1 Cross-border acquisitions vs. Greenfield investment: A ... · 2 Modes of Foreign Direct Investment June 2009 Cross-border acquisitions vs. Greenfield investment: A comparative performance

Modes of Foreign Direct Investment June 2009 1

Cross-border acquisitions vs. Greenfield investment: A comparative performance analysis in Greece

ABSTRACT

This paper compares cross-border acquisition with Greenfield foreign direct investment

(FDI). It investigates the impact of these two FDI modes on the long-term performance of

foreign subsidiaries. By focusing on the performance of the foreign affiliate, it departs from

the rich survival literature and for the first time explores the precise performance of these

ventures over a longer period of time. In particular, by drawing on the theory of industrial

organization (IO), we empirically examine to which extent the two different forms of market

entry help to explain the development of leading positions of affiliates in the host country.

Our field research is based on a wide original sample of 179 manufacturing subsidiaries of

foreign transnational corporations (TNCs) located in Greece. The econometric results indicate

that acquisitions exhibit specific signs of excellence in terms of market share, firm size,

capital intensity and product differentiation. We ascertain that at least as far as market share

and capital intensity are concerned, the superiority of the cross-border acquisitions rests on

both the fact that TNCs are eager to acquire the most efficient firms in the host country, and

actively engage in assisting these firms in their up-grading procedures.

Keywords: Transnational Corporation, Mode of Entry, Cross-border acquisition, Greenfield FDI, performance.


Cross-border acquisitions vs. Greenfield investment: A comparative performance analysis in Greece*

1. Introduction

Foreign TNCs have the option to choose between two competing modes of FDI in order to

penetrate foreign markets. One is Greenfield investment, the other one is taking over an

existing local firm. TNCs opting for Greenfield FDI and international acquisition respectively

are facing different types of implementation costs and may show different degrees of

economic performance in the host country during the post-entry period (Caves and Mehra,

1986; Dunning, 2000; Harzing, 2002).

The aim of this study is to shed some light on the question whether one of the two

modes of entry is associated with a better performance of the affiliate on the local market later

on. By offering a comparative performance evaluation of the two FDI modes at the affiliate

level, we add a new aspect to the existing bibliography. In contrast, most of the international

business literature approaches the Greenfields vs. acquisitions issue from the point of view of

the parent company and its decision-making procedures (e. g. Demirbag, 2008; Elango and

Sambharya, 2004; Larimo, 2003; Harzing, 2002; Mutambi and Mutambi, 2002; Brouthers and

Brouthers, 2000; Barkema and Vermeulen, 1998; Hennart and Park, 1993).

Another novelty is that we depart from the common survival literature which

concentrates on the subsidiary level and analyses survival vs. failure (e.g., Li and Guisinger,

1991; Pennings et al, 1994; Li, 1995; Caves, 1996; McCloughan and Stone, 1998; Shaver,

1998; Mata and Portugal, 2000). Following the critique of this literature (e.g., Pennings et al,

1994; Mitchell et al, 1994; Li, 1995; Reuer, 2000; Delios and Beamish, 2001) which

maintains that survival is a relatively weak and one-sided indicator of lasting success, we

propose to directly investigate the conditions of outperforming affiliates.

* We gratefully acknowledge the comments of two anonymous referees.


Based on the IO approach, we start from the notion that firm-specific advantages

(FSAs), no matter how powerful they are at the corporate level, are not a priori a guarantee

for a strong economic performance of a foreign affiliate. The crucial point is that specific

economic, cultural and political features of the national environment act as entry barriers and

affect the performance of the foreign affiliate during the post-entry stage. We investigate

whether, under the different entry modes, the chances for acting successfully in the long run

on the foreign market differ in fact systematically.

In order to measure performance, we employ several firm-specific indicators of

economic success such as market share, size, profitability, capital intensity, and product

differentiation. An affiliate, which is able to realize these attributes of success will also be

capable to erect substantial barriers to entry on its markets, which in turn makes it easier to

defend the present (leading) position more forcefully. In our econometric models we shall use

these qualifying indicators of a leading affiliate as dependent variables (see also such relevant

studies as Slangen and Hennart, 2008; Dikova, 2009; Demirbag et al, 2007).

To our knowledge, only very few studies have focused on this topic and they offer

controversial results. Woodcock et al (1994) analysed the financial performance of Japanese

Greenfield and acquired affiliates and concluded that Greenfields perform better than

acquisitions. Slangen and Hennart (2008) investigated foreign affiliates of Dutch TNCs and

found that acquisitions outperform Greenfields at low and intermediate levels of affiliate

integration, but Greenfields outperform acquisitions at higher integration levels. However,

this latter study only analyzes short-term performance.

Our study makes several contributions to prior research using long-run performance

estimates. To measure this performance, we analyze an original sample of 179 foreign

affiliates which have been established or acquired in Greece. After several years of operation,

we check their performance during the 2000-2003 period. Our paper also explores at which


time foreign affiliates manage to become an excellent performer in the specific host market.

In particular, in the case of acquisitions, we investigate whether the takeovers had been

outperformers on the local market already at the time when the acquisition took place and/or

whether they became leading affiliates later.

The investigation of TNC operations in Greece is important for several reasons. After

joining the EC in 1981, Greece has become a relatively open economy, subject to strong

international competition and particularly sensitive to developments in the global and

European markets. While the global developments apply to almost all FDI after the 1970s, we

will focus on the specific factors that have influenced the activities of TNCs on the Greek

market under these conditions. In particular, the share of total FDI stock in gross domestic

product continually rose from 1990 (6.2%) to 2000 (11.2%) and to 2007 (16.9%) (various

UNCTAD issues), while the rate of foreign participation in the total domestic industry

remained relatively stable at about 25%-30% (as measured by sales, employment and

exports). However, it grew much higher in distinct product markets (e.g., foods, beverages,

cement, petroleum products, chemical products).

Following our theoretical considerations we shall hypothesize that cross-border

takeovers have become the superior choice of entry mode of TNCs in Greece as compared to

Greenfield FDI. Our empirical results confirm this hypothesis. Thus, this particular study

offers new insights on the comparative performance analysis of Greenfields and acquisitions.

2. Theoretical background and hypotheses

In the following, we shall draw on the IO theory, and discuss the effects on excellence

of a local affiliate that might arise from the decision of a TNC to undergo a Greenfield FDI or

a cross-border acquisition respectively. We start from the notion that TNCs possess FSAs

(e.g., differentiated products, brand names) which they exploit nationally and internationally


(e.g., Hymer, 1960; Caves, 1971; Caves and Mehra, 1986; Caves, 1996; Dunning, 2000).

