UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDY
HO CHI MINH CITY ERASMUS UNIVERSITY OF ROTTERDAM
VIETNAM THE NETHERLANDS
VIETNAM – THE NETHERLANDS
PROGRAMME FOR M.A IN DEVELOPMENT ECONMICS
THE RELATIONSHIP BETWEEN
BUSINESS NETWORKING
AND SMES PRODUCTION EFFICIENCY
By
LE HOANG LONG
MASTER OF ART IN DEVELOPMENT ECONOMICS
HCMC, NOVEMBER 2013

University of Economics International Institute of Social Study
Ho Chi Minh City, Vietnam Erasmus University of Rotterdam, The Netherlands
VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN
DEVELOPMENT ECONMICS
THE RELATIONSHIP BETWEEN BUSINESS NETWORKING
AND SMEs PRODUCTION EFFICIENCY
by L H g L g
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of
Master of Art in
Development Economics
Academic Supervisor Dr. V H g
Vietnam – Netherlands Programme, November 2013

DECLARATION
This is to certify that this thesis e titled “The relationship between business
networking and SMEs production efficiency”, whi h is submitted by me in fulfillment
of the requirements for the degree of Master of Art in Development Economic to the
Vietnam – The Netherlands Programme. The thesis constitutes only my original work
and due supervision and acknowledgement have been made in the text to all
materials used.
L H g L g

iii
ACKNOWLEDGEMENT
I would not be possible to write this master thesis without the help and support
of people surrounding me.
Above all, I w uld li e t th y f ily, es e i lly y the – H g Th
Ki Hi , wh lw ys l ves, t es e f d su ts e the w y I have chosen.
I would like to express special appreciation to my supervisor, Dr. V H g ,
who I have learned a lot from his guidance, useful recommendations and valuable
comments.
I would like to acknowledge all the lecturers at the Vietnam – Netherlands
Programme for their knowledge of all the courses, during the time I studied at the
program. I ti ul , I g teful t ss f Nguy T g H i,
h Kh h N , T g g Th y, M h g Th h h d L V
Ch , who support me significantly in the courses as well as in the thesis writing
process.
Last, but not least, I would like to thank my friends and colleagues at Banking
University of HCMC for their helps.
HCMC, November 2013
L H g L g

iv
ABBREVIATIONS
AE Allocative efficiency
CIEM Central Institute for economic mangement
CRS Constant returns to scale
DEA Data envelopment analysis
DMU Decision making unit
GSO General Statistics Office Of Vietnam
SE Scale efficiency
SFA Stochastic frontier analysis
SMEs Small and medium sized enterprises
TE Technical efficiency
TFP Total-factor productivity
VRS Variable returns to scale

v
ABSTRACT
This study aims to examine the relationship between business networking and
the technical efficiency of small and medium sized enterprises (SMEs) in Vietnam. To
achieve this objective, this study proposes a framework to measure the production
efficiency of the SMEs; then, the study identifies the relationship between business
networking and their performance efficiency. Data Envelopment Analysis method is
employed in the first stage to measure the efficiency. In the second stage, the study
uses both Tobit and least squared regressions to examine the relationship between the
firm networking and its performance efficiency. The unbalanced data from the four
SMEs surveys, which cover the period of 6 years, from 2004 to 2010, will be
employed in this study. The research finds that the average technical efficiency scores
of SMEs in this period are moderately low, ranging from 48 percent to 70 percent
depending on the industries. Additionally, the relationship between business
networking and firm’s production efficiency appears to be different in different
indutries. For example, in food products and beverages, the network quantity is found
to have positive impact on the technical efficiency. However, network quality as well
as the network diversity might hinder the firms in this industry. The wood and wood
products and fabricated metal product experience a contradictory tendency when the
total network size and cluster size appear to have no impact, or even negative impact
on the technical efficiency. In these industries, the network quality appears to hold a
significantly crucial role than other dimensions of networking when it has positive
correlation with firm efficiency. Finally, the role of official business association
appears to be vague to firm efficiency.

vi
TABLE OF CONTENTS
LIST OF TABLES.........................................................................................................ix
LIST OF FIGURES........................................................................................................x
Chapter 1: INTRODUCTION ......................................................................................1
1.1 Problem statement.............................................................................................1
1.2 Research objectives...........................................................................................3
1.3 Research questions............................................................................................3
1.4 Research scope and data ...................................................................................3
1.5 The structure of this study.................................................................................3
Chapter 2: LITERATURE REVIEW ...........................................................................5
2.1 Production efficiency: Concepts, measurements and sources ..........................5
2.1.1 Concepts..................................................................................................5
2.1.2 Measurements .........................................................................................8
2.1.3 Efficiency measurement methods...........................................................9
2.1.4 Sources of technical efficiency.............................................................12
2.1.4.1 Exogenous sources................................................................................13
2.1.4.2 Internal sources....................................................................................14
2.2 Business networking .......................................................................................16
2.2.1 Business networking and related concepts ...........................................16
2.2.2 Components and roles of business networking ....................................17
2.2.3 Relationship between business networking and technical efficiency...19
Chapter 3: RESEARCH METHODOLOGY .............................................................23
3.1 An overview of Vietnamese Small and Medium sized Enterprises................23
3.1.1 Growth and contribution of SMEs in Vietnam.....................................23
3.1.2 An overview of manufacturing SMEs ..................................................26

vii
3.2 Conceptual framework and model specification ............................................27
3.2.1 The first stage: Efficiency measurement using the DEA method...........29
3.2.2 The second stage: Regression model..................................................32
3.3 Research hypotheses and concept measurements...........................................34
3.3.1 Research hypotheses ................................................................................34
3.3.2 Concept and variable measurements ....................................................35
3.4 Data source and filter process.........................................................................34
Chapter 4 EMPIRICAL RESULTS............................................................................37
4.1 Production efficiency of SMEs.......................................................................37
4.1.1 Data descriptions...................................................................................37
4.1.2 Production efficiency of SMEs in Vietnam..........................................39
4.2 The relationship between business networking and production efficiency....41
4.2.1 Data description.......................................................................................41
4.2.2 Regression results .................................................................................43
4.2.2.1 Network quantity ..................................................................................46
4.2.2.2 Network quality ....................................................................................49
4.2.2.3 Network diversity .................................................................................50
4.2.2.4 Cluster size............................................................................................52
4.2.2.5 Participation in a business association..................................................53
Chapter 5: CONCLUSION AND POLICY IMPLICATION ....................................55
5.1 Conclusion remarks ........................................................................................55
5.2 Policy implications..........................................................................................57
5.3 Limitations and recommendations for future research ...................................58
REFERENCES ............................................................................................................60
Appendix 1: Empirical studies on the sources of technical efficiency.......................65

viii
Appendix 2: Empirical studies on the relationship between business network and
firm performance ..........................................................................................................68
Appendix 3: Empirical studies on the technical efficiency measurements of
manufacturing firms in Vietnam ..................................................................................72

