Academy of International Business Best Paper Proceedings 2008

# AIB2008-0215

Competition, Transnational Learning, and Foreign Entry Strategy

Jiatao Li Hong Kong University of Science and Technology

mnjtli@ust.hk

Jing Yu (Gracy) Yang University of Sydney

g.yang@econ.usyd.edu.au

Paper presented on July 2, 2008

at the AIB Annual Conference, Milan, Italy http://aib.msu.edu/events/2008/

© 2008 Jiatao Li and Jing Yu (Gracy) Yang. Paper may be downloaded for personal use only and cannot be distributed without the explicit permission of the authors.

Competition, Transnational Learning, and Foreign Entry Strategy

Jiatao Li and Jing Yu Yang

Abstract: Drawing on organization theory perspectives, this study investigates how multinational corporations based in different home countries influence each other’s foreign entry decisions. The proposition that the subsidiaries of multinationals from different countries constitute a reference environment and that this environment provides important information for potential new entrants was tested with panel data on foreign entries from 55 home countries into China from 1979 to 1995. The rate of new entries from a focal home country was found to correlate with the number of foreign subsidiaries already established by firms from other home countries with cultures similar to that of the focal home country. This was interpreted as reflecting trans-national learning and competition. Uncertainty derived from home-host country trade ties and cultural differences was shown to moderate this trans-national mimetic learning. Key words: Trans-national learning, uncertainty, foreign subsidiaries Acknowledgements: The paper is based on a presentation at the “New Perspectives on Multinational Subsidiary Research” workshop at Newcastle, Australia on 13 November 2006. We would like thank our three editors (Joe Chang, Stephen Nicolas, and Elizabeth Maitland), and all other participants in the workshop for their helpful comments. We gratefully acknowledge support from the Research Grants Council of the Hong Kong government through grant HKUST6196/04H.

Jiatao Li Professor and Head Department of Management of Organizations Hong Kong University of Science and Technology Clear Water Bay, Hong Kong mnjtli@ust.hk Jing Yu Yang Assistant Professor Discipline of International Business University of Sydney 303 Institute Building H03 g.yang@econ.usyd.edu.au

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INTRODUCTION A salient feature of the world economy over the past two decades has been the

emergence of China as a leading destination for foreign direct investment (FDI) by

multinational corporations (MNCs). Investors from a large number of countries have

contributed, although unevenly, to this growing trend. For instance, the world’s leading

manufacturers of computers, electronic products, telecommunications equipment and

petrochemicals have expanded their production networks into China, turning China into a

global manufacturing center (United Nations, 2001). While China has undoubtedly become

an important part of the world for many multinationals, an intriguing question arises as to

how internationalizing firms based in different countries manage the uncertainties associated

with investing in China’s transition economy. This study focused on this question and

investigated how the entry strategy about entering into China has diffused across firms from

different countries.

Recent studies have begun to examine foreign market entry from macro perspectives

such as the institutional theory, interorganizational learning, and organizational ecology

(Guillén, 2002, 2003; Henisz & Delios, 2001; Li, Yang & Yue, 2007; Yiu & Makino, 2002).

Although the general themes of these perspectives on foreign entry are distinct, they share a

common focus on the interorganizational dynamics and social influences among firms. For

instance, the institutional perspective suggests that multinational corporations try to reduce

uncertainty in foreign markets by imitating the market entry strategies of other MNCs,

particularly those perceived as similar to themselves in terms of certain traits, such as being

from the same home country, in the same industry, or of similar size.

Nevertheless, this line of research has been limited in at least two aspects. First,

MNCs are embedded in a network of social relations with other MNCs spread all over the

world, and their strategic decisions, including foreign market entry strategies, are likely to

be influenced by the actions of other MNCs in this global network (e.g. Hannan & Freeman,

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1995; Li, Yang & Yue, 2007). Yet, previous studies have mostly examined firms’ foreign

expansion from a single home-country to a single or multiple host countries (e.g., Chan,

Makino & Isobe, 2005; Guillén, 2002; Henisz & Delios, 2001), and defined the community of

social influence within a home country boundary. These studies have been unable to examine

the actions of MNCs from different home countries, and thus have ignored any trans-national

social influences, which could crucially influence MNCs’ international expansion decisions.

