The relationship between business networking and SMEs production efficiency

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDY HO CHI MINH CITY ERASMUS UNIVERSITY OF ROTTERDAM VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONMICS THE RELATIONSHIP BETWEEN BUSINESS NETWORKING AND SMES PRODUCTION EFFICIENCY By LE HOANG LONG MASTER OF ART IN DEVELOPMENT ECONOMICS HCMC, NOVEMBER 2013 University of Economics International Institute of Social Study Ho Chi Minh City, Vietnam Erasmus University of Rotterdam, The Netherlands VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONMICS THE RELATIONSHIP BETWEEN BUSINESS NETWORKING AND SMEs PRODUCTION EFFICIENCY by L H gL g A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Art in Development Economics Academic Supervisor Dr V H g Vietnam – Netherlands Programme, November 2013 DECLARATION This is to certify that this thesis e titled “The relationship between business networking and SMEs production efficiency”, whi h is submitted by me in fulfillment of the requirements for the degree of Master of Art in Development Economic to the Vietnam – The Netherlands Programme The thesis constitutes only my original work and due supervision and acknowledgement have been made in the text to all materials used L H gL 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 Ki Hi , wh lw ys l ves, t es yf ily, es e i lly e f d su ts e y the – H g Th 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 h Ch Kh h N ti ul , I , T g teful t g ss g Th y, M f h Nguy g Th h T h g H i, d L V , who support me significantly in the courses as well as in the thesis writing process Last, but not least, I would like to thank my friends and colleagues at Banking University of HCMC for their helps HCMC, November 2013 L H iii gL g ABBREVIATIONS AE Allocative efficiency CIEM Central Institute for economic mangement CRS Constant returns to scale DEA Data envelopment analysis DMU Decision making unit GSO General Statistics Office Of Vietnam SE Scale efficiency SFA Stochastic frontier analysis SMEs Small and medium sized enterprises TE Technical efficiency TFP Total-factor productivity VRS Variable returns to scale iv 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 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 v TABLE OF CONTENTS LIST OF TABLES ix LIST OF FIGURES x 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 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 vi 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 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 vii Appendix 2: Empirical studies on the relationship between business network and firm performance 68 Appendix 3: Empirical studies on the technical efficiency measurements of manufacturing firms in Vietnam 72 viii 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 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 ix The policies designed to help the SMEs is desirable For example, the above result represents that firms with higher debt ratio will gain more technical efficiency score than firms with lower debt ratio This indicates that policy should concentrate on the finance to the SMEs because their current debt ratio does not support to the production efficiency This result also indicates that accessing capital from banks is still difficult to SMEs In addition, business networking and firm technical efficiency are correlated but its impact varies As such, firm manager should pay attention to their industry characteristics and the firm position in the network to take advantage of the maximum benefits from the networking In some cases such as firms in Wood and Metal, firm manager should focus its resources to build quality relationship rather than accumulating the network size Lastly, the policy makers should focus on the actitivities of official business association Many empirical studies (Parker, 2008; Schoonjans et al., 2011) have concluded that the government business association plays crucial role in enhancing firm performance However, this role appears to be vargue in Vietnam 5.3 Limitations and recommendations for future research This study has several limitations which offer possibilities for futher research Firstly, despite employing business networking in three dimensions, the study cannot measure the position of firm in the network as well as the characteristics of the relationship, for example, exploitation or colloboration Secondly, the problem of data could be serious in some cases For example, the proportion of available data of Food and Beverages is only around 60%; as a consequence, the sample may not represent the population at a reasonable level Furthermore, the SMEs surveys depend considerably on the perception and the honesty of the respondents, which are out of control of this study Finally, the causality problem is also an interesting problem It means that this study can only provide the relationship between business networking and the technical efficiency but not the direction of this relationship Both theories and 58 empirical studies provide evidence that business networking can enhance or hinder the technical efficiency Nevertheless, firm with higher efficiency can gain more relationship is also feasible can should be considered These problems are expected to be resolved in the future research Further studies are advised to include the impact of network characteristics and the position of firm in the network structure The limitations of data can be overcome by a survey, which can concentrate only on the business networking and firm performance The last limitation also suggests a direction for the technique in a future research The causalitiy problem can be identified by an instrumental variable or