ix
LIST OF TABLES
Table 3.1: Definition for SMEs in Vietnam ............................................................24
Table 3.2: Main indicators of enterprises as of 01/01/2012, by sizes......................26
Table 3.3: Number and proportion of manufacturing firms from 2006 to 2011 .....26
Table 3.4: Proportion of three main manufacturing industries................................27
Table 3.5: Concepts and measurements of variables in the study ...........................33
Table 3.6: Number of observations before and after filtering .................................35
Table 3.7: Number of observations before and after filtering in the stage 2...........36
Table 4.1: Descriptive statistic of production factor variables................................38
Table 4.2: Average value of technical efficiency scores .........................................39
Table 4.3: Proportion of efficient enterprises in the period 2004-2010 ..................41
Table 4.4: Descriptive statistic of efficiency index and its determinants................43
Table 4.5: The correlation matrix among variables and variance inflation factors.44
Table 4.6: Heteroscedasticity test for Pooled OLS model.......................................45
Table 4.7: Regression results of network size and efficiency score ........................46
Table 4.8: Regression results of network quality and efficiency score ...................49
Table 4.9: Regression results of network range and efficiency score .....................51
Table 4.10: Regression results of cluster size and efficiency score ..........................52
Table 4.11: Regression results of business association and efficiency score............54

x
LIST OF FIGURES
Figure 2.1: Production frontiers and technical efficiency.......................................6
Figure 2.2: Technical efficiency measurement.......................................................8
Figure 3.1 (a): Number of enterprises at 31/12 (by size of total assets) ....................25
Figure 3.1 (b): Number of enterprises at 31/12 (by size of employees).....................25
Figure 3.2: Conceptual framework .......................................................................28
Figure 4.1: CRS frontier and VRS frontier...........................................................42

1
Chapter 1:
INTRODUCTION
This chapter introduces the research topic and the problem statement. The
research objectives, the research questions and the research scope and data are also
included in this section. This chapter will end with the introduction of the thesis
organization.
1.1 Problem statement
Small and medium sized enterprises (SMEs) hold a crucial role in the
economic development, especially in developing countries including Vietnam.
Compared to large sized enterprises, SMEs appear to bring more merits to the
economy in terms of generating jobs, meeting the urgent demand immediately and
growing rapidly and efficiently (Assefa, 1997 in Admassie & Matambalya, 2002;
Hallberg, 1999). In the developing countries including Vietnam, SMEs have played
a major role to contribute significantly to reduce the unemployment rate. Often
being labor-intensive, SMEs help creating jobs for low skilled labor, which is
redundant in the developing countries (Schmitz, 1995; Hallberg, 1999). According
to the General Statistic Office of Vietnam, a number of formal SMEs (legally
registered firms) are 305,000 firms, accounting for 97.5 per cent of the total firms in
January 2012. This figure may be underestimated because of the lack of informal
SMEs statistics. These numbers of enterprises generate approximately 5 million
jobs and obtain about VND 4,600 billion revenue annually. In spite of the large
number and sustaintial contribution to the economy, SMEs have to deal with
countless problems to survive and develop. In the developing countries, SMEs often
face to the lack of resources such as capital, information, and knowledge. Hallberg
(1999) stated that information is a more serious problem to the SMEs rather than the
large firms, while Beck & Demirguc-Kunt (2006) advocated the influence of

2
capital shortage to the SMEs' growth. In this circumstance, business networking can
be a solution when it can help the SMEs overcome problems of resources.
Firms, particularly small and medium sized enterprises (SMEs), can exploit
the business network as a source of information, knowledge and competitive
advantage (Dyer & Singh, 1998). As such, business networking appears to be the
channel of resources. Furthermore, the benefits of business network have been
demonstrated in many empirical studies (e.g. Gulati, 1999; Dyer & Singh, 1998;
Lechner, Dowling & Welpe, 2006). Many scholars presented the positive
relationship between business network and firm growth and development (for
example, Schoonjans, Cauwenberge & Bauwhede, 2011; Lechner et al., 2006).
In Vietnam, network can bring the entrepreneurs many benefits such as
information, knowledge and other substitution resources. There appears to be a
significant correlation between network and firm efficiency in the case of Vietnam.
However, empirical studies to examine the link between business network and
Vietnam SMEs efficiency are limited. This study will present the evidence of this
linkage between business networking and production efficiency of the SMEs using
panel data and the data envelopment analysis (DEA) technique, which is an
effective method for measuring firm efficiency. The thesis deals with the
manufacturing SMEs in three major industries, which include food products and
beverages, wood and wood products and fabricated metal products. These three
industries, which account for over 50% of the total number of SMEs in Vietnam and
often deal with the problems of poor production capacity and the resource
constraint, can represent for the population of Vietnamese SMEs.

3
1.2 Research objectives
The study aims to examine the relationship between business networking
and production efficiency of SMEs in Vietnam. As such, it has two main objectives
which can be stated as follows:
(i) Estimating and analyzing the production efficiency of SMEs.
(ii) Investigating the relationship between the business networking and
the efficiency scores obtained from the first--stage. The study
attempts to exam the multi-dimensional impact of business
networking on the production efficiency such as network quantity,
network quality and network diversity.
1.3 Research questions
The main research question this paper attempts to answer is: Is there any
relationship between the business networking and the production efficiency of
SMEs in Vietnam? If yes, then how can business networking can influence the
production efficiency of SMEs?
1.4 Research scope and data
The study will examine the relationship between business networking and
the SMEs efficiency using the panel data for the period from 2004 to 2010. Three
selected industries include: (i) food products and beverages; (ii) woods and wood
products; and (iii) fabricated metal products. Of 18 industries, these three industries
have accounted for over 55 percent of the total number of SMEs in Vietnam (CIEM,
2011; CIEM, 2013); therefore, they can represent for the SMEs population.
1.5 The structure of this study
This study is presented in five chapters, which are constructed as follows:

4
Chapter 2 reviews the literature as well as empirical studies on the
relationship between business networking and firm production efficiency. It begins
with the definitions and determinants of the production efficiency. This chapter then
discusses the networking definition and its crucial role to the firms. Business
networking can influence production efficiency both directly and indirectly. In
addition, its impact on firm production efficiency can be etheir positive or negative
depending on the circumstances.
Chapter 3 presents the research methodology, in which both data
envelopment analysis and regression technique are discussed. This chapter also
provides the conceptual framework as well as the concept measurements. Five
hypotheses to examine the multi-dimensional impact of business networking on the
production efficiency are included. In addition, this chapter introduces the data
source and filter mechanism.
Chapter 4 presents the empirical results. The statistic descriptions of the
data are presented. Then, the findings of production efficiency of the SMEs will be
represented and discussed. This section also produces the regression results that
provide evidence on the relationship between business networking and production
efficiency.
Chapter 5 will summarize the main results along. Some policy implications
are proposed based on the results obtained from Chapter 4. This chapter also
outlines limitations and suggests the directions for future research.

5
Chapter 2:
LITERATURE REVIEW
This chapter will review the literature on the relationship between business
networking and firm production efficiency. Initially, the concepts, the
measurements and the determinants of the production efficiency will be analyzed.
This chapter then discusses the definitions of business networking as well as its
functions. The empirical studies on the relationship between business networking
and the production efficiency will be examined at the end of the chapter.
2.1 Production efficiency: Concepts, measurements and sources
2.1.1 Concepts
Production efficiency is one of the most central topics of economics
research at firm’s level. The concept of production efficiency is derived from the
production process, which converts input factors (including labor and capital) into
products (or production outputs). The overall or economic efficiency can be
decomposed into two components: (i) technical efficiency and (ii) allocative
efficiency.