In addition, the institutional perspective has emphasized the idea that uncertainty

increases the importance of social considerations relative to technical ones (Abrahamson &

Rosenkopf, 1993; DiMaggio & Powell, 1983; Festinger, 1954; Haunchild & Miner, 1997). In

the case of international expansion, firms deal with numerous uncertainties (Guillén, 2002;

Henisz & Delios, 1999; Martin, Swaminathan & Mitchell, 1998), yet studies of the process

have mainly focused on host country economic, cultural and institutional difficulties

(especially political hazards), or firm-specific uncertainties resulting from a firm’s lack of

foreign experience (Henisz & Delios, 2001; Lu, 2002). We know relatively little about the

impact of uncertainty emanating from home-host country relationships (such as trade ties and

cultural differences). There has been work on its direct effects on MNCs’ global strategies

(Hennart & Larimo, 1998; Johansen & Vahlne, 1977), but relatively little research has

examined its moderating effect on MNCs’ responses to social influences.

Our study was designed to focus on foreign market entry decisions by firms from a

number of countries investing in China, a market where high uncertainty prevails. It

investigated how firms based in different home countries influenced each other’s

international expansion decisions. Firms from the same home country were regarded as a

population of social actors embedded in an environment of firms from many other nations.

The focus was on the rate of entry of firms from a particular home country. The study

evaluated the proposition that this entry rate might be related to the number of foreign

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subsidiaries already in operation in the same industry launched by firms originating in other

home countries, particularly those culturally similar to the home country of the focal firm. A

high-level process of cross-national learning and competition in a specific industry was thus

being examined.

The perceived uncertainty of investing in China was treated as a potential moderator

of the trans-national learning. Uncertainty was defined in terms of two types of home-host

country relationships: home-host country trade ties, and cultural distance. A high level of

such uncertainty was expected to strengthen mimicry among MNCs in their decisions to enter

the China market.

Panel data on China manufacturing investments by firms from 55 home countries in

27 manufacturing industries from 1979 through 1995 was used in the analysis. Since China

did not officially open its economy to FDI until late 1978, this dataset allowed us to study FDI

from the beginning of China’s market transition. Consistent with previous research, the

hypotheses and tests reported in this study are cross-industry, and a comprehensive set of

control variables were also included in the models to account for alternative economic

explanations.

THEORY AND HYPOTHESES

Cross-national Learning in a Local Industry

According to the institutional theory perspective, firms operate within a social

framework of values and norms, and when coping with uncertainty, tend to behave socially,

imitating the actions of others. In other words, firms tend to search for practices and structures

which have already been legitimated because they have been adopted by other firms within

their immediate environment, particularly firms which are easily observable, similar to

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themselves, or socially prominent (DiMaggio & Powell, 1983; Haunschild & Miner, 1997;

Tolbert & Zucker, 1983).

In the case of foreign expansion, firms act not only strategically as suggested in the

traditional international management literature, but also socially, as suggested by the

organizational theory literature, to mimic the actions of other foreign firms, including those

from different countries (Li et al., 2007). In general, firms expanding into a new host market

face great uncertainty, especially when they lack the information they need to evaluate

present and future market opportunities there and how they can take advantage of them

(Guillén, 2002, 2003; Henisz & Delios, 2001). The pattern of foreign firms which have

already invested in the host market can provide important cues and serve as a reference point

for later entrants. They can to some extent mitigate their uncertainty in choosing which

market to enter and how to enter by observing, understanding and mimicking the actions of

their reference groups (Guillén, 2002; Henisz & Delios, 2001; Li et al., 2007).

In addition, foreign firms entering a foreign market often must evaluate their

legitimacy in the eyes of various audiences (Kostova & Zaheer, 1999; Li et al., 2007). Under

conditions of information asymmetry and bounded rationality, to make sense of the

legitimacy requirements of the host market, potential foreign investors may rely on observing

the actions of previous investors, expecting the audiences to judge the legitimacy of new

foreign entries by referring to the legitimacy of established foreign subsidiaries that belong to

some similar group (Kostova & Zaheer, 1999). Therefore, as the population of a certain group

of foreign subsidiaries in a host country increases, the visibility and legitimacy of such

foreign operations may be strengthened (Dobrev, 2001; Li et al., 2007). With enhanced

legitimacy, new foreign entrants may find it easier to overcome any local resistance and to fit

in with the host country environment (Guillén, 2002). So the presence of established foreign

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subsidiaries in a host country can serve as a signal of enhanced legitimacy for potential

entrants.

Previous work has demonstrated that the community of foreign firms originating from

the same home country constitutes an important institutional structure for subsequent

entrants, leading to home country-based mimicry (Guillen, 2002; Yiu & Makino, 2002). The

essential idea is that each home country has its distinctive cultural value system, and

regulatory, normative, and cognitive institutions which evolved through social interactions at

home (Westney, 1993; Murtha & Lenway, 1994). This encourages firms with the same

country of origin to consider each other as salient reference peers and then makes them more

likely to imitate each other’s foreign entry behaviors. Also, a large population of foreign

subsidiaries from a particular home country enhances the legitimacy of the home-host

country linkages, and encourages local acceptance of investors from this home country.

