOCUS? A RESOURCE DEPENDENCE EXAMINATION
Abstract
Drawing on the resource dependence theory, we contend that bribes paid in the home country
can help manage firms’ dependence on their home country government. By making domestic
markets more lenient, home country bribery dampens firms’ motivation to seek opportunities
abroad. Building on this baseline prediction, we also argue that the effect of home country
bribery may vary from new ventures to established firms. Moreover, our study also posits that
despite the benefits accrued in the home country through bribery, a high level of informal
competition in the home country reduces the effect of home country bribery. We test these
arguments using a unique sample of firms in transition economies. Our findings have crucial
implications for research on corruption and firm global strategies.
Introduction
Although illegal and unpalatable, business-related bribes exist in almost every society
(Donaldson and Dunfee, 1999; Getz, and Volkema, 2001). In transition economies especially,
the estimated amount of bribes paid is US $20 billion to $40 billion annually (Transparency
International, 2009). Bribery is relatively salient in these countries (Filatotchev, Stephan, and
Jindra, 2008; Newman, 2000; Spicer, McDermott, and Kogut, 2000) because governments have
certain discretion over the use of valuable resources, information, and law enforcement—all
upon which firms are critically dependent (Pfeffer and Salancik, 1978: 145-146; Lee, Oh, and
Eden, 2010). High levels of discretion and power attached to government officials allow them to
36
solicit illegal payments from firms (Doh et al., 2003; Rodriguez, Uhlenbruck, and Eden, 2005) in
exchange for needed resources and public services.
Although scholars have provided much insights for bribery (e.g., Cuervo-Cazurra, 2006;
Luo, 2005; Martin et al., 2007), most studies focus on the determinants of bribery rather than its
strategic implications. In particular, the literature has been relatively silent regarding how bribery
may affect firms’ foreign market strategies. To fill this gap, this study examines how bribery in
the home country may influence firms’ motivation to seek opportunities abroad. Specifically, we
ask: (1) how does bribery in the home country influence firms’ interests in foreign markets? (2)
Under which conditions is this effect strengthened or weakened?
Drawing on the resource dependence perspective (Hillman, Withers, and Collins, 2009;
Pfeffer and Salancik, 1978), we contend that bribery in the home country enables firms to
manage their dependence on the government to “reduce uncertainties related to that dependence
and the likelihood that depdence will have negative effects on the firm” (Getz, 1997: 43). In
other words, firms may pay their home country government additional money in order to manage
their dependence on the government, which in turn may grant them a better position in the
domestic market. We then propose that the improved position at home gives the bribing firms
more incentive to stay in their domestic market rather than to explore foreign markets. Building
on this central argument, we also examine the factors that may alter the effect of home country
bribery, including whether firms are new ventures or established firms and the level of informal
competition in the home country.
We test these arguments using a sample of firms located in 26 transition economies.
Transition economies denote countries that have experienced large-scale socio-economic
37
changes from socialist systems to market mechanisms (e.g., Poland and Russia). Although the
business environments of these countries have been much improved, some practices such as
bribery remain. We develop a unique database using the multi-country and multi-year surveys
initiated by the European Bank for Reconstruction and Development (EBRD) and the World
Bank. Our results provide broad support for our arguments.
Our study contributes to the international business (IB) literature in two ways. First, we
endeavor to bridge the resource dependence perspective (Hillman et al., 2009; Pfeffer and
Salancik, 1978) with the literature regarding government corruption and bribery (Cuervo-
Cazurra, 2006; Doh et al., 2003; Luo, 2005; Spencer and Gomez, 2011; Martin et al., 2007).
Second, we also show that the effect of home country bribery is conditioned on certain
contextual factors. For instance, we argue, and find that new ventures benefit more from bribery
than their established counterparts, as reflected in reduced motivation to explore foreign market
resulting from bribery. Consequently, once bribes are paid, new ventures have stronger
motivation to stay in the domestic markets rather than seeking opportunities abroad. This finding
may help to explain why ventures from transition economies remain less active in the global
market. In addition, we also find that the effect of home country bribery diminishes in countries
characterized by high informal competition from unregistered firms (Bruton, Ireland, and
Ketchen, 2012; La Porta and Shleifer, 2014). The pressure from these informal firms may reduce
the benefits resulting from bribery in the home countries, thus pushing firms to explore foreign
markets. Overall, these findings not only suggest that home country bribery has a decisive
influence on firms’ tendencies to seek opportunities abroad, but also indicate that the impact of
home country bribery is contextualized in nature.
38
Theoretical Background
Firm Dependences on Governments and Bribery
Since governments are an important source of influence on business policies and resource
control, firms attempt to manage their dependence on the government to obtain favorable
policies and resources (Hillman et al., 2009; Pfeffer and Salancik, 1978). The literature suggests
that firms can do so via bribery and such a behavior is quite common in transition economies
(Doh et al., 2003; Rodriguez et al., 2005; Hellman, Jones, and Kaufmann, 2003). In addition,
institutional voids in these countries (Khanna and Palepu, 1997, 2000) provide ample
opportunities for mismanagement and corruption. Many of the transition countries are marked by
the legacies of communism, in the form of political influence, bribery, and corruption (Devinney,
2013).
Bribery transactions constitute both demand and supply (Rose-Ackerman, 1997: 34; Martin
et al., 2007). On the demand side, officials’ discretionary power allows them to (mis)use this
authority toward increasing personal welfare (Shleifer and Vishny, 1993). Government officials
may accordingly request bribes and kickbacks from private agents to compensate for inadequate
salaries (Holmes, 1999). On the supply side, there is no shortage of evidence showing that bribes
are used by firms to “seek to influence the agent’s exercise of discretion” (Banfield, 1975: 596).
Pfeffer and Salancik note that, “when economic activity becomes increasingly subject to
government regulation or intervention, the attempts of formal organizations to adapt to the
government… become more frequent” (1978: 190, emphasis added). Bribes can be offered by
firms to ameliorate the inefficiency of formal channels for acquiring public resources and
ultimately managing government dependence to gain needed resources. Consequently,
39
researchers report that more than half of surveyed managers admitted to engaging in bribes
proactively rather than passively in less developed countries such as Nigeria (Ufere et al., 2012:
2444).
The Strategic Implications of Bribery
Firm dependence on the government is crucial in countries where government officials
hold discretionary power over the allocation of valuable resources, control of information,
rulemaking, and enforcement amidst a lack of sound institutions. In these settings, firms manage
their dependence on the government via connections so as to “alter the condition of the external
economic environment” (Pfeffer and Salancik, 1978: 190). For instance, several benefits and
preferential treatments can be gained through bribery—“illegal informal exchanges” with
government officials (Mudambi, Navarra, and Delios, 2013). First, bribery greases the wheel of
commerce (Banfield, 1975; Getz, and Volkema, 2001; Luo, 2005; Rose-Ackerman, 1997). Hsieh
and Moretti (2006), for instance, document government officials misusing their power to sell
public resources or products (e.g., oil) at prices below market values. Under this system of dual
prices consisting of “a low state price and a higher free market price” (Rose-Ackerman, 1997:
35), firms can obtain resources at a lower cost by bribing.
Second, bribes can buy favorable interpretations of the law and lenient treatments. This is
particularly important in transition economies where rules and regulations change frequently and
rapidly. Iakovleva, Solevsvik, and Trifilova (2013) interview managers in Russia and the
Ukraine, and find that managers “have to be ready to overcome various bureaucratic barriers” to
develop a business (p. 325). As governments impose regulations, levy taxes, enforce criminal
laws, and impose these costs selectively on firms, the firms’ competitive positions within the
40
domestic market are shaken. In response, firms may resort to bribes since government officials
have the discretion to “clarify regulatory requirements” and “lighten the regulatory load” (Rose-
Ackerman, 1997: 36).
To illustrate, in Russia, bribery fuels the extensive illegal timber trade: firms selling
illegally-sourced logs can avoid some of the compliance costs and taxes, allowing them to offer
logs at significantly reduced prices after bribing corrupt local officials (Vandergert and Newell,
2003). Bribery also allows firms to get away with other forms of corporate malfeasance: vodka
manufacturers and retailers in Russia have used money so that officials would turn a blind eye
toward tax evasion (Virkunen, 1999). As Pfeffer and Salancik insightfully argue, money has the
power to buy “exclusive coverage and competitive advantage” (1978: 195).
In addition, bribes can reduce the obstacles associated with home country operations and
speed up necessary bureaucratic processes. This suggests that additional payments could help
firms bypass the red tape (Luo and Han, 2009; Rose-Ackerman, 1997) and reduce the adverse
impact of red tape (Cuervo-Cazurra, 2006; Doh et al., 2003; Lee and Weng, 2013). Similarly,
key resources such as access to credit are usually controlled by governments (Barth et al., 2009).
Certain amounts of financial incentives therefore can be instrumental in helping firms acquire the
needed financial capital for their operations (Khwaja and Mian, 2005).
Hypotheses
How Home Country Bribery Affects Firm Foreign Market Focus
One important strategic decision firms must make is whether to move beyond its domestic
market by exploring international markets. While firms have different options for tapping into
foreign markets, making sales in foreign countries is a common method given the relatively low
41
commitment and risk involved (Johanson and Vahlne, 1977; Oviatt and McDougall, 1994). As
all firms have limited resources, an emphasis on foreign market sales would ultimately “affect
the allocation of resources…in the domestic market” (Campa and Guillén, 1999: 1463). On the
other hand, a greater emphasis in the domestic market would mean that firms become more
focused on their home countries and less on foreign markets.
We contend that firm bribery at home may influence its market focus and strategy to sell in
foreign markets. This argument is supported by several reasons. First, government officials can
“impose costs selectively in a way that affects the competitive position of firms in an industry”
(Rose-Ackerman, 1997: 36). Firms that bribe can avoid certain costs and obtain additional
benefits by seeking “unfair advantages and special treatments” relative to firms that do not
(Martin et al., 2007: 1403). Then the extent to which bribes can strengthen firms’ domestic
market positions and make the home country market more attractive.
While the lack of control over the needed resources—such as information regarding
changing policies and favorable treatment—creates substantial uncertainty for entities operating
within that environment, bribery can be one method to mitigate this uncertainty (Tan and
Chintakananda, 2016). Bribery may allow firms obtain inside information regarding future
policies and regulations before their competitors do, such that bribing firms can “occupy a
superior position in the market or grasp some early-mover opportunities” (Luo, 2005: 131).
Since the benefits resulting from firm bribery are mainly applied within the home country
(Claessens et al., 2008; Khwaja and Mian, 2005), firms that use bribery to acquire resources in
the home country may be less likely to abandon these benefits. In contrast, firms that bribe less
42
have relatively fewer benefits to give up should they decide to tap into markets beyond their
home bases.
Bribing firms may accordingly be motivated to focus on the reduced risks and enhanced
competitive positions within their domestic market (Ito and Pucik, 1993). In general, foreign
firms are inherently in a disadvantageous position relative to local competitors when operating
within foreign countries as local knowledge is difficult to obtain (Eden and Miller, 2004; Zaheer,
1995). The more firms bribe in the home country, the more attractive the domestic market will be,
to the extent that those bribes facilitate firms’ domestic operations. Put differently, when
dependence gives firms benefits, stronger dependence would provide firms more advantages and
these firms will be motivated to take advantage of the benefits resulting from bribery rather than
look for opportunities elsewhere (Hoetker, Swaminathan & Mitchell, 2007; Lee, Moon & Park,
2015). We therefore argue:
Hypothesis 1: Bribery in the home country will reduce firms’ focus on foreign markets.
Our earlier hypothesis (H1) posits that bribery paid to home country government officials
provide firms with preferential treatments. The argument is that the benefits resulting from
bribery will increase firms’ focus on home country markets, thereby lowering their motivation to
seek opportunities overseas. We note that this hypothesis is a general prediction and its effect
may vary depending on certain contextual factors. To better understand the effect of home
country bribery, it is useful to examine these critical contingencies. In the present study, we
consider two factors as such: the distinction between new ventures and established firms as well
as the competition from informal firms in a home country.
43
Moderating Role of New Ventures
While both established and newer firms depend on the government for resources, new
ventures are inherently in a more disadvantageous position since they have comparatively less
bargaining power and leverage over the government than established firms. As Sproul, Carnes,
Marvel, and Pozzuto put, “new ventures possess comparatively little power and may be at the
government’ mercy relative to firm performance” (2014: 1). Power and resource dependence are
interrelated: the importance and scarcity of resources are critical determinants of firm
dependence relationships with external entities such as home country governments (Pfeffer and
Salancik, 1978). Since new ventures face greater resource constraints and lack legitimacy
(Aldrich and Fiol, 1994), the resources that they could gain from the government would be more
crucial and valuable, compared to the same amount of government resources earned by
established firms. This in turn increases new ventures’ dependence on their home country
government vis-à-vis established counterparts.
Especially in transition economies that lack adequate and legitimate market mechanisms
that support new ventures (Dubini, 1988), ventures may resort to use bribes to manage their
dependence on the government to create a more favorable environment (Pfeffer and Salancik,
1978). For example, some entrepreneurs in Russia reported that bribery expedites public utility
service such as telephone installation, and the opportunity to purchase equipment from state
enterprises (Webster and Charap, 1993). Some of the managers in venture firms also recognized
that bribery was needed to obtain leases, lower raw material prices, and lock in contracts, as well
as bank credit (De Melo, Ofer, and Sandler, 1995).
44
Jancsics’ (2013) interviews with Hungarian entrepreneurs also find that bribery is
necessary to acquire needed resources. One manager even commented that, “I would say that
small entrepreneurs are trained for it [petty corruption] because it is necessary for survival”
(Jancsics, 2013: 329). The importance of bribery is perhaps best illustrated in an entrepreneur’s
comment that, “I see bribe payment as business investment…like buying input materials” (Ufere
et al., 2012: 2445; emphasis added). These remarks suggest that bribery helps new ventures
manage their dependence on the government and obtain the resources necessary for their
domestic operations.
Yet, as new ventures started off with a higher level of government dependence compared
to their established counterparts, the consequences of bribery—a type of political action to
manage government dependence—would have a different effect on their intention to seek foreign
opportunities. For instance, Peng and Luo (2000) find that while managers’ political ties help
improve firm performance, this relationship is even stronger for firms with greater dependence
on the government.
For new ventures with a greater dependence on the government, the benefits from
managing the dependence on home country government through bribery may seem more
important to leverage for better performance and survival. These benefits are obviously most
useful for domestic operations, and in most cases, “for new ventures…the local environment is
noted to be the primary source of resources needed for operations” (Fernhaber, Bilbert, and
McDougall, 2008: 267, emphasis added). Home country government resources procured through
bribery are therefore particularly important for new ventures’ survival and success. As new
45
ventures may react more strongly to the home country bribery, we argue that the effect of home
country bribery will be more pronounced for new ventures than for established firms.