Greenfield FDI is one particular form of a market penetration. TNCs consider this option

when their FSAs are strong enough to cover the additional transaction costs arising from the

operation in the foreign market, and when location advantages are abroad. Nevertheless, the

fact remains that the additional costs of the liability of foreignness (Zaheer, 1995) still have a

negative impact on the performance of the Greenfield venture. Slangen an Hennart (2008)

argue that Greenfields, unlike acquisitions, increase substantial external conformity costs (due

to the need to adaptation to the local environment) because they suffer from both a liability of

newness and a liability of foreignness. According to Pennings et al (1994), Greenfield

investments are more risky as compared to acquisitions, because as new projects they start at

the beginning of the learning curve (the liability of newness argument).

The situation might change for the better, if the TNC, instead of practicing Greenfield

FDI, acquires an existing local firm that is well-established in the market (e.g., Demirbag et

al, 2008). It may then try to combine the subsidiary’s advantages with its own core abilities,

thereby augmenting its overall FSA-system (Dunning, 2000). The new combined entity may

then be able to use these synergies to better overcome the transaction cost barrier and to

improve its position on the local market (e. g. Dunning, 2000; Anand and Delios, 2002). In

the case of Greenfield FDI, the parent company is relying entirely on its own capabilities. As

such, the typical Greenfield subsidiary is determined by the parent company’s FSAs and the

its organizational routines (Barkema and Vermeulen, 1998; Hennart and Park, 1993).

Cross-border acquisitions, by taking advantage of the FSAs of the local firm, might

also be able to react more quickly to changing market conditions and to strategic moves of the

competitors as the Greenfield venture could. At the time of market entry, in particular,

Greenfield investments use to need more time for planning, construction and market

positioning than takeovers. That way, they are loosing precious time in relation to cross-


border acquisitions before they can develop their operations (Carow et al, 2004; Larimo,

2003; Anand and Delios, 2002; Hennart and Park, 1993). Thus, foreign rivals opting for

cross-border acquisition are gaining time to react and to challenge market entry of the

competitors. That way, a foreign rival coming in second may still be able to outperform the

first mover. Alternatively, the first mover in an acquisition venture is hard to outpace and

enjoys the additional benefit of being able to select from among the "best targets".1

These arguments clearly favor takeovers in relation to Greenfield investments, and this

superiority is evident in the competitive advantages of foreign affiliates. In fact, econometric

studies support the view that foreign affiliates created by acquisition exhibit specific signs of

excellence. Reviewing the literature on this topic reveals that there are basically two

parameters for the explanation of the excellence of cross-border acquisitions. One parameter

is the strong position of the acquired local firm at the time of entry.2 The second parameter

represents the dynamic effects of synergy creation, responsiveness and speed during the post-

acquisition period.3

Based on these considerations we shall formulate distinct hypotheses of the impact of

the mode of entry on the evolution of affiliates as leaders in the Greek economy. In general,

Greenfield FDI placed in the small Greek economy in the past tend to be directed to new local

product markets which were initially rather small4. Locality, under these conditions, implies

that the growth potential of the Greenfield investment is closely linked to the expected rate of

growth of the overall economy and the elasticity of income of the respective commodity.

1 Yet, the analysis should not ignore the significance of learning effects resulting from the post-entry period. 2 International studies based on the IO approach, which primarily investigate pre-acquisition characteristics of target firms conclude that foreign buyers mainly pick the more productive and more profitable target firms (i.e., “cherries” and not “lemons”) (e.g., Goethals and Ooghe, 1997; Harris and Robinson, 2002; Fukao et al, 2005; Ruckman; 2005; Bellak et al, 2006). 3 Studies drawing also on the economics of IO theory associate economic success in the post-acquisition phase with the prominent position of the target at the time of takeover. They conclude that acquisition candidates use to possess specific characteristics of economic success that the foreign buyer can exploit and successfully combine with his own core abilities (Buckley and Casson, 1998; Caves, 1996; Goerg, 2000; Uhlenbruck, 2004; Girma, 2005). 4 Though this argument primarily applies to the time before Greece entered the EU, it also has general validity even in times of EU-integration as the overwhelming majority of FDI continue to be inward-looking in character.


Thus, Greenfield FDI usually takes place in a step-by-step growth process starting from

relatively small production units. In as much as economies of scale apply, this puts a heavy

cost burden on these firms. Foreign acquirers, in turn, tend to focus on relatively attractive

existing firms (Jensen, 1988) operating in well-developed oligopolistic local markets

(UNCTAD, 2000). Thus, the cross-border acquisitions are better prepared than Greenfield

FDI to attract sufficient demand on the local market from early on, and to reap economies of

scale. Also, large firm size implies that the target firm might offer favorable conditions for the

development of new FSAs. Thus, we hypothesize:

H1: foreign affiliates created by acquisition are more likely to be leading companies

with large market shares than foreign affiliates created by Greenfield FDI.

H2: foreign affiliates created by acquisition are larger in size than foreign affiliates

created by Greenfield FDI.

In Greece, many of the well established industries show deliberate signs of

oligopolistic behaviour even after joining the EU. As a consequence, firms holding large

market shares in these industries can be expected to be able to reap monopolistic profits. This

should also apply to the foreign affiliates, which are holding a leading position in the Greek

market. In addition, foreign acquirers can successfully exploit and further develop the

prominent market position of the target firm by adding own specific advantages. That way

they might also be able to positively influence long-term performance. In turn, Greenfield

investments, due to their start-up situation in new product markets, are in a quite different

position. They might well be able to take premium prices, if their new products are protected

by FSAs. Nevertheless, the small size of the new market and the specific costs of market

creation form a major obstacle for the generation of profits in the short run. Hence, foreign

acquisitions, as compared to Greenfields, may not only exhibit a better performance, in the

short and medium run, but also in the long-term. Thus we consider that international


takeovers are more profitable than Greenfield investments (Caves, 1996; Goethals and Ooghe,

1997; Elango and Sambharya, 2004) and hypothesize:

H3: foreign affiliates created by acquisition will have higher Return on Equity (ROE)

than foreign affiliates created by Greenfield FDI.

An important criterion for successful FDI in foreign markets derives from

specialization in industries which dispose of a competitive advantage in the local economy.

Drawing on conventional theories of international trade, competitive advantage is driven by

unit labor costs. That is, if unit labor costs are rising comparative advantage in labor intensive

industries is getting lost. Promising target industries, then, are those which apply capital

intensive technologies. In Greece, unit labor costs have risen substantially during the last 25

years. As a consequence, comparative advantage in labor intensive production has ceased to

exist. International acquisitions in Greece, due to their focus on large firms in well established

industries, tend to be characterized by a relatively high intensity of capital in production. This

should contribute to the economic success of this strategy. Greenfield investments, in turn,

use to be concentrated in industries where more labor intensive production prevails.