It is equally important to recognize that mimetic learning may take place among

foreign investors across national boundaries (Hannan, et al., 1995; Zaheer & Zaheer, 1997).

The community of foreign entries in the same industry but from different home countries

serves as another reference group which may affect subsequent foreign entries. Industries can

often become “pools of information about the characteristics and behaviors of firms” (Porac

& Rosa, 1996). Firms in an industry engage in “collective sense-making”, and are generally

regarded as a cognitively relevant peer group by firms that need to gain the legitimacy in a

host environment (Fligstein, 1985; Henisz & Delios, 2001). Foreign subsidiaries invested by

investors from different home countries operating in the same local industry have to compete

with similar product/service offerings, satisfy similar customer needs, and face a common set

of local legitimating pressures (Li, et al., 2007). So the greater the number of different home

countries’ foreign subsidiaries established in a specific local industry, the more effectively

that industry can help legitimate new foreign investors. That is to say, the entry rate of

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Australian multinationals into a specific industry in China can be spurred by a larger

population of established American, Japanese or European firms in the same industry.

We take a step further and argue that potential entrants from one home country will

not regard all other countries’ foreign subsidiaries in the local industry as equally informative

or legitimate. It is known that cultural notions and institutional arrangements shape

managerial ideologies and set constraints on firms’ strategic behaviors (Murtha & Lenway,

1994; Westney, 1993). Countries with similar cultures and value systems tend to share similar

attitudes toward and understandings about the host country, and thus to display similar

patterns of behavior. Accordingly, firms from culturally similar countries tend to develop a

similar mindset and adopt similar strategies. In their international expansion, they may

actively compare themselves to each other, and be inclined to consider each other as reference

points. The Australians may study American firms, but the Koreans may be more likely to

look to the Japanese. Therefore, we propose:

Hypothesis 1: In a specific industry in a specific host country, the greater the number of foreign firms from other home countries with which a focal country is culturally proximate, the greater will be the rate of entry from the focal country.

Cross-national Competition in a Local Industry

Competition is another important consideration in foreign market entry decisions.

According to the organizational ecology perspective, when the number of organizations in a

market becomes relatively high, the legitimation process gives way to the process of

competition (Carroll & Hannan, 1989; Hannan & Carroll, 1992). Competition intensifies

when organizations vie for limited common resources (Hawley, 1950: 202). Research from

the ecological perspective has consistently found that the potential for competition between

any two organizations is proportional to the overlap of their resource needs: the more

organizations’ niches overlap, the more they require similar resources to survive and thrive,

and the more intensely they compete (Baum & Singh, 1994; Hannan & Freeman, 1989).

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Organizations define competitive relations with other organizations by staking out market

niches, which may include claims about the clientele served, products and services provided,

and technology employed (Levine & White, 1961). Industry type is a criterion often used to

define market niches, and organizations operating in the same industry are often construed as

direct competitors (Hannan & Freeman, 1989).

In line with this logic, any increase in the total number of foreign subsidiaries in a

local industry, whether from culturally similar or dissimilar home countries, signals increased

competition for both potential entrants and entrenched foreign firms. The intensified

competition for the skilled labor, access to local distribution, suppliers and customers will

inevitably discourage additional foreign firms from entering that industry. Therefore, we

propose:

Hypothesis 2: In a specific industry in a specific host country, the rate of entry from a focal country will begin to decrease as the number of foreign firms from other home countries becomes too large.

The Moderating Effect of Uncertainty

These predictions about cross-national learning in a local industry, even after

considered the cultural differences between home countries, still fail to explain how firms from

various home countries might respond differently to a similar social learning situation. Firms

from Australia versus from USA, and firms from Japan versus Korea would be unlikely to deal

with social learning in the same way. Results of research applying the institutional perspective

suggest a significant factor accounting for the heterogeneity in firms’ mimetic actions is

uncertainty (Davis 1991; Festinger, 1954; Haunschild & Miner 1997; Palmer, Jennings, &

Zhou, 1993; Rogers, 1995). The institutional approach generally emphasizes the idea that

uncertainty increases the importance of social considerations (DiMaggio & Powell 1983;

Meyer & Scott 1983). Yet previous studies have largely assumed the existence of a general

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environmental uncertainty without specifying the sources of specific uncertainties. The present

study attempted to identify different sources of uncertainty in the context of FDI in an

emerging economy, and investigated how the uncertainties moderate the influence of social

learning on the entry rates of foreign firms from different home countries.

