The relationship between business networking and SMEs production efficiency

University of EconomicsHo Chi Minh City, Vietnam International Institute of Social StudyErasmus University of Rotterdam, The Netherlands VIETNAM – THE NETHERLANDSPROGRAMME FOR M.A INDEVE

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDY

HO CHI MINH CITY

ERASMUS UNIVERSITY OF ROTTERDAM

VIETNA M

THE NETHERLAN DS

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

THE RELATIONSH

IP BETWEEN BUSINESS NETWORKIN

G AND SMES PRODUCTIO

N

HCMC, NOVEMB

ER 2013

University of Economics

Ho Chi Minh City, Vietnam

International Institute of Social StudyErasmus University of Rotterdam, The Netherlands

VIETNAM – THE NETHERLANDSPROGRAMME FOR M.A INDEVELOPMENT ECONMICS

THE RELATIONSHIP BETWEEN BUSINESS NETWORKING AND SMEs PRODUCTION EFFICIENCY

Vietnam – Netherlands Programme, November 2013

I would not be possible to write this master thesis without the help and support

of people surrounding me

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Above all, I w uld li e t

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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 theprogram Iti 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

Data envelopment analysisDecision making unitGeneral Statistics Office Of VietnamScale efficiency

Stochastic frontier analysisSmall and medium sized enterprisesTechnical efficiency

Total-factor productivityVariable returns to scale

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

LIST OF TABLES

LIST OF FIGURES

Chapter 1: INTRODUCTION

1.1 Problem statement

1.2 Research objectives

1.3 Research questions

1.4 Research scope and data

1.5 The structure of this study

Chapter 2: LITERATURE REVIEW

2.1 Production efficiency: Concepts, measurements and sources

2.1.1 Concepts

2.1.2 Measurements

2.1.3 Efficiency measurement methods

2.1.4 Sources of technical efficiency

2.1.4.1 Exogenous sources

2.1.4.2 Internal sources

2.2 Business networking

2.2.1 Business networking and related concepts

2.2.2 Components and roles of business networking

2.2.3 Relationship between business networking and technical efficiency

Chapter 3: RESEARCH METHODOLOGY

3.1 An overview of Vietnamese Small and Medium sized Enterprises

3.1.1 Growth and contribution of SMEs in Vietnam

3.1.2 An overview of manufacturing SMEs

3.2.2 The second stage: Regression model

3.3 Research hypotheses and concept measurements

3.3.1 Research hypotheses

3.3.2 Concept and variable measurements

3.4 Data source and filter process

Chapter 4 EMPIRICAL RESULTS

4.1 Production efficiency of SMEs

4.1.1 Data descriptions

4.1.2 Production efficiency of SMEs in Vietnam

4.2 The relationship between business networking and production efficiency

4.2.1 Data description

4.2.2 Regression results

4.2.2.1 Network quantity

4.2.2.2 Network quality

4.2.2.3 Network diversity

4.2.2.4 Cluster size

4.2.2.5 Participation in a business association

Chapter 5: CONCLUSION AND POLICY IMPLICATION

5.1 Conclusion remarks

5.2 Policy implications

5.3 Limitations and recommendations for future research

REFERENCES

Appendix 1: Empirical studies on the sources of technical efficiency

Appendix 2: Empirical studies on the relationship between business network andfirm performance 68Appendix 3: Empirical studies on the technical efficiency measurements ofmanufacturing 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

Chapter 1:

INTRODUCTION

This chapter introduces the research topic and the problem statement Theresearch objectives, the research questions and the research scope and data are alsoincluded in this section This chapter will end with the introduction of the thesisorganization

1.1 Problem statement

Small and medium sized enterprises (SMEs) hold a crucial role in theeconomic development, especially in developing countries including Vietnam.Compared to large sized enterprises, SMEs appear to bring more merits to theeconomy in terms of generating jobs, meeting the urgent demand immediately andgrowing 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 Oftenbeing labor-intensive, SMEs help creating jobs for low skilled labor, which isredundant in the developing countries (Schmitz, 1995; Hallberg, 1999) According