Hypothesis 2: The negative relationship between home country bribery and firm focus on
foreign markets will be stronger for new ventures.
Moderating Role of Competition from Informal Firms
Another condition that could change the effect of home country bribery is the competition
from informal firms in firms’ home bases (Capelleras, Mole, Greene, and Stroey, 2008).
Informal firms are business entities that are not registered but still operate and compete against
legally registered firms (Bruton et al., 2012; La Porta and Shleifer, 2014). In other words, these
firms do not officially exist in the government registry, but they actually operate in the
marketplace. These underground firms are relatively away from government officials’ discretion
(Castells and Portes, 1989) and have less need to rely on the government. Competition from the
informal economy often places significant pressures on legally-operating firms (Gokalp, Lee,
and Peng, 2017). Two-thirds of the 40,757 firms in the World Bank Enterprise Surveys from
2006 to 2011 considered informal competition as a major obstacle to their business (Friesen and
Wacker, 2013).
We accordingly argue that the effect of benefits gained via bribery within the domestic
market may be dissipated by informal competition. In terms of resource dependence, while firms
depend on the government for resources and thus to improve their positions in the domestic
market, if these resources cannot be well protected and lose their value, firms’ dependence on the
government will be poorly managed. A high level of informal competition in the home country is
a factor that may erode the benefits from managing government dependence through bribery.
46
As the competitive pressure from informal firms in the home country diminishes the
benefits of managing government dependence through bribery, firms’ dependence on the
government may no longer be worth managing (Pfeffer and Salancik, 1978). In this case, firms
may choose to decrease their dependence on the government and thereby escape its control,
which could be done by investing more in foreign markets (Cuervo-Cazurra, Inkpen, Musacchio,
and Ramaswamy, 2014). If the government fails to curb the threat of unregistered firms, firms
that bribe home country officials would be inclined to reduce their dependence on the
government, and start to look for opportunities abroad. We thus argue that the effect of domestic
bribery may be reduced as the competition from informal firms in the home country increases.
Hypothesis 3: The negative relationship between home country bribery and firm focus on
foreign markets will be weaker for firms operating in countries with stronger competition
from informal entities.
Moderating Role of New Ventures and Informal Competition
We have argued that bribery within the home country can have differential impacts on new
ventures versus established firms (Hypothesis 2), and that the level of informal competition
within a country could alter the effect of home country bribery (Hypothesis 3). These arguments
consider the factors shaping the effect of home country bribery separately; yet these two
contingencies could interact in shaping a firm’s market focus. We accordingly consider how new
venture status and informal competition jointly alter the effect of home country bribery on firm
foreign market focus.
Because new ventures are inherently more dependent on the government for resources as
these resources are more valuable for them (Pfeffer and Salancik, 1978), we suggest that that the
47
resulting benefits from managing their dependence via bribery would be more crucial to them.
This, in turn, will motivate ventures to focus more on the domestic market (Dreher and
Gassebner, 2013). On the other hand, we have also stated that when the benefits of managing
government dependence lose value due to strong informal competition in the home country,
firms rather endeavor to escape the burdensome dependence (Cuervo-Cazurra et al., 2014).
New ventures face reduced survival prospects and are particularly vulnerable (Baik et al.,
2013) and thus are characterized by a greater dependence on the government for resources than
established firms. When competition from informal firms erodes the benefits of managing the
dependence on the government and rather makes this dependence burdensome, new ventures
would be even more motivated to escape this greater dependence which no longer helps them.
Cuervo-Cazurra and colleagues (2014) document that in the case of state-owned
enterprises (SOEs), the government exerts more power on them in the dependence relationship
through politicians on the board of directors, which motivates SOEs to reduce this excessive
government influence through internationalization. Following this reasoning, we argue that in the
case of new ventures with greater dependence on the government, they will attempt to manage
this dependence as long as it brings benefits. However, once the dependence becomes less
effective, new ventures will have higher tendencies to escape from the home country than
established firms do, despite having bribed in their home countries.
Meanwhile, since new ventures are more sensitive to informal competition compared to
established firms, they may perceive that the benefits from managing government dependence
through bribery is even more diminished as informal competition becomes intensified. Relatively
young and small firms often consider informal pressures to be major challenges (Gonzaelez and
48
Lamanna, 2007). Many entrepreneurs in Albania, a former transition economy, responded that
unfair competition from non-registered enterprises was “the largest obstacle to their success”
(Bitzenis and Nito, 2005).
Consequently, with greater informal competition, new ventures will have a stronger
motivation to seek opportunities overseas for resources than established firms. More developed
foreign markets may provide a better regulated environment as well as having less informal
competition (Ghemawat, 2001). New ventures from transition economies would therefore be
motivated to seek opportunities in a more developed foreign market where they face reduced
transaction costs (Lee et al., 2009; Luo and Wang, 2012). This is in line with prior research
noting that firms occupying non-dominant market positions are apt to explore foreign markets to
avoid keen competition at home, particularly that from informal firms (Ito and Pucik, 1993;
Sakakibara and Porter, 2001). We therefore contend that under the condition of strong informal
competition in the home country, the interaction effect of home country bribery and new
ventures will be weakened.
Hypothesis 4: While the relationship between home country bribery and firm focus on
foreign markets is stronger for new ventures, this effect will be weaker if the new ventures
operate in countries with stronger competition from informal firms.
Methods
Data
We test our hypotheses using a sample of firms from the Bank Business Environment and
49
Enterprise Performance Survey (BEEPS).1 The BEEPS is a cross-country, large-scale project
jointly conducted by the European Bank for Reconstruction and Development (EBRD) and
World Bank. Its objective is to assess a country’s institutional environment from the perspective
of businesses. Toward this goal, its researchers designed standardized questionnaires and
collected data in 2002, 2005, 2009, and onwards. During each wave, the BEEPS covered more
than twenty countries, primarily those in Eastern Europe and Central Asia.
This survey is one of the most suitable databases for studying firm bribery for three reasons.
First, it includes detailed questions regarding bribery, asking managers to reflect the amount of
bribe paid to officials. Second, the survey instrument was developed and modified multiple times
before implementation, to be applied to different countries with reasonable consistency. Finally,
it uses the stratified sampling method in each country, which ensures that the surveyed firms in
different countries are properly chosen. Given these merits, the database has been used by prior
studies (Ismail, Ford, Wu, and Peng, 2013; Lee and Weng, 2013; Spencer and Gomez, 2011;
Wang and Cuervo‐Cazurra, 2016).
While the BEEPS is a survey in nature there are reasons to believe that it is not severely
affected by potential response bias and common method variance (Chang et al., 2010; Podsakoff
et al., 2003). First, the main variables used in our study did not strongly rely on perceptual
measures. We used questions that specifically asked for actual behaviors. For example, our
primary variables such as firm focus on foreign markets and bribery within the home country are
based on behaviors rather than perception. These questions are less cognitively demanding and
encourage respondents to reply consistently. Second, following Svensson (2003), we test the
1 These questionnaires are available at http://ebrd-beeps.com/data/.
50
non-response bias by examining the observable firm attributes for companies reporting bribery
against those that did not. The t-tests on firm size and age were not significant (both p > 0.1),
suggesting that the non-response bias is not a major concern.
We gather our sample by taking three steps. First, we extract a set of firms that are
repeatedly observed in 2002, 2005, and/or 2009. Firms that are observed only once in the
observation period were excluded. Second, we focus on transition economies, which are defined
as formerly communist countries that adopted the socialist system (Hoskisson, Eden, Lau, and
Wright, 2000). Third, we exclude observations with missing values in our theoretical variables.
Taking these steps, we have 1,320 firms, or 2,616 firm-year observations for analysis. The
Appendix provides an overview of our sample.
Dependent Variable
Firms’ Foreign Market Focus refers to the extent that a firm emphasizes on the foreign
versus its domestic markets. This variable is measured using the ratio of foreign sales over total
sales, which suggests the importance of foreign markets relative to the domestic market for the
firm. The indicator has been used by prior research (Geringer, Tallman and Olsen, 2000; Zhang,
Li, Hitt, and Cui, 2007). This variable ranges from 0 to 100, with higher values indicating greater
emphasis on foreign versus domestic market.
Independent and Moderating Variables
Firms’ Foreign Market Focus refers to the extent that a firm emphasizes foreign versus its
domestic markets. This variable is measured using the ratio of foreign sales over total sales,
which suggests the importance of foreign markets relative to the domestic market for the firm.
The indicator has been used by prior research (Geringer, Tallman and Olsen, 2000; Zhang, Li,
51
Hitt, and Cui, 2007). This variable ranges from 0 to 100, with higher values indicating greater
emphasis on foreign versus domestic market.
Independent and Moderating Variables
Home Country Bribery. Following Fisman and Svensson (2007), we identify a firm’s home
country bribery by using the ratio of the firm’s payment to home country government officials
scaled by its domestic sales. Information on the bribery amount is developed using the survey
question from the BEEPS questionnaire: “[o]n average, what percent of total annual sales do
firms like yours typically pay in unofficial payments/gifts to public officials?” Because the
BEEPS is intended to “gather information and opinions about the investment climate in this
country” (emphasis added), we believe that this item is mainly about domestic bribery rather
than foreign bribery.
New Venture. The literature distinguishes new ventures from established firms using firm
age (Fernhaber, Gilbert, and McDougall, 2008; McDougall, Oviatt, and Shrader, 2003; Zhang
and Li, 2010). Following McDougall et al. (2003), we used a binary variable to categorize firms
that are five years old or younger as new ventures, while firms above this threshold are noted as
established firms (1 = new ventures, 0 = established firms). According to the Small Business
Administration (1992), the first five years are a critical period during which survival is
determined for most firms.
Competition from Informal Firms in the Home Country. Although prior research has
suggested that competitive pressure can be measured by the Herfindahl-type index, such an
indicator is unavailable as the information on unregistered firms’ sales is unobtainable. In this
study, we measured competition from informal firms in the home country using the amount of
52
goods or services in the market concealed from public authority scaled by that nation’s GDP.
The rationale behind is that a certain proportion of a country’s production is from unregistered
firms, and that the greater this proportion, the more problematic informal competition would be.
This variable is obtained from Schneider et al. (2010). This variable is a continuous indicator
with higher values indicating stronger competitive forces from informal firms in a home country.
In our data, the lowest value for this variable is 17 (Slovakia), and the highest is 67 (Georgia).
Control Variables
Our model includes several control variables that account for correlates with firms’ foreign
market focus. First, at the firm level we control for firm size, R&D, advertising intensity, and
government contract. Large firms are likely to have greater foreign market focus, and companies
with stronger technological know-how and marketing resources have higher tendencies to
explore foreign markets. Firm Size is measured by the number of employees (logarithm). R&D
intensity and advertising intensity are measured as the ratio of R&D expenditures and marketing
expenditures over firm domestic sales. Government contract, on the other hand, is a binary
variable indicating whether firms have home country governments as their clients (1= yes and 0
if no).
Second, the decision to venture out into foreign markets can be influenced by shareholders.
In contrast with foreign invested firms that often focus more on foreign markets (Filatotchev et
al., 2008), companies owned by home country governments generally act otherwise (Estrin,
Meyer, Nielsen, 2016). Given this, we include foreign ownership and government ownership as
control variables, which measure the proportion of ownership held by foreign shareholders and
home country governments.
53
In addition, since resources and assets are likely to be distributed unequally among firms,
firms that are efficient in managing their operations are likely to seek foreign markets. In light of
this, our model included capacity utilization as a control, measured by firms’ output over the
maximum of possible output found in the survey. In a similar vein, we control for new product
development, a dichotomous variable indicating whether firms had developed a new product
within the past three years (Golovko and Valentini, 2014).
Third, as home country contexts have a profound impact on firm bribery (Fisman and
Miguel, 2007; Martin et al., 2007), firms’ motivation to explore foreign markets can be affected
by the development of their home countries. In light of this, our models include GDP (logarithm)
and EU, a binary variable that was coded as 1 if a nation had a European Union membership at
the time of the survey year, and 0 otherwise. Finally, our models include a battery of year and
industry dummies. We create two dummies to denote years 2005 and 2009 to capture any
periodic effect in comparison to year 2002, which is the base year. Industry information, on the
other hand, was based on a question asking respondents to indicate from which sector firms
generated the most revenues: textiles, garments, chemicals, plastics and rubber, basic metals, etc.
The baseline category is “other manufacturing.”
Analytic Approaches
We have three issues to consider for the analytic approaches. First, as our data have firms
appearing multiple times, it is crucial to determine whether random or fixed effects models
should be used. The Hausman test is useful in examining the correlation between unobserved
individual effects and observed predictors (Greene, 2008: 200-210). As the Hausman (1978) test
is significant (χ2 = 154.67, p < 0.001), the null hypothesis that individual effects are uncorrelated
54
with the regressors is rejected, suggesting that the fixed effect model would be suitable. The use
of firm-fixed effects means that the reported models explain within-firm variation in the market
focus rather than inter-firm variation in the market focus. We used Stata’s “xtreg, fe” command
for the estimation.
Second, the measure of home country bribery deserves our attention. An assumption of the
OLS model is that independent variables are exogenous. As bribery is likely an endogenous
variable (Martin et al., 2007; Svensson, 2003), models without considering the endogeneity
problem may generate biased estimates. One way to alleviate the endogeneity issue is to use the
instrument variable (IV) approach (Greene, 2008; Wooldridge, 2002). A good instrument is
expected to be strongly correlated with the independent variable but weakly correlated with the
dependent variable. In the present study, we instrumented a focal firm’s bribery using the
average bribery level of other firms from an industry located within the same city. Firms’
locations were identified using an item in BEEPS which categorized all firms’ locations into 134
unique areas in 26 countries.
This instrument was considered as other firms’ bribery activities may be correlated with a
focal firm’s bribery but not necessarily affect the focal firm’s focus on foreign markets. We
obtained this instrument by first identifying a firm’s location using an item in BEEPS. We then
calculated the average bribery level for other firms within the same industry while excluding a
focal firm’s own bribery level. The average bribery level of other firms is strongly correlated
with a focal firm’s own bribery (r = 0.17, p < 0.001) but weakly correlated with foreign market
focus (r = –0.06, p < 0.05), suggesting that the instrument is suitable.
55
In our models, all the independent and control variables are lagged. Since our dataset
included multiple observations for the same firm, we used the Huber-White-sandwich estimator
that generates robust variance estimates and robust standard errors.