When expanding into a new host market, foreign firms may encounter different types

of uncertainties, including behavioral and contextual uncertainties (Root, 1988; Shan, 1991),

as well as firm-specific and policy-related uncertainties (Henisz & Delios 2001). Facing these

uncertainties, foreign firms generally refer to the actions of other relevant actors in the

immediate environment for making decisions cues (Guillén, 2002). But such social learning

processes differ among firms, and in fact firms’ perceptions of uncertainties vary as a

function of their surrounding environments, interorganizational networks and firm-level

attributes, such as age and experience in different fields or social groups (Guillén 2002;

Henisz & Delios 2001). Extending this logic to firms from the same home country investing

in China, we argue that firms from different nations will feel different levels of uncertainty,

and this should evoke different responses.

Prior research has confirmed that international firms operating in multiple

institutional environments face a variety of institutional pressures (Kostova & Zaheer, 1999).

Some of these pressures emanate from the host country and others from the home country, but

both influence global strategy (Rosenzweig & Singh, 1991; Westney, 1993). The differences

between home and host country and their interactions are likely to influence international

firms’ perceptions of uncertainty and in turn their foreign entry strategies.

This suggests the uncertainty which may moderate the responses to the social

learning, arises from home-host countries’ economic interactions and cultural differences.

Firms from a home country which has weak trade ties with the host country are unlikely to

have developed sufficient local knowledge to analyze host country opportunities correctly.

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As a result, such firms will tend to seek local information by referring to the established

foreign subsidiaries of firms from other culturally similar home countries. In contrast, if a

home country has built a strong trade relationship with the host country, new investors from

that home country can more easily access local market information and better understand the

host country market (Johansen & Vahlne 1977). These firms should be thus less likely to

model their foreign entry decisions on those of firms from other countries. So home-host

country trade ties can be expected moderate the relationship between the cross-national

mimetic learning and the entry rate from a particular country.

Hypothesis 3a: Any positive relationship between cross-national learning and the rate of entry from a focal home country will be weaker when the focal home country has stronger trade ties with the host country. In addition home-host country culture distance should also moderate the

cross-national learning process. When a home country is culturally very different from a

potential host country, firms from that home country may refrain from investing. This should

be so because business activity in a host country requires conformity to certain distinctive

cultural norms, institutional rules and business recipes that may be in conflict with those of

the home country (Kostova, 1999; Kostova & Zaheer, 1999). The larger the cultural distance

between the home and host countries, the higher the perceived contextual uncertainty about

entering the host country, hindering home country firms’ ability to tap into the collective

understanding of the local market (Hennart & Larimo, 1998; Xu & Shenkar, 2002). When

firms do consider entering a culturally distant host market, they are more likely to refer to

established foreign subsidiaries from culturally similar home countries for information.

Therefore, the relationship between the cross-national learning and foreign entry rates should

be stronger when the home country is culturally distant from the host country.

Hypothesis 3b: Any positive relationship between cross-national learning and the rate of entry from a focal home country will be stronger when the focal home country is culturally distant from the host country.

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RESEARCH METHODS

Sample and Data Sources

The hypotheses were tested using data on foreign-invested manufacturing ventures in

China over the 1979-95 period. China provides an excellent setting for examining the

influence of social process on organizational behavior (Guillén 2002, 2003, Scott 2002). The

FDI data series began from the start of China’s economic transition, and China’s institutional

context during the study period is widely considered to have been complex and highly

uncertain (Child 1994). In conditions of high environmental uncertainty, social influence

processes and social referents are crucial for organizations making decisions (Festinger,

1954; Greve, 1995, 1996; Janis, 1982; Salancik & Pfeffer, 1978; Scott, 2002).

The raw data were compiled by the research institute of the Ministry of Foreign Trade

and Economic Cooperation (MOFTEC) in Beijing. This database contains a brief profile of

each foreign-invested firm that operated in China during the 1979-95 period, with data on the

year of each investment, the national origin of the foreign parent, industry, and total

investment. All foreign ventures in the manufacturing industries were included in the sample,

excluding those in service sectors where the government tended to have more restrictions on

foreign investment (Child, 1994). Focusing on manufacturing facilitated comparison with the

results of previous studies (e.g., Bastos & Greve 2003; Guillén, 2002; Henisz & Delios,

2001). The analysis covered 85,528 foreign entries from 55 home countries in 27

manufacturing industries (2-digital SIC level) over the 16-year study period.