Therefore, we hypothesize:

H4: foreign affiliates created by acquisition have a higher capital intensity than

foreign affiliates created by Greenfield FDI.

Proceeding beyond the traditional model of price competition, product differentiation

emerges as an important parameter of economic success in advanced markets. In particular,

leading firms are eager to maintain and improve their market position by developing new

product varieties and creating brand names. The creation of variety enables price

differentiation, allows for the creation of entry barriers, fosters market concentration and

eventually generates monopolistic profits (e.g., Caves, 1971). These market conditions gain

importance with rising per capita income.


Greece, because of its historical heritage and its geographic position at the periphery

of Europe exhibits some specific signs of consumer behaviour, which gave rise to the

evolution of differentiated brands. These specific consumer brands have originally been

created and developed by Greek firms for the local market. TNCs might find it attractive to

incorporate these firms and their products into their (global) product differentiation strategy,

either for exploiting the local Greek market or by creating new global (regional European)

brands. That way, some highly developed Greek consumer industries have found attention of

TNCs as candidates for cross-border takeovers. Hence, we hypothesize:

H5: foreign affiliates created by acquisition offer more differentiated products than

foreign affiliates created by Greenfield FDI.

Foreign acquirers would appear to prefer entry into markets with low degree of

competition inter alia because competitive markets may increase the likelihood of mobility

and mortality. According to the conventional structure-conduct-performance approach this

would mean that TNCs prefer to engage in industries which are highly concentrated and

operate significant entry barriers. In fact, foreign entry by acquisition has been observed to be

more common in industries that are already concentrated, exhibit high barriers to entry (Caves

and Mehra, 1986; Buckley and Casson, 1998; UNCTAD, 2000; Goerg, 2000), and have a

relatively stable market structure. In contrast, Greenfield investors use to avoid entering

structurally “stabilized” markets. In these markets they expect to face major difficulties

regarding market penetration with new products and in the face of powerful local market

leaders. Thus, we hypothesize:

H6: foreign affiliates created by acquisition operate in more concentrated industries

than foreign affiliates created by Greenfield FDI.

3. The Empirical Analysis


3.1. Data

To test the above hypotheses, interviews were conducted among foreign affiliates

located in the Greek manufacturing sector. The sampling creation process was as follows. The

industrial population of Greenfield FDI (N1 = 170 subsidiaries) was taken from a research

study carried out in the foreign commercial-industrial chambers that have their registered

offices in the country and from previous field research. The corresponding population of

international acquisitions (N2 = 93 subsidiaries) was primarily obtained from TOPINVEST, a

management consultancy company specializing on mergers and acquisitions, and the foreign

commercial-industrial chambers. From a total 263 subsidiaries, 220 were selected for study.

The selection of subsidiaries from each total (N1 & N2 respectively) was made proportionately

on the basis of internal sector dispersion, so that a statistical bias at industry level is avoided

in the sample. The firms were asked to answer a questionnaire (see appendix) that consists of

three groups of questions. One group concentrates on some basic characteristics of the

subsidiary, the second one asks for subsidiary performance and the third one sheds light on

the managers’ general assessment of the performance of his firm under the selected entry

mode.

Following our initial communication by e-mail and repeated telephone calls thereafter,

179 subsidiaries responded to our questionnaire (participation rate: 68% of the total Ν). Thus,

the study draws on an original sample of 179 foreign affiliates that includes almost all the

most important units, which are presently operating in Greece; 124 of these establishments are

Greenfields, and 55 are cross-border acquisitions.

This is a particularly high participation rate. It could be realized, because most of the

central offices of the foreign affiliates were located in the area of Athens, so that personal

communication was relatively easy. These favorable conditions were also helpful for

completing the questionnaire with detailed interviews (about 90 minutes) carried out with the


respective financial managers. Firm-specific data were obtained from the directors of

accounting departments.

Table 1 presents some main characteristics of our sample concerning nationality of

foreign investor and industry of foreign subsidiaries.

All data of firm specific variables used in the study originates from this field research,

a small part of which was published recently (Georgopoulos and Preusse, 2006). Data for the

industry-specific variables are gathered from the Bulletin of Conjunctural Indicators (Bank of

Greece), the Annual Industrial Surveys (National Statistical Service of Greece), and the

annual publications on Greek Industry (Confederation of Greek Industries).

The data cover two periods: the period 2000 – 2003 (observation period) and the first

two year period immediately after starting operations (establishment period). This opens up

the chance to detect the development of the affiliates’ fate during its operation period. That

way, in the case of acquisitions, the model evaluates the result of the acquisition as a

combination of pre-acquisition advantages (of the Greek firm at the time of acquisition) and

the gains of reorganization during the whole post-acquisition period (up until the 2000-03

period). It is worthwhile stressing that the overwhelming majority of acquired subsidiaries had

been operating under the new ownership regime for at least ten years. Thus, it is reasonable to

conclude that any reorganization procedure after takeover had been completed at that time.

We use average values for all quantitative variables.

__________________________

Put Table 1 about here

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3.2. Models and Variables


In order to test our hypotheses, a series of regression models were estimated with the

dependent variable being each of the performance measures. Consequently, we employed six

corresponding regression models to regress the seven independent variables on subsidiary

performance. More precisely, we employed a binary logit model with the dependent variable

(PDIFF) as a dummy variable, and five ordinary least-squares (OLS) models, in which the

corresponding dependent variables (MSHARE, SIZE, CAP, PROF and CONCET) were

continuous. Such linear regression models are econometrically appropriate, when the

performance of affiliate is treated as the dependent variable (e.g., Slangen and Hennart, 2008;

Dikova, 2009; Demirbag et al., 2007).

The logistic regression links the dependent and the independent variables by the

following function: ( )( )

=

=−

=11

1ln

YP

YPβ0 + β1Χ1 + β2Χ2

Correspondingly, the OLS regressions are of the following form:

Yi = β0 + β1Χ1 + β2Χ2 + εi

In each of the six models, the dependent variable is explained by two sets of

independent variables: firm-specific (X1) and industry-specific variables (X2). X1 is a vector 1

* κ and X2 is a vector 1 * λ, where κ + λ = 7. That is, in each model the dependent variable is

explained by five firm-specific variables and two industry-specific variables.

