Deakin University
Faculty of Business and Law
SCHOOL OF ACCOUNTING, ECONOMICS AND FINANCE
School Working Paper - Economic Series 2006
SWP 2006/05
Internationalisation and Alliance Formation: Evidence from
Turkish SMEs
Mehmet Ulubasoglu, Muhammet Akdis and Sabahat Bayrak
The working papers are a series of manuscripts in their draft form. Please do not quote without
obtaining the author’s consent as these works are in their draft form. The views expressed in
this paper are those of the author and not necessarily endorsed by the School.
INTERNATIONALIZATION AND ALLIANCE FORMATION:
EVIDENCE FROM TURKISH SMEs
Mehmet A. Ulubaşoğlu †
Muhammet Akdiş ‡
Sabahat Bayrak ±
May 2006
Abstract
This study explores the issue of internationalization through forming alliances with foreign capital
in the small business sector in Turkey. Using a sample of 257 SMEs from this emerging market
economy, collected via a field study, we investigate the impact of size (measured by investment
levels and number of workers), sectoral preferences, factors causing low capacity utilization,
management control, and the education level of CEOs on the alliance decisions of small firms.
Multinomial logit estimations are employed to obtain a detailed and rich set of results. Overall, we
find that Turkish SMEs would like foreign capital to help them expand their production capacity,
while also serving as a conduit to convey their products to world markets. Size-specific, sectorspecific and management-specific factors are identified in the alliance behavior. We also investigate
whether alliance motivation is a product of unidimensional or multidimensional decision-making
process on the part of SMEs. Size, as shown by investment, and factors causing low capacity
utilization are found to be the main contributors to the multidimensionality of the alliance
motivation through influencing the alliance behavior predicted by other firm attributes.
JEL Classifications: C25, L22, M13.
Keywords: Internationalization; Alliances; Foreign Capital; SMEs; Turkey.
†
Corresponding author: School of Accounting, Economics and Finance, Deakin University, 221 Burwood
Highway, Burwood, Victoria 3125 Australia. E-mail: [email protected]
‡ Department of Economics, Pamukkale University, Turkey. E-mail: [email protected]
± Department of Management, Pamukkale University, Turkey. E-mail: [email protected]
2
1. Introduction
Small and medium-sized enterprises (SMEs) play pivotal roles in today's global economy.
Besides making substantial contributions to competitiveness, innovation and job creation in
local economies (Acs and Audretsch 1998, Audretsch 2002, Drnovsek 2004), they have
considerable shares in investment, production and trade within and across countries. They are
prolific producers of technology, transferring it to the use of firms of different sizes across
borders, initiating and/or facilitating revolutionary changes in production techniques (Acs,
1995, Marcotte and Niosi 2005). In addition to their economic and structural roles, SMEs may
even influence the general process of political and social democratization and liberalization in
a society (Futo and Kallay 1994). Reynolds (1997) predicts that the advanced economies of the
future will not be dominated by old, large firms. Therefore, one could expect that the future
structure of the world economy will become acceleratingly more connected to the evolution
and growth of SME activities.
In this context, two much related but distinct concepts about SMEs have attracted strong
attention from researchers: internationalization and inter-firm alliances. Stage theory of
internationalization predicts that small firms gradually progress on the path of
internationalization by exporting passively first, then setting up export branches and learning
the export business via independent representatives, and then establishing overseas sales
subsidiaries, and finally becoming multinationals and producing abroad (Johanson and
Vahlne 1990, Andersen 1993, Dollinger 1995). Recent research, however, has found that
patterns of SMEs are far from linear and gradual (Etemad and Wright 2003). First, some
firms are “born global” (McDougall et al. 1994, Madsen and Servais 1997). It is also known
theoretically and empirically that there are ample barriers for SMEs towards
internationalization due to their small sizes, limited financial and human resources,
constraints on information and management know-how, and market imperfections and
regulations (Buckley 1989, Acs et al. 1997). Thus, owner/enterprise-specific characteristics as
well as other country-specific factors are argued to be important in the internationalization
experience of small firms (Calof and Viviers 1995, Minguzzi and Passaro 2001, De Chiara
and Minguzzi 2002, Manolova et al. 2002, Obben and Magagula 2003).
Alliances have also been suggested as a distinct mode of internationalization. In the words of
Acs et al. (1997), SMEs can follow a strategy of “intermediated expansion” to international
3
markets. By intermediated expansion, it is meant that small firms rely on large partners to
obtain scale and scope economies often needed to operate abroad. In this respect, alliances
to be formed between domestic SMEs and foreign capital suggest themselves as a
convenient way of internationalization, provided that the intermediator does not seek for
rent extraction. Gomes-Casseres (1996, 1997), Kohn (1997) and Buckley and Casson (1998)
provide important theoretical and empirical foundations for the motives of SMEs for
alliances. For instance, Gomes-Casseres (1997) argues that firms that are small relative to
their rivals and markets tend to use alliances to gain economies of scale and scope. When
they are large, they avoid alliances. Kale, Singh and Perlmutter (2000), BarNir and Smith
(2002) and Chen and Huang (2004), among others, explored other important aspects of
inter-firm alliances, such as building relational capital, and the role of social networks. The
study of Bamford, Gomes-Casseres and Robinson (2003) is a zeal of efforts on the practical
side that provides corporate and business environment with a comprehensive guide for
alliance strategies.
Nevertheless, there is a dearth of research on the alliance issue for developing countries,
and more so, on its links with internationalization. 1 This paper addresses that part of the
literature by investigating the internationalization-cum-alliance problem with Turkish data.
We believe that SMEs in emerging economies need serious scholarly attention, because
such studies will contribute to the theoretical and empirical understanding of the issue by
analyzing the problem in a different setting. The results of such undertakings will also be
of great value to managers in domestic firms as well as those in multinationals, and public
policies. We collect a unique data set via a field study from 257 Turkish SMEs in a vast
array of dimensions, in order to retrieve the general outlook of SMEs on
internationalization, and explore their motivations for building alliances with foreign
capital. In terms of the latter, we aim to find out whether Turkish SMEs mainly seek,
through alliances with foreign capital, expanding their physical capacities, gaining access to
new markets, transferring technology, or transferring management expertise. Importantly,
we also investigate whether such motivations are a product of unidimensional or multidimensional decision-making process on the part of SMEs. In particular, we analyze
whether and how size, sectoral preferences, level of capacity utilization, type of
management (family vs professional) and the educational background of CEOs influence
the preferred function of foreign capital inflows within the domestic economy, given the
One exception is Chen and Huang 2004, who use Taiwanese data to study how different industries could
undertake strategic marketing alliances in an attempt to enter the international markets.
1
4
predictions on alliance associated with other firm attributes. We also make size
comparisons in the alliance behavior of small firms, where size is measured by number of
workers and amount of operating capital. Sector-specific and management type-specific
behaviors are also identified. Based on the nature of the data, multivariate multinomial
logit estimation technique has been used for the econometric analysis to obtain versatile
results (see Hollenstein 2005).
At this point it must be noted that the nature of the internationalization, and hence the
alliance formation problem in developing countries is rather different. For a developed
country like US, internationalization of SMEs may mean investing abroad and forming
alliances overseas (see Kohn 1997). For those in Japan, it may mean transferring
technology-intensive products to overseas through some mode of internationalization. For
SMEs in developing countries, however, it means first overcoming institutional, technical
and cultural obstacles, other things being equal (see Acs et al. 1997, Manolova and Yan
2002, Riddle and Gillespie 2003). In Turkey, for instance, statist development model
implemented until the 1990s within a heavily protectionist framework have had continued,
needless to say negative, lead and lag effects on internationalization. Also, a collectivist
culture avoding uncertainty has not been conducive to entrepreneural activities for a long
period of time (Kozan, Öksoy and Özsoy 2006). Thus, the fundamental question in
Turkey was not “how to internationalize”, but “whether or not to internationalize”. Once
a passage occurrs from “whether or not” to “how”, as has been shown it did in this study,
the next stage is to find out the way of internationalization. This paper also does this job.
We argue that while the developed-country-modes of internationalization cannot
necessarily be ruled out for Turkish SMEs, the majority is expected to be involved in a
hosting mode, i.e., foreign capital investing in Turkey. This is because, as in many other
developing countries, business environment lacks capital to expand to world markets
through its own means. Thus, a positive position on foreign capital investing and operating
in Turkey can be taken as a support for internationalization. This argument is indeed
substantiated in our empirical analysis.
Keeping the above issues in mind, the results of our study are very informative. We find
that the Turkish SMEs are really small (at least, they feel that they are small), and they seek
to expand their capacity by forming alliances with foreign capital. In doing so, they also
aim at gaining access to new markets abroad. In other words, they wish foreign capital to
5
serve as a conduit to convey their goods to the world markets. Importantly, transferring
technology and management know-how is of much lesser significance. We delve further
into these findings. There exist significant differences in the alliance behavior of SMEs of
different sizes. There is evidence that firms with small and big sizes have different alliance
motivations than firms with medium size, but the exact conclusion about the impact of
size on alliance behavior depends on how it is measured, i.e., investment or number of
workers, and other controlling factors. In addition, size, as shown by investment level (but
not number of workers), and the reasons for inefficiency contribute to the multidimensionality of the alliance motivation by influencing the other firm attributes on the
alliance behavior. In other words, one aspect of the business affects the other aspect. Such
an effect is especially pronounced on sectoral preferences, number of workers, and
education level of the CEOs. One exception is that the motivation of SMEs that are
managed by family members is not affected by any factor, making theirs a unidimensional
decision.
Our results are consistent with the conclusion of Gomes-Casseres (1997), who finds that
firms that are small relative to their rivals and markets tend to use alliances to gain
economies of scale and scope. Our results are also consistent with Acs et al. (1997), where
SMEs seek for intermediated expansion via larger multinational enterprises. A result in the
same vein as ours has also been found by Chen and Huang (2004) in the context of
Taiwanese SMEs: SME owners have a thorough understanding of market opportunities in
the global economy, but they feel that their own ability to develop markets is limited.
Importantly, we do not find transfer of technology as a significant motivation for alliance.
This is in support of Buckley (1997), who finds that SMEs will, in aggregate, not be the
major transferors of technology. Our results clearly show that the process by which the
Turkish SMEs would like to participate in the global economy is through forming alliances
with foreign capital.
The rest of the paper is organized as follows: section 2 provides a brief overview of the
position and the role of foreign capital and SMEs in the Turkish economy; section 3
describes the structural characteristics/attributes of the sampled SMEs; section 4 includes
the responses to the internationalization and alliance questions, section 5 has the
econometric analysis, and section 6 discusses the econometric results. Section 6 concludes
and links the findings to a policy stance.
6
2. Foreign Capital, SMEs, and the Turkish Economy
Until the 1980s, the position of the Turkish government (and the Turkish population in
general) on foreign capital was uncertain. There were a number of “negative” incidents
that had shaped, in a sense, a precautionary sentiment about foreigners and foreign capital
starting in the 19th century. Foreign capital owners were also relatively hesitant to commit
substantial investment funds to Turkey, largely due to persistent economic and sociopolitical instabilities. Also, closed, inward-oriented and statist development model resulted
in an environment with a plethora of institutional impediments. The decade of 1980s was a
turning point for both Turkish politics and the economy. Starting with the Özal
government in mid-1980s, a number of important structural and institutional reforms took
place, including opening up the economy and taking various steps for democratization.