6
Figure 2.1: Production frontiers and technical efficiency
y
0
A
B
C
technical change
The former component is proposed for long time, accompanied with the
concept of production possibility frontiers (PPF). Production frontiers describe the
maximum possible outputs for given inputs and technology level. In the production
process, due to the limited input factors, firms are only able to just produce on or
below the frontiers. Therefore, firms achieve technical efficiency when they
produce in the production frontiers (point B and point C in Figure 1). In a formal
definition, Koopmans (1951) stated that an efficient point is attained if it is feasible
and if there is no other point higher than it. Accordingly, a technically efficient firm
can increase its output if and only if there is a reduction in another output or at least
an increase in an input. The definition of Farell (1957) is well-accepted and is often
considered the pioneer definition of technical efficiency. Farell (1957, p. 254) stated
th t fi g i s effi ie y whe it su eeds i “ du ing as large as possible an
ut ut f give sets f i uts” This defi iti is ge e lly w s the
output-oriented viewpoint. As a supplement, Coelli et al. (2005) mentions the input-
orientated view as an efficient firm could produce a given output with the minimum
of inputs combinations. Derived from the production process, technical efficiency
can be understood as production efficiency.

7
The latter concept (allocative efficiency) reflects how efficient firms control
their costs. Allocative efficiency represents the capability of a firm to combine or
mix the inputs sets to produce the given output within the minimum budget. While
technical efficiency can be measured from the production function, estimation of
allocative efficiency requires cost, revenue or profit function.
Another crucial concept in efficiency is scale efficiency. In Figure 1,
although both firm B and firm C are in the production frontiers, they have different
productivity levels. Productivity is measured by the ratio of output and input
quantities, which is equal to the slope of a ray drawn from the origin through the
point. The productivity gap between firm B and firm C is derived from the impact
of scale. Many studies (Fä e, G ss f & L vell, 1983; Banker & Thrall, 1992;
Fä e, G ss f & R s, 1998; C elli et l , 2005…) represented the measurement
of scale efficiency. Nevertheless, they have not reached the final definition of scale
efficiency. Coelli et al. (2005, p. 58) stated th t: “S le effi ie y is si le
concept that is easy to understand in a one-input, one-output case, but it is more
difficult to conceptualize in a multi-input, multi- ut ut situ ti ” I this study,
scale efficiency can be understood as a difference between the firms in the most
technically productive scale and the firm with the remaining scales. It appears to be
a component which is derived from technical efficiency.
In order to identify the relationship between business networking and
production efficiency, this study will consider production efficiency as technical
efficiency in both assumptions: (i) constant returns to scale (pure technical
efficiency); and (ii) variable returns to scale (technical efficiency including scale
efficiency).

8
2.1.2 Measurements
This section will represent the basic measurements of efficiency in a simple
case with two inputs and one output under the assumption of a constant return to
scale. The below-mentioned measurements are from the input-orientated approach,
which will be employed in this study.
Figure 2.2: Technical efficiency measurement
The simple production model with two inputs 1 2
,
x x and one output y , the
measurements are demonstrated in Figure 2. Let ,
P Q
x x and *
x represent the input
vectors associated with point P ,Q and *
Q respectively. In addition, let w represent
the vector of input prices.
The iso-quant curve '
SS is a collection of many combinations 1 2
( , )
x x ,
which produce same amount of output. Therefore, firms working in this curve (at
pointQ and *
Q ) are technical efficient, while other firms (like point P ) are not. The
technical efficiency can be calculated by the ratio:
x /y
2
x /y
1
0
Q
P
R
Q*
S
S’
C’
C
TE
A
E

9
'
0
TE 1
0 0 '
Q
P
w x
Q QP
P P w x
   
Ratio
0
QP
P
represents the amount of required input reduction to be more
efficient (move form point P to pointQ ). Therefore, technical efficiency index (TE
index), which always takes the value between 0 and 1, can reflect the technical
efficiency of a firm.
The iso-cost curve '
CC represents the mix of inputs subject to the same and
minimum cost. Then, the allocative efficiency (AE) can be measured by the ratio:
0R 0 * ' *
AE
0 0 ' Q
Q w x
Q Q w x
  
Firm producing at point *
Q gains both TE and AE. As such, it achieves
overall economic efficiency (OE):
0 0 0 ' *
OE TE AE
0 0 0 ' P
Q R R w x
P Q P w x
     
The scale efficiency is resulted from the differences between the technical
efficiency in case of constant returns to scale (CRS) and this one in case of varied
returns to scale (VRS) (Fä e et l , 1983; C elli et l , 2005):
2.1.3 Efficiency measurement methods
Production efficiency is such an appealing area of research that many
studies have attempted t fi d ut the “best” eth d t esti te C elli et l (2005)
summarized that there are at least four popular methods to calculate these concepts:
1. Least square econometric production models
2. Total factor productivity indices (TFP index)
CRS
VRS
TE
SE
SE


10
3. Data envelopment analysis (DEA)
4. Stochastic frontier analysis (SFA)
Four techniques can be classified into two sub-groups based on their
assumptions and applications. Assuming that all firms are technically efficient, the
objectives of the initial two methods are to estimate the technical change rather than
the TE and AE. Without under the assumption that all firms are technically efficient
and taking into account the scale efficiency measurement, DEA and SFA are used
commonly in calculating relative efficiency among firms (Coelli et al., 2005). As
above-mentioned analyses, the technical efficiency can be derived from the concept
of production frontiers, where a firm can belong to the curve (technically efficient)
or stay below the curve (technically inefficient). However, the "true" curve is
unknown; therefore, based on their own assumptions, both methods attempts to
develop the curve by identifying the most efficient firms and forming the
production boundary.
SFA is a parametric method that needs to form a production function based
on some economic theories. When a functional form is specified (for example,
Cobb-Douglas’s production function), the parameters will be estimated. The error
term derived from the regression will contain both noise component and
inefficiency component. The strength of a parametric method is that if the selection
of the du ti fu ti is “t ue”, the e su e e t be l ulated more
accurately. Using a production function, SFA can fix the issue of statistical noise of
non-parametric methods. For example, SFA can include relevant variables into the
function to measure the accurate efficiency indices while DEA cannot. However,
this characteristic is also the drawback of the method. The production function is
difficult to define; even in some cases, it is unreasonable to identify the function.
Because this thesis is aiming to the large number of SMEs in three industries, the
"true" production function form becomes considerably difficult to identify.