Literature suggests the use of a combined measure of affiliate performance in order to

capture its multi-dimensional character. E.g., Pennings et al (1994, 635) argue that an “ideal

research design would incorporate multiple indicators of performance”. Li (1995) recognizes

that many criteria must be considered when evaluating the long-term potential of business.

Following these suggestions, we employ six (6) dependent variables as proxy for

performance. These variables are “market share” (MSHARE), "firm size" (SIZE),

"profitability" (ROE), "capital intensity" (CAP), "product differentiation" (PDIFF), and


“industry concentration” (CONCET). MSHARE is calculated as the market share of the

foreign unit on the local product market (at 4-digit industry level, NACE classification). A

foreign affiliate is supposed to have a leading position on this market if it holds the largest

market share of all firms operating on this market. Firm SIZE is measured by sales (millions

of Euro). PROF is calculated as the return before taxes on equity capital. CAP is measured as

the ratio of fixed assets per employee (thousands of Euros). PDIFF is a dummy variable

which takes the value one if the majority of the products of the affiliate are brand names, and

zero if they are not.5 CONCET is measured as the four-firm assets concentration ratio (at 4-

digit industry level, NACE classification).

We employ seven explanatory variables. The variable of primary interest for this study

is the dummy variable MODE, which indicates the chosen entry mode. The acquisition

dummy variable takes the value of one if entry is in the form of a cross-border acquisition and

zero if entry is in the form of a new plant. The six other explanatory variables control for

additional observable factors that might affect the characteristics of a leading affiliate. They

are: experience (TIME), market seeking (MOTIVE), ownership (OWNER), nationality

(NATIO) and two industry-specific variables (GROWTH and IMPORT).

First, we control for experience using the variable TIME. Growing experience helps to

accumulate competence on the market and contributes both to overcome initial competitive

disadvantages and to develop new country specific knowledge and capability. Thus,

experience is an important factor influencing the competitiveness of a firm. It also stands for a

better chance of survival of a foreign unit (Li and Guisinger, 1991; Woodcock et al, 1994;

Shaver et. al., 1997; McClougan and Stone, 1998; Pan and Chi, 1999; Delios and Beamish,

2001), and for success in the foreign market in the post-entry stage. Thus, we hypothesize that

host country experience improves the likelihood of creating a subsidiary with a strong

5 As we could not collect information from the enterprises about common PDIFF indicators (such as advertising expenditure as a percentage of sales etc.), we used the dummy variable as the “second best solution”.


position. TIME is a continuous variable. It is calculated as the difference between the year of

observation (2003) and the year of establishment.

Second, we control for market-seeking motives (MOTIVE). TNCs seeking new

markets abroad are likely to pay particular attention to the conditions of the local market, such

as the competitive situation, supply networks, supporting activities etc. (e. g. Pan and Chi,

1999). In turn, TNCs with an explicit focus on exports are likely to pay less attention to the

operating conditions on the local market, but concentrate on factor endowments, trade barriers

etc. Consequently, they will be less deeply involved in the local business scene and may not

be well prepared to respond to changes of the economic and political conditions in this

market. Thus, we expect that foreign subsidiaries with market-seeking motives will attain a

leading market position more easily than affiliates operating on resource-seeking or

efficiency-seeking motives (Dunning, 2000). MOTIVE is a dummy variable which takes the

value one for market-seeking motives and zero for others.

Third, we control for ownership (OWNER). Equity (ownership) is a frequently used

measure of control, commitment and involvement. The larger the share of equity the more

efficient does the control mechanism of the TNC work. In turn, minority joint ventures have

been recognized as being inherently instable, because of lack of control by the principal (e. g.

Dhanaraj and Beamish, 2004). One consequence is that minority joint ventures facilitate

opportunistic behaviour on behalf of the foreign partner. Because of this weakness, minority

joint ventures are often used as a transitory form of international involvement that will be

transformed into a majority joint venture, as soon as penetration of the foreign market is

believed to have been a successful venture (Pennings et al, 1994; Pan and Chi, 1999; Reuer,

2000; Mata and Portugal, 2000; Dhanaraj and Beamish, 2004). We hypothesize that foreign

affiliates in a majority ownership of a TNC have a greater chance to become a leader in a host

country.


Fourth, we control for EU-nationality. European TNCs tend to have an information

advantage over non-EU multinationals. This advantage may also apply to economic activities

which are carried out in Greece. For one thing, there are Greek particularities within European

Union which may be better recognized and understood by EU-multinationals. Another more

subtle aspect derives from the EU-process of convergence of financial and statutory policies,

an integral part of which is Greece. That is, national standards, restrictions and regulatory

policies are getting increasingly harmonized under the homogenizing framework of the

Maastricht Treaty and the ongoing integration process (Amsterdam, Lisboa).6 Since EU-

TNCs are directly involved in this process of statutory convergence, their perceived

investment risk declines and market commitment increases (e. g. Hadjikhani and Ghauri,

2001). They are, therefore, in an advantaged position relative to outsiders concerning the

efficient use of the knowledge that is generated by intimate participation. As a consequence,

subsidiaries of EU-TNCs operating in Greece are probably more familiar with the role that

this country plays in this process and this constitutes good preconditions for becoming leaders

on the Greek market. NATIO is a dummy variable that takes the value one if the parent

company is located in a EU- country and zero if it is not.

A fifth factor that potentially influences the entry mode choice and, later on, the

performance of a subsidiary on the local market is the so-called industrial environment (e.g.,

Elango and Sambharya, 2004). We use two industry-specific characteristics in order to control

for the influence of this environment. These are the rate of growth of the Greek industry

(GROWTH), measured by industry specific production indices (1980 = 100), and the import

penetration ratio (IMPORT),7 measured by the share of imports in domestic consumption.

6 The implementation of the Euro and of international accounting standards and the introduction of a European legal framework of mergers and takeovers are examples. 7The NACE classification was used at a 2-digit industry level and 3-digit level, wherever information was available.


4. Empirical Findings

4.1. Descriptive statistics

Table 2 reports descriptive statistics such as mean and standard deviation of the

dependent and independent variables for the total sample during the period 2000-2003. Some

remarkable differences between the two FDI modes can be observed in at least seven out of

thirteen variables. In particular, subsidiaries created by acquisition are, on average, four times

larger (SIZE) than subsidiaries created by Greenfield FDI and they are three times larger in

capital intensity (CAP). Their market share (MSHARE) is twice as large as that of Greenfield

ventures, and they are operating in product markets with a high degree of concentration

(CONCET). The mean values of the dummy variable PDIFF are significantly higher in the

case of cross-border acquisitions. Last, acquisitions more accurately express the “new

generation” of FDI (TIME), and they can be found mostly in sectors with a relatively low

import competition (IMPORT).