Most governments of the period followed policies that predicted some role for foreign
capital. 2
The tendency for reforms accelerated recently, with liberal and reform-oriented
government of Justice and Development Party, starting in early 2000s. Central to the
program of this party regarding the politics and the economy of Turkey has been the full
membership of Turkey to the European Union (EU). In this respect, various reform bills
that aimed at aligning and harmonizing the Turkish institutional structure with that of the
EU have been ratified by the parliament. The consequences of the reforms, which are still
ongoing, have already been realized. Inflation has decreased from around 60 percent to
less than 10 percent, a figure that has not been seen in the last 30 years. Interest rates
decreased, credit-worthiness increased, and exports and imports boomed. The Turkish
export figure for 2005 is 73.4 billion US dollars, meaning a two-fold increase as compared
to 2001. FDI figures for the year 2005 were realized around 10 billion US dollars, a nearly
three-fold increase over the year 2004. The period 2003-2005 has observed an FDI
magnitude that is equal to the amount received in the previous 30 years.
Turkish SMEs operate in such an economy. They undertake production in many key
industries like textile and apparel, metal products, and food products, and employ an
important proportion of the Turkish population. Their employment share in
Akdiş (1988) mentions that foreign capitalists started to be given incentives in accordance with the “Decree
on Operation Framework of Foreign Capital”, which was embedded in the stabilization program on 24
January 1980 with law no. 8/168.
2
7
manufacturing in the 1990s was around 60 percent, and value-added share was around 25
percent. Around 90 percent of total firms operating in the Turkish manufacturing industry
were SMEs (firms employing less than 200 workers) (KOSGEB, 2005). These figures
make it clear that a small number of large firms dominate the markets in terms of the value
of production. Thus, there is clearly a room for the expansion of SMEs in the production
and marketing front by allying with foreign capital, given that the economy is booming and
credit-worthiness has been achieved.
Turkish SMEs have attracted attention from the researchers in different aspects. Erzan and
Filiztekin (1997) compare the effects on European Customs Union on small firms and
large enterprises. Riddle and Gillespie (2003) examine how new venture firms in the
Turkish clothing industry use their informal and formal social networks to obtain
information on how to export successfully. They find that informal links are important
sources of information. Taymaz (2005) analyzes technical efficiency, returns to scale and
technical change in Turkish manufacturing firms over the period 1987-1997, and finds that
such factors are very important for the survival probability and growth of new
establishments. Finally, Kozan et al. (2006) investigate the impact of individual and
environmental factors on the growth plans of small businesses in Turkey. They find that
lack of know-how and financing problems are significantly linked to growth plans.
3. Data
The data were collected via a field study which was carried out in 2000 in Çorum, Denizli,
G.Antep, K.Maraş and Kayseri provinces of Turkey. These provinces were selected due to
their important roles in industrial production and high rankings in the provincial
distribution of investment shares. It is also fair to expect that foreign capital would invest
in provinces with some industrial base. Within each province, we randomly selected 75-130
SMEs, and delivered the questionnaires with the assistance of Chambers of Commerce,
Chambers of Industry, and Associations of Businessmen and Young Businessmen in the
provinces. These local organizations have had a friendly approach to our survey and
encouraged SMEs to fill in the questionnaires. The total number of questionnaires
delivered was 535, of which 257 were returned back. This makes the response rate close to
50 percent, which is generally considered as high for such surveys. SMEs were asked that
the questionnaire be completed by the chief executive officer (CEO). To maximize the
8
return, personal links were used with the local organizations, and SMEs were
communicated several times to return the survey.
The first part of the questionnaire contained questions on the structural characteristics of
SMEs. In particular, they are asked about the age and education characteristics of the
CEOs, sectors, number of workers, amount of fixed capital investments, type of the
management, and the reasons for inefficiency as shown by low capacity utilization.
The second part aimed at understanding the behavior of SMEs on internationalization and
alliance. As mentioned in the Introduction, in Turkey the stance on foreign capital is an
important and quite relevant reflector of the internationalization conception. Thus, the
SMEs are asked their general stance on foreign capital investing in Turkey, how
independent foreign capital can act within the country, and the reasons why full
independence of foreign capital within Turkey should be avoided. They are also asked their
stance on the adoption of international laws as part of the Turkish legislature. 3 The
questionnaire has been prepared in such a way that the questions and the answers reflect
the debates in media and the opinions and concerns of different political and economic
factions in the country. The least supportive option provided is that foreigners would be
attempting to exploit detrimentally the domestic resources. The second option was an
indifferent attitude, while the supportive option was about foreign capital acting in the
interests of Turkey. To understand their alliance behavior, the most relevant question was:
“What is the most important reason for you to form alliance with foreign capital?” In this
respect, the options provided were “Contributions to Operating Capital”, “Greater Access
to World Markets”, “Transfer of Technology”, “Transfer of Management Know-how”',
“No Contribution”' and “Other”. As it will turn out, this part of the survey has been able
to establish several important implications regarding the strategies of SMEs, small in both
absolute and relative sense, in an emerging market. 4
We also ask them the desired level of involvement of foreign capital in the privatization efforts in Turkey.
Because the responses mimic those of the “how independent foreign capital can act in Turkey” question, we
do not report the results to save space.
4 The questionnaire in fact included many questions, such as on their experience with financial crises etc.
However, the discussion and focus is beyond the scope of this paper. For that reason, we choose not to
include the whole questionnaire as part of this paper. However, the structural characteristics of the SMEs,
and the questions and answers on internationalization and alliance behaviors are mapped exactly from the
questionnaire to this paper. The whole questionnaire is available upon request, however.
3
9
The reliability of the second part of the survey is assessed through Cronbach's alpha
coefficient. The coefficient for all questions is found to be 0.70, which is right equal to the
acceptable reliability level suggested by Nunnally and Bernstein (1994, p. 265). We believe
that content validity is addressed in the survey, as the questions cover an important part of
the conceptual space of internationalization in Turkey, while construct validity is achieved
through distinguishing different SMEs on certain traits or behaviors (i.e., by addressing
both convergent and discriminant validities).
The item “No-response” provided as the answer to the internationalization and alliance
behaviors suggested little bias on the results. In fact, the pattern that emerged in the
answers is quite clear and dominated by certain options. Nevertheless, in an attempt to
address the issue formally, we sought for imputing the possible answers of such firms via
the corresponding firm attributes and parameter estimates obtained from the multivariate
regression analysis. Replacing those answers in the No-response items showed that the
extent of No-response bias is insignificant and hardly affects the results. 5
We now proceed with structural characteristics of the surveyed SMEs. Responses on
internationalization and alliance are provided in the following section.
3.1. Age and Education Level of CEOs
Of all SME owners in our sample, 65 percent fall into the age group of 25-44. In this
respect, our sample reflects the demographic structure in Turkey, where the population is
young. The education level of the CEOs is generally high, most of them being high school
or university graduates. 6 This level sums up to almost 80 percent in our sample.
Age Characteristics
Age Group
Quantity
Percent
No response
9
3.5
18-24
10
3.9
25-34
59
23.0
35-44
121
47.1
45-54
53
20.6
55+
5
1.9
TOTAL
257
100.0
Education Level of CEOs
Education
Quantity
Percent
No response
9
3.5
Primary Sch.
14
5.4
Secondary Sc.
11
4.3
High Sch.
82
31.9
University
128
49.8
Post-grad.
13
5.1
TOTAL
257
100.0
The share of “No-response” item provided as an answer to the structural characteristics of the firms never
exceeds 6%.
6 In Turkey, normally primary schooling is five years, secondary schooling is three years and high schooling is
three years. University education varies between two to four years.
5
10
3.2. Sector, Size and Management
The next point of interest is the sectoral distribution of SMEs, the number of workers
employed in these firms, total fixed capital investments, and the type of their management.
Sectoral Distr.
Sectors
Employment
Q.ty
Perc.
Workers
Q.ty
Perc.
Text. & Cloth.
Metal Prod.
Food & Prod.
Furn. Prod.
Other
105
54
31
13
54
40.9
21.0
12.1
5.1
21.1
TOTAL
257
100.0
20-50
51-100
101-150
151-200
201-300
301+
TOTAL
112
57
20
17
21
30
257
43.6
22.2
7.8
6.6
8.2
11.7
100.0
Investment
Total Inv.
Q.ty
($1,000)
No Resp.
16
0-100
33
100-500
62
500-1,000
39
1,000-5,000
69
5,000+
38
TOTAL
257
Managers
Perc.
Managers
Q.ty
Perc.
6.2
12.8
24.1
15.2
26.8
14.8
100.0
No Resp.
Family
Profess.
Other
5
168
75
9
1.9
65.4
29.2
3.5
TOTAL
257
100.0
SMEs in the textile and clothing sector constitute 41 percent of our sample, followed by
those in metal products, food products and furnishing sectors. These sectors have
important roles in the Turkish economy. Around 25 percent of the Turkish SMEs are
positioned in the textile and clothing industry, around 25 percent is in the metal products
sector, and around 20 percent is in food and tobacco sectors (KOSGEB, 2005).
Around 45 percent of SMEs in our sample employ 20-50 workers, add to that 22 percent
with 51-100 workers. Other SMEs are fairly equally distributed across the range of 100-300
workers.
Around 60 percent of SMEs have the value of fixed capital investments between $0 and
$1,000,000. This gives an idea as to the value of tangible assets in these enterprises, hence,
their size and capabilities. As will become clear, this low level of the value of fixed capital
is one of the reasons why SMEs are willing to form alliances with foreign capital. Another
interesting note is that a cross-tabulation between investment and the number of workers
indicates that firms with higher investment tend to employ more workers (p=0.00). 7 For
example, 92 percent of the firms with more than $5,000,000 of initial investment employ
more than 100 workers.
7
p-value is that of Pearson chi-square test statistic.
11
As per management type, a rather large portion of the enterprises (65.4 percent) is
managed by the members of the family that owns the firm. 8 The proportion of firms
whose executives are professional managers is about 30 percent. A cross-tabulation
indicates notably that enterprises with greater initial investment tend to employ
professionals for managerial positions (p=0.01).
3.3. Reasons for Under-Utilization of Capacity
We are also interested in the factors behind the under-utilization of capacity in SMEs.
These factors are extremely important, because low capacity utilization implies low
efficiency in production, and resources remaining idle.
Reasons for Under-Utilization of Capacity
Reason
Quantity
No Response
28
Shortage of Finance
48
Low Demand
105
Shortage of Raw Material
9
Frequent and long-lasting mechanical problems
7
Shortage of skilled technical staff
29
Other
31
TOTAL
257
Percent
10.9
18.7
40.8
3.5
2.7
11.3
12.1
100.0
Low demand seems to be a very important reason for under-utilization of capacity (41
percent). This hints a mismatch between the production capacity of firms and the demand
for their goods. This is followed by shortage of finance, which is related to the ability to
fund short-term expenditures on inventory, energy and labor costs, and inputs to diversify
the product composition with regards to emerging market needs. Shortage of raw material,
mechanical problems and shortage of skilled employees are the other factors, but seem to
be of lesser importance.
4. Internationalization and Alliance Behavior
4.1. Stance on Foreign Capital
There is widespread consensus in the development field that developing economies
typically suffer inadequate capital accumulation due to technical and institutional obstacles,
8
This result conforms to Buckley (1997), who argues that most SMEs are owned and managed by families.
12
and foreign capital can close the gap. Thus a starter question can be on general outlook on
investment opportunities to be extended to foreign capital in Turkey.
General Outlook on Foreign Capital
Outlook
Quantity
No Response
24
Other
17
Resource of Turkey would be exploited by foreigners
16
Their contributions would be limited
33
Resources of Turkey can be used better and more efficiently
167
TOTAL
257
Percent
9.3
6.6
6.2
12.9
65.0
100.0
We find that 65 percent of the SMEs support foreign capital on the basis that it will have
positive impact on the Turkish economy by facilitating better and more efficient use of
resources. A 13 percent assumes an indifferent posture on their contribution, while a six
percent subscribes to a nationalistic view that rejects foreign capital on the basis of their
serving Western colonial interests.