a simultaneousequation regression In conclusion, despite of several limitations, it is expected that this study contributes significantly to the research flow of business networking and firm efficiency Furthermore, this research may well be in an early stage of research on the issues and a more intensive research in the future is also expected 59 REFERENCES Admassie, A., & Matambalya, F A (2002) 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(1995) Developing relationships in business networks Londres: Routledge Timmer, C P (1971) Using a probabilistic frontier production function to measure technical efficiency The Journal of Political Economy, 776-794 Vu, Q N (2003) Technical Efficiency of Industrial State‐Owned Enterprises in Vietnam Asian economic journal, 17(1), 87-101 Watson, J (2007) Modeling the relationship between networking and firm performance Journal of Business Venturing, 22(6), 852-874 Zhao, L., & Aram, J D (1995) Networking and growth of young technologyintensive ventures in China Journal of business venturing, 10(5), 349-370 64 Appendix 1: Empirical studies on the sources of technical efficiency No References Objectives Timmer (1971) (1) Introduce technique to measure technical efficiency (2) Apply the technique to the empirical of the US agriculture (3) Compare to the results to traditional estimations Pitt and Lee (1981) Estimate production function and investigate sources of technical efficiency in the Indonesian weaving industry Admassie & Matambaly a (2002) Examine the technical efficiency in SMEs of Tanzania and identify the sources of technical efficiency Data/Data level Methodology Key conclusion & Policies Data of 48 states of the US in years 1960-1967 First stage: using Cobb-Douglas production function Output (gross agricultural output) is regressed with input set (labor, capital, land, fertilizer, livestock variable and seed and miscellaneous) to identify the inefficiency index (from the noise of regression) Second stage: regressing the inefficiency with some variables proxied for off-farm days, government payments, ages of farm operators, education and tenant The number of tenants, government payments, age of farm head and the race (Negro or not) have statistical relationship with the TE 50 Indonesian weaving firms for years 1972, 1973 and 1975 Using Cobb-Douglas production function and random effect variance components model to identify technical efficiency indices and maximum likelihood estimation to identify the sources of efficiency Mean efficiency of weaving industry is from 60% to 70% Age, size and ownership are main sources of technical efficiency While age influences negatively on efficiency, size has positive impact on firm efficiency Cross-sectional data of a Tanzanian survey about SMEs, from 1999 to 2000 Includes148 firms in sectors: food, textile and tourism Measurement: - Output: yearly sales income - Input set: Capital stock; Labor cost (labor bill); Material (real value of raw materials, fuel, electricity and water) - Attributed variables: Age; Size (number of employees); Skill intensity of labor (average period to train labor); Skill intensity of management staff (average period to train management staff); Dummy for regional control Method: using maximum likelihood estimates to identify the parameters of Cobb-Douglas production function - Mean technical efficiency level is around more than 50% - Technical efficiency has link to age (negative impact), size (positive impact), human capital development 65 Nikaido (2004) Rios & Shively (2004) Muller (1974) Measure technical efficiency of small scale industry (SSI) and investigate the relationship between firm size and geographical agglomeration and efficiency index Applying nonparametric method to identify efficiency of smallholder coffee farm and the relationship between this score and farm size Re-evaluate the concept of technical efficiency and the relationship between information and technical efficiency Cross-sectional data of SSI in India in 1992, including 505 observations in 18 industries Methodology: using maximum likelihood estimates for CobbDouglas stochastic frontier model to identify efficiency indices and 2-limit Tobit regression to figure out the sources of efficiency - Mean technical efficiency level is about 80% - The agglomeration of firms impacts positively on firm efficiency, while the firm size has a negative impact on it 209 farmers in Daklak, Vietnam in 2004 Two-step methodology: First step: use DEA method to calculate technical efficiency and cost efficiency (inputs: nine types of fertilizers and pesticide, hired labor, family labor; output Second step: use two-limit Tobit model to regress the efficiency indices and some measures: farm size (dummy with small size coded and otherwise); operator education (schooling years); land tenure (proportion of hired land size), infrastructure of farm (number of pump, length of irritation pipe); access to credit (instrumental variable from probit model) Small farms suffer a lower technical efficiency and cost efficiency Large farms tend to be more efficient than small farms Credit accessibility has positive correlation with both technical efficiency and cost efficiency Sample of Californian dairy farms in 1964 and the data set enlarged throughout period from 1960 to 1963 Measurement: using proxies for information concept: DH (level of fees paid to a trade organization which provide information to members); FY (an index performing exposed information type variable); LP (an management index that seemed to be related with the relative production costs) Methodology: Using augmented Cobb-Douglas production function where information proxies are added Least square estimations are employed