to the General Statistic Office of Vietnam, a number of formal SMEs (legallyregistered firms) are 305,000 firms, accounting for 97.5 per cent of the total firms inJanuary 2012 This figure may be underestimated because of the lack of informalSMEs statistics These numbers of enterprises generate approximately 5 million jobsand obtain about VND 4,600 billion revenue annually In spite of the large numberand sustaintial contribution to the economy, SMEs have to deal with countlessproblems to survive and develop In the developing countries, SMEs often face tothe 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 largefirms, 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 exploitthe business network as a source of information, knowledge and competitiveadvantage (Dyer & Singh, 1998) As such, business networking appears to be thechannel of resources Furthermore, the benefits of business network have beendemonstrated in many empirical studies (e.g Gulati, 1999; Dyer & Singh, 1998;Lechner, Dowling & Welpe, 2006) Many scholars presented the positiverelationship between business network and firm growth and development (forexample, Schoonjans, Cauwenberge & Bauwhede, 2011; Lechner et al., 2006)

In Vietnam, network can bring the entrepreneurs many benefits such asinformation, knowledge and other substitution resources There appears to be asignificant correlation between network and firm efficiency in the case of Vietnam.However, empirical studies to examine the link between business network andVietnam SMEs efficiency are limited This study will present the evidence of thislinkage between business networking and production efficiency of the SMEs usingpanel data and the data envelopment analysis (DEA) technique, which is aneffective method for measuring firm efficiency The thesis deals with themanufacturing SMEs in three major industries, which include food products andbeverages, wood and wood products and fabricated metal products These threeindustries, which account for over 50% of the total number of SMEs in Vietnam andoften deal with the problems of poor production capacity and the resourceconstraint, can represent for the population of Vietnamese SMEs

1.2 Research objectives

The study aims to examine the relationship between business networkingand production efficiency of SMEs in Vietnam As such, it has two main objectiveswhich 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 studyattempts to exam the multi-dimensional impact of businessnetworking 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 anyrelationship between the business networking and the production efficiency ofSMEs in Vietnam? If yes, then how can business networking can influence theproduction efficiency of SMEs?

The study will examine the relationship between business networking andthe SMEs efficiency using the panel data for the period from 2004 to 2010 Threeselected industries include: (i) food products and beverages; (ii) woods and woodproducts; and (iii) fabricated metal products Of 18 industries, these three industrieshave 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 therelationship between business networking and firm production efficiency It beginswith the definitions and determinants of the production efficiency This chapter thendiscusses the networking definition and its crucial role to the firms Businessnetworking can influence production efficiency both directly and indirectly Inaddition, its impact on firm production efficiency can be etheir positive or negativedepending on the circumstances.

Chapter 3 presents the research methodology, in which both dataenvelopment analysis and regression technique are discussed This chapter alsoprovides the conceptual framework as well as the concept measurements Fivehypotheses to examine the multi-dimensional impact of business networking on theproduction efficiency are included In addition, this chapter introduces the datasource and filter mechanism

Chapter 4 presents the empirical results The statistic descriptions of thedata are presented Then, the findings of production efficiency of the SMEs will berepresented and discussed This section also produces the regression results thatprovide evidence on the relationship between business networking and productionefficiency

Chapter 5 will summarize the main results along Some policy implicationsare proposed based on the results obtained from Chapter 4 This chapter alsooutlines limitations and suggests the directions for future research

Chapter 2:

LITERATURE REVIEW

This chapter will review the literature on the relationship between businessnetworking and firm production efficiency Initially, the concepts, the measurementsand the determinants of the production efficiency will be analyzed This chapterthen discusses the definitions of business networking as well as its functions Theempirical studies on the relationship between business networking and theproduction 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 economicsresearch at firm’s level The concept of production efficiency is derived from theproduction process, which converts input factors (including labor and capital) intoproducts (or production outputs) The overall or economic efficiency can bedecomposed into two components: (i) technical efficiency and (ii) allocativeefficiency

Figure 2.1: Production frontiers and technical efficiency

y

technical change

B C

A

0

The former component is proposed for long time, accompanied with theconcept of production possibility frontiers (PPF) Production frontiers describe themaximum possible outputs for given inputs and technology level In the productionprocess, due to the limited input factors, firms are only able to just produce on orbelow 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 increaseits 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 consideredthe pioneer definition of technical efficiency Farell (1957, p 254) stated

th t fig 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 orientated view as an efficient firm could produce a given output with the minimum

input-of inputs combinations Derived from the production process, technical efficiencycan be understood as production efficiency