We also ask SMEs about their position on adopting international laws. This question is
expected to provide some insights as to how Turkish SMEs view the harmonization of
domestic institutions with that of the EU, and how they feel they can handle such rules.
Adoption of International Laws
Opinion
No Response
Other
It will help the reincarnation of old Western interests
It will not help in transferring foreign capital
It is a necessity for and a consequence of globalization
TOTAL
Quantity
40
3
16
33
165
257
Percent
15.6
1.2
6.2
12.8
64.2
100.0
64 percent of the participants see it as an inevitable consequence of the globalization
process. About 13 percent of the entrepreneurs are hesitant about the adoption of new
laws for their use to attract foreign capital into the country, whereas 6 percent disfavor
such arrangements. These responses mimic closely those on general attitude above. In
sum, the SMEs in our sample feel capable enough in coping with the possible set of legal
arrangements required for accession to the EU, and globalization in general.
13
Within the context of the activities of foreign capital in Turkey, we ask SMEs about the
working conditions to be provided to foreign capital. One question of interest would be
how mobile foreign capital can act in Turkey; that is, whether it can take independent
actions for investment purposes or it should have a domestic partner to act together.
How Independent Foreign Capital can Invest in Turkey
Opinion
Quantity
No Response
17
Other
3
They can act independently under intense surveillance
26
They should be urged to act by allying with domestic firms
200
Foreign capital can act independently
11
TOTAL
257
Percent
6.6
1.2
10.1
77.8
4.3
100.0
Nearly four-fifths of the SMEs in our sample would like to see foreign capital act with a
partnership of a domestic firm. The proportion of those who favor independence for
foreigners remain low, at around 4 percent.
On this result, an inevitable question would be on why extending complete independence
to foreigners should be avoided and its possible consequences otherwise.
Why Independence of Foreign Capital should be Avoided
Opinion
Quantity
No Response
21
Other
4
They may use their power in Turkish politics
34
Foreign investment will not harm domestic interests
54
Some sectors may be of strategic importance
144
TOTAL
257
Percent
8.2
1.6
13.2
21.0
56.0
100.0
A large number of SMEs cast doubt about the full independence of foreign capital on the
basis that some sectors may be of strategic importance. We ascribe this standpoint to an
economic sensitivity (rather than a political one) that is related to a concern of not falling
behind the foreigners in their domestic economy. A cross-tabulation (unreported) between
the respondents that disfavor the complete independence of foreign capital and the
operating capital shows that 89 percent of the SMEs have initial investment of lower than
$5,000,000 (p=0.01). This means that the production and marketing capabilities of these
SMEs are relatively limited as compared to those of foreign firms, which are expected to
have greater capital. And being small in relation to both domestic and foreign markets,
14
they are worried that the independent activities of relatively large foreign firms in the
Turkish economy may eliminate them from the market. As will become clear below, this
finding is consistent with Gomes-Casseres 1997, who note that relatively small firms tend
to have a higher-than-average propensity to form alliances (p. 34).
4.2. Alliance with Foreign Capital
The importance and the advantages of working closely with foreign capital have been
hinted so far. Our final question is about SMEs’ motivations behind forming alliances with
foreign capital. 9 Since we are considering foreign investments into Turkey, these alliances
are assumed to be formed within Turkey. Acs and Preston (1997) note that the great
majority of alliances formed over the past decade have aimed at gaining access to new
product and processes, technologies, and organizational competencies. In conjunction with
this argument, our question is expected to help understand which of these factors are more
relevant for Turkish SMEs.
Motivations for Alliance with Foreign Capital
Opinion
Quantity
No Response
23
Other
2
It will not provide any contribution
10
It can help management and organization know-how
8
It can help in technology transfer
25
It can contribute to the firms’ operating capital
94
It can help in opening to the world markets
95
TOTAL
257
Percent
8.9
0.8
3.9
3.1
9.7
36.6
37.0
100.0
The responses above clearly indicate that foreign capital is expected to help Turkish SMEs
in opening to the world markets and increasing the operating capital of the firms.
Contribution to technology ranks third in significance, but far below the former two. A
fairly accurate proposition would be that, through partnerships with foreign capital, SMEs
in Turkey wish to expand their production capabilities, and in doing so, gain access to new
markets. Note that this is an understandable necessity if we re-consider the low levels for
the value of fixed capital in most of the firms, their desire to internationalize, and low
capacity utilization due to shortage of demand and finance.
Gomes-Casseres (1996) defines alliance as: any organizatonal structure used to govern an incomplete
contract between separate firms and in which each partner has limited control.
9
15
4.3. Cross Tabulation of Firm Attributes and Responses to the Alliance Question
Our aim is to build up a consistent framework that starts with looking at the patterns in
the raw data ex ante, and then exploring different aspects of these patterns using a
coherent econometric analysis ex post. In this vein, let us delve into the relationships
between firm attributes and the responses to the questions on internationalization and
alliance behavior. Table 1 in Appendix A presents a cross tabulation of detailed firm
attributes and general outlook. Firm attributes are categorized into the human capital level
of CEOs, sectors of the SMEs, their number of workers, investment magnitude, type of
management, and the reasons for low capacity utilization. For instance, the ‘category’ of
education level of CEOs includes the firm ‘attributes’ of whether the CEO is a graduate of
primary school, secondary school, high school, university or a post-graduate institution. 10
Table 1 presents, for each single attribute within each category, the number of responses
for a particular option and the percentage share of responses for that choice. For instance,
4 CEOs with primary school education (40 percent of all CEOs with primary school
education) are indifferent to foreign capital, while 6 CEOs (60 percent of all primary
school graduates) provide positive support. The positive outlook on foreign capital holds
regardless of firm attributes, within and across categories, as the responses concentrate
overwhelmingly on the support choice. This support is 77 percent (167/216) if the
attention is restricted only to those that cited one of the no-support, indifferent and
support views, i.e., when No Response and Other choices are disregarded. Thus
internationalization through foreign capital is welcomed in this emerging market.
Table 2 in the Appendix presents a cross tabulation of firm attributes and the responses to
the question “how independent foreign capital should act in Turkey”. It is evident that the
majority of firm attributes are associated with the partnership option.
Table 3 presents a cross tabulation of firm attributes and responses to the alliance behavior
question. An important result from this table is that the motivations Contributions to
Operating Capital and Access to World Markets hold regardless of many attributes within
and across all categories. However, there are variations in some cases.
10
Hence, the definitions of “firm attribute” and “category” used in this paper follow.
16
Dividing the sample sector-wise, we find that all sectors except Furniture Products have
high and equally strong preference for Contributions to Operating Capital and Access to
World Markets than the other motivations. For Metal Products and Other Sectors, there is
also some preference for Technology Transfer. On the other hand, for the Furniture
Products sector, the motivation for alliance is clearly Access to World Markets only.
Dividing the sample into investment magnitudes delivers striking results. For the smallest
group in our sample, i.e., those with operating capital of 0-$100,000, there is a significant
higher preference for alliance on the basis of Contributions to Operating Capital. For firms
with operating capital of $100,000-$500,000 the preference is more significant for Access
to World Markets, although Contributions to Operating Capital still holds important.
There is also some preference for Technology Transfer in this group of firms. For firms
with higher operating capital, i.e., the biggest three groups, the significance of the
operating capital and access to world markets motivations is generally high and equally
important. It is important to observe that even firms with the highest amount of operating
capital, i.e., those with 5,000,000+, the concern for opening to world markets is
substantial.
The number of workers category exhibits similar preference characteristics as the
investment category. In this case, the preference for Contributions to Operating Capital
and Access to World Markets is significantly higher than the other motivations, while firms
with less number of workers have some variations. The smallest group, i.e., firms with 2050 workers, have slightly higher preference for alliance on the basis of Access to World
Markets, while the preference of firms with 51-100 workers for alliance is high due to
contributions to operating capital.
For family and professional managers, the significance of the operating capital and world
markets motivations holds jointly, while for the Other Management attribute, the choice is
significantly on Access to World Markets. However, the number of such manager type is
low in our sample.
The associations between the reasons for low capacity utilization and motivations for
alliance are intuitive. For instance, among firms that stated demand shortage as the main
cause of low capacity utilization, a significant proportion (47 percent) prefers alliance on
the basis of Access to World Markets. Those that stated finance shortage as the main
17
reason for low capacity utilization, a considerably higher proportion (64 percent) prefer
alliance on the basis of Contributions to Operating Capital. Those with mechanical
problems prefer alliance totally for contributions to operating capital, while those firms
with shortage of skilled labor and raw material shortage are likely to choose Access to
World Markets as the motivation.
Upon these results, several conclusions can be drawn on Turkish SMEs by visiting
Gomes-Casseres (1997) on alliance strategies of small firms. Gomes-Casseres notes that
relative size is a key factor behind any firm’s alliance strategy, regardless of their absolute
size (p. 34). He goes on to note that relatively small firms are the kind of firms that tend to
have a higher-than-average propensity to form alliances. Thus, the first conclusion in our
case is that the relative size of Turkish SMEs is small. Relativeness here should be
understood as being relative to world markets, as the SMEs’ internationalization desire is
already substantial. Thus, the fact that they have a strong choice for alliance asserts the
reasoning that the Turkish SMEs are relatively small. 11
Gomes-Casseres also argues that deep-niche firms can overcome their smallness by finding
markets in which there are no large rivals, that is, markets in which they can act as large
players. The other firms -- which do have large rivals -- seek allies to nullify their
disadvantages (p. 43). Given this argument and the strong choice for alliance, Turkish
SMEs cannot be said to be deep-niche firms, i.e., they cannot provide distinct goods in
their locality so as to exploit a particular side of the market. This coincides with our casual
empiricism that most small firms in Turkey produce homogenous products within a
perfectly competitive market.
Our results are consistent with Acs et al. (1997: 14-15) as well, who argue that SMEs can
seek intermediated expansion to world markets through partnerships with large
enterprises.
5. Econometric Analysis
Cross-tabulations reveal important associations between firm attributes and alliance
behavior. However, these are unconditional associations. In other words, we look at only
11 In addition, Gomes-Casseres points out that “relatively small” implies that these firms are small relative to
the optimum size in their industry.
18
one category of attributes, e.g., sectoral division, ignoring the other attributes of the firms.
It may be relevant to hold certain characteristics constant. Note, for instance, that the
distributions of sectors, number of workers, type of management and reasons for low
capacity utilization are rather skewed in our sample, just like the skewness in the alliance
motivation, while the distribution for investment magnitudes is somewhat even. Thus,
some categories (and firms) may deliver observationally equivalent information. 12 It is of
interest to see how sectoral preferences will prevail after holding low capacity utilization
constant, or how they will prevail holding size constant. Or, it is of interest to explore how
size affects the alliance behavior, holding sectoral preferences and/or reasons for
inefficiency constant. This would also lead us to explore the multi-dimensionality of the
alliance behavior.