11
In a different approach, DEA is a mathematical technique, which compares
the inputs/outputs ratio to identify the "best" firms and form an envelopment curve.
As a non-parametric approach, the weakness of DEA is the statistical noise issue.
However, DEA has some merits that make it better than SFA in many cases. Firstly,
the materials of DEA can be chosen flexibly subject to the object of the researchers.
Shafer & Byrd (2000), for example, can choose three inputs related to investments
and two outputs to identify the efficiency of firm investments in information
technology. Secondly, the result of DEA can be used extensively for many
objectives. In many cases, DEA gives the efficiency indices for each Decision
Making Unit (DMU) and even presents a component that should be adjusted to
achieve efficiency. In other researches, the efficiency indices also can be used as a
variable for the second regression stage. Thirdly, extended DEA can fix some
problems of statistical noise. We can overhaul DEA by adding the environmental
factors as non-discretionary variables into the original DEA (in the case of using
only one-stage DEA) or running an additional regression (in the case of using two-
stage DEA). Finally, DEA appears to be fairly simple and easy to calculate for both
multi-outputs and multi-inputs. Thanks to these merits of DEA method, this study
will employ it to calculate the efficiency scores of the manufacturing SMEs in
Vietnam.
DEA method was introduced by Farrell (1957) and first applied in an
empirical by Charnes, Cooper & Rhodes (1978). In this first empirical study,
Charnes et al. (1978) proposed an input-orientation approach under the CRS
assumption. DEA also has been used as a formal term since this paper was realized
in a public domain. Contributing to the development of this method, Fä e et l
(1983) constructed it under the assumption of VRS. Since then, this technique has
been widely used in measuring production efficiency in many industries such as:
manufacturing, banking, public and non-profit organizations.

12
In the initial approach to DEA method, Farell (1957) represented a measure
of technical efficiency when he compared all given technology firms and calculated
the relative efficiency scores for each firm. In the input-orientation approach, firm
which produces a given output with minimum sets of input will gain a unity score
of technical efficiency. Inefficient firm's score will be calculated by one minus
maximum proportion of redundant input. In the output-orientation approach, with
given input and technology, firm is technical efficiency and gains unity if it can
produce maximum quantity of output. Meanwhile, score of technically inefficient
firm is calculated as the proportion of its output compared to output of the efficient
firm and, as such, this score is less than one.
This study also uses this technique in the first stage to identify the relative
production efficiency of SMEs in Vietnam.
2.1.4 Sources of technical efficiency
Timmer (1971, p. 777) concluded that "The extent of technical efficiency in
an industry is, then, important. Knowledge of the sources of any inefficiencies is
doubly important". This study is generally considered as a pioneer study using two-
stage approach to identify the determinants of technical efficiency. Traditional
inputs of production such as capital, labor, material, land and natural resources
influence directly technical efficiency. Additionally, there are also a number of
other factors that have significant impact on firm’s performance. Fried et al. (1999)
and Fried et al. (2002) classified these factors into three categories: (i) managerial
components, (ii) ownership components and (iii) regulatory components. The first
category may also be understood as internal components, while the two latter may
considered as exogenous components. Aiming to identify the relationship between
business networking and technical efficiency, this study organizes these
determinants in only two groups as following: (1) Exogenous factors, which are
related to firm demographic or characteristics such as: age, ownership, size; and (2)

13
Internal factors, which influence firm management ability to translate the inputs into
outputs.
This study will present empirical studies on two exogenous factors (age and
size) and two internal factors (information and credit accessibility). Although many
studies demonstrate that ownership is a crucial determinant of the technical
efficiency, the empirical of SMEs in Vietnam shows that Vietnamese SMEs are
almost in private sector and do business as a household enterprise. Therefore, the
ownership may be not the source of differences in the technical efficiency of
Vietnamese SMEs.
2.1.4.1 Exogenous sources
Empirical studies in the first-group factors such as age and size are plentiful
such as Timmer (1971), Pitt & Lee (1981), Admassive & Matambalya (2002),
Binam et al. (2003). As the pioneer, Timmer (1971) applied his proposal of two-
stage regression in the case of the US agricultural production at the State level. In
the first stage, Timmer ran a regression for the traditional Cobb-Douglas production
function to investigate the inefficiency of each state. In the next phase, other
variables such as age proportion, education and tenant were employed to examine
their impacts on the inefficiencies. Timmer concluded that higher proportion of
middle age operators have positive impact on technical efficiencies. Pitt & Lee
(1981) also used two-stage regression approach in the case of Indonesian weaving
industry and concluded that age of firm, size and ownership are main resource of
technical efficiency. This study found that age has negative relationship with
efficiency. Studying on small and medium scale firms, Admassie & Matambalya
(2002) based on Tanzanian SMEs survey in three sectors: food, textile and tourism
to identify the linkage between external factors such as age, size and technical
efficiency of firm. They argued that age of firm can positively influence the
technical efficiency according to theory of learning-by-doing. However, learning-

14
by-doing has the decreasing marginal effect when firm is mutual. Furthermore,
young firms tend to have better ability of applying new technology than old firms.
Therefore, firm age can have negative impact on efficiency as the results of
Admassie & Matambalya (2002) and Binam et al. (2004).
In term of firm size, Admassie & Matambalya (2002) argued that both too
small firms and too big firms have trouble with management and supervision. In
case of SMEs, firm size was found to have positive impact on firm efficiency. This
result is in line with Pitt & Lee (1981) and Hallberg (1999). Rios & Shively (2004)
applied non-parametric method (DEA) to identify technical efficiency and cost
efficiency of 209 small farming households in Vietnam. In the second stage, they
employed two-tail Tobit model to regress the efficiencies with some farms'
characteristic factors, which includes farm size. The result also indicated the same
with above-mentioned studies when farm size has positive impact on farm
efficiency. Also objecting to small scale firms, Nikaido (2004) showed opposite
result when firm size influences negatively on technical efficiency. This study
argued that small firms may receive large supports from government rather than the
bigger ones, so they have no incentive to become bigger.
2.1.4.2 Internal sources
Internal sources include factors that influence the firm management ability
and lead to differences in firm efficiency. This section will discuss the impact of
information and credit accessibility on the technical efficiency.
The role of information significantly influences on firm behavior and
performance. As mentioned in many microeconomics textbooks, for example,
Pindyck & Rubinfeld, 7th
edition, 2008, asymmetric information can lead to adverse
selection and damage the firm performance as well as social welfare. Raju & Roy
(2000) demonstrated that information is more valuable in a more competitive

15
market, where the ability of product substitution is higher. While the influence of
information on other measurements of firm performance such as profit, return on
equity, productivity is demonstrated in many empirical studies (Morishima, 1991;
Raji & Roy, 2000; Hsu et al., 2008), the study of relationship between information
and the technical efficiency is limited. This impact can be demonstrated in the
empirical study of Muller (1974), which was carried out on the data from
Californian farms. In his study, Muller adjusted the traditional Cobb-Douglass
production function by adding information proxies into the model. To measure
information concept, he used some proxies such as the fees paid for associations to
obtain information, index reflecting exposed information ability and management
index which related to production costs. After transforming from the Cobb-Douglas
function into log-linear form and regressing by least square procedure, the marginal
impact of information variables were estimated. This study presented that the
augmented production function is more significant than the traditional and the role
of information in the technical efficiency is examined.
Theories and empirical studies provide demonstration of relationship
between credit accessibility and production efficiency. Theory of principle-agency
and free cash flow advocates the positive influence of debt on firm efficiency
(Jensen, 1986). These theory argues that firm in debt will have incentives to
produce more efficiently. To prevent the problem of asymmetric information
between lenders and borrowers, debtors are required to be monitored and supervised
by the lenders. As a result, firms with loans appear to be more efficient than
indebted firms. On the other hand, in the case of awfully high agency costs and
under the pressure of paying high level of interest, firm can suffer from troubles of
illiquidity. Nickell & Nicolitsas (1999) found that high financial pressure can
constrain the policy of employment and capital investment, which are main
determinants of firm efficiency. In another approach, more efficient firms can
access the loans more straightforwardly. The credit risk evaluation concept proposes