The latter concept (allocative efficiency) reflects how efficient firms controltheir costs Allocative efficiency represents the capability of a firm to combine ormix the inputs sets to produce the given output within the minimum budget Whiletechnical efficiency can be measured from the production function, estimation ofallocative 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 differentproductivity levels Productivity is measured by the ratio of output and inputquantities, which is equal to the slope of a ray drawn from the origin through thepoint 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 scaleefficiency 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 iseasy to understand in a one-input, one-output case, but it is more difficult toconceptualize 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 productivescale and the firm with the remaining scales It appears to be a component which isderived from technical efficiency

In order to identify the relationship between business networking andproduction efficiency, this study will consider production efficiency as technicalefficiency in both assumptions: (i) constant returns to scale (pure technicalefficiency); and (ii) variable returns to scale (technical efficiency including scaleefficiency)

2.1.2 Measurements

This section will represent the basic measurements of efficiency in a simplecase with two inputs and one output under the assumption of a constant return toscale The below-mentioned measurements are from the input-orientated approach,which will be employed in this study

Figure 2.2: Technical efficiency measurement

measurements are demonstrated in Figure 2 Let and x* represent the input

vectors associated with point P , Q and Q * respectively In addition, let w representthe vector of input prices

The iso-quant curve SS ' is a collection of many combinations ( x1 , x2 ) ,which produce same amount of output Therefore, firms working in this curve (atpoint Q and Q * ) are technical efficient, while other firms (like point P ) are not.The technical efficiency can be calculated by the ratio:

x P , x Q

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

SE=TE CRS SE VRS

2.1.3 Efficiency measurement methods

Production efficiency is such an appealing area of research that manystudies 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)

3. Data envelopment analysis (DEA)

4. Stochastic frontier analysis (SFA)

Four techniques can be classified into two sub-groups based on theirassumptions and applications Assuming that all firms are technically efficient, theobjectives of the initial two methods are to estimate the technical change rather thanthe TE and AE Without under the assumption that all firms are technically efficientand taking into account the scale efficiency measurement, DEA and SFA are usedcommonly in calculating relative efficiency among firms (Coelli et al., 2005) Asabove-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 isunknown; therefore, based on their own assumptions, both methods attempts todevelop the curve by identifying the most efficient firms and forming the productionboundary

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 errorterm derived from the regression will contain both noise component andinefficiency 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 aproduction function, SFA can fix the issue of statistical noise of non-parametricmethods For example, SFA can include relevant variables into the function tomeasure the accurate efficiency indices while DEA cannot However, thischaracteristic is also the drawback of the method The production function isdifficult 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

In a different approach, DEA is a mathematical technique, which comparesthe 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 investmentsand two outputs to identify the efficiency of firm investments in informationtechnology Secondly, the result of DEA can be used extensively for manyobjectives In many cases, DEA gives the efficiency indices for each DecisionMaking Unit (DMU) and even presents a component that should be adjusted toachieve efficiency In other researches, the efficiency indices also can be used as avariable for the second regression stage Thirdly, extended DEA can fix someproblems of statistical noise We can overhaul DEA by adding the environmentalfactors as non-discretionary variables into the original DEA (in the case of usingonly 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 bothmulti-outputs and multi-inputs Thanks to these merits of DEA method, this studywill employ it to calculate the efficiency scores of the manufacturing SMEs inVietnam

DEA method was introduced by Farrell (1957) and first applied in anempirical by Charnes, Cooper & Rhodes (1978) In this first empirical study,Charnes et al (1978) proposed an input-orientation approach under the CRSassumption 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 beenwidely 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 calculatedthe relative efficiency scores for each firm In the input-orientation approach, firmwhich produces a given output with minimum sets of input will gain a unity score oftechnical efficiency Inefficient firm's score will be calculated by one minusmaximum proportion of redundant input In the output-orientation approach, withgiven input and technology, firm is technical efficiency and gains unity if it canproduce maximum quantity of output Meanwhile, score of technically inefficientfirm is calculated as the proportion of its output compared to output of the efficientfirm and, as such, this score is less than one

This study also uses this technique in the first stage to identify the relativeproduction 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 isdoubly important" This study is generally considered as a pioneer study using two-stage approach to identify the determinants of technical efficiency Traditionalinputs of production such as capital, labor, material, land and natural resourcesinfluence directly technical efficiency Additionally, there are also a number of otherfactors that have significant impact on firm’s performance Fried et al (1999) andFried et al (2002) classified these factors into three categories: (i) managerialcomponents, (ii) ownership components and (iii) regulatory components The firstcategory may also be understood as internal components, while the two latter mayconsidered as exogenous components Aiming to identify the relationship betweenbusiness networking and technical efficiency, this study organizes thesedeterminants in only two groups as following: (1) Exogenous factors, which arerelated to firm demographic or characteristics such as: age, ownership, size; and (2)

Internal factors, which influence firm management ability to translate the inputsinto outputs.