Thus, we seek to enrich our results set with multivariate regression analysis using a
polychotomous dependent variables technique -- multinomial logit model. 13 This model
facilitates exploring the associations between firm attributes and responses (motivations
for alliance), adjusted for other factors (i.e., size, as measured by investment and number
of workers, sectors, reasons for inefficiency, type of management and the education level
of the CEOs). To proceed with the estimation, we code the responses on alliance
motivation to 0, 1, 2, 3, 4 and 5. As the choice set has no natural ordering, multinomial
logit estimation will address the characteristic of the dependent variable created. All firm
attributes are represented with dummy variables, where the relevant dummy takes 1 for a
particular attribute and 0 otherwise. 14 Let us define the following equation:
ALLIANCEi = β 0 + β1 FIRM _ ATTRIBUTEi , j ,k + β 2 OTHER _ CONTROLLING _ ATTRIBUTESi , j + ε i
(1)
where ALLIANCE is a polychotomous choice variable created out of the responses to
the alliance question, i =1,....n is an index for firms where n is the number of firms in the
regression (changes with data availability), j is an index for categories (i.e., sectors,
investment level, number of workers, reasons for inefficiency, type of management, and
12
For instance, reasons for low capacity utilization may be associated with certain sectors, e.g., Shortage of
Demand with Textile and Clothing, and Shortage of Finance with Metal Products, and this may lead to the
choice of Contributions to Operating Capital and Access to World Markets as alliance motivations,
respectively.
13
Hausman tests for independence of irrelevant alternatives indicate that each option is different than each
other significantly.
14 We excluded the “No Response” item from both responses and firm attributes since no information can
be extracted from them.
19
education level of CEOs), k is an index for single firm attributes within categories, and ε is
an error term that follows the logistic distribution.
At this point, we discuss the details of our econometric approach. Note that we do not
take the approach of presenting coefficient estimates from multinomial logit models and
interpreting their signs and significance levels. The coefficient estimates from these models
in fact show whether and how a vector of right-hand side variables impact the direction of
the choice of a particular option relative to a base option. In addition, if a sub-vector of the
right-hand side variables is constituted by dummy variables, such as all the sub-vectors
(categories) in our case, then the coefficient estimates in a full-fledged multinomial logit
model would indicate the impact of a particular firm attribute with reference to a base
attribute on the choice of a particular option with regards to a base option. 15 Needless to say,
this interpretation is demanding. It demands comparisons of various attributes and various
motivations.
In addition, the directions of the choices with respect to a base motivation are too obvious
to explore here. They are already known from Table 3. We can observe that all firm
attributes within and across categories are relatively equally likely to be associated with the
Contributions to Operating Capital and Access to World Markets motivations over the
other motivations. Therefore, by looking at the directions of the choices, there is no
possibility to elicit further information on the firm attributes, within and across categories.
Not surprisingly, the coefficient estimates from multinomial logit regressions (simple and
full-fledged) that we have run confirm this argument (unreported). 16
What we focus on, then, is the predicted probabilities of a particular firm attribute being
associated with a certain member of the choice set (e.g., a Textile and Clothing firm’s
probability of choosing Contributions to Operating Capital, Access to World Markets,
Technology Transfer, etc.). In other words, the focus is on the predicted probabilities and
15
For a brief example, assume that we set the base choice on No Contribution. Assume also that we explore
the sectoral preferences, say, that of Textile and Clothing, and set the base sector to Other Sectors. Thus the
coefficient estimate of Textile and Clothing would indicate the direction of the choice of the Textile and
Clothing sector relative to Other Sectors for a particular motivation for alliance relative to No Contribution.
The coefficient estimates show that each firm characteristic is insignificantly different than others in
choosing the members of the choice set. In terms of the above example, Textile and Clothing does not differ
significantly from any of the other sectors in making the choices from the choice set. This is because each
firm characteristic has a tendency to be associated with the first two options, and none of the characteristics
deviates significantly from this tendency.
16
20
marginal effects associated with FIRM_ATTRIBUTE in Equation (1). This means that
predicted probabilities are adjusted for size, sector, reasons for inefficiency, type of
management and education level of CEOs, where appropriate. If there is a significant
change in the predicted probabilities after holding a factor constant, then this factor is said
to affect the impact of the relevant firm attribute on the choice. Holding-a-factor-constant
in essence means that this factor is incorporated into the decision-making process for
alliance. In other words, the decision-maker has information on that factor, and states
his/her opinion on the basis of a particular attribute accordingly. Thus, our tool to explore
the multi-dimensionality of the alliance decisions is comparing the adjusted and unadjusted
predicted probabilities of firm attributes. Note that the unadjusted probabilities are already
provided in Table 3 -- i.e., the percentage values.
Finally, we also provide the marginal effects of firm attributes on stating a certain
motivation for alliance. These figures indeed amount to differences among the attributes
of the same category on selecting a particular outcome. They show, for instance, how firms
with Investment level of $0-$100,000 compare with firms of other size about choosing
Contributions to Operating Capital as the motivation for alliance, and so on. 17 This
answers how different size indicators differ than each other in affecting a choice (see,
among others, Moen 1999, Lopez-Gracia and Aybar-Arias 2000, Pope 2002, Hollenstein
2005, who make size comparisons among firms of different size). Marginal effects, too, are
adjusted for size, sector, the reasons for inefficiency, type of management and education
level of the CEOs, where appropriate.
A brief overview of the multinomial logit model is provided in Appendix B.
6. Results
6.1. Predicted Probabilities
Tables 4a through 4f in Appendix A present the predicted probabilities for each firm
attribute, adjusted, one at a time, for size, sectors, reasons for inefficiency, type of
management, and the education level of the CEOs, where appropriate. Table 4g presents
the predicted probabilities when all factors, other than the attribute category itself, are
In fact for ‘unadjusted’ probabilities, marginal effects amount to the difference between the predicted
probability of the firms with a particular level of investment choosing Contributions to Operating Capital
and the average of the predicted probabilities of all other firms with other investment magnitudes choosing
the same outcome, and so on.
17
21
jointly held constant. Let us first summarize the results from the outset. One striking result
is that after adjusting for all factors, predicted probabilities still are concentrated on
Contributions to Operating Capital and Access to World Markets, though a few changes
also occur regarding Technology Transfer. In addition, probability changes occur often
when investment levels and the reasons for inefficiency are held constant. The impact of
controlling for sectors, the number of workers, type of the management, and the education
level of the CEOs on predicted probabilities is very little. Also, most changes related to the
motivations of Contributions to Operating Capital and Access to World Markets are in
opposite directions. We consider changes as significant when the predicted probabilities
change by at least 5%. In particular, the changes are classified into 5-9%, 10-14% and
15%+ ranges.
To start with the link between sectoral preferences and alliance motivation, none of the
predicted probabilities associated with different sectors is affected significantly when the
number of workers, type of management and education level of the CEOs are held
constant (Tables 4d, 4e, 4f). Some changes are observed when investment and reasons for
inefficiency are held constant, i.e., when those factors are made known to the decisionmaker to incorporate into the decision-making process before stating an opinion on
sectoral preferences. Importantly, when Investment is held constant, the probability of a
Metal Products firm choosing Contributions to Operating Capital decreases and that for
Access to World Markets increases (Table 4a). This implies that, incorporating the
investment level into the decision-making process for alliance, the expectations of this
sector from an alliance decrease for contribution to their operating capital, but those for
greater access to world markets increase. Other things being equal, it can be said that firms
in this sector have relatively higher operating capital, but this level of investment does not
endorse them better opportunities in the world markets. The firms are willing to close this
gap with alliance. Exactly the same result is obtained for Other Sectors as well. 18
Controlling for Investment also increases the predicted probability of Contributions to
Operating Capital in the Food Products sector. In lieu of the same above, firms in this
sector can be said to have relatively low levels of operating capital, and they wish to ally
with foreign capital for that reason. However, such alliance is not expected to bring other
opportunities. When the reasons for inefficiency are held constant (Table 4b), Metal
Examples of other sectors in our sample include Leather and Products, Paper board and Print, Marble
Processing, Chemical Products and Motor Vehicle Parts. These are all low in numbers to analyze them as
separate sectors.
18
22
Products firms are more likely to choose Contributions to Operating Capital. Ceteris
paribus, this implies that those firms have higher inefficiency factors that are related to
their level of operating capital (such as shortage of finance or mechanical problems), thus
would be willing to ally with foreign capital to mend that deficiency. Other Sectors, too,
have higher inefficiency due to operating capital and lower inefficiency due to marketing
opportunities, thus incorporating these inefficiency factors into their decisions, they are
more likely choose Contributions to Operating Capital and less likely to choose Access to
World Markets. No impact is observed on Textile and Clothing due to holding any factor.
Some important results are also obtained when the focus is set on the number of workers
employed. As above, controlling for only Investment level and Reasons For Inefficiency
have fairly significant effects. Controlling for Investment 19 results in changes in the alliance
choices of firms that are in the range of 20-50 and 201-300 workers (Table 4a). This
implies that for firms that are in the other ranges of number of workers, Number of
Workers and Investment must be proportional so that controlling for Investment makes
insignificant changes in the predicted probabilities of the corresponding range of Number
of Workers. In terms of the range of 20-50 workers, these firms are less likely to choose
Contributions to Operating Capital and more likely to choose Access to World Markets if
investment is made part of the decision making process. So, these firms have
proportionally higher level of initial investment, but this investment level is not conducive
to higher access to world markets. Foreign capital is expected to help at that point.
Whereas for firms that are in the range of 201-300 workers, proportionally higher initial
investment is conducive to higher access to world markets but probably associated with
lower level of technology. Thus, on that basis, they are willing to choose Technology
Transfer. Controlling for Reasons for Inefficiency, on the other hand, affects ranges 151200, 201-300 and 300+, which are indicators of relatively greater size (Table 4b). The most
significant change is on the range 151-200. For firms in this range, the predicted
probability of choosing Contributions to Operating Capital increases from 0.44 to 0.63,
while that for Access to World Markets decreases from 0.56 to 0.37. Thus higher
inefficiency factors related to operating capital tempt these firms to form alliance with
foreign capital, while lower inefficiency factors related to marketing opportunities (such as
It is a legitimate question to ask the reason for holding one size variable constant, i.e., investment, to
explore another size variable - number of workers, as it is expected that higher number of workers would be
associated with higher levels of investment. Note that the number of workers may be higher or lower in
firms relative to their investment level. This is exactly the point we would like to explore.
19
23
less shortage of demand) lead to lower probability of choosing Access to World Markets.
On the other hand, firms in the range of 201-300 and 300+ workers are less likely to
choose Contributions to Operating Capital and more likely to choose Access to World
Markets for exactly the opposite reasons. Controlling for sectors, type of management and
education level of CEOs does not result in any significant change in the predicted
probabilities (Tables 4c, 4e, 4f).
Next, predicted probabilities associated with investment. Importantly, the impact of
Investment on alliance behavior is significantly affected only when the reasons for
inefficiency are held constant, except a few other cases. Controlling for Reasons for
Inefficiency affects the alliance behavior for firms that are with the smallest, medium and
the highest size in our sample (Table 4b). Firms with investment levels of 0-$100,000 and
$500,000-$1,000,000 experience decreasing predicted probabilities for choosing operating
capital. Firms in these ranges may possess lower inefficiency due to their operating capital,
thus incorporating this into the decision, they expect less contribution to their operating
capital. Firms with investment level of $5,000,000+ experience increasing predicted
probability for operating capital and decreasing predicted probability for world markets.
Thus, these firms may have higher inefficiency due to factors about operating capital and
lower inefficiency due to factors about their marketing capabilities. Controlling for sectoral
differences, too, affect the firms that are in the investment range of 0-$100,000 (Table 4c).
Their predicted probability of choosing Contributions to Operating Capital increases and
that for Technology Transfer decreases. Firms in this range probably belong to sectors
with a higher need of operating capital but lower need of technological endowment.
Finally, controlling for Number of Workers only affects the firms with investment level of
0-$100,000; their predicted probability of choosing Contributions to Operating Capital
increases by 0.10 (Table 4d). This may imply that firms with this level of investment
employ proportionally higher number of workers than their operating capital, i.e., they are
relatively overstaffed, thus they wish to see foreign capital contribute to their capital. As
above, controlling for Type of Management and Education Level of CEOs does not result
in any significant effect (Tables 4e and 4f), nor is the impact of investment levels $100,000$500,000 and $1,000,000-$5,000,000 affected by holding any factor constant.