_________________________


__________________________

4.2. Logit and OLS models

In this section the most important econometric findings will be presented (Tables 3, 4,

5, 6 and 7). For each of the regression models, variance inflation factors (VIF) were examined

to determine the existence of multicollinearity. The largest VIF factor in our models was 2.05

(see the last column in the Tables 3,4 & 5), which is much lower than the multicollinearity

threshold of 10 (Neter et. al., 1996). Thus, multicollinearity has not been found.

As regards the regression analysis of the total sample (Table 3), the goodness of fit

and power criteria of the logit model are very satisfactory. For instance, model 1 has a high


overall explanatory power, with a chi-square of 47.513 (p< 0.001). In addition, 76.9% of all

observations are correctly classified, which indicates a very good performance. The

Nagelkerke R-square measure confirms that the model has a good explanatory power (0.327).

The overall interpretative ability of the OLS models 3 and 4 is quite satisfactory, too (adjusted

R2: 0.342 and 0.215 respectively), while the corresponding interpretative ability of models 2,

5 and 6 is relatively weak. In the five OLS models, there seems to be no problem of

autocorrelation in the residuals. The Durbin-Watson coefficient is about two (2.146 in model

2, 2.065 in model 3, 1.786 in model 4, 1.978 in model 5 and 2.325 in model 6). It is worth

mentioning that, initially, in models 2 and 6, this coefficient was 1.149 and 0.837

respectively, which indicated autocorrelation of the residuals. We verified this by the ARCH

procedure and resolved the problem using the Cochrane-Orcutt iterative procedure (Asteriou,

2006). The final results were ρ̂ =0.481 for model 2 and ρ̂ =0.588 for model 6.

__________________________


__________________________

In the secondary models, the statistically significant variables are the following. Model

1: MODE (***), ΤΙΜΕ (***), MOTIVE (**) and GROWTH (**). Model 2: MODE (***).

Model 3: MODE (***), ΤΙΜΕ (***), MOTIVE (**) and OWNER (*). Model 4: MODE (***)

and IMPORT (**). Model 5: MODE (*), OWNER (**) and ΝΑΤΙΟ (**). Model 6:MODE

(**), TIME (***), OWNER (**), and GROWTH (*). To sum up, our explanatory variable

(MODE) is statistically significant at the 1% level in four out of six models. This means that

in the observation period 2000-2003, takeovers clearly exhibited a higher degree of product

differentiation, and larger market share. They also had a bigger size and a higher capital

intensity as compared to Greenfields. Also, these units operated in more concentrated product

markets. Differences in profitability were rather of minor importance (statistically significant


at the 10% level).8 We thus find full support for the hypotheses 1, 2, 4 and 5, but also for

hypothesis 6, though to a lesser extend. Yet, we find weak support for hypothesis 3.

As far as the control variables are concerned, it is worthy noting that three firm-

specific variables are found to be statistically significant, that is, TIME, MOTIVE and

OWNER.9 As regards TIME (experience), this variable is important for product

differentiation, size and market concentration (significance at 1% level). Moreover, MOTIVE

(market-seeking), too, is significant for product differentiation and size (5% level), while the

variable OWNER (foreign capital participation) is positively associated with profitability (5%

level), operation in oligopolistic industries (5% level), and business size (10% level). Contrary

to our hypothesis concerning NATIO (nationality), non-EU origin does not seem to have a

negative effect on foreign subsidiaries on the Greek market. In fact, the opposite seems to be

true in the case of profitability (5% level).10 Last, the industry-specific variables GROWTH

and IMPORT were statistically significant to a limited extend.

Subsequently, we split our sample into two time periods, i.e. the 1981-2003 period and

the 1991-2003 period (Tables 4 & 5 correspondingly) in order to test whether foreign units

established (or acquired) during different periods of time show different performance. This is

not the case. Specifically, the explanatory variable (MODE) was found to be statistically

significant at the 1% level in three out of six models (PDIFF, MSHARE, SIZE). In the fourth

model (CAP) its explanatory power was less (10% and 5% level respectively).

8 Several scholars such as Slangen and Hennart (2008) and Demirbag et al (2007) have expressed scepticism concerning the use of profitability as appropriate performance measure. 9 Most of these findings are in line with the literature on these topics. See for example Pennings et al, 1994; Pan and Chi, 1999; Reuer, 2000; Mata and Portugal, 2000; Dhanaraj and Beamish, 2004; Li and Guisinger, 1991; Woodcock et al, 1994; Shaver et. al., 1997; Delios and Beamish, 2001. 10 In fact, many non-European TNCs, mainly those from the USA, have been operating in the European markets for many years and had already gained rich institutional and political experience on these markets for quite some time. Also, many of them had been active in Greece before via non-equity investments such as licensing or exports. Consequently, when entering the Greek market they will probably have acted as quasi-European firms rather than foreigners.


The control variables were much less important statistically than the FDI mode. For

example, in the sub-sample 1981-2003, the most significant firm-specific variables were

MOTIVE and NATIO (but with a negative sign) for both product differentiation and

profitability (different levels of significance). Furthermore, in the two sub-samples, the

importance of the control variables has somewhat increased as compared to the total sample

(compare Tables 4 and 5 with Table 3).

__________________________


__________________________

__________________________


__________________________

So far, the analysis has not provided an answer to how and when the foreign affiliates

have attained their leading market position. These questions can be tackled by comparing the

state of the acquired units and the Greenfields in the observation period 2000-2003 with their

respective state at the time of acquisition or establishment (that is, during the first two post-

entry years). Employing a T-test, we compare the mean values of three variables (MSHARE,

CAP, PROF) in three different samples, the total sample, the sub-sample 1981-2003 and the

sub-sample 1991-2003 (Table 6). The empirical analysis reveals statistically significant

differences in performance between the two types of affiliates in MSHARE and CAP (1%

level). The differences are existent during both the establishment and the observation period

[see Table 6, in the three samples the models (1) and (2)]. Unfortunately it is not possible to

give an exact quantitative statement on the relative performance, but our analysis suggests

that the cross-border acquisitions at least stabilized there leading role during the post-

acquisition period [Table 6, total sample, model (3)]. Stabilizing a leading position in a


growing national market certainly means that these acquisitions had a particularly strong

performance.

To sum up, we present our main findings in Table 7. High support has been found for

the hypotheses 1, 2, 4 and 5. Hypotheses 3 and 6 only found weak support, except for the T-

test for hypothesis 3, which is rejected.