This study will present empirical studies on two exogenous factors (age andsize) and two internal factors (information and credit accessibility) Although manystudies demonstrate that ownership is a crucial determinant of the technicalefficiency, the empirical of SMEs in Vietnam shows that Vietnamese SMEs arealmost in private sector and do business as a household enterprise Therefore, theownership may be not the source of differences in the technical efficiency ofVietnamese SMEs

2.1.4.1 Exogenous sources

Empirical studies in the first-group factors such as age and size are plentifulsuch 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 Inthe first stage, Timmer ran a regression for the traditional Cobb-Douglas productionfunction to investigate the inefficiency of each state In the next phase, othervariables such as age proportion, education and tenant were employed to examinetheir impacts on the inefficiencies Timmer concluded that higher proportion ofmiddle age operators have positive impact on technical efficiencies Pitt & Lee(1981) also used two-stage regression approach in the case of Indonesian weavingindustry and concluded that age of firm, size and ownership are main resource oftechnical efficiency This study found that age has negative relationship withefficiency 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 technicalefficiency of firm They argued that age of firm can positively influence thetechnical 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 ofAdmassie & Matambalya (2002) and Binam et al (2004).

In term of firm size, Admassie & Matambalya (2002) argued that both toosmall firms and too big firms have trouble with management and supervision Incase of SMEs, firm size was found to have positive impact on firm efficiency Thisresult is in line with Pitt & Lee (1981) and Hallberg (1999) Rios & Shively (2004)applied non-parametric method (DEA) to identify technical efficiency and costefficiency of 209 small farming households in Vietnam In the second stage, theyemployed two-tail Tobit model to regress the efficiencies with some farms'characteristic factors, which includes farm size The result also indicated the samewith above-mentioned studies when farm size has positive impact on farmefficiency Also objecting to small scale firms, Nikaido (2004) showed oppositeresult when firm size influences negatively on technical efficiency This studyargued that small firms may receive large supports from government rather than thebigger ones, so they have no incentive to become bigger

2.1.4.2 Internal sources

Internal sources include factors that influence the firm management abilityand lead to differences in firm efficiency This section will discuss the impact ofinformation and credit accessibility on the technical efficiency

The role of information significantly influences on firm behavior andperformance As mentioned in many microeconomics textbooks, for example,Pindyck & Rubinfeld, 7th edition, 2008, asymmetric information can lead to adverseselection 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 ofinformation on other measurements of firm performance such as profit, return onequity, productivity is demonstrated in many empirical studies (Morishima, 1991;Raji & Roy, 2000; Hsu et al., 2008), the study of relationship between informationand the technical efficiency is limited This impact can be demonstrated in theempirical study of Muller (1974), which was carried out on the data fromCalifornian farms In his study, Muller adjusted the traditional Cobb-Douglassproduction function by adding information proxies into the model To measureinformation concept, he used some proxies such as the fees paid for associations toobtain information, index reflecting exposed information ability and managementindex which related to production costs After transforming from the Cobb-Douglasfunction into log-linear form and regressing by least square procedure, the marginalimpact of information variables were estimated This study presented that theaugmented 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 relationshipbetween credit accessibility and production efficiency Theory of principle-agencyand 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 producemore efficiently To prevent the problem of asymmetric information betweenlenders and borrowers, debtors are required to be monitored and supervised by thelenders As a result, firms with loans appear to be more efficient than indebtedfirms On the other hand, in the case of awfully high agency costs and under thepressure of paying high level of interest, firm can suffer from troubles of illiquidity.Nickell & Nicolitsas (1999) found that high financial pressure can constrain thepolicy of employment and capital investment, which are main determinants of firmefficiency In another approach, more efficient firms can access the loans morestraightforwardly 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 thepositive 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 theidentifying 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 alsoargued 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 linkindividuals of many different groups and form the larger The interpersonal ties arethe basis of larger ties in community level