The impact of Inefficiency on alliance behavior is significantly affected only when
Investment is held constant (Table 4a). Add to that, three important reasons for
24
inefficiency, i.e., demand shortage, finance shortage and mechanical problems, have no
different impact on alliance behavior when any factor is held constant (Tables 4a - 4f).
Holding Investment constant, firms with Shortage of Skilled Staff are associated with
lower predicted probability of Contributions to Operating Capital, and those with Other
Reasons have lower predicted probability for Access to World Markets. Although firms
with Raw Material Shortage exhibit more significant changes, the number of such firms is
only nine in our sample. None of the other factors affect the impact of inefficiency on
alliance behavior.
As per Type of Management, an important result is about the firms with family and
professional managers. None of the controlling factors affect significantly the alliance
behavior of such firms (Tables 4a - 4f). In both groups of firms, around 80% is equally
likely to choose Contributions to Operating Capital and Access to World Markets as the
motivation for alliance. Firms with Other Management have a very significant tendency to
choose Management Transfer but de-emphasize Access to World Markets when
Investment and Sectoral differences are held constant (Tables 4a and 4c). However, the
number of such firms is only nine in our sample.
About Education Level of the CEOs, none of the predicted probabilities associated with
different education levels is affected significantly when Sectors, Number of Workers and
Type of Management are held constant. Some changes are observed when Investment and
Reasons for Inefficiency are held constant. For instance, the probability of Primary School
Graduate CEOs choosing Contributions to Operating Capital decreases (from 0.42 to
0.36) and that for Access to World Markets increases (from 0.33 to 0.41) when Investment
is held constant (Table 4a). Other things being equal, this may be due to the fact that firms
with CEOs that are primary school graduates have relatively high levels of operating
capital, but given this level of investment their access to world markets is limited. Foreign
capital is expected to help with the latter. When Reasons for Inefficiency are held constant,
for High School Graduates, the probability of choosing Contributions to Operating
Capital decreases and that for Access to World Markets increases. For Primary School
Graduates only the probability of choosing Contributions to Operating Capital decreases,
while for University Graduates, only the probability of choosing Access to World Markets
increases. The impact of Secondary Schooling is not affected by holding any factor
constant. All these results imply that, overall, when investment and the reasons for
25
inefficiency constitute part of the decision making parameters for alliance (separately, not
jointly), education of the CEOs tends to favor Access to World Markets more, as opposed
to Contributons to Operating Capital.
Finally, we hold all factors constant jointly (Table 4g), except, of course, for the category in
question. 20 Although this results in a disadvantage of not being able to find the predicted
probabilities associated with some attributes due to insufficient degrees of freedom, it is
helpful in figuring out whether the choices, i.e., the motivations for alliance, are
unidimensional or multi-dimensional. This exercise produces some different results than
above. Needless to mention, most significant changes are driven by the joint control of
Investment and Reasons for Inefficiency. 21 Note also that the most significant impact of
holding all factors constant is on the predicted probabilities associated with Sectors and
Number of Workers.
As per sectors, Metal Products firms experience a significant decrease in predicted
probability of choosing Contributions to Operating Capital and a significant increase in the
predicted probability of Access to World Markets. When all factors are considered as part
of the motivation for alliance, the related effect found above strengthens. For Textile and
Clothing and Food Products, predicted probabilities for Contributions to Operating
Capital increases, however. Other Sectors are less likely to choose Access to World
Markets when all factors are held constant.
Significant changes are also observed in Number of Workers. Firms in the range of 51-100
workers experience an increase in the predicted probability of choosing the operating
capital motivation. Those in the range of 151-200 workers experience a very significant
increase in the probability of choosing Contributions to Operating Capital (i.e., from 0.44
to 0.68) and a very significant decrease in the probability of choosing Access to World
Markets (i.e., from 0.56 to 0.31). Those that are in the range of 201-300 workers
experience a very significant drop in the operating capital motivation (i.e., from 0.35 to
0.19), but significant rises in the Access to World Markets and Technology Transfer
motivations. Firms that employ more than 300 workers have lower predicted probability of
choosing the operating capital motivation. This implies that there is a cut-off point in the
Note that when controlling for all factors, we only use one size variable, Investment, in the regression.
That is, two size variables are not utilized simultaneously on the right hand side. We, however, use Number
of Workers, when finding the predicted probabilities of Investment, and vice versa.
21 This result is also confirmed in our (unreported) regressions, where we try the controlling factors only in
pairs and triplets.
20
26
number of workers, 200, below which operating capital motivation increases and above
which the same motivation becomes weaker. This pattern is also observed in a slightly
different way for Access to World Markets. Thus, 200 workers can be said to be a key size
for Turkish SMEs.
In addition, while a few changes are observed in the High School Graduate CEOs,
Invesment Level $1,000,000-$5,000,000 and Shortage of Skilled Labor attributes, the
changes in Professional Management are worth discussing. In that group of firms, the
probability of choosing Contributions to Operating Capital and Access to World Markets
decreases jointly, while the motivation for Technology Transfer doubles. This implies that
in such firms, when all factors are incorporated in the decision-making process, capabilities
as shown by operating capital and access to new markets may be already strong enough what they significantly need is to obtain higher technology through alliances.
To sum up our results, different attributes of the firms tend to be associated dominantly
with Contributions to Operating Capital and Access to World Markets as the motivations
for alliance. However, the impact of some of the attributes on these choices may be
influenced by some factors, while some others are not influenced at all. The decision in the
case of former can be said to be multi-dimensional, and in the case of the latter,
unidimensional. For instance, the decisions of Family Management are unidimensional.
They hold strongly, and to the same extent, regardless of other factors, whether held
constant separately or jointly. Sectoral preferences, on the other hand, are
multidimensional. Other factors influence the sectoral decisions. Several attributes, such as
different sizes are affected by only a few factors. Meanwhile, size, as shown by investment
level, and the factors causing inefficiency highly influence the predictions associated with
most attributes on the alliance behavior.
6.2. Marginal Effects
So far we have discussed the absolute values of and the changes in predicted probabilities
associated with single firm attributes. A very important point is how each attribute of the
firms compares with other attributes within the same category. For instance, how small
firms as shown by 0-$100,000 are motivated for alliance as compared to firms of greater
size. Although some hints have been received above, marginal effects of firm attributes
27
facilitate such comparisons by providing the magnitudes of such differences. Such effects
can as well be investigated with and without being adjusted for a factor. As per the
significant results above, we control only for Investment, Sector, Reasons for Inefficiency,
separately and jointly. The unadjusted marginal effects are reported in Table 5a while
adjusted results are in Tables 5b through 5e. Reported in the last columns are Δs,
indicating the variability in the relevant firm attribute for selecting a particular alliance
motivation. 22 A Higher Δ indicates that the relevant firm attribute has a high variation in
predicted probabilities across the members of the choice set on alliance motivation.
Positive marginal effects indicate that the likelihood that the related firm attribute being
associated with the corresponding motivation is higher than average. Negative marginal
effects, thus, show that it is below the average.
A remarkable result is that a great majority of marginal effects concentrate only on
Contributions to Operating Capital and Access to World Markets when all factors are held
constant (Table 5e). In other words, adjusting for all factors, the associations of most firm
attributes with Technology Transfer, Management Transfer, No Contribution and Other
as the motivations for alliance are zero.
It is useful to provide a brief flavor on how to interpret marginal effects. Among sectoral
groups, unadjusted marginal effects show that Furniture Products firms are 33% less likely
to choose Contributions to Operating Capital than the firms in the other sectors, while
they are 24% more likely to choose Access to World Markets than the firms in the other
sectors (Table 5a). These figures vary, but remain at high levels, when other factors are
controlled separately. When all factors are held constant, firms in Metal Products appear to
be the most different then the other sectors; they are least likely to choose Contributions
to Operating Capital and the most likely to choose Access to World Markets (Table 5e).
Among size groups, small firms as shown by investment level 0-$100,000 seem to be the
most different. When all factors are held constant jointly and separately, they are more
likely to choose Contributions to Operating Capital and less likely to choose Access to
World Markets than the firms with greater size.
22 They are the averages of the absolute values of the marginal effects for each firm attribute. Horizontal
summation of marginal effects for each firm attribute is zero.
28
In terms of number of workers, firms in the range 20-50 appear to be most different than
others when all factors are held constant (Table 5e). In this case, they are the most likely
group to choose Access to World Markets and the least likely group to choose
Contributions to Operating Capital. In fact, firms in the smallest and the highest two range
of number of workers (i.e., 20-50, 201-300, 300+) are less likely to choose Contributions
to Operating Capital and more likely to choose Access to World Markets than the firms in
the other ranges, which prefer the opposite.
In terms of Reasons for Inefficiency, unadjusted results indicate that firms with mechanical
problems are the mostly likely to choose Contributions to Operating Capital and the least
likely to choose Access to World Markets. When all factors are held constant, however, the
usual intuitive result is obtained: Firms with Shortage of Demand are the most likely to
choose Access to World Markets and the least likely to choose Contributions to Operating
Capital, while for firms with Finance shortage, the reverse is true.
As expected, firms with Family and Professional Management attributes do not really
differ than each other when all factors are held constant, although Other Management
provides more extreme results when nothing is adjusted for. Education Level of CEOs,
too, do not provide differing choices when all factors are adjusted, but CEOs with Postgraduate Education are the most likely to choose Contributions to Operating Capital and
the least likely to choose Access to World Markets, when controlling factors are held
constant separately.
7. Conclusions
This paper on Turkey contributes to the literature on internationalization of SMEs and
formation of inter-firm alliances in developing economies. An inward oriented approach to
economic development in Turkey over a long period created institutional, technical and
cultural obstacles for small firms to internationalize. This study provides strong evidence
that Turkish SMEs are willing to overcome such obstacles. We also identified that the
process by which they wish to integrate with the global economy is via alliances with
foreign capital. Our analysis also shows that Contributions to Operating Capital and
Access to World Markets are the most important motivations for such alliances. Some of
our results are consistent with the findings of Gomes-Casseres (1997), Acs et al. (1997),
29
Buckley (1997) and Chen and Huang (2004) on the internationalization and alliance
behaviors of small firms, but we have gone beyond those findings to generate further
results.
The study shows that there exist significant differences in the alliance behavior of SMEs of
different sizes. There is evidence that firms with smallest size and biggest size have
different alliance motivations than firms with medium size, but the exact impact of size on
alliance behavior depends on how it is measured, i.e., investment or number of workers,
and other controlling factors.
In addition, size and reasons for inefficiency strongly influence other firm attributes in
determining their likely association with alliance motivation. The motivation of firms with
20-50 and 201-300 workers is affected by those two factors, which implies that the
proportionality of their investment levels and the number of workers employed is
questionable. However, if all factors are held constant, a clear breakage occurs at firms
with 200 workers. Firms with 200 or more workers have different motivations for
internationalization than firms with less than 200 workers. Sectoral preferences of Metal
Products and to some extent Food Products sectors are affected by Investment and
Reasons for Inefficiency. Around 45 percent of Turkish SMEs operate in these sectors.
Education Level of the CEOs is another category affected by these two factors. It is found
that CEOs with low education level, be it primary or high school graduate, are unable to
access world markets using their own means. Foreign capital is expected to assist at this
point. The motivation of Family Management, however, is not affected by any controlling
factor, while holding-all-factors-constant affects the motivation of Professional
Management in favor of Technology Transfer.