16
that lenders tend to finance more efficient firms to lessen the risks. From this
theory, technical efficiency can lead to credit accessibility. Many empirical studies
(Rios & Shively, 2004, using DEA method; Binam et al., 2004, using SFA method)
found the positive correlation between credit accessibility and technical efficiency.
However, others such as Binam et al. (2003) cannot identify this relationship.
Appendix 1 produces a summary of all empirical studies related to the
identifying the determinants of firm technical efficiency.
2.2 Business networking
2.2.1 Business networking and related concepts
There are several approaches to understand networking. At individual level,
interpersonal networking can be considered as similar as other concepts such as:
interpersonal ties, interpersonal relationship, and interpersonal interaction.
Granovetter (1973) divided the individual ties into strong ties and weak ties. He also
argued that strong ties, which require joining person more time to interact, are likely
to have access less information than weak ties. Therefore, weak ties can link
individuals of many different groups and form the larger. The interpersonal ties are
the basis of larger ties in community level.
At the organizational level, Snehota & Hakansson (1995, p. 25) defined "a
relationship is mutually oriented interaction between two reciprocally committed
parties". Developed from this definition, business network is depicted as a form of
structure connecting business relationships with specific properties. In line with this
study, Cook & Emmerson (1984 in Zhao & Aram, 1995) also described the
business networks as a system of power and commitment. Kumon (1992, in Zhao &
Aram, 1995, p. 350) has a more formal definition of business network as a
lle ti , i whi h the ti i ts “sh e useful i f ti / wledge with the
members, to achieve mutual understanding, and to develop a firm base for mutual

17
trust that may eventually lead to collaboration to achieve actors' individual as well
as collective goals". In the case that small firms can form a both geographical and
sectoral network, a cluster is established (Schmitz, 1995). Schmitz also stated that
the relationship among firms in a cluster can be either exploitation or collaboration.
Another crucial concept is often mentioned when we discuss about the
business network is the social capital. Many researchers agree that social capital has
a strong link with social networks (Coleman, 1988; Portes & Sensenbrenner, 1993;
Bourdieu, 2008). In a short definition, Molina-M les & M tí ez-Fe dez (2010,
p. 261) stated that social capit l is defi ed “ s the s d s i l el ti s
embedded in the social structures of society that enable people to coordinate action
d t hieve desi ed g ls” t a firm’s level, Koka & Prescott (2002) stated that
inter-firm networks can represent the social capital due to its functions. The first
function of inter-firm networks is the means of information transportation. The
second function of the networks is to create the obligations and expectations based
on norms of all joining firms. Therefore, business network appears to be defined as
social capital in a narrow extent of business environment.
In conclusion, business networking can be understood as a system
accommodating many business relationships, where participants can share their own
sources with others to obtain mutual business objectives.
2.2.2 Components and roles of business networking
Business networks can be classified into groups based on some criteria.
Some studies (Watson, 2007; Parker, 2008; Schoonjans et al., 2011) divided
business networks into formal and informal networks. Parker (2008) provided a
common definition of formal business network as "organizations that bring
entrepreneurs together in order to share business information and experience for
mutual advantage" (p. 628). In his empirical study of Australian SMEs, Watson

18
(2007) argued that formal networks can include six sub-categories: banks, business
consultants, external accountants, industry associations, Small Business
Development Corporation (the official Australian government agency focus on the
development of small business sector), solicitors/lawyers. Whereas, the informal
business networks included networks with: family and friends, local businesses and
others in the industry.
In another classification, Lechner et al. (2006) proposed the model of
rational mix including five parts: (1) social networks, (2) reputational network, (3)
marketing information networks, (4) co-opetition networks and (5) co-operative
technology networks.
The functions of business networking can be derived from the definition of
Kumon (1992). Business network is characterized as a channel of transporting
information and knowledge. Snehota & Hakansson (1995) identified three layers of
a business relationship (or a business network, in an extending definition) as below:
 Activity layer: a relationship maintains and promotes both internal and
interactional activities of parties.
 Resource layer: resources are connected and tied together in a
business network.
 Actor layer: business network connects the joining parties and
influences their behavior.
On the ground of the above analysis, business network holds a crucial role
that can enhance the firm production performance. In many studies of SMEs (Zhao
& Aram, 1995; Gulati, 1999; Dyer & Singh, 1998; Koka & Prescott, 2002; Lechner
et al., 2006), the resource layer was emphasized when business network can enable
firm to access inadequate resources.

19
2.2.3 Relationship between business networking and technical efficiency
Business networking can influence the technical efficiency directly and
indirectly through other resources. As previous analysis, business networking can
manipulate firm activity (layer of activity) and firm behavior (layer of actor). As a
result, the firm's management ability of transformation from inputs into outputs can
be influenced by firm network. In the indirect path, business networking can affect
the technical efficiency through the main sources of the technical efficiency
(resource layer). In a business network, participants can share from traditional
production inputs such as labor, capital to internal sources such as information and
credit accessibility (Schmitz, 1995; Hallberg, 1999; Koka & Prescott 2002). In
empirical studies, relationship between business networking and firm performance
has been researched extensively. On the one hand, network can positively influence
firm performance, which can be represented by several measurements. On the other
hand, over-embeddedness can impose constraints on firms.
Dyer & Singh (1998) found that firm network can produce the sustainable
competitive advantage through generating relation-specific assets, conducting
knowledge and providing supplementary resources and effective governance.
Therefore, business network can boost the super-normal profit. Gulati (1999) also
contributed to the set of studies. His study employed the panel data in the period of
1980-1989 and demonstrates that business networking can lead to long-term
performance. Using a different approach, Lechner et al. (2006) proposed a model of
network mix and claims the network mix plays a significantly important role in firm
development. This study was carried out based on the case of venture-capital
financed companies in five selected nations for six months. They identified that
network size and network relational mix were linked to firm performance, which
was measured by time-to-break-even at founding year and sales in the next years.
However, different networks were crucial in different situations. Reputational
networks contributed moderately, whereas cooperate technology networks have