The unit of analysis for the study was defined at the level of the home country, host

country and industry. The foreign entry data was first aggregated from the individual

investments to the home country-local industry level. Following the methods of previous

studies, the year of the first FDI at the home country-local industry level was defined as the

beginning of the observations (Carroll & Hannan, 2000; Henisz & Delios, 2001; Ruef, 2000).

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Year one observations for each home country-industry pair were lost, since all independent

and control variables in the models were lagged by one year. The final sample consisted of

4,973 home country-industry-year cells.

Variables and Measures

FDI entry rate. The dependent variable in this study was the entry rate, measured as

the number of foreign subsidiaries newly established in China in a particular year in a

particular industry and from a particular home country. This was a count variable with an

average value of about 17 entries.

Cross-national Learning. To examine cross-national learning, a composite measure

was devised to capture the extent to which a country’s cultural values resembled those of

other countries, and this was analyzed in tandem with the number of FDI entries from those

countries already in place in China in the same industry. Cultural distance was calculated

based on Hofsete’s four culture dimensions (Kogut & Singh, 1988). Cultural similarity was

expressed as a transformation from the cultural distance, specifically measured as one minus

a proportion calculated as the cultural distance between focal country i and another country j

in an industry k divided by the maximum cultural distance in that industry. This index was

multiplied by the number of same-industry FDI entries into China by firms from country j.

The cross-national learning effect was thus highest when a focal country i’s cultural values

overlapped those of the other countries which had established a large number of subsidiaries

in China in the same industry. Formally, the measure of cross-national learning for focal

country i in industry k at time t-1 was

n

∑ FDI jkt-1 (1 - CDijk(t-1) / CDk(t-1) )

j=1=

Culture-weighted FDI density in a local

industryik(t-1)

where CDijk(t-1) is the cultural distance between countries i and j for industry k during year t-1,

CDk(t-1) is the maximum cultural distance in the same industry, and FDI jk(t-1) is the number of

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investments in industry k at t-1 by firms from country j. The logarithm of this variable was

used in the analysis.

Cross-national competition. To examine the cross-national competition effects in a

local industry, a cross-national FDI density variable was constructed as the number of

existing foreign subsidiaries from all the other foreign countries (other than the focal home

country) in an industry in the previous year. The square of this count variable was used to

represent the competition effect.

To explore the moderating effects, each home country’s trade volume with China was

included in the analysis, which would be expected to have a positive direct effect on FDI

entries according to the staged model of international expansion (Dunning 1993, Johanson &

Vahlne, 1977; Kogut & Zander, 1993; Vernon, 1979). Each home country’s cultural distance

from China was then calculated using the methods of Kogut and Singh (1988). This was

expected to have a negative relationship with foreign expansion (e.g.Dunning, 1988). The

trade volume and cultural distance variables were tested as moderators of the relationship

between learning and entry rates, as discussed in H3.

Control Variables

The logarithm of a focal home country’s own FDI density in the local industry was

first tested as a control variable. Several industry-level controls were also included in the

analysis, including concentration, average profitability, and sales growth. The local industry

concentration was measured as the percentage of industry revenues accounted for by the eight

largest firms in China. The data was collected from China’s Top 100 Companies Across

Industries (1993-96). Comparable data was obtained only for 1992-95, and the four-year

average of this ratio was used to control for the industry structure in China. High local

industry concentration would be expected to have a negative relationship with new FDI

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entries (Caves, 1996; Hennart & Park, 1994). The average profitability of the local firms in

the industry in China was controlled for as well. Because of data limitations, it was necessary

to use the percentage of local firms which had suffered losses as an indication of this

construct (reverse coded and labeled as the ratio of negative profit firms). This negative

profitability ratio would be expected to show a negative relationship with new entries. The

annual growth in industry sales was also controlled for, a variable which should relate

positively with the rate of new foreign entries. The data was obtained from the China

Statistical Yearbook (various years).

Following the lead of Cosset & Roy (1991), China’s political risk level was also

controlled for in the models, using the annual country risk ratings provided by Institutional

Investor magazine. GDP per capita was also included to control for home country

heterogeneities. Finally, home-country and industry fixed effects were included.

RESULTS

The means, standard deviations and correlation coefficients for all the variables are

presented in Table 1. Table 2 reports the results of negative binomial regression models1

(Greene, 1996) of the FDI entry rate. Model 1 included the control variables only. Models 2

and 3 added the main effects of cross-national learning and local competition. Models 4-5

added individual interactions between the two moderating variables, i.e. home-host trade

relationships and cultural difference, and mimicry (measured by the culture-weighted FDI

density in the local industry). Finally, Model 6 included all the main and interaction terms.