Results
Main Findings
Table 2.1 summarizes the descriptive statistics and correlations of our variables. As shown,
our sample firms have a relatively low focus on foreign markets (Mean = 10.63), and show a
reasonable variation (S.D. = 25). On average, these firms paid 0.83 percent of their sales to home
country government officials. Multi-collinearity is not a major concern since the highest VIF
value is 3.23, well below the recommended threshold of 10.
Table 2.2 summarizes the results of regression models. Model 1 includes the control
variables, Model 2 tests the effect of home country bribery, and Model 3 adds the interaction of
bribery in the home country and new venture. Models 4 tests the moderating effect of informal
competition, and Model 5 includes the three-way interaction term. All models are significant (p <
0.001), suggesting that the independent variables have high explanatory power.
Hypothesis 1 argues that firms that pay bribes in their home countries will focus less on
foreign markets. In Model 2, the coefficient of bribery in the home country is negative (β =-1.65,
p < 0.05). It means that holding all other factors as equal, a standard deviation increase of home
country bribery will reduce firm focus on foreign markets by 4.43 percent. As the average firm’s
foreign market focus in our data is not high, the effect is economically relevant. This finding
provides strong support to Hypothesis 1.
56
Table 2.1. Descriptive Statistics and Correlations Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13
Firms’ Foreign Market Focus 10.63 25.11 —
Home Country Bribery 0.04 2.68 -0.05 —
New Venture 0.19 0.39 -0.08 0.07 —
Competition from Informal Firms 38.00 11.00 -0.11 0.02 0.18 —
Firm Size 3.16 1.62 0.27 -0.09 -0.16 0.01 —
R&D Intensity 1.68 11.19 -0.04 0.05 0.09 0.09 -0.08 —
Advertising Intensity 3.48 31.87 -0.04 0.01 0.07 0.05 -0.07 0.56 —
Capacity Utilization 79.98 18.63 0.01 -0.08 0.05 -0.07 -0.01 0.00 -0.02 —
Government Contract 0.22 0.41 -0.02 0.32 0.02 0.01 0.03 0.04 -0.01 -0.06 —
Government Ownership 8.50 26.38 -0.01 -0.09 -0.07 0.03 0.29 0.00 0.00 0.00 -0.06 —
Foreign Ownership 0.09 0.29 0.14 -0.01 0.05 0.09 0.13 0.09 0.08 0.03 -0.01 -0.04 —
New Product Development 0.65 0.48 0.13 0.07 -0.04 0.02 0.19 0.02 0.02 -0.04 0.09 -0.05 0.05 —
GDP 23.99 1.41 -0.01 -0.01 -0.12 -0.35 0.00 -0.04 -0.01 0.06 0.00 -0.02 0.05 0.02 —
EU 0.20 0.40 0.06 -0.02 -0.12 -0.47 -0.06 -0.06 -0.03 0.05 -0.03 -0.06 -0.01 0.00 0.32
Note: N = 2616; correlations greater than 0.05 are significant at the 0.05 level; correlations greater than 0.07 are significant at 0.01 level.
57
Table 2.2. Results of Fixed-effect Regression Modelsa
Independent Variables Model 1 Model 2 Model 3 Model 4 Model 5
Industry Dummies Incl. Incl. Incl. Incl. Incl.
Year Dummies Incl. Incl. Incl. Incl. Incl.
Firm Size 6.52† 4.34 4.78* 5.74* 5.43*
(3.56) (2.70) (2.24) (2.42) (2.10)
R&D Intensity -0.23 -0.25 -0.14 -0.24 -0.17
(0.27) (0.20) (0.17) (0.18) (0.15)
Advertising Intensity -0.02 0.17 0.15 0.14 0.15
(0.32) (0.24) (0.20) (0.21) (0.18)
Capacity Utilization 0.04 0.04 -0.04 0.09 -0.00
(0.11) (0.08) (0.07) (0.07) (0.07)
Government Contract 1.61 5.07 5.65+ 5.61 5.54+
(4.91) (3.93) (3.27) (3.49) (3.03)
Government Ownership -0.37 -0.77* -0.65* -0.66* -0.51†
(0.29) (0.34) (0.29) (0.31) (0.27)
Foreign Ownership 7.77 8.46 3.59 11.97* 7.05
(7.92) (5.87) (4.97) (5.29) (4.75)
New Product Development -3.64 -3.25 -1.73 -2.11 -2.53
(5.20) (3.91) (3.26) (3.48) (3.08)
GDP -15.04 14.47 12.23 7.23 2.10
(22.66) (17.96) (14.93) (16.06) (14.22)
EU -2.21 -0.94 -0.85 0.18 -1.11
(5.48) (4.24) (3.52) (3.78) (3.30)
New Venture 0.69 0.26 -0.11 0.47 -1.34
(5.21) (4.17) (3.46) (3.70) (3.42)
Competition from Informal Firms -3.12 5.52 4.76 1.53 -0.14
(7.19) (5.75) (4.78) (5.21) (4.67)
Home Country Bribery (H1, –) -1.65* -1.54** -3.55*** -2.73***
(0.63) (0.53) (0.74) (0.69)
Bribery x New Venture (H2, –) -5.42*** -4.65***
(1.08) (1.14)
Bribery x Competition from Informal Firms (H3, +) 0.15*** 0.10**
(0.04) (0.03)
New Venture x Competition from Informal Firms 0.39†
(0.20)
Bribery x New Venture x Competition from Informal Firms (H4, –) 0.09
(0.08)
Intercept 4.72 -5.11 -4.69 -2.97 -6.95
(8.56) (6.96) (5.59) (5.61) (5.44)
R2 0.40 0.47 0.64 0.59 0.45
Model F 1.78 ** 3.38 ** 1.78 ** 2.72 ** 3.96 **
† p < 0.10;* p < 0.05; ** p < 0.01; *** p < 0.001.
a Robust standard errors clustered at the firm level are shown in the parentheses.
Hypothesis 2 contends that the effect of home country bribery will be stronger for new
ventures than for established firms. According to Model 3, the interaction between bribery and
new ventures is negative and significant (β = -5.42, p < 0.001). The economic significance of this
variable can be examined by adjusting the values of home country bribery and new ventures. A
58
standard deviation increase of home country bribery from the mean would lower an established
firm’s focus on foreign markets by 4.11 percent. In contrast, for new ventures, a standard
deviation increase in home country bribery will reduce foreign market focus by 18.50 percent.
As the change of the foreign market focus is fairly large (14.39 percent), Hypothesis 2 is
supported.
Hypothesis 3 argues that the negative effect of home country bribery will be reduced when
firms face greater competition from the informal sector within their home countries. A positive
interaction between home country bribery and competition from informal firms would support
this hypothesis. In Model 4 we find a positive interaction between bribery within the home
country and competition from informal firms (β = 0.15, p < 0.01). The effect of this estimate is
also economically relevant. If we set informal competition at the mean level, then a standard
deviation in home country bribery will decrease firm focus on foreign markets by 9.58 percent.
However, if we move informal competition to a one standard deviation above the mean, the same
amount of bribery would lower firm focus on foreign markets only by 4.88 percent. As the
change of foreign market focus is non-trivial (4.7 percent) in our context, the finding provides
support for Hypothesis 3.
Hypothesis 4 proposes that the interaction effect between bribery and new ventures will be
reduced in countries where informal competition is intense. This hypothesis predicts a positive
three-way interaction of between country bribery, new ventures, and informal competition that
affects firm focus on foreign markets. The result is shown in Model 5. While the sign of the
interaction variable is positive as expected, it is non-significant (p < 0.1), thus Hypothesis 4 is
not supported.
59
To gain additional insight, we graphed the interaction effects in Figure 2.1. In creating
Figure 2.1 we set the low (high) value as one standard deviation below (above) the mean and
calculated the predicted values for the outcome variables. The plots shown in Figure 2.1 are
consistent with our hypotheses. Plot (a) shows that although bribery in the home country
generally reduces firms’ focus on foreign markets, the impact of bribery is greater for new
ventures than for established firms, which supports Hypothesis 2. Similarly, Plot (b) indicates
that while the relationship between home country bribery and foreign market focus are both
negative for firms facing lower and higher informal competition, the slope of the firms operating
in countries with higher informal competition is flatter than the slope than those firms operating
in countries with lower informal competition. Such a finding is consistent with the idea of
Hypothesis 3.
Robustness Checks
Aside from the main findings, we also perform several additional analyses to ensure that our
results are robust. (In the interest of space, only part of the robustness checks is shown here but
the full results are available upon request). First, we consider several additional country-level
variables that may be relevant given our research topic including (1) Government corruption
(Kaufmann et al., 2009) (2) rule of law (Kaufmann et al., 2009), (3) political constraint (Henisz,
2000), and (4) incidence of national leader change. Government corruption was measured by
control of corruption index; the original values range from -2.5 to 2.5, with higher values
indicating high control of corruption. To facilitate interpretation, we reverse-code it such that
higher values indicate that the government corruption issue is more prevalent. The variable of
rule of law is from Kaufmann et al. (2009), and the information of political constraint index is
60
(a) Interaction of Home Country Bribery and New Ventures
(b) Interaction of Home Country Bribery and Competition from Informal Firms
Figure 2.1. Interaction Plots
0
5
10
15
20
25
30
Low Bribery in the Home Country High Bribery in the Home Country
Pre
dic
ted
Fo
reig
n M
arket
Fo
cus
Established Firm New Venture
0
10
20
30
40
50
60
70
80
Low Bribery in the Home Country High Bribery in the Home Country
Pre
dic
ted
Fo
reig
n M
arket
Fo
cus
Low Informal Competition High Informal Competition
61
from Henisz (2000). We create a dummy variable National leader change to indicate whether a
nation’s leader changed in each year. We add these variables separately into our models, and
perform estimations. As shown in Table 2.3, the results with these additional controls are
consistent with our main findings.
Second, although our measure of bribery provides useful information regarding a firm’s
home country bribery, a potential limitation is that it may include both passive bribery (e.g.,
bribes extorted by home country government officials) and active bribery (e.g., bribes used
proactively to seek preferential treatments). We therefore endeavor to differentiate active bribery
from the passive one. To do so, we first regress the observed bribery amount on government
corruption, GDP, firm size, research and development (R&D) intensity, advertising intensity,
government contract, new ventures, informal competition, and other control variables. Using the
estimate results, we calculate the predicted home country bribery.
Table 2.3. Robustness Checksa
Independent Variables
Model 6
Adding Government
corruption
Model 7
Adding Rule
of law
Model 8
Adding
Political
constraint
Model 9
Adding
National
leader change
Model 10
Alternative
measure of
bribery
Industry Dummies Incl. Incl. Incl. Incl. Incl.
Year Dummies Incl. Incl. Incl. Incl. Incl.
Firm Size 5.42* 5.73** 5.56* 5.48* 4.68*
(2.15) (2.10) (2.10) (2.12) (1.96)
R&D Intensity -0.17 -0.16 -0.14 -0.18 -0.28+ (0.15) (0.15) (0.15) (0.15) (0.14)
Advertising Intensity 0.15 0.18 0.14 0.17 0.19
(0.18) (0.18) (0.18) (0.18) (0.17)
Capacity Utilization 0.00 0.01 -0.02 0.00 0.03
(0.07) (0.07) (0.07) (0.07) (0.06)
Government Contract 5.56+ 4.98 5.12+ 6.14+ 5.15+ (3.13) (3.03) (3.03) (3.21) (2.84)
Government Ownership -0.51+ -0.42 -0.54+ -0.48+ -0.53*
(0.27) (0.28) (0.27) (0.27) (0.25)
Foreign Ownership 7.05 6.97 6.32 7.04 6.59
(4.80) (4.71) (4.76) (4.78) (4.59)
New Product Development -2.52 -2.80 -1.38 -2.20 -3.23 (3.12) (3.06) (3.20) (3.14) (2.94)
GDP 2.20 -1.71 3.72 0.76 0.00
(14.67) (14.39) (14.20) (14.48) (19.13)
EU -1.12 0.02 -0.83 -0.46 -0.87
(3.35) (3.38) (3.29) (3.49) (3.07)
New Venture -1.35 -0.97 -0.95 -1.51 -1.97
62
(3.45) (3.40) (3.41) (3.45) (3.16)
Competition from Informal Firms -0.08 -2.25 0.80 -0.19 0.23 (5.10) (4.89) (4.71) (4.70) (4.41)
Government Corruption -0.21
(6.24)
Rule of Law 10.27
(7.67)
Political Constraint 49.63 (39.84)
National Leader Change 1.76
(2.92)
Home Country Bribery (H1, –) -2.73*** -2.82*** -2.63*** -2.70*** -3.05***
(0.71) (0.69) (0.69) (0.69) (0.62)
Bribery x New Venture (H2, –) -4.64*** -5.01*** -4.83*** -4.63*** -4.70*** (1.15) (1.16) (1.14) (1.14) (1.02)
Bribery x Competition from Informal Firms
(H3, +)
0.10** 0.10** 0.09* 0.10** 0.10**
(0.03) (0.03) (0.03) (0.03) (0.03)
New Venture x Competition from Informal
Firms
0.39+ 0.28 0.40+ 0.38+ 0.33
(0.22) (0.22) (0.20) (0.21) (0.20)
Bribery x New Venture x Competition from
Informal Firms (H4, –)
0.09 0.09 0.08 0.09 0.05
(0.08) (0.08) (0.08) (0.08) (0.08)
Intercept -7.07 5.62 -1.86 -2.97 -3.09
(5.42) (5.62) (5.13) (5.61) (5.11)
R2 0.70 0.72 0.64 0.71 0.75
Model F 1.78** 3.38** 1.78** 2.72** 4.77**
† p < 0.10;* p < 0.05; ** p < 0.01; *** p < 0.001.
a Robust standard errors clustered at the firm level are shown in the parentheses.
Accordingly, an alternative home country bribery measure can be developed by subtracting
the predicted bribery amount from the observed bribery amount. For instance, if one firm X had
the predicted home country bribery of 2.2% and its reported bribery amount is 3%, then the firm’
home country bribery can be coded as 0.8% (3-2.2 = 0.8). Alternatively, if another firm Y had
the predicted home country bribery of 1.4%, while the reported bribery amount is 0.9%, the
home country bribery of this firm would be documented as -0.5% (1.4-0.9 = -0.5). As can be
reasoned, negative values suggest firms paid less bribes than the predicted levels; these are
passive bribers. Alternatively, positive values indicate that firms paid bribes more than the
predicted amounts and thus are active bribers. Using this alternative bribery variable, we
performed additional analyses. The estimations based on this alternative measure of briery are
consistent with our main results, as shown in Model 10.
63
Third, we considered an alternative measure of informal competition in the home country.