____________________________________.


____________________________________


____________________________________

5. Discussion

This paper has compared acquisitions and Greenfield FDI in terms of their economic

performance in the small open Greek economy. A lot of the literature on this topic focuses on

the impact of these entry modes on the survival chances of affiliates. But pure survival of an

affiliate does not necessarily mean that it holds a leading position on the local market and, by

extension, that it shows an excellent performance. We take up this weakness and suggest that

the “reverse side of the coin”, i.e. the exact extend of the economic success needs to be further

explored. In doing this, we depart from the common survival literature. Until today only

Slangen and Hennart (2008) have systematically examined the performance consequences of

the entry mode choice directly. However, these authors only compare the ex ante expectations

of the managers with the subsidiaries’ ex post performance during the first two post-entry

years. This means that their investigation is limited to this short period of time. The authors

recognize this limitation and point out that it is important to also capture the long-run benefits.


In the current paper we argue that FDI is a productive investment that needs time until

all adjustments have been made and the benefits can be reaped. In the case of acquisitions,

value creation via synergies or value destruction through cultural conflicts use to take quite

some time. In turn, in Greenfields, the adaptation of the new entity to the host-country

environment is a most challenging task. Hence, the establishment mode choice is a typical

long-term management decision. This study takes care about these aspects and presents long

term performance estimates. In particular, we propose that the establishment mode choice has

a long term impact on the post-entry performance of affiliates. In order to test this proposition,

we have interviewed 179 subsidiaries located in Greece, which have been operated for a

longer period of time in the host country (observation period 2000-2003).

Our econometric results provide a satisfactory explanation of the superior performance

of international acquisitions relative to Greenfield FDI for the case of Greece. More precisely,

we detect that, after a sufficiently long operation period, foreign subsidiaries created by

acquisitions exhibited larger market shares, a bigger firm size, higher capital intensity of

production, and more differentiated products as compared to the Greenfield units. These

results highly support four out of our six research hypotheses. We could qualify and confirm

these results after splitting up our sample into two different time periods.

One important reason for the excellent performance of the cross-border acquisitions is

that these investments have been concentrated on domestic firms, which had a strong position

on the local market even at the time when the takeover took place. Consequently, the new

foreign entity could start running its operations out of a particularly good initial position. But

this is not the whole story. A good starting point may be helpful, but it is not a sufficient

condition for ongoing success. The core argument in favor of sustained superiority of cross-

border acquisition derives from the industrial organization literature. It holds that, via

acquisition, a TNC combines its own FSAs with the advantages of the acquired enterprise. In


the positive case, this merging of FSAs will eventually lead to a new powerful augmentation

of core abilities. In this scenario, over the years, the buyer might be able to establish an

advanced competitive position on the foreign market that becomes unchallengeable for quite

along time. This position might be further stabilized, if the new firm is able to erect new entry

barriers on this market, which are appropriate to keep potential rivals out and enhance the

scope of monopolistic power as suggested by other scholars (e.g., Caves and Mehra, 1986;

Buckley and Casson, 1998 Goerg, 2000). Apparently, market entrance barriers are so high in

some cases that even attractive profit margins do not persuade foreign competitors to enter.

Our empirical findings confirm the above scenario. In particular, using three variables

of performance, we compare the performance of subsidiaries during the first two post-entry

years of operation (establishment period) with the corresponding performance in the

observation period. We find that, in fact, acquired affiliates improved or at least maintained

their leading position concerning market shares and capital intensity as compared to

Greenfields. As regards market share, a compelling explanation for the strategic positioning

on the local market is the fact that sales and distribution systems do not function sufficiently

well across borders, and that they are time-consuming to build-up out of a Greenfield

position. Hence, foreign TNCs source, exploit and further develop these networks locally via

takeovers rather than by establishing new plants and distribution systems (e.g., Anand and

Delios, 2002; Chen and Zeng, 2004).

In our study, these merits were long-lasting (more than a decade as a matter of rule)

and have not been threatened by any adjustment process following after the takeover. This

runs counter to the argument that acquisitions incur high resource deficiency costs in terms of

redundancies and duplications (Woodcock et al, 1994), show a weak managerial performance

(Woodcock et al, 1994; Li, 1995; McCloughan and Stone (1998), and face high post-


acquisition costs in general (McCloughan and Stone, 1998). We did not discern such effects

in the case of the Greece economy.

Generally speaking, the main explanation for the economic success of takeovers in

Greece rests on two components. One is that they are focused on particularly attractive target

firms, which are operating in well developed oligopolistic industries (e.g., foods and

beverages). This provides these subsidiaries with an excellent starting position. The second

draws on a dynamic process that is difficult to catch. Apparently, the TNCs engage in

assisting and promoting their subsidiaries to merge their own FSAs with the specific strength

of the acquired local firm. Our data coincide with the thesis that both effects have taken place

in Greece during most of the EC (EU) era.11 In our view, the most significant contribution of

this paper is that it identifies cross-border acquisition as an attractive TNC strategy to enter a

foreign market, and to stay there as a strong competitor in the long run. Takeovers appear to

have an advantage over Greenfield FDI in overcoming the obstacle of the liability of

foreignness and in achieving the objective of becoming a leader and defending this position.

Moreover, our findings explain why, in the global division of labor, international acquisitions

prevail as a means of entry to a local market (UNCTAD, 2000).

Finally, some limitations of this approach should be considered. First, the subsidiaries

created by Greenfield investment should be subject to further and more thorough examination.

The fact that they fall short of acquisitions does not necessarily mean that all of them can be

categorized as “lemons”. Vice versa, not all acquisitions are necessarily “cherries” . The

econometric models, in fact, indicate statistically significant differences within the two groups

of subsidiaries, but they do not necessarily apply to any single subsidiary. Certainly, the issue

of gains of competitive advantages resulting out of experience requires further investigation,

11 The basic reason for this development was that trade barriers in Greece dropped considerably after the country had joined the EC (EU) in 1981. As a result, much of the tariff hopping Greenfield FDI of former times became obsolete. Under the new conditions, the relative attractiveness of cross-border acquisitions increased, because import competition on the Greek market intensified and speed has become an important new parameter in the investment calculus of TNCs.


since many Greenfields have been operating for quite a long time in the domestic market.

Moreover, in order to extend these results to more general applications (e.g. to a wider range

of economic settings), further studies will have to be carried out in other open economies.

This is deemed particularly necessary, because this research issue has been investigated

systematically for the first time in this paper, and Greece might be a special case. Finally, we

have controlled for the subsidiary- and for the industry-level in the host country, but, due to

lack of relevant information, we failed to control for the FSAs of the parent companies. Most

certainly the strength of these “inherited” FSAs may also influence the performance of foreign

subsidiaries. Hence, it is left to future research to broaden our knowledge on the relative

importance of these effects.


