The relationship between business networking and SMEs production efficiency

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDY VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONMICS THE RELATIONSHIP BETWEEN BUSINESS NETWORKING AND SMES PRODUCTION EF

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDY

VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONMICS

THE RELATIONSHIP BETWEEN BUSINESS NETWORKING AND SMES PRODUCTION EFFICIENCY

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

L H g L g

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,

DEA Data envelopment analysis

DMU Decision making unit

GSO General Statistics Office Of Vietnam

SFA Stochastic frontier analysis

SMEs Small and medium sized enterprises

TE Technical efficiency

TFP Total-factor productivity

VRS Variable returns to scale

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

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

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

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

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

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

1.1 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

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

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:

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

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

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

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)

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 x x1, 2 and one outputy, the measurements are demonstrated in Figure 2 Let x P,x Q and *

x represent the input vectors associated with pointP,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( ,x x1 2), which produce same amount of output Therefore, firms working in this curve (at pointQandQ*) are technical efficient, while other firms (like pointP) are not The technical efficiency can be calculated by the ratio:

'0

Q P

The iso-cost curveCC'represents the mix of inputs subject to the same and minimum cost Then, the allocative efficiency (AE) can be measured by the ratio:

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



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

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

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)

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-

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

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

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

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

(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:

interactional activities of parties

business network

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

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

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

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

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

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

Average no

of employees Agriculture,

forestry and fishery 10 VND 20 billion 10-200 VND 20-100 billion 200-300 Industry and

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

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

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)

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

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

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

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

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 &

Business network variables:

- Network quantity (NW size)

- Network quality (Assistance

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