Although measuring the strength of informal competition is “inherently difficult” (La Porta &
Shleifer, 2008: 280), we strove to capture the level of competition from informal firms in a
particular industry by following two steps. First, we sorted out an item in BEEPS that asked
managers to indicate the challenges posed by informal firms: “To what extent the practices of
competitors in the informal sectors create obstacle to this establishment?” This item used a 0-4
scale, with 0 being “no obstacle” while “four being very severe obstacle.” Second, we averaged
the values by firms within an industry in a home country to measure the competition from
informal firms for each industry. To ease interpretation, we re-scaled this variable to 1-5, with
higher values indicating more obstacles created by informal firms in an industry. Results with
these industry averaged values for informal competition were very consistent with our main
findings, and these results can be provided upon request.
Finally, we also examined the issue of reverse causality. While our findings show that
home country bribery decreases firms’ interest in seeking opportunities in foreign countries, one
might argue that an opposite effect could be possible. That is, firms may first determine their
market focus and then decide on how much to bribe in the home country. In our view, this
conjecture is unlikely for two reasons. First, the analyses shown earlier involve a temporal
order—home country bribery was measured at one period before (year t-1) firm foreign market
focus (year t). Second, to further ensure that home country bribery influences foreign market
focus and not the other way around, we perform regressions with home country bribery as the
dependent variable while foreign market focus as the predictor, along with other control
variables mentioned earlier. The results are shown in Table 2.4. As the effect of foreign market
64
focus on home country bribery is not significant (β = 0.01, p = 0.37), reverse causality is not a
major concern for our finding.
Table 2.4. Reverse Causality
Independent variables Model 11
DV: Home country bribery
Model 12
DV: Home country bribery
Industry Dummies Incl. Incl.
Firm size -0.04
(0.02)
-0.05†
(0.03)
R&D intensity 0.02
(0.02)
0.02
(0.02)
Advertising intensity -0.00
(0.01)
-0.00
(0.00)
Government contract 0.46**
(0.13)
0.45**
(0.13)
Foreign ownership -0.01**
(0.00)
-0.01**
(0.00)
Government ownership -0.01**
(0.00)
-0.01**
(0.00)
Capacity utilization -0.00
(0.02)
-0.00
(0.02)
GDP -0.04†
(0.02)
-0.04†
(0.02)
Government corruption 0.26**
(0.08)
0.27**
(0.08)
New venture 0.04
(0.01)
0.04
(0.10)
Competition from informal firms 0.01
(0.01)
0.01
(0.01)
Foreign market focus — 0.01
(0.01)
Intercept -0.10
(0.62)
-0.14
(0.63)
χ2 211.56** 212.19**
R2 0.094 0.094
Number of observations 2616 2616
Number of firms 1320 1320
Note: Robust standard errors clustering at the firm level in parentheses. Mundlak instruments (country average of all
firm-level predictors) and year dummies are included but not shown to save space.
† p < 0.10; * p < 0.05; ** p < 0.01.; *** p < 0.001. Two-tailed tests.
65
Discussion
The purpose of this study is to examine the effect of home country bribery on firms’
foreign market focus. Researchers have begun to investigate the determinants of firm bribery, a
growing topic in the management literature (Cuervo-Cazurra, 2006; Luo, 2005; Spencer and
Gomez, 2011). Despite the rich and useful findings, a less explored question is: what are the
strategic implications of bribery? In addition, while many studies focus on firms’ bribery
activities in host countries, we look at firms’ bribery in their home country and how this affects
their foreign market strategies. In doing so, we join the literature on home country institutions
and the research on firm global strategies (Cuervo‐Cazurra, 2011, Tan and Chintakananda, 2016).
In this study, we utilize the resource dependence theory (Hillman et al., 2009; Pfeffer and
Salancik, 1978) and the literature on corruption (Cuervo-Cazurra, 2006; Martin et al., 2007) to
study the consequences of firm bribery. We contend that the more firms bribe to manage their
dependence on the government and gain resources, the more their domestic markets appear
attractive, and the less motivation they will have in exploring foreign markets. Building on this
baseline argument, we also examine the conditions that may alter the effect of home country
bribery, depending on whether firms are new ventures or established firms and the level of
informal competition in the home country. We tested these arguments using a multi-country
sample, and the results provide broad support to our arguments.
Theoretical Implications
This study contributes to the IB literature in two major ways. First, our study adds to the
literature on government corruption by investigating the implications of home country bribery.
We contend that bribery has strategic implications for firms via the benefits and resources it
66
provides. This finding resonates with the comment that “the political environment is one
important means by which the organization links itself into the social system” (Pfeffer and
Salancik, 1978: 190). Bribery in the home country allows firms to manage their government
dependencies and acquire preferential treatments. This, in turn, would induce bribing firms to
focus more on the domestic market where obtained government resources and favorable
treatments can be harnessed, therefore decreasing their interest in foreign markets.
The second contribution is that our study examines the contingencies that shape the effect
of home country bribery. While both established firms and new ventures have incentives to
approach officials to achieve their strategic goals, the impact of home country bribery is not the
same for them: new ventures are generally more dependent on government resources than their
established counterparts, and thus the effect of home country bribery is more pronounced for
new ventures. Managing the dependence via bribery accordingly creates a more favorable home
country environment for new ventures. Due to these benefits—which is often the primary market
for new ventures—new ventures’ tendencies to focus on foreign markets diminish considerably
as they pay bribes to home country government officials.
The other contingent factor is the competition from informal firms in the home country
(Bruton et al., 2012; La Porta and Shleifer, 2008). These unregistered businesses can move
quickly without being regulated and pose challenges to the domestic operations of registered
firms. With much informal competition, the benefits of managing government dependence
through bribery reduces. In such a case where the dependence on the government becomes
burdensome without much benefits, firms may seek to escape home countries by exploring
foreign markets (Cuervo-Cazurra et al., 2014). This reasoning suggests that while bribery can
67
enhance a firm’s focus on its domestic market, the effect decreases when firms are challenged by
intense competition from the informal entities in the home country. By considering these
contingencies both theoretically and empirically, our study wishes to provide a more nuanced
picture regarding how firms’ bribery in their home country affects foreign market focus.
Practical Implications
Our findings have several implications for practitioners. First, managers should be aware
that bribery has consequences for their global strategies. Given that the benefits from
government resources are country-specific, many bribing firms may find their domestic markets
more attractive and thus overlook opportunities in foreign markets. Consequently, practitioners
should consider their home country activities (especially bribery) in their evaluation of foreign
market opportunities. Firms that use money to influence home country government officials are
advised to have a more holistic view in evaluating foreign market opportunities. Despite the
potential challenges, new markets in other nations may have the unique resources and
endowments that home countries do not readily offer (Ghemawat, 2001). Missing these
opportunities may not be in the best interest of firms.
Second, managers in new ventures should be particularly mindful of the implications of
bribery. We find that relative to established firms, new ventures generally have greater
dependences on the government, and that such reliance will intensify the effect of home country
bribery. Due to their newness and resource constraints, ventures are more challenged to compete
for public resources against established counterparts. Using money to influence government
officials thus is one method to support new ventures’ domestic operations. Aside from this
method, entrepreneurial firms could also consider strategically allocating their resources and
68
efforts on nascent industries or to operate in sectors with fewer dominant players, or where
governments provide more generous support. By choosing their domains strategically, new
ventures will have more chances to secure public resources and improve their survival prospects
in the home countries.
Limitations and Future Directions
Our paper has several limitations that provide opportunities for future research. First,
although our study focuses on firms’ dependence on governments, we did not directly measure
firms’ dependence on governments, but utilized proxies to capture the effect. To gain additional
insights, we encourage researchers to adopt other methods such as qualitative interviews or more
sophisticated questionnaires to capture this effect. Second, our theoretical account suggests that
bribery is one way for firms to manage resource dependence. In addition to this approach, firms
can also cultivate interpersonal relationships or develop political connections with home country
governments (Pfeffer and Salancik, 1978). As extensions, researchers can develop frameworks to
study these alternative methods.
Third, our research setting is transition economies. Aside from countries included in this
study, future studies can examine other emerging economies (such as India or China). Despite
the market potential and resources, the institutions in these countries are not fully developed yet
and government corruption issues may exist. Scholars therefore can design research to examine
how the domestic activities may influence firm global strategies. Fourth, while BEEPS provides
rich and detailed information regarding firms’ bribery activities in the home country, information
regarding the host countries which these firms explore is largely unavailable. It would be
69
interesting to know whether the effect of home country bribery influence critical decisions in
global strategies such as location choices and entry modes.
Conclusion
This study examines how home country bribery may influence firms’ foreign market
focus. We contend that domestic bribery allows firms to better manage their dependence on the
government and receive favorable treatments, which makes their domestic environments more
attractive. Consequently, home country bribery can facilitate domestic operations and in turn
change firms’ market focus. Building on this premise, we also argue that the effect of home
country bribery may be altered depending on two crucial conditions, including whether firms are
new ventures or established companies and the competition from informal firms in the home
country. Amassing a sample of firms in multiple transition economies, we find that new ventures
have higher tendencies to focus on their domestic markets when they gain benefits from bribery.
Moreover, competition from informal firms in the home country prompts firms to seek
opportunities abroad despite the resources and favorable treatments acquired from bribery. These
findings collectively suggest that firms’ decisions to explore foreign markets are closely related
to their home country activities and domestic contexts. In closing, we hope these arguments and
findings can simulate more research examining home country activities and their strategic
implications for firms.
70
APPENDIX A
Table A.1. Sample Firms, Home Countries, and Bribery Activities
Country Number of Firms Number of
Observations
Percentage of
Firms Reporting
Bribery
Average Amount
of Bribery
(Percentage in Firm
Sales)
Albania 37 55 63 2.01
Armenia 77 95 21 0.82
Azerbaijan 4 12 39 2.02
Belarus 26 76 57 1.14
Bosnia 14 43 14 0.24
Bulgaria 73 134 33 0.87
Croatia 62 71 23 0.58
Czech Republic 29 35 32 0.53
Estonia 59 87 23 0.18
Georgia 64 102 60 1.07
Hungary 35 80 46 0.71
Kazakhstan 60 93 43 0.95
Kyrgyz 46 89 53 1.79
Latvia 55 87 31 0.58
Lithuania 45 72 38 0.43
Moldova 50 86 39 0.96
Macedonia 39 104 25 0.37
Poland 87 101 25 0.75
Romania 63 109 38 1.05
Russia 35 65 54 0.77
Serbia 41 100 36 0.62
Slovakia 25 41 34 0.59
Slovenia 75 98 12 0.26
Tajikistan 36 56 70 1.41
Ukraine 113 181 67 1.90
Uzbekistan 46 92 68 1.33
Total 1,296 2,164
71
CHAPTER 3
DIVERGENT LEARNING EFFECTS FROM ALLIANCE EXPERIENCE AND CROSS-
BORDER M&A ENTRIES
Abstract
Drawing on the learning perspective, we argue that prior alliance experience may have divergent
learning effects on subsequent merger and acquisition (M&A) entries into a host country. While
alliances as strategic choice leads firms to focus only on learning about the particularities of
managing alliances, alliances chosen as an institutionally embedded entry mode where
regulations do not allow foreign MNCs to engage in M&As may prompt MNCs to stretch
themselves and learn beyond what the alliance relationship offers. Thus, alliance experience
gained where M&As are not allowed may help MNCs in their subsequent M&As while that
where M&As are allowed may hamper future M&As. Analyses based on a sample of 1,561
MNCs from 40 different home countries entering China through M&As over an 11-year period
(2003–2013) provide considerable support to our predictions.
Introduction
Several studies have shown mixed findings on the effect of international alliance
experiences on subsequent cross-border M&A entries. On the one hand, some studies show that
multinational corporations (MNCs) continue to enter in the alliance mode that they initially
chose for subsequent alliance entries (Chang and Rosenzweig, 2001). On the other hand,
however, others find that prior alliance entries are positively associated with the acquisitive
mode in later entries (Brouthers, Brouthers, and Werner, 2008; Guillén, 2003; Li et al., 2007;
72
Meyer and Tran, 2006; Nadolska and Barkema, 2007; Xia et al., 2009), especially in emerging or
transition market economies.
Then the question would be: how does the effect of learning from alliance experience
differ on choosing subsequent M&A entries? One possible explanation for the difference is
contingent upon the institutional conditions that MNCs may face behind the choice of the
alliance entry mode. For instance, learning can be different when alliances are made because the
M&A mode of entry is restricted in a host country, compared to when they are deliberately
chosen in an environment with free entry mode choices. In the former case, we name those
alliances influenced by the institutional context as “institutionally embedded choices”, and the
latter as “strategic choices” (Child and Rodrigues, 2005). Prior research is silent on how the
effects of alliance experience vary in these two different cases.
To approach the aforementioned question, we rely on literatures on organizational learning and
international business and explore the boundaries of learning from the alliance entry mode. We
focus on the role of alliance experience and identify different regulatory conditions in which this
experience may lead to or inhibit subsequent entry via M&As. Previous studies have revealed
that prior foreign market entries in a particular mode fuel future entries of the same type (Chang,
1995; Delios and Henisz, 2003; Erramilli, 1991) and locks MNCs down in rigid routines (Levitt
and March, 1988). They suggest that as experience generates value when applied to a similar
setting (Argote, Beckman, and Epple, 1990), not in different settings, knowledge obtained from
managing alliances would be useless or misapplied when making acquisitions later (Zollo et al.,
2002).
73
However, what happens if an MNC is forced to choose the alliance entry mode although
it prefers an M&A? In our research context for example, because the legal basis for the foreign
takeover of local firms was not established prior to 2003 in China (Xia, Tan, and Tan, 2008),
most MNCs invested in alliances although many preferred M&As. In this case, since their
original aim is M&A, MNCs have the intent to learn from their alliance whatever is necessary
for future M&As, and leverage the learning to subsequent M&A initiatives. The foreign partner
can choose how much to focus on gaining local knowledge from alliances (Inkpen and Beamish,
1997): while usually MNCs focus less on learning about the local environment from an alliance
relationship (Jiang and MacKenzie, 2011), those MNCs that were forced to choose the alliance
entry mode due to the regulatory constraints may strive to learn about the host country for future
M&A endeavors.
On the other hand, under an environment where there are opportunities to opt for cross-
border M&As, alliances made in this period are strategic choices for reasons such as risk sharing
and cooperation. As a result, these alliances would be helpful in future alliance endeavors
(Meyer, Estrin, Bhaumik, and Peng, 2009), but very likely harmful to other entry modes such as
M&A, as MNCs suffer from learning penalties (Perkins, 2014; Zeng et al., 2013) and core
rigidity (Leonard-Barton, 1992) from experience in a different entry mode. Thus, the regulatory
environment plays a big part on how alliance experience affects subsequent entries via M&As,
and the learning occurred in an environment with limited entry mode choices is qualitatively
different from that with free choice.