Appendix: Questionnaire items used

Name of person I………………………………..Name of person II………………….

Position in the subsidiary……………………. Position in the subsidiary………….

1. Characteristics of the subsidiary

• Industry (NACE Classification)………….………………………………..

• Year of establishment (Greenfield/ acquisition)……………..:…………….

• Type of investment (Greenfield/ acquisition):………………………………

• Motive of investment (market-seeking, resource-seeking FDI etc.):..………

• Nationality of foreign investor……………………………………………….

• Foreign participation (in %) in the share capital of subsidiary……………….

•

2. Subsidiary performance

Period of establishment/ acquis. Observation period

(average for the first two years) (average for the period 2000-2003)

• Market share ……… ……….

• Capital intensity ………. ……….

• ROE ………. ………..

• Product differentiation ……….

• Size ………..

Definitions:

Market share: the market share of the foreign subsidiary on the local industry

Capital intensity: the ratio of fixed assets per employee (thousands of Euro)

ROE: the return before taxes on equity capital

Product differentiation: yes, if the majority of the products are brand names/ no, if otherwise

Size: sales (millions of Euro)

3. General assessment of performance of the subsidiary

• In the case of Greenfield investment: How the foreign subsidiary has been developed up to now? Are you satisfied?

• In the case of acquisition: How the acquired subsidiary has been developed up to now? Are you satisfied?


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TABLE 1: Sample characteristics

Country of origin __________________________________________________________________________________ Number of affiliates % European Union (EU-15) 127 70.9 Germany 50 27.9 Franch 31 17.3 U.K. 9 5.0 Netherlands 12 6.7 Italy 19 10.6 Belgium 3 1.6 Denmark 1 0.6 Spain 1 0.6 Sweden 1 0.6 Other Western Europe 8 4.5 Switzerland 8 4.5 Non – European Countries 44 24.6 U.S. 32 17.9 Japan 3 1.6

Other countries 9 5.0 TOTAL 179 100.0 __________________________________________________________________________________ Industry of affiliates __________________________________________________________________________________ Number of affilates % Foods 28 15.6 Beverages 10 5.6 Tobacco 3 1.7 Textiles 10 5.6 Clothing 8 4.5 Leather – Footwear 1 0.6 Paper 7 3.9 Printing – Publishing 3 1.7 Petroleum and coal products 6 3.4 Chemical products 35 19.6 Rubber products and plastics 7 3.9 Non-metallic minerals 14 7.8 Basic metals 4 2.2 Metal products 9 5.0 Machines and equipment 7 3.9 Electrical machinery and appliances 24 13.4 Transportation 1 0.6 Other industries 2 1.0 TOTAL 179 100.0 _________________________________________________________________________________


Table 2. Descriptive statistics (total sample, average for the observation period 2000-2003)

_____________________________________________________________________

all observations cb-acquisition Greenfield FDI Variables mean SD mean SD mean SD _____________________________________________________________________

Dependent variables

PDIFF 0.61 0.490 0.78 0.417 0.53 0.501

MSHARE 24.69 18.891 41.82 21.419 21.75 15.942

SIZE 64.47 104.993 137.87 161.824 33.89 40.818

CAP 54.87 64.278 96.12 93.189 36.47 32.381

PROF 17.73 17.831 18.17 17.528 17.49 18.071

CONCET 56.87 17.672 69.64 17.995 55.92 16.763

Independent variables

MODE 0.31 0.463 - - - -

TIME 22.65 11.247 11.42 8.452 22.84 8.173

MOTIVE 0.87 0.342 0.89 0.315 0.85 0.354

OWNER 73.25 23.615 71.42 22.228 74.06 24.248

NATIO 0.75 0.435 0.78 0.417 0.73 0.444

GROWTH* 35.18 40.166 38.76 31.376 33.60 43.523

IMPORT* 51.81 21.256 37.73 20.289 58.06 18.577

__________________________________________________________________________________

Note: * industry variables/ PDIFF, MODE, MOTIVE and NATIO dummy variables


Table 3. Regression models (TOTAL SAMPLE, average for the observation period 2000-2003)

___________________________________________________________________________

Variables Models

Logit OLS

Dependent PDIFF MSHARE SIZE CAP PROF CONCET

___________________________________________________________________________

Independent (1) (2) (3) (4) (5) (6)

VIF

___________________________________________________________________________

Intercept -3.663 9.883 -27.488 15.826 -2.498 20.509

(2.823) (1.819) (-1.162) (0.744) (-0.336) (4.690)

Firm-specific variables

MODE 2.435*** 10.488*** 76.328*** 35.783*** 6.451* 5.392** 2.04

(3.845) (3.995) (7.198) (3.791) (1.947) (2.403)

TIME 0.069*** 0.022 1.740*** 0.262 0.174 0.032*** 2.05

(2.815) (1.339) (3.911) (0.690) (1.238) (2.776)

MOTIVE 1.554** 3.685 25.371** 9.313 5.722 0.507 1.26

(2.589) (1.035) (2.181) (0.910) (1.521) (0.166)

OWNER 0.012 0.038 0.295* 0.181 0.108** 0.075** 1.08

(1.562) (0.853) (1.863) (1.348) (2.158) (1.981)

NATIO -0.736 -1.587 -11.219 5.096 -5.927** -1.823 1.07

(1.640) (-0.610) (-1.349) (0.717) (-2.235) (-0.807)

Industry-specific variables

GROWTH 0.011** 0.025 0.111 -0.017 0.040 0.073* 1.28

(2.210) (0.588) (1.097) (-0.197) (1.181 (1.888)

IMPORT 0.002 -0.081 -0.174 -0.353** 0.066 0.015 1.41

(0.210) (-0.878) (-0.834) (-2.025) (1.008) (0.179)

__________________________________________________________________________________

Nagelkerke R2 0.327 - - - - -

Model chi square 47.513*** - - - - -

CCR (%) overall 76.9 - - - - -

Adjusted R2 - 0.116 0.342 0.215 0.099 0.094

Durbin-Watson - 2.146 2.065 1.786 1.978 2.325

__________________________________________________________________________________

Notes: * Significant at 10 per cent; ** Significant at 5 per cent; *** Significant at 1 per cent / In parentheses: t-values


Table 4. Regression models (SUB-SAMPLE 1981-2003, average for the observation period 2000-2003)