The incremental chi-square statistics show a significant improvement in fit for models 2-6

compared with their respective baselines.

1 The Poisson process serves as a natural baseline model for organizational entries (e.g. Carroll & Hannan 2000, Hannan & Freeman 1989). However, the variance of the FDI entry count (87.8) was nearly 5 times larger than the mean (17.2), a sign of over-dispersion. To correct for the over-dispersion in the data, we used the negative binomial model (see Carroll & Hannan 2000, McKendrick et al. 2003).

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------ Insert Tables 1-2 here ------

Hypothesis 1, predicting a relationship between mimetic learning and entry rates,

received strong support. The results show that a home country’s FDI entry rate was

significantly higher when other home countries with similar culture values had previously

invested extensively in China in the same industry. Yet, models 3-6 also predict that as the

total number of subsidiaries from other countries (similar and dissimilar) gets too high,

potential foreign entrants start to encounter competitive pressures, supporting Hypothesis 2.

Hypothesis 3 predicts that high uncertainty (as measured by a large cultural distance

and weak trade ties between the home country and China) will moderate the mimetic learning

relationship described in Hypothesis 1. The results generally corroborated the predictions.

Models 3 to 5 show that mimetic learning was significantly weakened by strong home

country trade ties with China (p ≤ 0.001), but was significantly strengthened by cultural

distance (p ≤ 0.001). Therefore, Hypotheses 3a and 3b were fully supported in the analysis.

The two graphs in Figure 1 illustrate these significant moderating effects as presented in the

full model (Model 6). They demonstrate the trans-national mimicry effect in a local industry

at different levels of the home country’s cultural distance from China, and its trade ties with

China. The first graph illustrates that the trans-national learning effect is strong when trade

with China is low, but this learning effect attenuates as the home country builds closer trade

ties with China. The second graph demonstrates that trans-national learning is more salient

when the home country is at a greater cultural distance from China.

------ Insert Figure 1 here ------

Home country trade with China showed a significant and positive relationship with

FDI entry, while cultural distance showed a significant negative relationship, consistent with

expectations. The models showed that the home country’s own prior investment in the same

industry in China had a positive correlation, confirming the findings of previous studies

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(Chan, et al., 2005; Guillén, 2002). Local industry concentration showed the expected

negative correlation with further FDI entry. This may indicate that multinational firms tend to

avoid entering industries dominated by a few large domestic players. Local industry growth

and profitability were also related to new FDI entries, with foreign investors entering

high-growth and avoiding lower profitability industries. China’s significant country risk had

a negative effect in all models. As expected, a home country’s GDP per capita had a positive

relationship with its propensity to invest in China.

DISCUSSION AND CONCLUSIONS

It has been argued that national economies are steadily becoming more global as

cross-border flows of trade, investment, and capital increase. This study has dealt with

trans-national learning and competition forces, and tested their influence on the foreign

expansion strategies of multinationals. Specifically, we found that: 1) the entry rate of foreign

subsidiaries was significantly higher in situations where firms from other culturally similar

home countries had already invested extensively (learning effect); yet 2) this entry rate

declined as the local industry became crowded with many foreign subsidiaries (competition

effect); and 3) the cross-national learning effect was strengthened in highly uncertain

situations, for example, when the trade volume between the home country and China was low

or the cultural distance between them was great.

This study has applied macro organizational theories to examine trans-national

influences on firms’ international entry strategies. Internationalization of firms has received

relatively little attention from organizational theorists up to now (Guillén, 2002; Henisz &

Delios, 2002), and insufficient attention has been paid to international influences on firms’

strategies, such as the relationships and differences between countries and their positions in

global networks (Guler et al., 2002; Xu & Shenkar, 2002). In response to these concerns, this

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study applied institutional theory and organizational ecology to the analysis of foreign

expansion strategies, considering the cross-national social influences involved. Such macro

perspectives seem particularly germane to this context, because the economic, regulatory, and

political situation in an emerging host country is often highly uncertain, leading foreign firms

to examine the actions of peer firms for cues about whether or not to enter that particular

market. Treating firms from one home country as a unique population of actors, we have

argued that they are typically embedded in a complex network of multinationals from a

diverse set of home countries. These international influences constitute an additional

dimension in a firm’s foreign expansion decisions.