As such, we view that while generally learning takes place within the boundaries of a
given entry mode, MNCs may learn beyond a specific entry mode that they take on if they
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possess certain intentions to leverage the learning on other, new entry modes. Although
institutional constraints may restrict MNCs to a certain type of entry mode, these constraints
cannot discourage MNCs from learning beyond that specific entry mode, if MNCs possess a
specific goal for learning. At the same time, however, when all entry mode options are available,
given that there is no restriction, the need to go above and beyond what the institutions allow
diminishes.
We also explore how the economic distance (Tsang and Yip, 2007) changes the effect of
learning and posit that it strengthens the positive relationship between prior experience in
alliance made as institutionally embedded choice and subsequent M&A entry. The reason is that
experience in an economically distant environment makes the local knowledge more valuable for
the acquiring firm. Because firms that intentionally learned about the local environment for
potential future M&As from alliances will find that this learning is more important for M&As in
the presence of a high economic distance, they will probably heed more attention to learning for
future M&As. On the other hand, MNCs that have made alliances as a strategic choice would be
focusing in their alliance routines and thus feel less need to gain local knowledge.
We examine these ideas in the context of China. No legal basis for M&A deals was
available before 2003 in China (Huang and Kao, 2006). A crucial turning point was in 2003
when the Chinese government introduced a number of laws and guidelines intended to stimulate
foreign M&A activities, reflecting the change in foreign direct investment (FDI) regulations
(Huang and Kao, 2006). This contrast in the regulatory environment allows us to test the
different effect of alliance experience on subsequent M&As.
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Theoretical Background
The organizational learning literature (Cyert and March, 1963; Levitt and March, 1988; Nelson
and Winter, 1982) attests to the role of experience and learning in conducting FDIs (Johanson
and Vahlne, 1977). Based on organization learning theory, many studies find that prior
experience in a host country encourages MNCs to undertake subsequent entries in that same
country. For example, Chang (1995) finds that MNCs are more likely to sequentially invest in
markets where their capabilities and prior experiences are advantaged. Moreover, repeat entrants
into the same country were found to fail less than their first-time counterparts, as they learn
through the knowledge spillover effects of prior experience (Li, 1995; Shaver, Mitchell, and
Yeung, 1997).
On top of this, the concept of experiential learning suggests a within-form entry effect,
manifested in such a way that firms retain the same entry mode, whether hierarchical (e.g., M&A)
or relational (e.g., alliance), when making subsequent entries (Xia et al., 2009). While the effect
of M&A experience for acquiring firms has been amply studied in the literature, in this paper, we
consider past alliance experience, an alternative organizational form to M&A.
Experiential Learning and Alliance Experience
We conceptualize an MNC’s alliance experience as the sequential combination of entries
in the alliance mode into the same host country, which gives the chance for the MNC to create
unique knowledge patterns acquired within this given entry mode (Perkins, 2014). From the
concept of experiential learning, experience generates repetitive momentum (Amburgey and
Miner, 1992; Beckman and Haunschild, 2002), which makes firms to retain the same alliance
entry mode over other forms for subsequent entry attempts (Meyer et al., 2009; Xia et al., 2009).
76
This is because in general, prior alliance experience generates knowledge and
understanding on that particular entry mode (Lyles, 1987, 1988), through which MNCs can
leverage when reinvesting in new projects in the same entry mode. Experience generates value
only when applied to a similar setting (Argote, Bechman &Epple, 1990). Prior experience of
operating joint undertakings with a foreign partner would prepare MNCs against the challenges
of managing shared ownership (Mitchell, 1988; Shenkar and Zeira, 1987). As such, experience
attached to each entry-mode tends to be persistent. The features that are distinctive of alliances
would be more useful to leverage for future alliance entries, but not for M&A entries.
However, when the learning from an experience is applied to a different setting, it is
likely to backfire. For example, a firm utilizing regulatory experiences gained in one country to
enter another country is more likely to fail as it faces “learning penalty” (Perkins, 2014)—a steep
penalty for using the wrong knowledge in a different context. Thus, experience in a different
entry mode may not only be irrelevant, but harmful to leverage on a new, different entry mode.
Forcing unfit knowledge into a different setting can be worse than not having any knowledge. No
wonder “inappropriate sets of knowledge” can hamper innovation in new projects that firms
undertake (Leonard-Barton; 1992: 118).
Although learning from experience develops core capabilities needed in the current
activity, at the same time it may also lead to a competency trap (Levitt and March, 1988) acting
as an obstacle or rigidity, preventing organizations from seeking novel knowledge (Nelson and
Winter, 1982; Tripsas and Gavett, 2000). The greater the depth of knowledge and experience, the
more likely organizations become resistant to change, and the less likely they engage in
information searching activities (Weiss and Heide, 1993). In the same way, although alliance
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experience breeds a useful capability for future alliances, this capability becomes inappropriate
knowledge when applied to M&As, a different entry mode.
While alliances and M&As are both “double-layered acculturation entry strategies”
(Barkema et al., 1996), alliances experience leads to a more focused learning about partnership,
coordination, conflict resolution, etc. with the local partner (Barkema et al., 1996; Lyles, 1987,
1988; Madhok, 1997; Shenkar and Zeira, 1987), rather than knowledge about the local
environment. In fact, studies find that local knowledge is ambiguous and hard to transfer from
the local to foreign partner (Jiang and MacKenzie, 2011; Szulanski, 1995) because it is “sticky”
and that consequently, foreign partners in cross-border alliances hardly tries to acquire local
knowledge but rather depend highly on the local partner.
It is often difficult for MNCs to understand the local regulatory, or the normative, socio-
cognitive systems (Scott, 1995), and they remain shortsighted in the local environment, which is
all the more critical in emerging countries that is subject to unpredictable political and economic
transformations (Meshi, 2005). Thus, these MNCs rely on the local partner for dealing with
issues concerning relations with the local environment, such as access to distribution channels,
knowledge of local regulations, and relations with state authorities (Meshi, 2005; Teece, 1986).
As two partners contribute expertise in highly differentiated areas, this may lead to a split
control structure (Killing, 1982) in which the local partner deals with local marketing and
institutions while the foreign partner oversees research, development, and production. The split
control structure is frequently observed in joint ventures in developing countries (Yan and Luo,
2016), and this division makes it even more likely for the MNC to ignore the importance of local
knowledge and not try to gain it (Jiang and MacKenzie, 2011). In this sense, learning about the
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host country environment is hardly achieved in alliances, although M&A entries require great
knowledge on the host market environment. In addition, the alliance’s local labor force is also
left to manage by the local partner (Kogut and Singh, 1988). MNCs pick alliances over
acquisitions because they do not want to deal with managing a foreign labor force due to a lack
of skill (Hennart and Reddy, 1997), and thus they would not learn about this aspect from
alliances, which on the other hand is critical for M&As.
Besides these factors, while in the case of alliances only selected resources are exchanged
with the core businesses of the partners kept separate, in M&As acquirer MNCs must undergo
major restructuring challenges from the local target (Li et al., 2007). They are thus focused on
retaining control, transferring organizational rules, practices, and capacities (Xia et al., 2009),
achieving a global standardization if needed (Uhlenbruck and de Castro, 2000), and internal
consistency (Davis et al., 2000; Kostova and Zaheer, 1999). These additional features of M&As
are difficult to learn from alliances, and thus experience in managing alliances may not be
helpful when making acquisitions later (Zollo et al., 2002). However, we argue that when the
alliance entry mode is chosen due to regulatory restrictions against M&A entries, those MNCs
that wanted to invest in M&As but could not would endeavor to learn and draw the right lessons
for subsequent M&As. In other words, they will display a special case of learning.
Hypotheses
Experience in Alliances Made as an Institutionally Embedded Choice
History has witnessed many countries banning foreign acquisitions in some or all sectors,
or making them difficult by various legal restrictions (Hennart and Reddy, 1997). In particular,
the idiosyncrasies of the emerging market context usually make it harder for MNCs to initiate
79
M&As (Meyer and Tran, 2006). In emerging markets, this was often the case before the FDI
regulations restricting M&A deal-making were replaced with new ones allowing MNCs to
acquire local firms (Perez-Gonzalez, 2004).
In the case of China, before the new legislation promulgated liberalizing foreign M&A
activities (Huang and Kao, 2006), foreign investors were forced to choose less “aggressive”
entry strategies such as alliances (Meyer and Tran, 2006; Peng, 2006), which became the most
popular solution to the “Chinese puzzle” (Kengelbach, Klemmer, and Roos, 2013). Thus, while
the regulatory environment before new legislation presents opportunities to pursue both alliances
and M&As, the alliance entry mode in this period can be seen as an institutionally embedded
choice—strongly influenced by the institutional context (Child and Rodrigues, 2005).
For instance, during an interview, IKEA’s PR manager revealed that IKEA’s first entry
mode choice into China was a joint venture with a local partner. The choice was made passively
as joint venture was the sole way to operate business due to government’s restrictions in entry
mode (Wang, 2011). IKEA eventually closed down its first store. Since 2003, it made several
subsequent entries as wholly owned subsidiaries through establishing new operations or
acquiring existing firms in the host country, which IKEA believes is the best way to achieve its
way of doing business (Wang, 2011). In such cases, alliance experience gained when M&As are
not allowed may lead to entries in the originally intended entry mode such as M&As.
While MNCs may have preferred hierarchical (e.g. M&A) modes to relational modes
(alliances) for reasons such as to achieve a higher level of internal consistency (Davis et al., 2000;
Kostova and Zaheer, 1999), they were forced to engage in alliances suboptimally. In this case,
contrary to the typical notion that alliance experience leads to organizational inertia and learning
80
penalties, it could be helpful for firms as they invest in M&As. This is because learning
accumulated from such alliances is different than that from ordinary alliances that occur in an
environment with free choice of entry modes, since they are intended to be in lieu of M&As.
MNCs with such alliance would keep their original intention of acquiring, and anticipate the
eventual change in FDI regulations in the emerging market host country.
Thus, they would display a different type of learning, since they would go out of their
way and to learn more. For example, while typically MNCs do not focus on learning about the
host-country environment or managing the local workforce in alliances (Davis et al., 2000; Jiang
and MacKenzie, 2011; Kogut and Singh, 1988), they may seek to learn about this aspect bearing
in mind that this knowledge would be useful for future M&As. This is in line with the notion of
strategic intent (Hamel and Prahalad, 2005) or learning intent referred to as the “level of desire
and will of the parent with respect to learning from the joint venturing experience” (Tsang, 2002:
839). For firms to learn successfully, they need to set the explicit objective to learn and facilitate
learning by building appropriate mechanisms and systems (Ghoshal, 1987).
MNCs that have entered into alliances against their wishes of engaging in M&A deals
would make it their learning intent to break free from the typical pattern and acquire knowledge
that would be useful for eventual M&As such as aspects about the local environment and
employees. This special type of learning from alliances could be used and/or recombined to fit
with subsequent entries in M&As due to the effect of cross-form knowledge spillover (Xia et al.,
2009).
Perkins (2014: 151) finds that the heterogeneity of the country-specific environments
may challenge firms “to identify and build in-depth capabilities across each type of institutional
81
experience over time”. According to this notion, MNCs can parse out and focus on a specific
aspect that has relevance in M&A from their alliance experience (Kogut and Zander, 1992), if
they actively seek out to do so. In this sense, more alliance experience of this type would allow
the MNC to accumulate more knowledge that is applicable to subsequent M&As. In other words,
this knowledge base will develop learning in the host-country condition and thus enhance the
MNC’s ability to engage in future M&As.
Case in point, in China, while prior to the issuance of the 2003 M&A regulations MNCs
opted for the often-used investment model of Sino-foreign joint alliances (Huang and Kao, 2006)
due to the lack of regulatory support, some of these firms shifted their entry mode to M&As after
the regulation change. For example, an MNC in our sample, Nestlé—headquartered in
Switzerland, initially entered the Chinese market through alliances with local partners such as
Qingdao Milk and Shanghai Sugar, Cigarette & Wine, before 2003. However, it then initiated
subsequent M&A endeavors after the 2003 regulatory change, acquiring local companies such as
Yunnan Dashan Drinks, Yinlu Foods Group, and Hsu Fu Chi International in China. Taken
together, we expect:
Hypothesis 1: The likelihood of making M&A entries into an emerging market will be
positively associated with the number of alliances made as institutionally embedded
choices when M&As are restricted in that country.
Experience in Alliances Made as a Strategic Choice
Research shows that firms selectively use alliances over acquisitions in well-defined
contexts (Kogut and Singh, 1988; Balakrishnan and Koza, 1993; Hennart and Reddy, 1997). In a
host-country regulatory environment that allows for both alliance and M&A entries, alliances are
82
chosen only by MNCs that prefer this entry mode for strategic reasons. In this type of
environment, because these MNCs have entered through alliances out of preference and intention
for this entry mode, they would be more challenged to move outside their strategic comfort zones
(Shortell and Zajac, 1990).
Based on their alliance experience, these MNCs establish their own knowledge base
about this particular entry mode that was intended from the beginning. Then they can replicate
the same strategy configured from their valuable experience such as managing partner
relationships, which would be useful mostly in running new alliances (Barkema et al., 1997).
Once an MNC’s competence in an entry mode is established in the host country, it is easier for
the firm to maintain its current course of action than to change. Managers in these MNCs are
likely to draw on routines that have been successful, instead of engaging in information search
and processing (Zeng et al., 2013).
Literature finds that accumulated experience tends to generate optimism and
overconfidence in decision makers, resulting in suboptimal search behavior (Cooper et al., 1995;
Hmieleski and Baron, 2009). With routines from alliance experience imprinted on them, MNCs
could suffer from learning myopia—the tendency to overlook beyond their current activity to
distant activities (Zeng et al., 2013). This means that MNCs will keep making subsequent
investments in the same mode rather than contemplating on a different entry mode.
This is why Finkelstein and Haleblian (2002) talk of the “negative transfer effect” of
experience on subsequent activities when the prior and subsequent activities are dissimilar (Zeng
et al., 2013: 45). An MNC’s ability to effectively learn from its experience erodes when it enters
dissimilar cultures (Barkema et al., 1996; Zeng et al., 2013) because cultural difference creates a
83
gap between its prior experience and new knowledge needed to enter a new market (Johanson
and Vahlne, 1977). In a similar manner, the inherent differences in two different entry modes
may erode the MNC’s ability to learn and apply its alliance experience when making M&A
entries. It is difficult for MNCs to distinguish appropriately between routines that could or could
not be transferred from cross-border alliances to acquisitions (Nadolska and Barkema, 2007).