At the organizational level, Snehota & Hakansson (1995, p 25) defined "arelationship is mutually oriented interaction between two reciprocally committedparties" Developed from this definition, business network is depicted as a form ofstructure connecting business relationships with specific properties In line with thisstudy, Cook & Emmerson (1984 in Zhao & Aram, 1995) also described the businessnetworks 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 achievemutual 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 andsectoral network, a cluster is established (Schmitz, 1995) Schmitz also stated thatthe 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 thatinter-firm networks can represent the social capital due to its functions The firstfunction of inter-firm networks is the means of information transportation Thesecond 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 associal capital in a narrow extent of business environment

In conclusion, business networking can be understood as a systemaccommodating many business relationships, where participants can share their ownsources 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) dividedbusiness networks into formal and informal networks Parker (2008) provided acommon definition of formal business network as "organizations that bringentrepreneurs together in order to share business information and experience formutual advantage" (p 628) In his empirical study of Australian SMEs, Watson

(2007) argued that formal networks can include six sub-categories: banks, businessconsultants, external accountants, industry associations, Small BusinessDevelopment Corporation (the official Australian government agency focus on thedevelopment of small business sector), solicitors/lawyers Whereas, the informalbusiness networks included networks with: family and friends, local businesses andothers in the industry.

In another classification, Lechner et al (2006) proposed the model ofrational mix including five parts: (1) social networks, (2) reputational network, (3)marketing information networks, (4) co-opetition networks and (5) co-operativetechnology networks

The functions of business networking can be derived from the definition ofKumon (1992) Business network is characterized as a channel of transportinginformation 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 enablefirm to access inadequate resources

2.2.3 Relationship between business networking and technical efficiency

Business networking can influence the technical efficiency directly andindirectly through other resources As previous analysis, business networking canmanipulate firm activity (layer of activity) and firm behavior (layer of actor) As aresult, the firm's management ability of transformation from inputs into outputs can

be influenced by firm network In the indirect path, business networking can affectthe technical efficiency through the main sources of the technical efficiency(resource layer) In a business network, participants can share from traditionalproduction inputs such as labor, capital to internal sources such as information andcredit accessibility (Schmitz, 1995; Hallberg, 1999; Koka & Prescott 2002) Inempirical studies, relationship between business networking and firm performancehas been researched extensively On the one hand, network can positively influencefirm performance, which can be represented by several measurements On the otherhand, 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) alsocontributed 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 firmdevelopment This study was carried out based on the case of venture-capitalfinanced companies in five selected nations for six months They identified thatnetwork 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 Reputationalnetworks contributed moderately, whereas cooperate technology networks have

weak impact on firm performance Social networks had no relationship with firmperformance in the start-up phase but played a considerable role in firmdevelopment Besides that, this study also found the strong impact of marketingnetworks 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 thedependent variables, Watson included demographic variables (age, dummy forindustry, size) and network variables (size, intensity, range) as independentvariables 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 reducewhen the networks are congested

Koka & Prescott (2002) approached the social capital as the network leveland constructed the social capital/inter-firm network as a structure of threeinformation dimensions including: information volume, information diversity andinformation richness Applying structural equation model (SEM) and factorsanalysis method to confirm the validity of the social capital model, this paperconstructed 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 thefirm 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 ofthe technical efficiency This study employed DEA

method under both assumption of constant returns to scale (CRS) and variablereturns to scale (VRS) in the first stage to achieve the technical efficiency indices.Traditional inputs included: Land, Age, Labor, Tools value and Fertilizer, whileoutput was measured by coffee yield The results showed that the mean technicalefficiency of coffee farms is 36 percent (under the assumption of constant returns toscale) 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 thedependent variable Some key variables including household size, age and a dummyfor joining a business groups were expected to be correlated with the technicalefficiency The dummy for network was found to have highly significant impact onthe firm efficiency Although the impact was negative and it was not expected, therelationship is a crucial result to suggest that the policy should pay more attention tothe business network.