Adjusting for all factors and making overall comparisons among the firm attributes, most
firm attributes (both within and across categories) share similar features about choosing
Technology Transfer, Management Transfer, No Contribution and Other as the
motivations for alliance. However, they differ in two other motivations, namely,
Contributions to Operating Capital and Access to World Markets, other things being
equal.
30
Finally, in terms of marginal effects results, firms in Metal Products sector, those that are
smallest in terms of investment and number of workers, and those firms with demand and
finance shortage are strongly different than attributes within their categories.
Our results provide useful insights to develop policy scenarios. Turkey initiated
negotiations for membership with EU in the late 2005. These negotiations are expected to
last for at least 10 years due to various structural characteristics of the Turkish economy
and society. One such characteristic is the young and dynamic nature of the Turkish
population. This observation is of concern to the EU because EU membership can trigger
mass exodus of the young people to the EU countries in search of better employment
opportunities. The formation of alliances between European capital and Turkish SMEs
within Turkey could provide better employment prospects for the Turkish population
which could stem the potential flow of younger migrants to other EU countries.
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APPENDIX A. Alliance Motivation – Cross-Tabulations and Econometric Results
Table 1. Cross Tabulation of Firm Attributes and General Outlook
35
Firm Attributes
CEO - Primary School
CEO - Secondary Sch.
CEO - High School
CEO- University
CEO- Post-graduate
Textile & Clothing
Metal Products
Furniture. Products
Food & Products
Other Sectors
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (301+)
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Family Management
Professional Man.
Other Man.
Demand Shortage
Finance Shortage
Mechanical Problems
Short. of Skilled Labor
Raw Material Shortage
Other Reasons
No Support
Indifferent
Support
Total
0
0
0
0
6
0.09
9
0.08
1
0.09
5
0.05
4
0.09
1
0.10
4
0.20
1
0.02
10
0.11
2
0.04
0
0
1
0.07
1
0.06
2
0.07
1
0.04
7
0.14
2
0.06
4
0.07
2
0.06
11
0.08
4
0.06
0
0
7
0.08
5
0.13
0
1
1
0.04
3
0.33
0
0
4
0.40
0
0
14
0.20
14
0.13
0
0
19
0.20
6
0.14
2
0.20
1
0.05
5
0.11
13
0.14
12
0.26
0
0
3
0.21
2
0.13
3
0.11
5
0.21
10
0.19
4
0.11
7
0.12
6
0.18
21
0.16
9
0.13
2
0.22
11
0.13
5
0.13
1
0.17
16
0.23
3
0.33
3
0.12
6
0.60
8
1
50
0.71
87
0.79
10
0.91
71
0.75
34
0.77
7
0.70
15
0.75
38
0.86
71
0.76
33
0.70
17
1
10
0.72
13
0.81
23
0.82
18
0.75
35
0.67
29
0.83
46
0.81
26
0.76
102
0.76
56
0.81
7
0.78
67
0.79
30
0.75
5
0.83
19
0.73
3
0.33
24
0.88
10
11
70
110
11
95
44
10
20
44
94
47
17
14
16
28
24
52
37
57
34
134
69
9
85
40
7
27
9
26
Number of SMEs selecting a choice, and the share of that choice in the corresponding firm attribute underneath. §: in thousand US
dollars.
Table 2. Cross Tabulation of Firm Attributes and Independence of Foreign Capital
Firm Attributes
No
Indifferent
36
Support
Total
Support
CEO - Primary School
CEO - Secondary Sch.
CEO - High School
CEO- University
CEO- Post-graduate
Textile & Clothing
Metal Products
Furniture. Products
Food & Products
Other Sectors
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (301+)
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Family Management
Professional Man.
Other Man.
Demand Shortage
Finance Shortage
Mechanical Problems
Short. of Skilled Labor
Raw Material Shortage
Other Reasons
1
0.09
2
0.18
10
0.13
9
0.08
1
0.08
8
0.08
5
0.10
5
0.18
2
0.18
6
0.12
15
0.15
6
0.11
0
0
2
0.11
1
0.11
1
0.04
1
0.04
13
0.22
4
0.11
3
0.05
3
0.09
21
0.14
4
0.06
0
0
10
0.11
2
0.05
0
0
7
0.25
1
0.11
2
0.07
10
0.91
7
0.64
63
0.83
105
0.88
10
0.83
84
0.87
40
0.82
23
0.82
9
0.82
41
0.84
80
0.79
45
0.83
20
1
12
0.84
2
0.89
26
0.93
26
0.90
41
0.71
33
0.87
57
0.92
31
0.89
123
0.80
65
0.93
9
1
84
0.88
38
0.88
6
0.86
18
0.64
6
0.67
28
0.93
0
0
2
0.18
3
0.04
5
0.04
1
0.08
5
0.05
4
0.08
0
0
0
0
2
0.04
6
0.06
3
0.06
0
0
1
0.05
17
0
1
0.04
2
0.07
4
0.07
1
0.03
2
0.03
1
0.03
9
0.06
1
0.01
0
0
1
0.01
3
0.07
1
0.14
3
0.11
2
0.22
0
0
11
11
76
119
12
97
49
28
11
49
101
54
20
15
19
28
29
58
38
62
35
153
70
9
95
43
7
28
9
30
Number of SMEs selecting a choice, and the share of that choice in the corresponding firm attribute underneath. §: in thousand US
dollars.
Table 3. Cross Tabulation of Firm Attributes and Alliance Motivation
Firm Attributes
Operating
World
Tech.
37
Manag.
No
Other
Total
Capital
Mkt.
Transfer
Transfer
Contr.
CEO - Primary School
5
4
2
0
1
0
12
0.42
0.33
0.17
0
0.08
0
CEO - Secondary Sch.
4
4
0
2
1
0
11
0.36
0.36
0
0.18
0.09
0
CEO - High School
32
30
7
3
3
0
75
0.43
0.40
0.09
0.04
0.04
0
CEO- University
42
55
15
2
3
1
118
0.36
0.47
0.13
0.02
0.02
0.01
CEO- Post-graduate
7
1
0
1
1
1
11
0.64
0.09
0
0.09
0.09
0.09
Textile & Clothing
44
42
4
5
4
1
100
0.44
0.42
0.04
0.05
0.04
0.01
Metal Products
19
18
10
0
2
0
49
0.39
0.37
0.20
0
0.04
0
Furniture. Products
1
7
1
0
2
0
11
0.09
0.64
0.09
0
0.18
0
Food & Products
11
9
2
3
2
1
28
0.39
0.32
0.07
0.11
0.07
0.04
Other Sectors
19
17
7
0
0
0
43
0.44
0.40
0.16
0
0
0
Workers (20-50)
31
41
13
3
8
0
96
0.32
0.43
0.14
0.03
0.08
0
Workers (51-100)
28
16
7
2
0
1
54
0.52
0.30
0.13
0.04
0
0.02
Workers (101-150)
8
9
2
0
1
0
20
0.40
0.45
0.10
0
0.05
0
Workers (151-200)
7
9
0
0
0
0
16
0.44
0.56
0
0
0
0
Workers (201-300)
7
8
2
2
0
1
20
0.35
0.40
0.10
0.10
0
0.05
Workers (301+)
13
12
1
1
1
0
28
0.46
0.43
0.04
0.04
0.04
0
Inv. (0-100) §
15
7
5
0
2
0
29
0.52
0.24
0.17
0
0.07
0
Inv. (100-500) §
15
22
11
0
3
1
52
0.29
0.42
0.21
0
0.03
0.02
Inv. (500-1000) §
17
15
4
0
1
0
37
0.46
0.41
0.11
0
0.03
0
Inv. (1000-5000) §
23
28
3
8
2
1
65
0.35
0.43
0.05
0.12
0.03
0.02
Inv. (5000+)§
17
16
2
0
1
0
36
0.47
0.44
0.06
0
0.03
0
Family Management
63
61
17
3
5
2
151
0.42
0.40
0.11
0.02
0.03
0.01
Professional Man.
29
26
7
4
4
0
70
0.41
0.37
0.10
0.06
0.06
0
Other Man.
1
6
1
1
0
0
9
0.11
0.67
0.11
0.11
0
0
Demand Shortage
27
44
13
6
3
1
94
0.29
0.47
0.14
0.06
0.03
0.01
Finance Shortage
28
12
3
0
1
0
44
0.64
0.27
0.07
0
0.02
0
Mechanical Problems
7
0
0
0
0
0
7
1
0
0
0
0
0
Short. of Skilled Labor
8
12
1
2
4
0
27
0.30
0.44
0.04
0.07
0.15
0
Raw Material Shortage
2
4
2
0
1
0
9
0.22
0.44
0.22
0
0.11
Other Reasons
13
13
4
0
0
0
30
0.43
0.43
0.14
0
0
0
Number of SMEs selecting a choice, and the share of that choice in the corresponding firm attribute underneath. §: in thousand US
dollars.
Table 4a. Alliance Motivation – Predicted probabilities (adjusted for investment)
Firm Attributes
Operating
Capital
World
Mkt.
38
Tech.
Transfer
Manag.
Transfer
No
Contr.
Other
CEO - Primary Sch.
0.36 ↓
0.41 ↑
0.13
0
0.10
0
CEO – Second. Sch.
0.39
0.38
0
0.15
0.08
0
CEO - High Sch.
0.44
0.39
0.09
0.05
0.04
0
CEO - University
0.34
0.46
0.15
0.02
0.02
0.01
CEO - Post-grad.
0.64
0.09
0
0.08
0.10
0.10
Textile & Clothing
0.47
0.39
0.05
0.04
0.05
0.01
Metal Products
0.34 ↓
0.42 ↑
0.20
0
0.04
0
Furniture Products
0.11
0.61
0.08
0
0
0
Food & Products
0.48 ↑
0.29
0.06
0.11
0.03
0.03
Other Sectors
0.38 ↓
0.47 ↑
0.15
0
0
0
Workers (20-50)
0.25 ↓
0.48 ↑
0.12
0.06
0.10
0
Workers (51-100)
0.56
0.28
0.12
0.03
0
0.02
Workers (101-150)
0.38
0.44
0.12
0
0.06
0
Workers (151-200)
0.48
0.52
0
0
0
0
Workers (201-300)
0.33
0.35 ↓
0.19 ↑
0.06
0
0.08
Workers (301+)
0.45
0.41
0.05
0.04
0.05
0
Family Management
0.42
0.41
0.11
0.03
0.03
0.01
Professional Man.
0.39
0.37
0.14
0.04
0.06
0
Other Man.
0.08
0.54 ↓↓
0.08
0.30 ↑↑↑
0
0
Demand Shortage
0.26
0.47
0.15
0.06
0.03
0.01
Finance Shortage
0.64
0.28
0.06
0
0.02
0
Mechanical Problems
1
0
0
0
0
0
Short. of Skilled Labor
0.25↓
0.47
0.04
0.08
0.16
0
Raw Material Short.
0.18
0.54 ↑↑
0.28 ↑
0
0 ↓↓
0
Other Reasons
0.45
0.38 ↓
0.17
0
0
0
↑, ↑↑ and ↑↑↑: Adjusted predicted probability is 5-9%, 10-14%, 15%+ higher than the unadjusted predicted
probability, respectively. The same criteria apply to downward arrows as well, which show lower adjusted
predicted probabilities.
Table 4b. Alliance Motivation – Predicted probabilities (adjusted for inefficiency factors)
Firm Attributes
CEO - Primary Sch.
CEO – Second. Sch.
CEO - High Sch.
CEO - University
CEO - Post-grad.
Textile & Clothing
Metal Products
Furniture Products
Food & Products
Other Sectors
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (300+)
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Family Management
Professional Man.
Other Man.