Indeed, accumulated alliance experience may increase additional costs and inefficiency
because this old experience would have to be unlearned (Huber, 1991; Tsang and Zahra, 2008) in
order to reorient itself toward a new entry mode. Consequently, those that have chosen the
alliance mode out of choice would be content to let their experience take its normal course of
action, and get locked in. They will not choose to apply learning from their alliance experience
on a new form—M&A. As such, we offer the following hypothesis:
Hypothesis 2: The likelihood of making M&A entries into an emerging market will be
negatively associated with the number of alliances made as strategic choice when M&As
are allowed in that country.
Moderating Effect of Economic Distance
Economic distance (Ghemawat, 2001) can be seen as the difference between the income
level, intermediate inputs, information or knowledge, and the cost and quality of natural,
financial, and human resources between the home and host countries (Ghemawat, 2001).
Additionally, it implies differences in the technological level (Tsang and Yip, 2007), the nature
of demand, economies of standardization or scale, and the distribution or business systems
(Ghemawat, 2001).
84
While economic distance comprises of MNCs entering markets that are less or more
developed than their own (Tsang and Yip, 2007), our setting—China, naturally leads us to focus
on the former: firms entering into an emerging economy from a developed country, which is the
majority case. When developed country MNCs enter less developed host countries, they can
exploit their firm-specific advantages such as better technology. This is facilitated by the host
country’s lower wages and rent, and local competitors that are relatively backward in terms of
technological, managerial, and marketing expertise. Less developed economies are also attractive
for their high economic growth and rapidly increasing demand from a sizeable, developing
consumer base (Meyer and Tran, 2006).
However, MNCs are likely to be challenged by major economic distance creating
disparities in economic developments. Ghemawat (2001) finds that the costs incurred in the
distribution process within the host country is substantial. It is harder for MNCs to replicate
existing business models such as economies of scale and standardization in a country of greater
economic distance, rather than in a country with a similar economic profile. Ghemawat (2001)
gives the example of how operating Wal-Mart—a US company—in India would be much more
challenging than running Wal-Mart in Canada, which would practically be a “carbon copy” of
that in the US.
Literature finds that economic distance also leads to disparities in education and political
systems, and may limit a MNC’s ability to communicate effectively in the host market (Johanson
and Wiedersheim, 1975). Economic distance thus may intensify cross-country differences,
complexity and uncertainty, which in turn makes experience and knowledge about the host
market critical when MNCs enter the host market through M&As.
85
For acquirer MNCs, their existing knowledge can be leveraged when making M&As in
countries of similar economic characteristics (Johnson and Tellis, 2008), but the importance of
host market knowledge may weigh in heavier when making M&A entries into economically
dissimilar markets. The harder it is to operate in emerging host country due to greater economic
distance (Ghemawat, 2001), the more valuable the learning from the host market would be for
future M&A entries. Accordingly, we posit that MNCs that have intentionally learned about the
local environment from alliances will find that this learning is more helpful for M&As in
economically remote markets. Thus, we expect that economic distance strengthens the positive
relationship between prior experience in alliance made as institutionally embedded choice and
subsequent M&A entry.
Hypothesis 3: When the economic distance between the investing firm’s home country
and host country is higher, there will be a stronger positive relationship between
experience in alliances as an institutionally embedded choice and subsequent M&A
entries.
On the other hand, for firms that have formed alliances as a strategic choice, economic
distance would have different implications. As stated, firms are more successful when entering
into countries with closer economic distance from their home market because of the similarities
in consumer demand, market segments and business institutions (e.g., distribution channels,
infrastructure, media, and advertising agencies) (Mitra and Golder, 2002). Competencies or
knowledge-based resources developed in their home markets (Madhok, 1997) can be leveraged
in host countries that are similar in economic development (Johnson and Tellis, 2008). On the
86
flip side, MNCs bear more risks entering into host countries that differ from their home country
economically (Ghemawat, 2001).
When MNCs from significantly more advanced countries undertake M&A deals in the
host country, they would have less understanding and familiarity with the environment (i.e.
distribution channel and other local conditions) than those from economically proximate
countries. These MNCs from widely different home countries would need to adjust to the new
market conditions (Dunning, 1998), and knowledge about the local environment would be
critical in this case. However, those MNCs that made alliances as a strategic choice would have
been bound in their alliance routines (Levitt and March, 1988) and not focused on gaining local
knowledge needed for M&As. This lack of knowledge that is necessary for M&As would seem
more intimidating and discouraging with higher economic distance, and the moderating effect of
economic distance thus strengthen the negative relationship.
Hypothesis 4: When the economic distance between the investing firm’s home country
and host country is higher, there will be a stronger negative relationship between
experience in alliances as a strategic choice and subsequent M&A entries.
Research Setting: China
We choose China as our research setting as it offers an appropriate context to study entry
modes to an emerging market, and to test the effect of alliance experience on subsequent M&As:
it has been the top destination for FDI (Peng, 2006) with the highest volume of inbound M&A
deals, and holds a critical position in the global economy (Kengelbach et al., 2013). China is also
worthy of observation because of its relatively fast speed of change during the economic
transition, with potential instances of regulatory changes (Peng, 2006).
87
Figure 3.1. Hypotheses Setup
We focus particularly on the issuance of M&A regulations in 2003 which created a new
environment in China, facilitating foreign M&As (Figure 3.2). In 2003, the Chinese government
issued the “Provisional Regulation on Foreign Investors Merging with or Acquiring Domestic
Enterprises” (Huang and Kao, 2006; Xia et al., 2008), which introduced several breakthroughs.
Most prominently, it offered a legal basis for cross-border M&As and provided rules to be
followed in carrying out M&A transactions, except in a few “strategic” industries where foreign
firms were still restricted in terms of entry mode choice (The Economist, 2015). Thus, although a
high proportion of FDI projects in China were alliances until the early 2000s due to regulatory
restrictions as a major factor (Beamish, 1985; Puck et al., 2008), under the new regulation, firms
could freely choose M&As as an alternative entry mode. We thus choose this setting to test the
effect of alliance experience and how it differs by before and after this regulatory change.
Alliance Experience as a Strategic
Choice (After 2003 in China) (H2)
(+)
Alliance Experience as an
Institutionally Embedded Choice
(Before 2003 in China) (H1) Subsequent M&A Entries
(-)
(-)
(+)
Economic Distance (H3, H4)
88
Figure 3.2. Number of cross-border M&A deals into China and total M&A amount (source:
SDC)
Method and Data
We used a sample of MNCs that entered China through M&As. Our observation period
starts in 2003 and extends until 2013. We use the Securities Data Corporation’s (SDC) database,
which extensively documents foreign alliance and acquisition entries in China. As the most
authoritative and up-to-date database with information on firms’ international activities since
1985, SDC is used as the primary source of acquisition information in research. We include all
foreign entry announcements of all MNCs into China from the SDC database. Among these
firms, those publicly owned ones listed on Standard & Poor’s (S&P) COMPUSTAT annual data
were used to derive our final sample consisting of 1,561 foreign public firms from 40 different
home countries entering China (Appendix). Our sample size was cut down due to the limited
data availability for important control variables.
0
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Number of Deals Total Amount (in millions of USD)
89
Our unit of analysis is firm-year. COMPUSTAT provides the annual firm-level data
necessary for our longitudinal analysis such as performance measures and the primary standard
industrial classification (SIC) assignment. We gathered firm data from 2002 to calculate one-
year lagged values for our independent variables. The potential number of firm-year
combinations was 17,171 (1,561 firms by 11 years). 311 firms did not exist throughout the full
observation period. For those firms that had discontinued financial information, we removed the
missing values for the years at the end, which created an unbalanced panel data structure.
Longitudinal observations on our independent variables allows us we to include annual time-
varying covariates in the analysis.
Dependent Variable
Our dependent variable is the number of entries made via M&As for each firm in a given
year. The foreign entries in our study included not only completed transactions but also entry
attempts (i.e., M&A announcements) for conceptual reasons: the announcement of an M&A
initiative reflects a firm’s attempt to enter a country, although it may not actually be realized.
Our approach is consistent with prior work (Schneper and Guillén, 2003; Xia et al., 2008) where
those M&A transactions that were announced but not completed are included to avoid sampling
biases (Beckman and Haunschild, 2002). Overall, 52 percent (1,492 out of 2,832) of these entries
in our sample were eventually completed. Our dependent variable was measured by the number
of M&A attempts initiated by each firm and year. In the SDC database, each M&A attempt
carries a deal code, which describes the specific form of the related M&A transaction. We take
into account only majority acquisitions, which consists of mergers, full acquisitions, and
acquisition of majority interest.
90
Independent and Moderating Variables
We measured alliance experience by the accumulated number of mutual cooperative
arrangements such as a joint venture or strategic alliance an MNC had in China. We take into
account those completed deals only. We further separate them into alliances that occurred as an
institutionally embedded choice (when M&As were restricted before 2003), and those as
strategic choice (when MNCs could freely chose entry modes after 2003). We developed both
variables using the SDC database, and log-transform them to reduce skewness.
Economic distance was calculated following Tsang and Yip (2007), whereby it is
measured as the difference, in US dollars, in the real per capita gross domestic product (GDP)
between the home and a host country. Because most MNCs entering into China are from more
developed countries, economic distance is measured as ln(𝑌) minus ln(𝑌𝐶ℎ𝑖𝑛𝑎), where 𝑌𝐶ℎ𝑖𝑛𝑎 and
𝑌 represent the real gross domestic product (GDP) per capita in US dollars of China and the
home country, respectively. The natural logarithmic difference is used because it is a standard
practice in econometrics. It is a good approximation of the percent difference in real GDP per
capita when 𝑌𝐶ℎ𝑖𝑛𝑎 and 𝑌 are not too far away from each other.
Control Variables
All time-varying country-level variables and distance variables are from the year prior to
an acquisition announcement. These control variables are likely to affect the acceptance,
desirability, or appropriateness of acquisitions of MNCs by regulatory agencies and thus affect
their acquisition outcomes (Xia et al., 2009).
Firm-level controls Literature finds that the choice of M&A entry mode depends on the
characteristics of the investing firm (Anderson and Gatignon, 1986; Hennart and Park, 1993).
91
We control for year since IPO, computed as the log of the number of years since an MNC’s
initial public offering (IPO) (Guillén, 2002). We also include firm size, measured as the natural
log of a firm’s total assets in the prior year. Having more resources available leads firms to
choose a higher control mode of entry (Xia et al., 2009). We also add a measure of performance,
the annual return on assets (ROA: net income divided by total assets), as past performance can
affect foreign market decisions (Ellstrand, Tihanyi, and Johnson, 2002). We control for a firm’s
financial leverage using the debt-to-equity ratio, calculated by total debt divided by total assets.
Then we include a firm’s level of international exposure—the ratio of foreign sales to total sales.
In addition, we control for prior M&A experience, measured as the accumulated number
of completed M&A deals that a firm has in the past. Further, we control for whether a foreign
firm has experience in acquiring Chinese state-owned enterprises (SOEs) as this may affect the
learning by the foreign firm: SOEs may behave differently and government intervention in the
M&A deal can be extensive (Lebedev, Peng, Xie, and Stevens, 2015; Peng and Heath, 1996;
Uhlenbruck and De Castro, 2000). In the SDC database, we identify those foreign firms that have
had an M&A in the past with the Sell-side Government Owned Involvement Flag.
Country-level controls We included GDP growth to capture the host country’s market
potential. We control for the geographic distance and cultural distance between China and the
home countries represented in our sample. Geographic distance between the capital cities of a
MNC’s home country and Beijing was constructed using measures from the CEPII database
(Mayer and Zignago, 2011). Following Kogut and Singh (1988), we use Hofstede’s 4-
dimensional framework to construct the cultural distance index:
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𝐶𝐷𝑗 = ∑{(𝐼𝑖𝑗 − 𝐼𝑖𝐶ℎ𝑖𝑛𝑎)2
4
𝑖=1
/𝑉𝑖}/4
𝐶𝐷𝑗 is the cultural distance between country j and China, the host country. 𝐼𝑖𝑗 is country j’s score
on the ith cultural dimension, 𝐼𝑖𝐶ℎ𝑖𝑛𝑎 is the score of China on this dimension, and 𝑉𝑖 is the
variance of the score of the dimension. Literature finds cultural distance to be relevant in
discussing cross-border M&A (i.e. Aybar and Ficici, 2009; Morosini, Shane, and Singh, 1998).
We measure institutional distance between China and home countries using the World
Bank governance indicators, including government effectiveness, regulatory quality, rule of law,
voice and accountability, quality political stability and absence of violence, and control of
corruption. We then use a Euclidean distance calculation (Kogut and Singh, 1988), to calculate
the Institutional Distance using the following formula:
𝐼𝐷𝑘 = ∑{(𝐼𝑘 − 𝐼𝐶ℎ𝑖𝑛𝑎)2
𝑛
𝑖=1
/𝑉𝑖}/𝑛
where 𝐼𝑘 refers to the institutional indicator (I) for country k, 𝐼𝐶ℎ𝑖𝑛𝑎 refers to the institutional
indicator (I) for China, and 𝑉𝑖 is the variance of indicator I. 𝐼𝐷𝑘 is the institutional distances of
country k from China. The symbol n refers to number of indicators for a particular measure. The
Euclidean distance calculation method reduces the sensitivity of our measures to differences in
any one indicator. The larger the values of institutional distance, the greater are the differences
between host country and home country in the institutional aspects (Gaur and Lu, 2007).
Bilateral trade is operationalized as the log sum of total import and export values in US
dollars between a home country and China to capture the intensity of their economic interactions
(Xia et al., 2011). It is measured by using the information collected from the United Nations
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Comtrade Database, which is the most comprehensive trade database available. Finally, we also
fixed the acquirer firm’s industry effect by creating a series of dummy variables to account for a
firm’s broad level of industry participation as based upon their primary SIC Code.
Model Specifications
Following early studies (Henisz and Delios, 2001; Henisz and Macher, 2004), we test our
hypotheses using the negative binomial analysis which is appropriate for our count dependent
variable: the number of M&A deals occurred annually. The negative binomial model can
accommodate overdispersion in the dependent variable (Barron, 1992) and have often been used
in prior research to analyze overdispersed count data (Kozhikode and Li, 2012; Zelner, Henisz,
and Holburn, 2009).
We estimate robust standard errors using the method of generalized estimating equations
(GEE) which relaxes the assumption of independence of errors within the firm clusters (Krishnan
and Kozhikode, 2015). This specification also has inherent advantages over random effects and
fixed effects: while random effects estimators use all the observations, they do not account well
for unobserved heterogeneity at the group level (i.e., among firms). Fixed effects estimators are
better suited for this purpose, but at the expense of dropping all observations from groups with
no events (Krishnan and Kozhikode, 2015). GEE overcomes these limitations, by accounting for
unobserved heterogeneity (Katila and Ahuja, 2002), producing standard errors robust to group-
level heteroskedasticity (Zelner et al., 2009), and allowing for autocorrelation.