By considering firms from other home countries as key constituents of the

environment within which firms from a specific home country are embedded, the results

show that learning and competition in a local industry serve as two critical forces linking

firms from different home countries, and that they convey important information about

legitimacy and opportunities for potential foreign entrants. This is consistent with the

findings of organization studies which have shown that firms’ strategic decisions are shaped

not only by the technical merits of the strategy itself, but also by the sociological context in

which they are taken (Rogers, 1995). By demonstrating the substantial roles of

trans-national learning and competition, this study lends credence to the institutional and

ecological processes of internationalization, and extends the range of applicability of macro

organization theory.

On top of the main effects, this study identified two variables which moderate the

trans-national mimetic learning process. The home country’s trade ties with the host and its

cultural distance from the host country have been shown to predict the variance in the

uncertainties perceived in regard to entry decisions. This in turn helps explain how such

uncertainties may moderate the cross-national learning process in determining subsequent

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entry rates. These results are consistent with but go beyond the previous work on the

moderating effect of uncertainty in inter-firm imitation (Haunschild & Miner, 1997; Henisz

& Delios, 2001). They provide new evidence to support the contention that cross-national

isomorphism in a local industry is stronger in highly uncertain situations, particularly when a

firm invests in a host country which is culturally distant from and/or has weak trade ties with

its home country. These results thus extend the study of uncertainty and mimetic learning to a

multinational context.

The findings of this study provide useful information for managers faced with foreign

entry decisions, particularly entry into emerging markets such as China. Emerging countries

are characterized by weak capital markets, poorly specified property rights, and high

institutional uncertainty (Nee, 1992). Our findings suggest that international firms investing

in such markets need to consider their home country’s economic, institutional and cultural

similarity with the host market, as well as the actions taken by peer firms from other

countries.

Despite its promising results and useful implications, this study has several

limitations which suggest areas for future research. First, the country-industry level of

analysis did not allow for a direct comparison between social effects and technical efficiency

considerations in the formulation of multinationals’ foreign entry decisions. Institutional

theorists have argued that organizational practices adopted on the basis of social

considerations can be decoupled from the technical considerations affecting organizations

(Meyer & Rowan, 1977). This study included industry controls, but, because of the

aggregated level of analysis, could only use proxies measured at the population level. Further

research is clearly needed to theoretically and empirically isolate the institutional forces at

18

work from other plausible and well-established technical mechanisms influencing firms’

international strategies2.

Of course, this study focused on only one host country, China. China’s emerging

economy served as an ideal empirical context for such a study, but additional research is

warranted to examine cross-national learning and competition forces in other settings. In

addition, this study focused on how firms from different home countries influenced each

others’ entry decisions, looking particularly at the cultural similarities between home

countries. Our theories would be further validated if we could identify alternative types of

linkages among home countries in their learning dynamics, such as worldwide trade

networks, and examine these influences on entry decisions.

A proper understanding of the complexities of internationalization apparently cannot

be achieved by focusing on a single country. To fully understand how organizational

practices and norms diffused internationally, a broader definition of the institutional

environment is required. The core logic of the present study – that cross-national learning and

competition influence the entry of foreign subsidiaries in a host market – can be applied to a

number of other international decisions such as the choice of entry mode, the use of expatriate

managers and the design of human resource management practices. These should be

interesting topics for future studies.

2 We have conducted a robustness check using 940 Japanese publicly listed firms. The results showed that individual firm’s foreign entry rates in China were influenced not only by traditional firm-level determinants such as their prior experience in the host country and R&D intensity, but also by the investments of firms from other countries. This supported our conclusions about the trans-national mimetic learning effect. Detailed results are available on request.

19

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Table 1: Summary and Correlation Statistics a

Mean s. d. 1 2 3 4 5 6 7 8 9 10

1 FDI entry 17.20 87.82 -

2 Culture-weighted FDI density in local industry t-1 2.37 1.37 0.01 -

3 (Cross-national FDI density in local industry t-1) 2/10 8

0.06 0.19 0.04 0.50 -

4 Home country trade with China 0.06 0.11 0.54 -0.27 -0.07 -

5 Cultural distance from China 0.21 0.13 -0.19 0.11 0.04 -0.27 -

6 Home country FDI density in local industry t-1 /102 1.63 1.56 0.49 0.22 0.16 0.63 -0.26 -

7 Industry concentration 43.33 2.76 0.04 0.09 -0.06 0.02 0.02 -0.01 -

8 Ratio of negative profit firms t-1 10.87 9.47 -0.05 -0.27 -0.17 0.07 -0.01 -0.07 0.00 -

9 Industry sales growth t-1 0.26 0.19 0.02 0.05 0.11 -0.04 0.00 -0.01 -0.19 0.05 -

10 China’s political risk 0.23 0.19 0.02 0.44 0.50 -0.06 0.04 0.13 0.03 0.03 0.06 -

11 GDP per capita t-1 /104 1.30 0.94 0.07 0.05 -0.02 0.21 0.57 0.25 0.06 0.04 -0.06 0.02

Note. a N = 4,973. All correlations > | 0.025| are significant at the 5 percent level.

