Business-Government Relationship and Foreign Market Entry

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



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



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.

http://scholar.google.com/citations?user=OLNLSlMAAAAJ&hl=en&oi=sra


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.


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.


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)


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)


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/.

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.


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.

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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).

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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

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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

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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

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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

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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

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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).

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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.

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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

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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).

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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)

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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.

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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.

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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).

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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×


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

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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


export ratio

Additional variable:

diplomatic relations


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×


-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: [email protected]

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).


Academy of Management Annual Conference (Vancouver, Canada).


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

Documents

Business-Government Relationship and Foreign Market Entry