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Table 3.1. Summary Statistics and Correlation Matrix Mean S.D. Min. Max. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Majority M&A entry 0.10 0.38 0 10 1
Year since IPO 2.73 0.80 0 4.16 -0.01 1
Size 5.21 3.36 0 16.54 0.02* 0.28* 1
ROA 0.00 0.03 -2 1.30 0.00 0.00 -0.02* 1
Debt-to-equity ratio 1.30 61.31 0 3297 -0.01 0.00 -0.02* 0.00 1
Foreign sales 0.09 0.22 0 1 0.04* 0.14* 0.25* -0.01 0.02* 1
M&A experience 0.48 1.16 0 20 0.11* 0.13* 0.09* 0.00 -0.01 0.07* 1
Government Involvement Experience 0.17 0.38 0 1 0.04* 0.05* 0.04* 0.01 -0.01 -0.01 0.11* 1
GDP growth 2.97 3.16 -9 17.30 0.01 -0.22* -0.22* 0.00 -0.01 -0.14* -0.04* 0.00 1
Bilateral trade 25.42 1.32 15 27.01 -0.01 0.29* 0.02* 0.00 0.01* 0.01 0.07* -0.01* -0.05* 1
Geographic distance 9.01 0.44 7 9.86 0.02* 0.14* 0.00 -0.01 0.02* 0.19* 0.01 0.02* -0.15* 0.27* 1
Cultural distance 3.84 0.69 3 4.57 0.01 0.22* 0.39* 0.01 0.01* 0.30* -0.02* -0.04* -0.42* -0.28* 0.22* 1
Institutional distance 4.21 1.38 1 6.30 -0.01 -0.02* -0.14* 0.01 -0.02* -0.20* 0.00 0.02* 0.02* -0.17* -0.03* -0.17* 1
Economic distance 1.50 0.61 -2 2.68 0.01* 0.06* 0.06* 0.00 0.01* 0.18* -0.03* 0.06* -0.20* 0.15* 0.59* 0.32* 0.07* 1
Alliance experience
(before 2003)
0.26 0.53 0 4.98 0.02* 0.16* 0.07* 0.00 0.00 0.13* 0.06* 0.02* -0.06* 0.05* 0.10* 0.11* -0.06* 0.08* 1
Alliance experience
(after 2003)
0.14 0.42 0 5.44 0.00 0.05* 0.02* 0.00 0.00 0.07* 0.01 -0.01 -0.03* 0.04* 0.06* 0.06* -0.05* 0.04* 0.57*
* p < .05 (two-tailed test)
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Table 3.2. Coefficients of GEE Negative Binomial Estimates Predicting M&A Entry Variables, Hypotheses Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Year dummies Incl. Incl. Incl. Incl. Incl. Incl.
Industry Dummies Incl. Incl. Incl. Incl. Incl. Incl.
Year since IPO -0.090* -0.094* -0.101* -0.098* -0.096* -0.100*
(0.040) (0.040) (0.040) (0.040) (0.040) (0.040)
Size 0.021* 0.021* 0.019* 0.032** 0.032** 0.032**
(0.010) (0.010) (0.010) (0.010) (0.010) (0.010)
ROA -0.110 -0.111 -0.122 -0.158 -0.155 -0.155
(1.034) (1.035) (1.034) (1.006) (1.005) (1.007)
Debt-to-equity ratio -0.016 -0.015 -0.016 -0.016 -0.016 -0.016
(0.021) (0.021) (0.021) (0.021) (0.021) (0.021)
Foreign sales 0.449*** 0.430*** 0.410*** 0.376** 0.378** 0.369**
(0.121) (0.122) (0.122) (0.123) (0.123) (0.123)
M&A experience 0.142*** 0.141*** 0.139*** 0.140*** 0.138*** 0.140***
(0.011) (0.011) (0.011) (0.012) (0.012) (0.012)
Government Involvement Experience 0.343*** 0.340*** 0.326*** 0.325*** 0.324*** 0.325***
(0.066) (0.066) (0.066) (0.066) (0.066) (0.067)
GDP growth -0.009 -0.009 -0.009 -0.008 -0.007 -0.007
(0.017) (0.017) (0.017) (0.017) (0.017) (0.017)
Bilateral trade -0.044 -0.046 -0.046+ -0.048+ -0.048+ -0.048+
(0.028) (0.028) (0.028) (0.028) (0.028) (0.028)
Geographic distance 0.157+ 0.153+ 0.148+ 0.150+ 0.152+ 0.148+
(0.085) (0.085) (0.085) (0.085) (0.085) (0.085)
Cultural distance -0.018 -0.023 -0.027 -0.040 -0.041 -0.041
(0.067) (0.067) (0.067) (0.067) (0.067) (0.067)
Institutional distance -0.034 -0.033 -0.036 -0.037+ -0.039+ -0.038+
(0.022) (0.022) (0.022) (0.022) (0.022) (0.022)
Economic distance 0.021 0.022 0.023 0.017 0.024 0.019
(0.064) (0.064) (0.064) (0.063) (0.064) (0.063)
Alliance experience (before 2003),
H1(+)
0.009* 0.033*** -0.078 0.033*** -0.120
(0.004) (0.008) (0.057) (0.009) (0.074)
Alliance experience (after 2003), H2(-) -0.027** -0.044** -0.069 0.060
(0.009) (0.015) (0.079) (0.083)
Alliance experience before 2003×
Economic distance, H3 (+)
0.064* 0.088*
(0.032) (0.042)
Alliance experience after 2003×
Economic distance, H4 (+)
0.021 -0.054
(0.039) (0.045)
Constant -2.70** -2.40* -2.53* -2.40* -2.390* -2.368*
(0.983) (0.987) (0.980) (0.987) (0.988) (0.988)
Wald Chi-squared 342.64*** 367.80*** 340.12*** 354.54*** 376.01*** 379.34***
Obs. 15,681 15,681 15,681 15,681 15,681 15,681
Note: year and industry fixed effects included in all models; independent error structure; robust standard errors in parentheses.
Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.1
Results
Summary statistics and correlations are reported in Table 3.1. Table 3.2 presents the
coefficients of the GEE negative binomial models. Model 1 includes control and moderating
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variables only. Models 2–6 test the relationships predicted in H1–4 and Model 7 is the full model.
In Model 1, several control variables are significant. We find that year since IPO, firm size, and
foreign sales show significant positive impacts on M&A entries. Geographic distance M&A
experience are positive and significant as well.
In Model 2 we find alliance experience before 2003 to be positive and significant (β =
0.01, p = 0.047). These results support H1 that the likelihood of subsequent M&A entry
increases with alliance experience before 2003. For a one unit change in alliance experience
before 2003, the difference in the logs of expected counts of the response variable, M&A entries,
is expected to change by the respective regression coefficient. Since the alliance experience
before 2003 is log transformed, we can interpret β (0.01) as the percent change in the number of
M&A entries for a one percent change in the number of alliance experience before 2003. The
chance that the results would have come up in a random distribution is 4.9%. The robust standard
error is 0.004, and the 95% confidence interval is between -0.001 and -0.017.
Model 3 tests the different effects of alliance experience after 2003. In Model 3, the
coefficient is statistically significant and negative (β = -0.03, p = 0.004) as predicted in H2. For a
one percent increase in the number of alliance experience after 2003, there would be β (0.03)
percent decrease in the number of M&A entries. The robust standard error is 0.009, and the 95%
confidence interval is between -0.001 and -0.017.
In Models 4 and 5, the moderating effect of economic distance is tested on each
independent variable individually. We see that in Model 4 alliance experience before
2003× economic distance is positive and marginally significant (β = 0.06, p = 0.048). The robust
standard error is 0.032, and the 95% confidence interval is between -0.001 and -0.124. The
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economic magnitude of the coefficient of the interaction term is slightly higher than that of the
direct effect for alliance experience before 2003, supporting H3 that higher economic distance
strengthens the positive effect of learning from alliances as institutionally embedded choice. This
interaction is plotted to further validate the moderating effects. On the other hand, alliance
experience after 2003× economic distance is insignificant (β = 0.20, p = 0.292), thus we fail to
find statistical support for our H4. The full model, Model 6 confirms our findings.
Figure 3.3. Moderating effects of economic distance on alliance experience before 2003
Consistent with the results in Table 3.2, Figure 3.3 reveals that while both the high and
low values for the economic distance moderator have positive slopes, the one for higher
economic distance is slightly steeper, telling us that again that higher economic distance tends to
intensify the positive effect of M&A experience after 2003 even more. On the other hand, the
non-finding for H4 has been explored further by split sample analyses: we test the effect of
alliances made after 2003 in the group of firms that have higher than mean economic distance,
-2
-1.9
-1.8
-1.7
-1.6
-1.5
-1.4
-1.3
-1.2
-1.1
-1
Low IV High IV
M&
A E
ntr
ies
Alliance Experience Before 2003
Low Economic
Distance
High Economic
Distance
98
versus the other group that has lower than mean economic distance. While results show that our
argument is supported and significant in the former group, it loses significance in the latter group.
These results can be provided upon request.
Robustness Checks
Alternative specifications, operationalization of the variables, and additional variables
were tried to test for the robustness of our findings. To measure the level of institutional
development and confirm that our findings persist, we operationalize institutional distance in a
different way, by four items following Dow and Larimo (2009). The first two items are the
democracy and autocracy indicators collected from the POLITY IV Database for each country
and year. The third and fourth items are the indicators of political rights and civil liberties
published by Freedom House for each country and year. We computed the distance for each item
as the absolute value of the difference between the two partners’ home countries. Since they are
highly correlated, we used factor analysis to create a single composite measure. This variable
produced similar results overall, as show in Table 3.3.
We then also replace the bilateral trade control variable with export ratio which is
operationalized as export to China from each home country over its total export values to the
world in US dollars. This is to capture the intensity of economic interactions between the home
country and China. We also include an additional control, which may be relevant along with
trade relations: diplomatic relations. For this variable, we use the number of foreign embassies
and consulate generals of the investing firm’s home country in China, and the number of years
that China had established diplomatic relations with the home country. The information on China
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foreign relations was drawn from ChinaToday.com. Even with the inclusion of alternative or
additional variables, our model showed reasonable robustness.
Table 3.3. Robustness Checks
Variables, Hypotheses Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Alternative variable:
polity
Alternative variable:
export ratio
Additional variable:
diplomatic relations
Alternative variable:
low v. high economic distance
Year dummies Incl. Incl. Incl. Incl. Incl. Incl. Incl. Incl.
Industry Dummies Incl. Incl. Incl. Incl. Incl. Incl. Incl. Incl. Year since IPO -0.106** -0.105** -0.107** -0.107** -0.093* -0.101* -0.094* -0.107**
(0.040) (0.040) (0.039) (0.039) (0.041) (0.039) (0.041) (0.039)
Size 0.019+ 0.032** 0.024* 0.036*** 0.019+ 0.035*** 0.018+ 0.036*** (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.010)
ROA -0.133 -0.169 -0.077 -0.115 -0.100 -0.104 -0.104 -0.115
(1.004) (0.978) (1.052) (1.024) (1.053) (1.037) (1.051) (1.022) Debt-to-equity ratio -0.016 -0.016 -0.016 -0.016 -0.017 -0.017 -0.017 -0.016
(0.023) (0.023) (0.021) (0.021) (0.022) (0.022) (0.022) (0.021)
Foreign sales 0.408*** 0.366** 0.360** 0.317* 0.382** 0.301* 0.385** 0.336** (0.121) (0.122) (0.124) (0.125) (0.123) (0.125) (0.123) (0.124)
M&A experience 0.139*** 0.139*** 0.138*** 0.138*** 0.139*** 0.138*** 0.139*** 0.136***
(0.011) (0.012) (0.011) (0.012) (0.011) (0.012) (0.011) (0.012) Government Involvement
Experience
0.312*** 0.309*** 0.329*** 0.325*** 0.326*** 0.324*** 0.327*** 0.326***
(0.067) (0.067) (0.066) (0.067) (0.066) (0.067) (0.066) (0.066)
GDP growth -0.007 -0.007 -0.021 -0.020 -0.006 -0.017 -0.007 -0.020 (0.017) (0.017) (0.017) (0.017) (0.017) (0.018) (0.017) (0.017)
Bilateral trade -0.033 -0.032 -1.279*** -1.293*** -0.039 -1.158** -0.037 -1.212***
(0.029) (0.029) (0.370) (0.371) (0.029) (0.384) (0.029) (0.362) Geographic distance 0.146+ 0.138 0.083 0.081 0.146+ 0.086 0.165* 0.137*
(0.088) (0.088) (0.083) (0.083) (0.086) (0.084) (0.076) (0.069)
Cultural distance 0.080 0.101 -0.317** -0.334** -0.066 -0.332** -0.060 -0.302** (0.157) (0.157) (0.117) (0.117) (0.070) (0.118) (0.069) (0.113)
Institutional distance -0.028 -0.043 -0.039+ -0.040+ -0.050* -0.050* -0.049* -0.039+
(0.058) (0.058) (0.021) (0.021) (0.023) (0.023) (0.023) (0.021) Diplomatic Relations 0.048* 0.034
(0.023) (0.024)
Economic distance 0.008 0.002 0.081 0.076 0.031 0.076 0.047* -0.116 (0.063) (0.063) (0.068) (0.067) (0.064) (0.067) (0.023) (0.157)
Alliance experience (before 2003),
H1(+)
0.033*** -0.138 0.032*** -0.135 0.033*** -0.138+ -0.022 0.032***
(0.008) (0.074) (0.008) (0.074) (0.008) (0.074) (0.152) (0.008) Alliance experience (after 2003),
H2(-)
-0.026** 0.062 -0.026** 0.059 -0.026** 0.060 0.033*** -0.026**
(0.009) (0.085) (0.009) (0.085) (0.009) (0.085) (0.008) (0.009)
Alliance experience before 2003× Economic distance, H3 (+)
0.099* 0.097* 0.098* 0.119 (0.042) (0.042) (0.043) (0.622)
Alliance experience after 2003×
Economic distance, H4 (+)
-0.057 -0.055 -0.056 -4.792
(0.046) (0.046) (0.046) (4.691)
Constant -3.243*** -3.139** -1.840* -1.679+ -2.563* -1.759+ -2.754** -2.177**
(0.977) (0.975) (0.899) (0.898) (0.996) (0.905) (0.912) (0.775)
Wald Chi-squared 359.51***
374.58***
372.66***
386.94***
370.83***
388.42***
370.63***
379.44***
Obs. 15,395 15,395 15,395 15,395 15,395 15,395 15,395 15,395
Note: year and industry fixed effects included in all models; independent error structure; robust standard errors in parentheses. Standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.1
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We also consider that our moderator, economic distance, is asymmetric so we broke it
down into that of MNCs entering markets that are less or more developed than their own (Tsang
and Yip, 2007). In the case of the former, the results were aligned with the main findings. In turn,
we considered the moderating effect of the latter and found no statistical significance for the
results. The non-findings may be explained by the fact for those MNCs from home countries that
are less developed than China, their motivations for entry and subsequent entry lack clear
explanations. Traditional explanations cannot totally cover the rise and motivations for cross-
border M&As from emerging economies (Buckley et al., 2007; Dunning and Lundan, 2008;
Gubbi et al., 2010; Sun et al., 2012). Also, while M&As in developed countries from developing
countries use the exploration mode, M&As from emerging economies into other developing
countries such as China was found to not have any effect (Rabbiosi et al., 2012).