20
weak impact on firm performance. Social networks had no relationship with firm
performance in the start-up phase but played a considerable role in firm
development. Besides that, this study also found the strong impact of marketing
networks and competitor networks on the firm development.
Watson (2007) found an interesting relationship between the networks and
SMEs possibility of survival and growth. Forming a logistic regression model with
SMEs possibility of survival, income growth and return on equity growth as the
dependent variables, Watson included demographic variables (age, dummy for
industry, size) and network variables (size, intensity, range) as independent
variables. The result showed that the relationship between firm survival and
etw f s t i ve ted U sh e It e s th t the ssibility f SMEs’
survival and growth rate can be boost until they gain enough the optimum number
of relationships and reduce when the networks are congested.
Koka & Prescott (2002) approached the social capital as the network level
and constructed the social capital/inter-firm network as a structure of three
information dimensions including: information volume, information diversity and
information richness. Applying structural equation model (SEM) and factors
analysis method to confirm the validity of the social capital model, this paper
constructed the score of information dimensions for each firm and regressed these
variables with the dependent variables of sales-per-employee (firm productivity).
The result provided evidence that social capital/inter-firm network can influence the
firm productivity differently through information factors.
Binam et al. (2003) and Binam et al. (2004) used two approaches to identify
the relationship between business network and technical efficiency. Using data of 81
s ll ffee f e s i Côte d'Iv i e i 1998, i et l (2003) tte ted t
identify the determinants of the technical efficiency. This study employed DEA

21
method under both assumption of constant returns to scale (CRS) and variable
returns to scale (VRS) in the first stage to achieve the technical efficiency indices.
Traditional inputs included: Land, Age, Labor, Tools value and Fertilizer, while
output was measured by coffee yield. The results showed that the mean technical
efficiency of coffee farms is 36 percent (under the assumption of constant returns to
scale) and 47 per cent (under the assumption of variable returns to scale). The two-
limit Tobit model was employed in the second stage, with the TE being the
dependent variable. Some key variables including household size, age and a dummy
for joining a business groups were expected to be correlated with the technical
efficiency. The dummy for network was found to have highly significant impact on
the firm efficiency. Although the impact was negative and it was not expected, the
relationship is a crucial result to suggest that the policy should pay more attention to
the business network.
As an extension study, Binam et al. (2004) applied SFA method in the
empirical of 450 farmers in Cameroon in 2001/2002 season. In the first stage, this
study constructed a Cobb-Douglas production function with production inputs
including land size, labor and capital. In the next stage, the dummy of participation
in an association and dummy for extension contact are used to proxy social
network. The maximum-likelihood estimates provided the result that joining
association contributed positively to the technical efficiency, while the dummy for
extension contact was not significantly statistical. The weakness of these papers is
the simplicity in measurement of business networking, so that the results could not
represent the full effect of network on the technical efficiency.
In contrast, other papers found no relationship between business networking
and firm performance (Aldrich & Reese, 1993 in Watson, 2007). Forming a
theoretical framework, the paper of Portes & Sensenbrenner (1993) demonstrated
that networks can constrain firm actions or even make firms leave far from their

22
own objectives. Networks can cause pressure on the participants, restrict the
freedom and create the cost of community (free rider issue). Koka & Prescott
(2002) concluded that the dimensions of social capital/inter-firm network can
influence firm performance differently and may be negatively. Appendix 2
summarizes empirical studies on the issues.
In general, business network appears to impact on many aspects of firm
performance such as net asset (Schoonjans et al., 2011), comparative advantages
and super normal profit (Dyer & Singh, 1998), productivity (Koka & Prescott,
2002), growth (Schoonjans et al., 2011; Watson, 2007). Concomitantly, the studies
examining the relationship between network and technical efficiency are limited
and the measurement of network in these studies is fairly simple. This study is to
identify the relationship between business networking and technical efficiency in
the case of small and medium firms in Vietnam.

23
Chapter 3:
RESEARCH METHODOLOGY
Firstly, this chapter will provide an overview of the small and medium sized
enterprises in Vietnam. Next, it will construct the conceptual framework and the
concept measurements based on the literatures. The research methodology,
including data envelopment analysis and regression technique, will also be
discussed. Thirdly, this chapter presents five hypotheses to examine the multi-
dimensional impact of business networking on the production efficiency. Finally,
the data source and filter mechanism will be mentioned at the end of this chapter.
3.1 An overview of Vietnamese Small and Medium sized Enterprises
3.1.1 Growth and contribution of SMEs in Vietnam
There are various official definitions of SMEs, according to the summary of
Gibson & van der Vaart (2008). The classification of most of international
institutions and countries is often based on the maximum number of employees,
maximum revenues and/or maximum total assets. In Vietnam, the definition of
SMEs is officially enacted by the government through the decree number
90/2001/ND-CP in November 2001, and updated by 56/2009/ND-CP in June 2009.
According to the latest decree 56, a manufacturing firm is defined as a SME when it
has equal to or fewer than 300 persons or maximum total capital of VND 100
billion. The details of SMEs definition is represented in Table 3.1 below.

24
Table 3.1: Definition for SMEs in Vietnam
Types of industry
Micro enterprises Small enterprises Medium enterprises
Average no. of
employees
Maximum value
of total asset
Average no.
of employees
Maximum value of
total asset
Average no.
of employees
Agriculture,
forestry and fishery
10 VND 20 billion 10-200 VND 20-100 billion 200-300
Industry and
construction
10 VND 20 billion 10-200 VND 20-100 billion 200-300
Services 10 VND 10 billion 10-50 VND 10-50 billion 50-100
Source: Government's Decree No. 56/2009/ND-CP
Many studies provide evidence that SMEs bring significant benefits to the
economy in terms of employment creation, efficiency and growth because of
utilizing efficiently the national resources (Assefa, 1997 in Admassie &
Matambalya, 2002; Hallberg, 1999). In the developing countries, where the supply
of unskilled labors is relatively surplus, SMEs play an even more crucial role in job
generation. Furthermore, SMEs are often dynamic and adaptable to the local market
when they can meet the market demand immediately. In Vietnam, since the
implementation of the Enterprise Law in 2005, a number of SMEs have
significantly increased (Figure 3.1 (a) and (b)). These figures show that, along with
the increasing trend in the number of total enterprises, the number of SMEs has also
gone up with the average growth rate being approximately 21 percent per year in
the period 2006-2011. In term of total assets, the number of small firms, which have
less than or equal to VND 20 billion, is the largest and accounted for approximately
84 percent of the number of total firms in 2011. The average proportion of medium
firms, whose total assets was between VND 20-100 billion, is about 12 percent,
while the number of large firms was only 5 percent in 2011. In term of employees,
the micro firms with only 1-10 employees accounted for approximate two third of
total firms in 2011, whereas the share of small firms (11-200 employees) is in the
second rank with the figure of 29 percent in 2011. The medium firms (201-300
employees) and large firms (over 300 employees) accounted for only 4 percent of
total firms.