Operating
Capital
0.37 ↓
0.36
0.36 ↓
0.40
0.64
0.46
0.30 ↑
0.12
0.42
0.50 ↑
0.34
0.54
0.38
0.63 ↑↑↑
0.28 ↓
0.38 ↓
0.47 ↓
0.32
0.40 ↓
0.37
0.54 ↑
0.42
0.42
0.17 ↑
World
Mkt.
0.35
0.36
0.46 ↑
0.42 ↑
0.12
0.42
0.41
0.63
0.29
0.35 ↓
0.43
0.29
0.47
0.37 ↓↓↓
0.46 ↑
0.49 ↑
0.26
0.41
0.44
0.43
0.39 ↓
0.41
0.35
0.63
Tech.
Transfer
0.18
0
0.10
0.12
0
0.04
0.24
0.10
0.06
0.15
0.13
0.12
0.11
0
0.13
0.05
0.18
0.20
0.12
0.05
0.04
0.11
0.12
0.11
Manag.
Transfer
0
0.16
0.05
0.02
0.11
0.05
0
0
0.12
0
0.03
0.04
0
0
0.13
0.05
0
0
0
0.13
0
0.02
0.06
0.08
No
Contr.
0.11
0.12
0.03
0.03
0.13
0.03
0.05
0.15
0.08
0
0.08
0
0.05
0
0
0.04
0.08
0.05
0.03
0.02
0.04
0.03
0.05
0
Other
0
0
0
0.01
0
0
0
0
0.03
0
0
0.02
0
0
0
0
0
0.02
0
0
0
0.01
0
0
See the notes to Table 4a.
Table 4c. Alliance Motivation – Predicted probabilities (adjusted for sectoral differences)
Firm Attributes
Operating
Capital
World
Mkt.
39
Tech.
Transfer
Manag.
Transfer
No
Contr.
Other
CEO - Primary Sch.
CEO – Second. Sch.
CEO - High Sch.
CEO - University
CEO - Post-grad.
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (300+)
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Family Management
Professional Man.
Other Man.
Demand Shortage
Finance Shortage
Mechanical Problems
Short. of Skilled Labor
Raw Material Short.
Other Reasons
0.45
0.35
0.44
0.35
0.68
0.32
0.54
0.41
0.41
0.32
0.46
0.58 ↑
0.30
0.48
0.34
0.45
0.43
0.40
0.09
0.28
0.65
1
0.30
0.20
0.44
0.33
0.38
0.39
0.48
0.10
0.43
0.29
0.45
0.59
0.40
0.42
0.22
0.44
0.40
0.46
0.44
0.40
0.37
0.60 ↓
0.47
0.27
0
0.42
0.45
0.44
0.13
0
0.10
0.12
0
0.11
0.13
0.09
0
0.14
0.05
0.12 ↓
0.19
0.09
0.06
0.08
0.11
0.12
0.07
0.15
0.06
0
0.04
0.24
0.12
0
0.15
0.04
0.02
0.05
0.04
0.03
0
0
0.08
0.03
0
0
0
0.10
0
0.02
0.04
0.24 ↑↑
0.06
0
0
0.09
0
0
0.09
0.11
0.03
0.03
0.10
0.10
0
0.06
0
0
0.04
0.08
0.05
0.03
0.03
0.03
0.02
0.07
0
0.03
0.02
0
0.15
0.11
0
0
0
0
0
0.07
0
0.01
0
0
0.06
0
0
0.02
0
0.01
0
0.02
0
0
0.01
0
0
0
0
0
See the notes to Table 4a.
Table 4d. Alliance Motivation – Predicted probabilities (adjusted for number of workers)
Firm Attributes
CEO - Primary Sch.
CEO – Second. Sch.
CEO - High Sch.
CEO - University
CEO - Post-grad.
Textile & Clothing
Metal Products
Furniture Products
Food & Products
Other Sectors
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Family Management
Professional Man.
Other Man.
Demand Shortage
Finance Shortage
Mechanical Problems
Short. of Skilled Labor
Raw Material Short.
Other Reasons
Operating
Capital
0.42
0.36
0.45
0.34
0.67
0.43
0.40
0.08
0.38
0.48
0.62 ↑↑
0.30
0.46
0.33
0.44
0.43
0.39
0.12
0.29
0.65
1
0.27
0.22
0.42
World
Mkt.
0.36
0.37
0.38
0.48
0.09
0.41
0.38
0.67
0.33
0.38
0.21
0.45
0.41
0.43
0.41
0.41
0.35
0.67
0.47
0.26
0
0.44
0.46
0.44
Tech.
Transfer
0.14
0
0.10
0.13
0
0.05
0.18
0.08
0.06
0.14
0.13
0.18
0.10
0.05
0.09
0.10
0.12
0.11
0.13
0.07
0
0.04
0.21
0.14
Manag.
Transfer
0
0.17
0.04
0.02
0.08
0.05
0
0
0.12
0
0
0
0
0.14
0
0.02
0.06
0.10
0.06
0
0
0.08
0
0
No
Contr.
0.08
0.09
0.04
0.03
0.09
0.06
0.04
0.17
0.07
0
0.04
0.05
0.03
0.05
0.05
0.03
0.08
0
0.03
0.02
0
0.17
0.11
0
Other
0
0
0
0
0.07
0
0
0
0.04
0
0
0.03
0
0.01
0
0.01
0
0
0.01
0
0
0
0
0
See the notes to Table 4a.
Table 4e. Alliance Motivation – Predicted probabilities (adjusted for management)
Firm Attributes
Operating
Capital
World
Mkt.
40
Tech.
Transfer
Manag.
Transfer
No
Contr.
Other
CEO - Primary Sch.
CEO – Second. Sch.
CEO - High Sch.
CEO - University
CEO - Post-grad.
Textile & Clothing
Metal Products
Furniture Products
Food & Products
Other Sectors
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (300+)
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Demand Shortage
Finance Shortage
Mechanical Problems
Short. of Skilled Labor
Raw Material Short.
Other Reasons
0.41
0.35
0.42
0.36
0.67
0.43
0.39
0.08
0.41
0.47
0.32
0.52
0.39
0.41
0.35
0.48
0.52
0.29
0.49
0.34
0.46
0.29
0.63
1
0.31
0.24
0.46
0.34
0.33
0.41
0.46
0.10
0.43
0.36
0.62
0.30
0.37
0.42
0.29
0.47
0.59
0.40
0.42
0.23
0.42
0.40
0.43
0.46
0.46
0.30
0
0.45
0.38 ↓
0.41
0.16
0
0.09
0.13
0
0.04
0.20
0.09
0.07
0.16
0.13
0.13
0.10
0
0.10
0.04
0.17
0.22
0.11
0.04
0.05
0.14
0.07
0
0.04
0.25
0.14
0
0.21
0.04
0.02
0.16 ↑
0.05
0
0
0.11
0
0.04
0.04
0
0
0.09
0.03
0
0
0
0.14
0
0.06
0
0
0.06
0
0
0.09
0.11
0.04
0.03
0↓
0.03
0.04
0.21
0.07
0
0.09
0
0.04
0
0
0.03
0.08
0.06
0
0.03
0.03
0.03
0
0
0.14
0.13
0
0
0
0
0
0.07
0.01
0
0
0.04
0
0
0.02
0
0
0.05
0
0
0.02
0
0.02
0
0.01
0
0
0
0
0
See the notes to Table 4a.
Table 4f. Alliance Motivation – Predicted probabilities (adjusted for CEOs’ education)
Firm Attributes
Textile & Clothing
Metal Products
Furniture Products
Food & Products
Other Sectors
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (300+)
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Demand Shortage
Finance Shortage
Mechanical Problems
Short. of Skilled Labor
Raw Material Short.
Other Reasons
Family Management
Professional Man.
Other Man.
Operating
Capital
0.43
0.39
0.09
0.38
0.48
0.31
0.53
0.43
0.45
0.31
0.46
0.54
0.29
0.46
0.36
0.46
0.28
0.63
1
0.31
0.23
0.40
0.40
0.43
0.13
World
Mkt.
0.44
0.40
0.62
0.32
0.36
0.44
0.29
0.42
0.55
0.44
0.44
0.25
0.42
0.40
0.43
0.46
0.48
0.28
0
0.45
0.42
0.46
0.43
0.35
0.65
Tech.
Transfer
0.04
0.18
0.09
0.08
0.16
0.14
0.11
0.10
0
0.11
0.04
0.18
0.21
0.11
0.04
0.06
0.14
0.07
0
0.04
0.19
0.14
0.12
0.08
0.11
Manag.
Transfer
0.05
0
0
0.10
0
0.03
0.05
0
0
0.09
0.03
0
0
0
0.12
0
0.06
0
0
0.08
0
0
0.02
0.08
0.11
No
Contr.
0.04
0.03
0.20
0.08
0
0.08
0
0.05
0
0
0.03
0.03
0.06
0.03
0.03
0.03
0.03
0.02
0
0.12
0.16
0
0.03
0.06
0
Other
0
0
0
0.04
0
0
0.02
0
0
0.04
0
0
0.02
0
0.01
0
0.01
0
0
0
0
0
0.01
0
0
See the notes to Table 4a.
Table 4g. Alliance Motivation – Predicted probabilities (adjusted for all categories other
than itself)
Firm Attributes
Operating
World
Tech.
41
Manag.
No
Other
CEO - Primary Sch.
CEO – Second. Sch.
CEO - High Sch.
CEO - University
CEO - Post-grad.
Textile & Clothing
Metal Products
Furniture Products
Food & Products
Other Sectors
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (300+)
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Demand Shortage
Finance Shortage
Mech. Prob.
Short. of SkilledL
Raw Mat. Shor.
Other Reasons
Family Management
Professional Man.
Other Man.
Capital
n.a.
n.a.
0.37 ↓
0.39
n.a.
0.49 ↑
0.24 ↓↓↓
n.a.
0.48 ↑↑
0.48
0.31
0.57 ↑
0.37
0.68 ↑↑↑
0.19 ↓↓↓
0.38 ↓
0.55
0.32
0.50
0.32
0.45
0.26
0.66
n.a.
0.29
n.a.
0.46
0.42
0.34 ↓
n.a.
Mkt.
n.a.
n.a.
0.44
0.43
n.a.
0.40
0.49 ↑↑
n.a.
0.33
0.35 ↓
0.43
0.28
0.45
0.31 ↓↓↓
0.49 ↑↑
0.46
0.27
0.38
0.41
0.48 ↑
0.42
0.47
0.29
n.a.
0.52 ↑
n.a.
0.39
0.43
0.29 ↓
n.a.
Transfer
n.a.
n.a.
0.11
0.13
n.a.
0.04
0.24
n.a.
0.06
0.15
0.11
0.11
0.08
0
0.21 ↑↑
0.07
0.16
0.19
0.09
0.07
0.04
0.16
0.05
n.a.
0.04
n.a.
0.15
0.10
0.20 ↑↑
n.a.
Transfer
n.a.
n.a.
0.05
0.01
n.a.
0.04
0
n.a.
0.08
0.02
0.04
0.03
0
0
0.11
0.03
0
0
0
0.11
0
0.05
0
n.a.
0.09
n.a.
0
0.03
0.04
n.a.
Contr.
n.a.
n.a.
0.03
0.02
n.a.
0.03
0.03
n.a.
0.03
0
0.11
0
0.10 ↑
0
0
0.05
0.02 ↓
0.09 ↑
0.00
0.00
0.09
0.05
0.00
n.a.
0.06
n.a.
0
0.02
0.12
n.a.
n.a.
n.a.
0
0.01
n.a.
0
0
n.a.
0.02
0
0
0
0
0.01
0
0.01
0
0.01
0
0
0
0.01
0
n.a.