Discussion and Contribution
Our study contributes to literatures on foreign entry mode and experiential learning by
considering what firms learn from their past alliances, and whether alliance experience can have
different effects on subsequent M&A entries. Typically, the common logic from organizational
learning theory is that learning from experience is helpful for future international expansion (Luo
and Peng, 1999), and that prior experience expedites learning in related knowledge (Cohen and
Levinthal, 1994). Accordingly, MNCs that have undertaken alliances leverage those experiences
on subsequent alliances, and do not choose other entry modes such as M&As.
However, our study sheds insight on whether past alliance experience made with
different strategic motivations may have diverging impacts from encouraging to impeding future
M&A endeavors. To effectively solve the alliance experience effect puzzle, we distinguish
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between the learning effects of alliances in different regulatory environments. We view that
alliances where M&As are banned are made as institutionally embedded choice, but the ones
made after a regulatory change allowing for M&As are out of strategic choice, and they would
lead to different types of learning.
First, our results show that the effect of alliances made when M&As are not allowed can
lead MNCs to have learning intent, which may be beneficial for subsequent M&As. Generally,
alliances allow MNCs to draw on the local partner’s knowledge (Hennart and Reddy, 1997)
which makes them heavily reliant on their local partner’s skills in dealing with host-country-
specific issues (Meyer and Tran, 2006). However, MNCs making alliances in in an environment
where M&As are restricted may foresee new investment policies geared towards M&A
liberalization and promotion, keep in mind that they would invest in M&As when the right time
comes, and endeavor to learn about the local environment to leverage on M&As.
What is noteworthy is that this type of learning from alliance experience only takes place
when the MNC consciously takes charge of the process, is attentive and willing. Learning intent
plays a critical part in how the effect of this alliance experience manifests. Because these
alliances were made as the second-best but available choice instead of M&As where M&As are
not allowed, they would have been made with the intent to stretch the boundaries of general
learning from alliances and leveraging this learning on future M&As.
However, after the change in the regulatory system creating a different environment
allowing for M&As, MNCs choose the alliance entry mode out of their own strategic motivation
and goals despite the new environment that is supportive to M&As. These MNCs would develop
proficiency with the alliance mode and become more likely to pursue the same entry mode in the
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future, where their experience is relevant and helpful (Haleblian and Finkelstein, 1999), and not
shift to M&As. Indeed, our results indicate that firms that have chosen alliance entry after the
regulatory change are unlikely to make subsequent M&A deals.
We also view that the different strategic motivations leading to diverging learning effects
of alliance experience are contingent on the regulatory environment and its change through the
introduction of new M&A regulations. Regulatory changes in a host economy were found to
influence foreign market entry strategies (Xia et al., 2009): we extend this body of research to
show how regulatory changes also affect the learning outcomes of MNCs in terms of alliance
experience, and in doing so answer a recent call to study the evolution paths of foreign entry
mode choices (Hennart and Slangen, 2015). In a sense, we suggest that a change in the
regulatory environment from restricting to encouraging M&As can “reset” the value of prior
alliance experiences, which our setting allows us to test.
Additionally, we find economic distance between a host and home country enhances the
effect of learning through experience. In economically distant environments, firms are more
likely to rely on their local knowledge to draw inferences needed to handle local procedural
complexities when making M&A entries. Only those alliances made as institutionally embedded
choice (before 2003) lead to the learning of local knowledge, and this experience would be more
valuable for MNCs looking to undertake M&A deals with a greater economic distance.
The implication of our findings for practitioners is that they need to be aware of the role
of experience in organizations: the influence of the past orientation and action has a cumulative
effect, which makes it very powerful (Wiebe et al., 2010). The learning-curve effects of repeat
investments in each entry mode increases MNC’s future investment frequency in that same entry
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mode (Madhok, 1997), as search rules stay conditioned by solutions tried in the past (Chang and
Rosenzweig, 2001). The accumulation of alliance experience may lead to the use of
institutionalizing mechanisms (e.g. alliance departments) and lock the MNC down in this path.
Another implication is that if managers of the MNC had intent to learn about the local
environment through alliance deals, larger economic distance works in their favor for subsequent
M&As. Firms indeed gain more from difficult learning environments, particularly in emerging
market economies (Perkins, 2014).
Limitations and Future Directions
Our study is not without limitations, which naturally flows into directions for further
study. Given changes in the preferences of MNCs in choosing entry mode into China over time
(Puck et al., 2008), the country provides a good setting for studying how the prior entry
experience of firms affects the likelihood of subsequent M&A entries differently according to the
regulatory change. However, it also raises the issue of generalizability. Future research may
expand our framework to other contexts, especially emerging markets.
One notable environmental factor pertaining to the acquired firm’s characteristic which
may have contingency on our hypothesized relationship is the level of regional economic
development in which the acquired firm is located within China. This has been found to vary
from coastal to inland areas in China (Luo, 2007), because economic reforms in China occurred
first in the coastal areas. Accordingly, local firms in coastal regions can provide more useful
resources and knowledge, such as sophisticated marketing knowledge and well-developed
distribution channels (Li, Zhou, and Zajac, 2009). This would have an influence on the M&A
entry decision of the acquiring firm, for the benefits that coastal targets provide by
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complementing the advanced technology and knowhow of the acquirer. We hope future research
tests such additional contingencies that explain the role of experience on M&A behavior.
Conclusion
In general, when experience gets accumulated in a certain direction, firms would keep the
direction, meaning that those that have invested in alliances would keep the same entry mode at
the expense of trying out different entry modes such as M&As. However, with learning intent,
we find that MNCs can break free from this pattern. When the alliance mode is chosen where
M&A is impossible, a different type of learning can occur with special intention: MNCs may
endeavor to learn about the local environment from their alliances and transfer the learning to
subsequent M&A entry into the host country. The effect of alliance experience may thus vary
from acting as barriers to drivers for subsequent M&A entries. Depending on the MNC’s goals
and motivation, their approaches to the learning may diverge, and this would be reflected in their
selection of entry mode for subsequent entries.
We also find that given the same level of experience from alliances made as
institutionally embedded choice when M&As are not allowed, greater economic distance will
make its positive effect on subsequent M&A entry more strongly pronounced. Alliance
experience made when M&As are not allowed can lead firms to learn about the host market
which is relevant for subsequent M&As, and economic distance makes firms rely more on this
learning when making subsequent M&A entry decisions.
105
APPENDIX B
Table B.1. Distribution of Firms in the Sample by Home Country
Country Number of firms Number of total
M&A entries
Australia 58 92
Austria 5 6
Belgium 8 14
Canada 23 24
Chile 1 1
Denmark 10 19
Finland 8 8
France 52 131
Germany 29 68
Greece 3 2
Hong Kong 378 853
Iceland 2 2
India 7 9
Indonesia 4 9
Ireland-Rep 4 8
Israel 4 4
Italy 12 13
Japan 185 286
Kuwait 1 2
Luxembourg 1 1
Malaysia 63 77
Mexico 2 2
Netherlands 23 54
New Zealand 4 3
Norway 6 4
Philippines 4 7
Portugal 1 2
Russian Fed 1 1
Singapore 119 196
Slovenia 1 2
South Africa 7 6
South Korea 46 56
Spain 9 11
Sweden 17 33
Switzerland 23 46
Taiwan 63 109
Thailand 16 15
Turkey 1 1
United Kingdom 74 156
United States 286 499
Total 1,561 2,832
106
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BIOGRAPHICAL SKETCH
Jinsil Kim is a PhD candidate of International Management Studies in the Department of
Organizations, Strategy and International Management at the Jindal School of Management, at
The University of Texas at Dallas. Before coming to The University of Texas at Dallas, she
completed her Master’s level studies at Seoul National University in Korea and ESSEC Business
School in France. Prior to that, she attended Cedarville University in Ohio where she studied
International Studies and Social Sciences. Her research interests lie in the field of strategic
management and international business, especially in the domain of corporate political strategies
in transition economies. She has presented several papers at prominent international conferences,
and has also taught International Business and Strategic Management courses at The University
of Texas at Dallas.
CURRICULUM VITAE
JINSIL KIM
Address: Jindal School of Management, The University of Texas at Dallas. 800 W. Campbell
Road, SM 43 | Richardson, TX 75083
E-mail: Jinsil.Kim@utdallas.edu
EDUCATION
May, 2017 (expected) THE UNIVERSITY of TEXAS at DALLAS TX, USA
PhD Candidate (ABD)
Organizations, Strategy, and International Management
2013 SEOUL NATIONAL UNIVERSTIY Seoul, Korea
MA in International Studies with Honors
2012 ESSEC BUSINESS SCHOOL Cergy, France
MBA
2009 CEDARVILLE UNIVERSITY OH, USA
BA in International Relations and Social Science with High Honors
PROFESSIONAL EXPERIENCE
AXA Group: International Project Management (Paris, France) 2012
Centre for EU Studies, Seoul National University: Researcher (Seoul, Korea) 2010-2011
TEACHING/RESEARCH ASSISTANT EXPERIENCE
Instructor
• Strategic Management (undergraduate, capstone course) Summer 2014, The University
of Texas at Dallas. Evaluation: 4.2/5.0
• International Business (undergraduate, junior-level course) Spring 2016, The University
of Texas at Dallas. Evaluation: 4.8/5.0
Teaching/Research Assistant
• Global Strategy, for Seung-Hyun Lee, Undergraduate 2013-2015
• International Business, for Seung-Hyun Lee, Undergraduate 2013-2015
• International Business, for Seung-Hyun Lee, PhD 2015
• Macro-Organizational Empirical Investigation, for Richard Harrison, PhD 2014
• Research Assistant, for Sheen Levine 2014
• Social Entrepreneurship, for Toyah Miller, Undergraduate 2015
• Entrepreneurship in the Social Sector, for Toyah Miller, MBA 2015
RESEARCH INTERESTS
Strategic management, International business, Corporate political strategy, Foreign market entry,
Joint ventures, Strategic alliances, Mergers and acquisitions
PUBLICATIONS
Kim, J. & Kim, K. 2017. Local Partners’ Network Effect on IJV Longevity in Different
Institutional Contexts at Asia-Pacific Journal of Management, Forthcoming.
WORKING PAPERS
Kim, J., & Lee, S.-H. 2016. Political Market Competition in Transition Economies.
Organization Science, Under Review.
Kim, J., Weng, D.H., & Lee, S.-H. 2016. Home Country Bribery and Interest in Foreign Markets:
A Study Based in Firms in Transition Economies. Global Strategy Journal, Under Review
Shin, H., & Kim, J. 2016. Asymmetry in Asset Specificity, Opportunism, and Safeguard in
Bribery Transactions. Strategic Management Journal, Reject and Resubmit
Kim, J. 2016. Managing Increased Dependencies from Product Recalls in a Highly Regulated
Industry: A Stakeholder Approach. Business and Society, Under Review.
Kim, J., Xia, J., & Lee, S.-H. 2016. Divergent Alliance Learning and Cross-Border M&A
Entries. Work in Progress.
Lee, S.-H., Gokalp, O., & Kim, J. 2016. Corporate Tax Compliance Decision in Transition
Economies. Work in Progress.
CONFERENCE PRESENTATIONS
Shin, H., & Kim, J. 2016. Asymmetry in Asset Specificity, Opportunism, and Safeguard in
Bribery Transactions at Academy of Management Annual Conference (Anaheim, CA).
Kim, J. 2016. Managing Increased Dependencies from Product Recalls in A Highly Regulated
Industry: A Stakeholder Approach at Academy of Management Annual Conference (Anaheim,
CA).
Lee, S.-H., Gokalp, O., & Kim, J. 2016. Corporate Tax Compliance Decision in Transition
Economies at Academy of Management Annual Conference (Anaheim, CA).
Kim, J., & Lee, S.-H. 2016. Intercorporate Political Competition in Transition Economies at
Academy of International Business Annual Conference (New Orleans, LA).
Kim, J. 2015. Local Partners’ Network Effect on IJV Longevity in Different Institutional
Contexts at Academy of Management Annual Conference (Vancouver, Canada).
Kim, J., & Lee, S.-H. 2015. Intercorporate Political Competition in Transition Economies at
Academy of Management Annual Conference (Vancouver, Canada).
Kim, J., & Lee, S.-H. 2015. Intercorporate Political Competition in Transition Economies at
Management Scholars Professional Development Workshop, Oklahoma State University,
Stillwater, OK. (Won the Best Paper Award).
Kim, J., & Lee, S.-H. 2014. Patent Litigation as Real Options Exercise in a Highly Competitive,
Dynamic Market at Academy of Management Annual Conference (Philadelphia, PA).
Lee, S.-H., Weng, D.H., & Kim, J. 2014. The Effect of Firm Bribery in Transition Economies
on Firm's Market Focus between Domestic and Foreign Markets at Academy of International
Business Annual Conference (Vancouver, Canada).
Chair for BPS Paper Session # 1868 “Uncertainty and Firm & Technological Scope” at Academy
of Management Annual Conference (Philadelphia, PA).
2016 Doctoral Consortium at Academy of International Business Annual Conference (New
Orleans, LA).
2016 BPS and IM Division Doctoral Consortium at Academy of Management Annual Conference
(Anaheim, CA).
AWARDS & HONORS
2016 Runner-up for AIB Best Doctoral Dissertation Proposal Award
2016 Scholarship Winner for Academy of International Business (AIB) Doctoral
Stipend Program
2016 Finalist for The University of Texas at Dallas OWLIE Best PhD Student Award
2013–2017 The University of Texas at Dallas Graduate Teaching Assistantship and Tuition
Scholarship
LANGUAGUES
English, Korean, French
PROFESSIONAL MEMBERSHIPS
Ad hoc Reviewer, Academy of Management Annual Meetings 2014 – present
Member of the Academy of Management 2014 – present
Ad hoc Reviewer, Academy of International Business Annual Meetings 2014 – present
Member of the Academy of International Business 2014 – present
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