25
Figure 3.1 (a): Number of enterprises at 31/12 (by size of total assets)
Source: General statistic office (2006-2011)
Figure 3.1 (b): Number of enterprises at 31/12 (by size of employees)
Source: General statistic office (2006-2011)
Growing rapidly and accounting for the largest proportion of total
enterprises, SMEs also contribute considerably to the economy. Table 3.2 is a visual
representation that provides some indicators to evaluate the contribution of SMEs.
While the large firms have created 5.8 million jobs, the SMEs have also generated
over 5 million jobs, equivalent with 46.2 percent of total jobs created. More
0
50,000
100,000
150,000
200,000
250,000
300,000
2006 2007 2008 2009 2010 2011
Small
Medium
Large
0
50,000
100,000
150,000
200,000
250,000
2006 2007 2008 2009 2010 2011
Micro
Small
Medium
Large

26
importantly, the majorities of 5 million employees in the SMEs are often low-
skilled and appear to be difficult to gain a job in the larger enterprises. Moreover,
the growth of SMEs may reduce the migrations because they can create job locally.
Another important indicator which should be considered is the total amount of tax
and fees contributed by the SMEs. The SMEs contributed almost VND 164,000
billion to the government budget in 2011, accounting for 31.8 percent of total tax
and fees.
Table 3.2: Main indicators of enterprises as of 01/01/2012, by sizes
Enterprise sizes
Number of enterprises
(Enterprises)
Number of
employees (Persons)
Total assets
(Bil. VND)
Net turnover
(Bil. VND)
Tax and fees
paid (Bil. VND)
Large 7,737 5,829,741 9,410,077 5,797,118 351,376
Proportion (%) 2.50 53.80 63.70 55.70 68.20
Medium and small 304,903 5,009,658 5,369,536 4,610,582 163,812
Proportion (%) 97.50 46.20 36.30 44.30 31.80
Source: General statistic office (2006-2011)
3.1.2 An overview of manufacturing SMEs
Table 3.3 presents a summary of manufacturing firms in Vietnam for the
period 2006-2011. In general, the proportion of manufacturing firms declined from
20 percent in 2006 to 16 percent in 2011. However, the number of manufacturing
firms has been doubled in a period of 5 years. While the number of micro and small
enterprises increased sharply, the number of medium and large enterprises also
increased, but at a lower speed. Since 2010, the number of micro and small
enterprises has reached to over 20 thousand enterprises and continues to increase
despite of the economic crisis.
Table 3.3: Number and proportion of manufacturing firms from 2006 to 2011
Year
Proportion of
manufacturing
firms
Total of
manufacturing
Micro Small Medium Large
2006 20% 25,086 8,904 13,022 908 2,252
2007 20% 29,182 10,617 15,055 1,046 2,464
2008 19% 36,459 14,514 18,345 1,096 2,504
2009 18% 42,894 19,551 19,593 1,142 2,608
2010 16% 45,472 20,018 21,429 1,215 2,810
2011 16% 52,587 23,834 24,516 1,334 2,903
Source: General statistic office (2006-2011)

27
According to the report from SMEs survey (CIEM, 2011; CIEM, 2013), 30
percent manufacturing SMEs are located in ten major provinces including: Hanoi,
Phu Tho, Ha Tay, Hai Phong, Nghe An, Quang Nam, Khanh Hoa, Lam Dong, Ho
Chi Minh city (HCMC) and Long An. Manufacturing enterprises have activities in
various industries (about 18 industry codes in 2011). However, the three main
industries including food products and beverages (Food and Beverages), wood and
wood products (Wood) and fabricated metal products (Metal) contribute more than
55 percent of the total number of SMEs.
Table 3.4: Proportion of three main manufacturing industries
Year of survey 2005 2007 2009 2011
Total surveyed firms 2,739 2,492 2,543 2,449
Share of no. of SMEs in Food and Beverages (%) 22.5 27.9 29.2 30.1
Share of no. of SMEs in wood and wood products (%) 5.4 11.9 12.0 10.2
Share of no. of SMEs in fabricated metal products (%) 18.1 16.9 17.0 17.6
Total share of three main industries 46.0 56.7 58.2 57.9
Source: Author's calculation from report of SMEs' surveys
Food and Beverages and Metal are the two leading industries that have
attracted the participation of the major of SMEs, while the number of SMEs in
Wood industry has increased annually and took the third rank since 2007 (Table
3.4). Some main products of those three industries can be listed as follows:
 Food and Beverages products: noodle, cake, bread, tofu, sausage, fish
s u e, beve ges…
 Wood products: products made from wood and bamboo for constructions,
ges… ex e t fu itu e su h s des s, beds…
 Met l du ts: d s, t s, g i ultu l equi e t… de f et l
In general, the products of these industries appear to be from the simple
production, which is labor- and material-intensive rather than capital-intensive.
3.2 Conceptual framework and model specification
On the ground of theories and empirical studies, a conceptual framework
for this study is developed as illustrated in Figure 3.2.

28
Figure 3.2: Conceptual framework
Source: Author's analysis
The relationship between business networking and production efficiency
will be examined in two stages:
(1) Production efficiency identification: production efficiency is the capacity
of converting the inputs into the outputs and can be derived as a relative
index from the DEA method (Farell, 1957; Charnes, Cooper and Rhodes,
1978; Banker, Charnes & Cooper, 1984; Binam et al., 2003; Rios &
Shively, 2004).
(2) Relationship investigation: the efficiency indices from stage (1) will then
be regressed against business networking variables and control variables.
The impact of business networking on firm efficiency can be demonstrated
by networking theories (Kumon, 1992; Snehota & Hakansson, 1995;
Portes, 2000; Koka & Prescott, 2002) and empirical studies (Koka &
Input sets:
- Labors
- Physical capital
- Materials
Output
Production efficiency
(Technical efficiency)
Business network variables:
- Network quantity (NW size)
- Network quality (Assistance
Intensity)
- Network range (NW diversity)
- Cluster size
- Association participation
Control variables:
- Firm size
- Firm age
- Firm capital structure

29
Prescott, 2002; Lechner, Dowling & Welpe, 2006; Watson, 2007;
Schoonjans et al., 2011). The direct influence of business networking and
technical efficiency is provided by several empirical studies including
Binam et al. (2003), and Binam et al. (2004). Furthermore, the business
networking can influence technical efficiency indirectly through
information (Muller, 1974). This study extends the networking variables
into multi-dimension including: network quantity, network quality,
network diversity, cluster size and dummy variable for joining an
association, which will provide a comprehensive view on the relationship
between business networking and SMEs' production efficiency.
Together with the networking variables, this stage will employ control
variables, which may have considerable impact on technical efficiency,
such as: firm size (Pitt & Lee, 1981; Admassie & Matambalya, 2002;
Nikaido, 2004; Rios & Shively, 2004; Binam et al., 2003; Binam et al.
2004), firm age (Timmer, 1971; Pitt & Lee, 1981; Admassie &
Matambalya, 2002; Binam et al., 2003; Binam et al., 2004) and firm
capital structure (Jensen, 1986; Nickell & Nicolitsas, 1999; Rios &
Shively, 2004; Binam et al., 2004).
The detailed descriptions of the two stages can be represented as below.
3.2.1 The first stage: Efficiency measurement using the DEA method
Over four decades from its first introduction by Farrell (1957), the DEA
method has been consistently applied and improved significantly. In the first stage,
the approach adopted in this study will be based on the extension DEA model by
Charnes et al. (1978) and further developed by Banker et al. (1984).
As previously discussed, there are two approaches to apply the DEA
method that are input-orientated approach and output-orientated approach. The
measurement of efficiency in both approaches is similar. With the assumption of