0
n.a.
0
0
0
n.a.
See the notes to Table 4a. n.a.: The regression does not have sufficient degrees of freedom.
Table 5a. Alliance Motivation – Marginal Effects (Unadjusted)
Firm Attributes
Operating
Capital
World
Mkt.
Tech.
Transfer
42
Manag.
Transfer
No
Contr.
Other
∆
CEO - Primary Sch.
CEO – Second. Sch.
CEO - High Sch.
CEO - University
CEO - Post-grad.
Textile & Clothing
Metal Products
Furniture Products
Food & Products
Other Sectors
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (301+)
Family Management
Professional Man.
Other Man.
Demand Shortage
Finance Shortage
Mechanical Problems
Short. of Skilled Labor
Raw Material Shortage
Other Reasons
0.02
-0.04
0.05
-0.08
0.25
0.07
-0.02
-0.33
-0.01
0.05
0.14
-0.14
0.08
-0.06
0.09
-0.13
0.15
0
0.04
-0.06
0.07
0.04
0.01
-0.31
-0.21
0.30
0.62
-0.12
-0.19
0.04
-0.09
-0.05
-0.02
0.11
-0.34
0.03
-0.05
0.24
-0.10
-0.01
-0.19
0.02
0
0.04
0.05
0.04
-0.14
0.05
0.17
-0.01
0.03
0
-0.05
0.27
0.12
-0.16
-0.42
0.05
0.04
0.04
0.06
-0.11
-0.02
0.05
-0.11
-0.12
0.12
-0.02
-0.04
0.07
0.07
0.13
-0.01
-0.10
-0.07
0.05
0.03
-0.01
-0.12
-0.01
-0.08
0.01
-0.01
0
0.05
-0.05
-0.11
-0.08
0.12
0.03
-0.04
0.15
0.01
-0.04
0.06
0.03
-0.04
-0.04
0.08
-0.04
-0.04
-0.05
-0.04
0.12
-0.04
-0.01
0
-0.04
-0.04
0.07
0
-0.04
0.03
0.08
0.05
-0.05
-0.04
0.04
-0.04
-0.04
0.05
0.05
0
-0.03
0.05
-0.01
0
0.15
0.03
-0.05
0.03
0.02
-0.02
-0.01
-0.02
0.07
0.06
0.01
-0.05
-0.05
-0.01
-0.02
0.03
-0.04
-0.02
-0.03
-0.04
0.12
0.07
-0.05
-0.01
-0.01
-0.01
0
0.09
0
-0.01
-0.01
0.03
-0.01
-0.01
0.01
-0.01
0.01
-0.01
-0.02
0.01
-0.01
-0.01
0.05
-0.01
0.01
0.01
-0.01
0.01
-0.01
-0.01
-0.01
-0.01
-0.01
0.04
0.07
0.02
0.05
0.15
0.04
0.04
0.13
0.05
0.04
0.08
0.06
0.03
0.06
0.05
0.05
0.07
0.02
0.07
0.04
0.03
0.02
0.02
0.12
0.08
0.10
0.21
0.07
0.08
0.03
Marginal effects rounded to second decimal points. ∆: Average of the absolute values of the marginal effects for each firm characteristic.
Table 5b. Alliance Motivation – Marginal Effects (adjusted for investment)
Firm Attributes
CEO - Primary Sch.
CEO – Second. Sch.
CEO - High Sch.
CEO - University
CEO - Post-grad.
Textile & Clothing
Metal Products
Furniture Products
Food & Products
Other Sectors
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (301+)
Family Management
Professional Man.
Other Man.
Demand Shortage
Finance Shortage
Mechanical Problems
Short. of Skilled Labor
Raw Material Shortage
Other Reasons
Operating
Capital
World
Mkt.
Tech.
Transfer
Manag.
Transfer
No
Contr.
Other
∆
-0.06
0.03
0.07
-0.12
0.38
0.13
-0.09
-0.32
0.12
-0.04
-0.24
0.23
-0.04
0.05
-0.04
0.06
0.06
0
-0.34
-0.23
0.30
0.63
-0.16
-0.24
0.00
-0.02
0.02
-0.04
0.10
-0.36
-0.05
-0.02
0.20
-0.10
0.06
0.15
-0.19
0.01
0.10
-0.01
-0.01
-0.03
-0.05
0.38
0.18
-0.21
-0.47
0.11
0.12
-0.06
0.02
-0.10
-0.04
0.07
-0.10
-0.09
0.10
-0.04
-0.05
0.03
0
0.01
0.01
-0.11
0.10
-0.06
-0.03
0.05
-0.04
0.07
-0.07
-0.11
-0.08
0.16
0.05
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.06
0.05
0
0.04
0.08
0.02
0
0.16
-0.01
0.05
0.08
-0.05
0.02
0.04
-0.04
0.01
0
0
0
-0.01
-0.02
-0.04
0.13
-0.04
-0.05
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.03
0.03
0.02
0.05
0.16
0.05
0.03
0.12
0.05
0.03
0.08
0.08
0.01
0.05
0.03
0.02
0.02
0.02
0.13
0.08
0.10
0.20
0.08
0.09
0.03
See the notes to Table 5a.
Table 5c. Alliance Motivation – Predicted probabilities (adjusted for sectoral differences)
Firm Attributes
Operating
Capital
World
Mkt.
Tech.
Transfer
43
Manag.
Transfer
No
Contr.
Other
∆
CEO - Primary Sch.
CEO – Second. Sch.
CEO - High Sch.
CEO - University
CEO - Post-grad.
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (300+)
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Family Management
Professional Man.
Other Man.
Demand Shortage
Finance Shortage
Mechanical Problems
Short. of Skilled Labor
Raw Material Shortage
Other Reasons
0.07
0.04
0.06
-0.14
0.45
-0.12
0.18
-0.01
-0.04
-0.06
0.05
0.24
-0.15
0.07
-0.06
0.03
0.05
0.01
-0.34
-0.23
0.30
0.58
-0.07
-0.23
0
-0.10
0.06
-0.06
0.12
-0.35
0.11
-0.20
0.04
0.14
0.03
0
-0.25
0.05
-0.04
0.12
0.01
-0.03
-0.03
0.37
0.16
-0.24
-0.48
0.13
0.08
-0.01
0.03
-0.10
-0.01
0.02
-0.10
0.02
0.03
-0.02
-0.10
0.03
-0.06
0
0.10
-0.03
-0.06
-0.04
-0.01
0.02
-0.03
0.07
-0.06
-0.10
-0.07
0.15
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.03
0.03
0.02
0.05
0.15
0.04
0.07
0.01
0.05
0.02
0.02
0.08
0.05
0.03
0.04
0.02
0.02
0.01
0.12
0.08
0.10
0.19
0.04
0.08
0
See the notes to Table 5a.
Table 5d. Alliance Motivation – Marginal Effects (adjusted for reasons for inefficiency)
Firm Attributes
CEO - Primary Sch.
CEO – Second. Sch.
CEO - High Sch.
CEO - University
CEO - Post-grad.
Textile & Clothing
Metal Products
Furniture Products
Food & Products
Other Sectors
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (300+)
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Family Management
Professional Man.
Other Man.
Operating
Capital
-0.02
0.03
-0.05
-0.01
0.25
0.08
-0.15
-0.44
0.08
0.07
-0.11
0.15
-0.05
0.13
-0.14
-0.03
0.07
-0.13
-0.01
-0.02
0.11
0.02
0.03
-0.33
World
Mkt.
-0.03
0.02
0.05
0
-0.19
-0.02
0.05
0.40
-0.06
-0.08
0.08
-0.13
0.05
-0.08
0.11
0.06
-0.09
0.05
0.01
0.06
-0.06
0
-0.04
0.30
Tech.
Transfer
0.05
-0.05
0
0.02
-0.06
-0.06
0.10
0.04
-0.03
0.01
0.03
-0.01
0
-0.05
0.03
-0.03
0.02
0.08
0
-0.04
-0.05
-0.01
0.01
0.04
Manag.
Transfer
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
No
Contr.
0
0
0
0
0
0
0
0.01
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Other
∆
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.02
0.02
0.02
0.01
0.08
0.03
0.05
0.15
0.03
0.03
0.04
0.05
0.02
0.04
0.05
0.02
0.03
0.04
0
0.02
0.04
0.01
0.01
0.11
See the notes to Table 5a.
Table 5e. Alliance Motivation – Marginal Effects (adjusted for all factors)
Firm Attributes
Operating
Capital
World
Mkt.
Tech.
Transfer
44
Manag.
Transfer
No
Contr.
Other
∆
CEO - Primary Sch.
CEO – Second. Sch.
CEO - High Sch.
CEO - University
CEO - Post-grad.
Textile & Clothing
Metal Products
Furniture Products
Food & Products
Other Sectors
Inv. (0-100) §
Inv. (100-500) §
Inv. (500-1000) §
Inv. (1000-5000) §
Inv. (5000+)§
Workers (20-50)
Workers (51-100)
Workers (101-150)
Workers (151-200)
Workers (201-300)
Workers (301+)
Family Management
Professional Man.
Other Man.
Demand Shortage
Finance Shortage
Mechanical Problems
Short. of Skilled Labor
Raw Material Shortage
Other Reasons
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
-0.04
-0.02
0.04
0.01
0
0.01
0
0
0
0
0
0
0.01
0.01
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.10
-0.21
-0.10
0.21
0
0
0
0
0
0
0
0
0.03
0.07
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.07
0.08
0.19
-0.08
0.06
-0.14
0.10
-0.36
0.23
0.01
0.18
-0.25
-0.10
0.05
0.02
-0.07
-0.08
-0.19
0.08
-0.06
0.14
0.10
0.36
-0.23
-0.01
-0.18
-0.25
0.10
-0.05
-0.02
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.03
0.03
0.06
0.03
0.02
0.05
0.04
0.12
0.08
0
0.06
0.09
0.04
0.02
0.01
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
-0.26
0.32
0.25
-0.31
0.01
-0.01
0
0
0
0
0
0
0.09
0.11
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
-0.14
0.15
-0.01
0
0
0
0.05
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
0.07
-0.07
0
0
0
0
0.02
See the notes to Table 5a.
APPENDIX B. The Multinomial Logit Model
45
Let an individual i be faced with J choices. The utility from choice j is:
U ij = β ' z ij + ε ij
[
(A.1)
]
where z ij = x ij , w i and x ij is the vector of aspects that are specific to the individuals and
to the choices, and w i contains the characteristics specific to the individual, which are the
same across all choices. The probability that the choice j will be selected depends on the
probability Pr(U ij > U ik ) for all k ≠ j . McFadden (1974) showed that, taking into
account the distribution of the J disturbances distributed with Weibull Distribution
F (ε ij ) = exp(e
− ε ij
) , the probability of selecting the choice j is defined by:
Pr(Yi = j ) =
e
β ' z ij +α 'w i
∑e
β ' z ij +α 'w i
(A.2)
j
where Yi is the random variable that indicates the choice made. However, a modification
must be made to the model since, in this case, individual specific characteristics fall out of
the probability. The coefficient must vary across the choices rather than characteristics,
which leads to the multinomial logit model:
Pr(Yi = j ) =
e
β 'xi i
J
∑e
β 'j x i
(A.3)
k =0
The estimation of the model in the equation (A.3) will provide the probabilities for the J+1
choices for an individual with characteristics x i . The estimation is carried out by
maximizing the loglikelihood function:
J
n
J
⎛
β'x ⎞
l( β1 ,......, β 2 ) = ∑ ∑ β 'j x i − ∑ log⎜1 + ∑ e j i ⎟
j =1 Yi = j
i =1
i =1
⎝
⎠
46
(A.4)