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Table 2: Negative Binominal Analysis: Entry Rate of Foreign Subsidiaries in China, 1979-95 a

Variable M1 M2 M3 M4 M5 M6 Culture-weighted FDI density in local industry t-1

H1+ 0.17 *** 0.23 *** 0.23 *** 0.16*** 0.17***

(0.03) (0.03) (0.03) (0.04) (0.04)(Cross-national FDI density in local industry) t-1 2/10 8 H2- -1.08 *** -1.10 *** -1.09*** -1.10***

(0.11) (0.11) (0.11) (0.11)Culture-weighted FDI

density x Trade value H3a+ -0.59 *** -0.54***

(0.11) (0.11)Culture-weighted FDI density x Cultural distance H3b- 0.35*** 0.26**

(0.11) (0.11)Home country trade with 2.38 *** 2.75 *** 2.75 *** 3.50 *** 2.88*** 3.54*** (0.24) (0.24) (0.24) (0.28) (0.24) (0.28)Cultural distance from China -11.29 *** -17.72 *** -18.75 *** -16.8*** -17.69*** -16.24 (1.74) (2.05) (2.04) (2.06) (2.05) (2.07)Control variables Home country FDI density in local industry t-1 /102 0.49 *** 0.41 *** 0.42 *** 0.43 *** 0.42*** 0.44***

(0.02) (0.02) (0.02) (0.02) (0.02) (0.02)Industry concentration 0.05 *** 0.05 *** 0.05 *** 0.05 *** 0.05*** 0.05*** (0.01) (0.01) (0.01) (0.01) (0.01) (0.01)Ratio of negative profit firms -0.32 *** -0.29 *** -0.27 *** -0.29 *** -0.27*** -0.29*** (0.03) (0.04) (0.03) (0.04) (0.03) (0.04)Industry sales growth t-1 1.06 *** 0.96 *** 1.00 *** 1.06 *** 1.01*** 1.06*** (0.09) (0.09) (0.09) (0.09) (0.09) (0.09)China’s political risk -1.06 *** -1.21 *** -0.79 *** -0.78 *** -0.80*** -0.78*** (0.10) (0.10) (0.11) (0.11) (0.11) (0.11)GDP per capita t-1 /104 0.89 *** 0.71 *** 0.64 *** 0.76 *** 0.63*** 0.75*** (0.06) (0.06) (0.06) (0.07) (0.06) (0.07)Intercept -1.86 *** -1.41 *** -1.24 *** -1.61 *** -1.30*** -1.63*** (0.26) (0.27) (0.27) (0.28) (0.27) (0.28) Dispersion parameter (α) -0.73 *** -0.74 *** -0.78 *** -0.80 *** -0.79*** -0.81*** (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) Degrees of freedom 85 86 87 88 88 89Log likelihood -10905.28 -10886.72 -10843.61 -10830.01 -10838.26 -10827.04Chi-square change (df) vs. - 37.12(1)*** 86.22(1)*** 27.2(1)*** 10.7(1)*** 33.14(2)***

baseline - M1 M2 M3 M3 M3

Note. a N = 4, 973. Non-standardized coefficients are reported with standard errors in parentheses. Industry and home-country fixed effects are not reported here. * Significant at the p≤0.05 (** p≤0.01; *** p≤0.001) level.

25

Figure 1a: Moderating Effects of Trade Ties on the Relationship between Cross-national Learning and Foreign Market Entry

26

0.00

0.40

0.80

1.20

1.60

2.00

-1.5 sd -1.0 sd -0.5 sd mean +0.5 sd +1.0 sd +1.5 sd +2 sd

Mul

tiplie

r Rat

e of F

orei

gn E

ntry

Mean trade value +1 sd

Mean trade value + 2 sd

Standard Deviations of Culture-weighted FDI Density from Mean

Figure 1b: Moderating Effects of Cultural Distance on the Relationship between Cross-national Learning and Foreign Market Entry

Mul

tiplie

r Rat

e of F

orei

gn E

ntry

0.00

0.40

0.80

1.20

1.60

2.00

-1.5 sd -1.0 sd -0.5 sd mean +0.5 sd +1.0 sd +1.5 sd +2 sd

Mean trade value - 1sd

Mean trade value + 1sd

Standard Deviations of Cross-national FDI Density from Mean