30
constant return to scale, the input-orientated measurement and output-orientated
measurement will provide same results (Coelli et al., 2005). In their study, Coelli et
al. (2005) also claim that the selection of input-orientated or output-orientated
measurement is not considerably crucial. This paper chooses the approach of input-
orientation for several reasons. The first and most important reason is that the paper
deals with small and medium firms, which consider more about how to mix the
input factors to gain outputs rather than they can change output providing given
resources. SMEs are often lack of resources; therefore, they often attempt to
exchange and exploit their abundant resources (such as labor) in the production
process. Second, the objective of this study is to identify the efficiency indices for
the second regression rather than to find out the capacity utilization. Output-
orientated approach is more suitable in the circumstance of study in capacity
utilization. Therefore, this study will employ the input-orientated approach, which
means that firm attempts to control (minimize) the inputs set to gain the given
output. The method represented below will also in the line with input-orientated
approach.
The main idea of efficiency measurement is to compare the output/input
ratio between firms under some assumptions such as: the number of input and
output must be positive and total output value must be less than or equal to the total
input value. The firm with the highest ratio will be the most efficient and scores 1,
while the inefficient firm will score less than 1.
In mathematic expression, let consider the efficiency measurement for J
firms, which produce M outputs Y from N inputs X. Firstly, the mathematical
function of the relative output/input ratio can be represented as:

31
1 1
,
1 1
max (1)
: 1 1,2,...,
, 0 1,2,...,
1,2,...,
m n
M N
j m mj n nj
m n
u v
M N
m mj n nj
m n
m n
z u y v x
subject to u y v x j J
u v m M
n N
 
 
   
    
   
   
 
   
   
 

 
 
Where: zj : the relative efficiency index of jth
firm
ymj : the observed mth
output of jth
firm
xnj : the observed nth
input of jth
firm
um : the weight for mth
output
vn : the weight for nth
input
Model (1) is a nonlinear and non-convex fraction that attempts to maximize
the relative ratio of sum of weighted outputs over sum of weighted inputs. The first
constraint aims to keep the efficiency scores less than or equal to 1, while the
second constraint ensures the existence of factors for production progress. The
problem of this ratio is that if 
*, *
u v is a solution, then *, )
*
( u v
  should be a
solution. Therefore, the ratio system provides infinitive number of solutions. To fix
the problem, we can constrain the sum of weighted inputs equal 1. As such, the
objective is to maximize the sum of weighted outputs. The further constraint is
presented in the model (2), which can also be called primal form:
'
' '
1
1
'
1 1
'
max (2)
: 1 1,2,...,
0 1,2,...,
, 0 1,2,...,
j m
m
m
m
M
mj
m
u
N
n nj
n
M N
mj n nj
m n
n
z u y
subject to v x n N
u y v x j J
u v m M


 
 
  
 
 
 
  
 
 
 


 
Second, using duality in linear programming, the ratio system (2) can be
rewritten into an equivalent envelopment form as following:

32
,
min (3)
: 0 1,2,...,
0
0
j
j
subject to y Y j J
x X
 


 

   
 

Where:  is a scalar, which is the efficiency index of the firms and is a
1
I  vector of constants.
Model (3) is under the assumption of CRS (Charnes et al., 1978). In the
further extension, Banker et al. (1984) handled the assumption of VRS by adding
the st i t J1’λ=1, whe e J1 is Jx1 ve t f 1:
,
min (4)
: 0 1,2,...,
0
1' 1
0
Z
j
j
Z
subject to Y Y j J
ZX X
J





   
 


The model (3) and (4) will be processed by the computer software called
DEA program, which is written by Coelli (1996). The results, which include
technical efficiency under CRS assumption and VRS assumption, will be employed
in the second stage.
3.2.2 The second stage: Regression model
In this stage, the efficiency indices can be used as the independent variables
in the Tobit regression or OLS regression. Many empirical studies such as Binam et
al. (2003), Rios & Shively (2005) used Tobit regression because of the specification
of the dependent variable. When using efficiency indices as the dependent variable,
the scores are in the range from 0 to 1; therefore, the Tobit model appears to be
more suitable. Whenever the data is censored, for example, left-censoring point of 0
and right-censoring point of 1 in this case, OLS may not yeild consistent parameter
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estimates (Cameron & Trivedi, 2009). However, recent studies specialized in DEA
second stage (Hoff, 2007; McDonald, 2009) provided evidence that Tobit model
may not be the only and the best method to use. By mathematical analysis and
empirical study, Hoff (2007) showed that OLS regression can be more reliable than
Tobit regression in some circumstances. In more details, MacDonald (2009)
indicated that the OLS regression and Tobit regression will provide the same
outcomes when the dependent variables locate far from the limits. Another
consideration is that the Tobit model is very vulnerable in the case of
heteroscedasticity existence, which means that the results from Tobit regression will
be bias. As such, if the dependent variable concentrates more on the frontiers (0 and
1, in this case), Tobit regression is a good solution. Otherwise, the OLS regression
is more suitable.
This study will employ both Tobit regression and feasible generalized least
squares (FGLS) estimation with option panels(heteroskedastic) to control for the
heteroscedasticity. Using FGLS model or Tobit model depends on the density of
technical efficiency scores resulted from Stage 1 and the heteroscedasticity testing
for Tobit model. In the existence of heteroscedasticity in Tobit regression, the
results will be inconsistent because of the biased variance. In that case, FGLS
regression is more reliable. However, the coefficient of Tobit model is not biased
and can be used in the comparison with the results of FGLS model.
The linear functional form is employed for two reasons: (1) to identify the
relationship between efficiencies and business networking, and (2) to compare with
the results from Tobit model. This choice is also compliant with many empirical
studies such as Binam et al. (2003), Rios & Shively (2005).
Both least squared model and Tobit model are represented as follows:
(1) Least squared model:
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jt jt jt jt
EI X
  
  
Where: EI : efficiency indices resulted from the first stage
ji
X : independent variables of jth
firm
(2) Two-limit Tobit model:
*jt jt jt jt
EI X
  
  
Where: *
EI : latent value of efficiency indices
If *
EI ≤ 0, the effi ie y i di es EI = 0
If *
EI ≥1, the effi ie y i di es EI = 1
If 0 < *
EI < 1, the efficiency indices EI=EI*
ji
X : independent variables of jth
firm
3.3 Research hypotheses and concept measurements
3.3.1 Research hypotheses
As discussed in the previous chapter, business networking can influence
positively firm performance as well as production efficiency in both direct and
indirect ways (Portes, 2000; Koka & Prescott, 2002; Binam et al., 2004; Lechner et
al., 2006; Watson, 2007; Parker, 2008; Schoonjans et al., 2009). In addition, other
theory and empirical studies argue that business networking can have negative
impact on production efficiency (Portes & Sensenbrenner, 1993; Binam et al.,
2003). This study proposes the hypotheses supporting to both positive effect and
negative effect of networking and efficiency. The business networking concept will
be considered in five components: (i) business network quantity, (ii) business
network quality, (iii) network diversity, (iv) cluster size and (v) participation in a
business association or not.
In more details, there are five research hypotheses that will be tested in this
study which can be illustrated as follows:
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