As an extension study, Binam et al (2004) applied SFA method in theempirical of 450 farmers in Cameroon in 2001/2002 season In the first stage, thisstudy constructed a Cobb-Douglas production function with production inputsincluding 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 associationcontributed positively to the technical efficiency, while the dummy for extensioncontact was not significantly statistical The weakness of these papers is thesimplicity in measurement of business networking, so that the results could notrepresent the full effect of network on the technical efficiency

In contrast, other papers found no relationship between business networkingand firm performance (Aldrich & Reese, 1993 in Watson, 2007) Forming atheoretical framework, the paper of Portes & Sensenbrenner (1993) demonstratedthat networks can constrain firm actions or even make firms leave far from their

own objectives Networks can cause pressure on the participants, restrict thefreedom and create the cost of community (free rider issue) Koka & Prescott (2002)concluded that the dimensions of social capital/inter-firm network can influencefirm performance differently and may be negatively Appendix 2 summarizesempirical studies on the issues.

In general, business network appears to impact on many aspects of firmperformance such as net asset (Schoonjans et al., 2011), comparative advantagesand super normal profit (Dyer & Singh, 1998), productivity (Koka & Prescott,2002), growth (Schoonjans et al., 2011; Watson, 2007) Concomitantly, the studiesexamining the relationship between network and technical efficiency are limited andthe measurement of network in these studies is fairly simple This study is toidentify the relationship between business networking and technical efficiency inthe case of small and medium firms in Vietnam

Chapter 3:

RESEARCH METHODOLOGY

Firstly, this chapter will provide an overview of the small and medium sizedenterprises in Vietnam Next, it will construct the conceptual framework and theconcept measurements based on the literatures The research methodology,including data envelopment analysis and regression technique, will also bediscussed 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

3.1.1 Growth and contribution of SMEs in Vietnam

There are various official definitions of SMEs, according to the summary ofGibson & van der Vaart (2008) The classification of most of internationalinstitutions and countries is often based on the maximum number of employees,maximum revenues and/or maximum total assets In Vietnam, the definition ofSMEs is officially enacted by the government through the decree number90/2001/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 ithas equal to or fewer than 300 persons or maximum total capital of VND 100billion The details of SMEs definition is represented in Table 3.1 below

Source: Government's Decree No 56/2009/ND-CP

Many studies provide evidence that SMEs bring significant benefits to theeconomy in terms of employment creation, efficiency and growth because ofutilizing 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 jobgeneration Furthermore, SMEs are often dynamic and adaptable to the local marketwhen they can meet the market demand immediately In Vietnam, since theimplementation of the Enterprise Law in 2005, a number of SMEs havesignificantly increased (Figure 3.1 (a) and (b)) These figures show that, along withthe increasing trend in the number of total enterprises, the number of SMEs has alsogone up with the average growth rate being approximately 21 percent per year in theperiod 2006-2011 In term of total assets, the number of small firms, which haveless 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 mediumfirms, 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 oftotal firms in 2011, whereas the share of small firms (11-200 employees) is in thesecond rank with the figure of 29 percent in 2011 The medium firms (201-300employees) and large firms (over 300 employees) accounted for only 4 percent of

24

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)

Medium

Large

Source: General statistic office (2006-2011)

Growing rapidly and accounting for the largest proportion of totalenterprises, SMEs also contribute considerably to the economy Table 3.2 is a visualrepresentation that provides some indicators to evaluate the contribution of SMEs.While the large firms have created 5.8 million jobs, the SMEs have also generatedover 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-skilledand appear to be difficult to gain a job in the larger enterprises Moreover, thegrowth of SMEs may reduce the migrations because they can create job locally.Another important indicator which should be considered is the total amount of taxand fees contributed by the SMEs The SMEs contributed almost VND 164,000billion to the government budget in 2011, accounting for 31.8 percent of total taxand fees.

3.1.2 An overview of manufacturing SMEs

Table 3.3 presents a summary of manufacturing firms in Vietnam for theperiod 2006-2011 In general, the proportion of manufacturing firms declined from

20 percent in 2006 to 16 percent in 2011 However, the number of manufacturingfirms has been doubled in a period of 5 years While the number of micro and smallenterprises increased sharply, the number of medium and large enterprises alsoincreased, but at a lower speed Since 2010, the number of micro and smallenterprises has reached to over 20 thousand enterprises and continues to increasedespite of the economic crisis

Table 3.3: Number and proportion of manufacturing firms from 2006 to 2011