___________________________________________________________________________

Variables Models

Logit OLS


___________________________________________________________________________

Independent (1) (2) (3) (4) (5) (6)

VIF

___________________________________________________________________________

Intercept -3.969 20.641 -32.013 86.412 -3.155 9.010

(-1.674) (1.366) (-0.298) (1.291) (-0.242) (0.392)


MODE 3.237*** 18.930*** 120.988*** 47.917* 7.236 -0.849 1.87

(3.017) (3.176) (2.848) (1.793) (1.396) (-0.094)

TIME 0.049 -0.188 -6.090* -2.558 -0.519 -0.067 1.70

(0.706) (-0.424) (-1.937) (-1.314) (-1.279) (-0.100)

MOTIVE 2.649* 9.581 102.225 -37.531 17.588** 16.203 1.37

(1.740) (0.949) (1.423) (-0.853) (2.083) (1.056)

OWNER -0.010 0.039 -0.534 0.129 0.082 0.063 1.04

(-0.630) (0.409) (-0.759) (0.304) (0.988) (0.434)

NATIO -1.612* -3.305 -30.541 1.834 -13.624*** -4.329 1.13

(-1.662) (-0.643) (-0.803) (0.080) (-3.075) (-0.554)


GROWTH 0.043*** -0.010 0.126 -0.103 -0.031 0.186* 1.37

(3.288) (-0.140) (0.240) (-0.324) (-0.488) (1.682)

IMPORT 0.001 -0.126 2.069** 0.466 0.213** 0.194 1.49

(0.031) (-1.042) (2.385) (0.875) (2.036) (1.055)

__________________________________________________________________________________

Nagelkerke R2 0.598 - - - - -

Model chi square 45.455*** - - - - -

CCR (%) overall 82.3 - - - - -

Adjusted R2 - 0.228 0.188 0.077 0.139 0.040

Durbin-Watson - 1.785 1.702 2.229 2.081 1.690

__________________________________________________________________________________



Table 5. Regression models (SUB-SAMPLE 1991-2003, average for the observation period 2000-2003)

___________________________________________________________________________

Variables Models

Logit OLS


___________________________________________________________________________

Independent (1) (2) (3) (4) (5) (6)

VIF

___________________________________________________________________________

Intercept -2.662 2.467 -14.715 108.855 -11.265 30.095

(-0.927) (0.125) (-0.100) (1.251) (-0.682) (1.176)


MODE 3.229*** 24.362*** 149.711*** 70.014** 10.342 -0.712 1.25

(2.744) (3.303) (2.738) (2.094) (1.637) (-0.074)

TIME -0.040 0.108 -11.907* -6.638* -0.326 -2.271** 1.06

(-0.314) (0.136) (-1.990) (-1.916) (-0.495) (-2.204)

MOTIVE 2.198 13.219 124.114 -41.339 18.613* 8.491 1.25

(1.501) (1.118) (1.421) (--0.804) (1.920) (0.554)

OWNER -0.021 0.096 -0.896 0.003 0.094 0.065 1.05

(-1.089) (0.797) (-0.988) (0.005) (0.949) (0.415)

NATIO -0.901 -1.754 -23.963 4.612 -12.784** 1.284 1.08

(-0.765) (-0.264) (-0.474) (0.160) (-2.340) (0.149)


GROWTH 0.046*** -0.042 0.059 -0.156 -0.058 0.282** 1.27

(3.035) (-0.441) (0.081) (-0.376) (-0.744) (2.289)

IMPORT 0.000 -0.020 2.353** 0.611 0.278** 0.069 1.23

(0.000) (-1.188) (2.042) (0.900) (2.167) (0.730)

__________________________________________________________________________________

Nagelkerke R2 0.576 - - - - -

Model chi square 32.121*** - - - - -

CCR (%) overall 85.0 - - - - -

Adjusted R2 - 0.123 0.153 0.089 0.116 0.112

Durbin-Watson - 1.605 1.600 2.399 2.033 1.631

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Table 6. T-test results. Observation period (average for the period 2000-2003) vs. period of Establishment (average for the first two years of operation)

_____________________________________________________________________ TOTAL SAMPLE

(1) cb-acquisition (establ.per.) vs. Greenfield FDI (establ.per.)

mean SD mean SD t-test (sig. (2-tailed)

MSHARE 26.27 10.332 11.29 10.332 0.000

CAP 56.46 31.334 21.55 22.128 0.000

PROF 14.72 11.942 14.66 15.187 0.978

(2) cb-acquisition (observ.per.) vs. Greenfield FDI (observ.per.)


MSHARE 41.82 21.419 21.75 15.942 0.000

CAP 96.12 93.189 36.47 32.381 0.000

PROF 18.17 17.528 17.49 18.071 0.822

(3) cb-acquisition (establ.per.) vs. cb-acquisition (observ..per.)


MSHARE 26.27 10.332 41.82 21.419 0.000

CAP 56.46 31.334 96.12 93.189 0.000

PROF 14.72 11.942 18.17 17.528 0.234

(4) Greenfield FDI (establ.per.) vs. Greenfield FDI (observ..per.)


MSHARE 11.29 10.332 21.75 15.942 0.000

CAP 21.55 22.128 36.47 32.381 0.000

PROF 14.66 15.187 17.49 18.071 0.173

____________________________________________________________________________

SUB –SAMPLE 1981-2003



MSHARE 25.44 9.194 11.21 10.815 0.000

CAP 91.88 95.311 24.81 24.233 0.000

PROF 15.46 15.495 10.47 12.247 0.179


Table 6. (continued)



MSHARE 42.20 19.906 19.72 15.229 0.000

CAP 99.03 96.099 39.69 33.218 0.000

PROF 17.13 17.615 12.14 14.135 0.239

______________________________________________________________________

SUB –SAMPLE 1991-2003



MSHARE 25.50 8.617 11.58 15.900 0.000

CAP 94.77 96.208 16.27 19.550 0.000

PROF 15.76 15.758 11.18 14.871 0.384



MSHARE 42.81 19.722 19.08 19.630 0.000

CAP 101.94 97.004 26.97 26.338 0.000

PROF 17.39 17.946 11.61 16.918 0.336

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Table 7. Summary of findings

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Performance Period of Degree of

Variables Hypotheses Models investigation support

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Market share H1 OLS observation period high support

T-test observ. vs. establ. period high support

Size H2 OLS observation period high support

Profitability H3 OLS observation period weak support

T-test observ. vs. establ. period not supported

Capital intensity H4 OLS observation period high support

T-test observ. vs. establ. period high support

Product differentiation H5 Logit observation period high support

Market concentration H6 OLS observation period weak support

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