in log-linear form; then, the marginal impact of information variables is calculated from the regression results Both regression results from data set of 1964 and enlarged data set of 1960-1963 suggest the significant role of information for the production efficiency Adding information variables facilitates the production function when it can reduce the productivity differences 66 Binam, Sylla, Diarra & Nyambi (2003) Binam, Tonye, Nyambi & Akoa (2004) Examine the technical efficiency in small coffee farmers and identify the key determinants of technical efficiency 81 farmers in Vavoua - a lowincome zone in Côte d'Iv i e i 1998 Using DEA (under assumptions of CRS and VRS) in the first stage to identify technical efficiency indices Input set includes: Land (area); Age (years); Labor (worker per day); Tools value; Fertilizer Output includes coffee yield 2-limit Tobit model is employed in the second stage to regress TE with some key variables such as: Dummy for own management; Dummy for credit accessibility; Distance from house to farm; Dummy for education level; Age; Household size (number of household's members); Dummy for joining associations; Dummy for land tenure; Dummy for origin of farmer and Dummy for monocrop Examine the technical efficiency in small household farmers in Cameroon and identify the key determinants of technical efficiency 450 farmers in Cameroon's 15 agricultural villages in 2001/2002 crop season types of crop were surveyed: groundnut monocrop, maize monocrop and maize-groundnut farming First stage: using maximum likelihood estimates of CobbDouglass stochastic frontier function Output is measured in kg, while inputs include total land size, total labor and capital Second stage: using maximum likelihood estimates to identify the determinants of TE Key independent variables include Education (schooling years), Age (years), Dummy for credit access (1 if the farmer has a loan in the past year and otherwise), two proxies for social capital (Dummy for joining association, Dummy for extension contact) 67 Average TE of farmers is 36% (under the assumption of CRS) and 47% (under the assumption of VRS) Household size, age, participation in an association and the origin of farmer are key determinants of TE While young farmers are more technically efficient, large households appear to be less efficient Participation in an association is strongly statistically significant, but has negative impact on TE The average TE ranges from 73% to 75%, depended on the types of crop Sources of the TE include education, credit access, and association participation All of these factors have positive effect on the TE Appendix 2: Empirical studies on the relationship between business network and firm performance No References Gulati (1999) Portes (2000) Objectives Data/Data level Methodology Key conclusion & Policies Examine the importance of firm alliance and identify the determinants of alliance forming decision Interview 153 managers in 11 large multi-national companies to identify the key factors of alliance decision Data of firm: Firm level panel of firms from 1970 and 1989 Some measurements of networking are used: Cliques: number of cliques firm participate in; Closeness: Freeman measurement (1979) to represent how closeness of firm linkage with other firms; Experience: cumulative total of alliances firm joined until the previous year; dummy for alliance with for joining an alliance and otherwise Model: Forming probit model with the dependent variable being the probability of alliance participation Network resources can help to create new networks and long-term performance Methodology: meta-analysis the studies of Bourdieu (1979, 1980, 1985), Glen Loury (1977, 1981), Coleman (1988a, 1990) and others Definition of social capital (in the framework): "Ability to secure benefits through membership in networks and other social structures" Four sources of social capital: value introjection, bounded solidarity, reciprocity exchanges and enforceable trust Social capital can be extended from individuals to communities Positive effects of social capital: social capital plays the role as: (1) source of social control (2) source of family support (3) source of benefits through networks Negative consequences: (1)exclusion of outliers (2) excess claims on group Review the origin and definitions of social capital, classify the sources of social capital and examine both positive impact and negative impact of social capital on individuals and communities 68 members (3) restrictions on individual freedoms downward leveling norms Koka & Prescott, (2002) Conceptualize social capital as the network level Establish a multidimensional model of social capital and examine the social capital as a source of information (in dimensions of information: volume, diversity and richness) Develop the social capital theory focusing on the impacts of this information on the firm performance Population of 422 firms in 706 alliances in steel industry in America through the period of 19801994 Variables of social capital: measured by dimensions of information as following: (1) Information volume: Eigence: Eigence measure of centrality; Partner: total number of partners; Ties: total number of ties (2) Infor diversity: Holes: using network measure structural holes developed by Burt (1992, 1997); Country: nationality of partners; Tech: groups of sub-industries (3) Infor richness: CumAct: total number of years of alliance activities/total number of years of all of alliance activities in the network; Multiple: total number of multiplex partners/total number of partners; Repeated ties: total number of partners which have repeated activity/total number of partners Method: using Structural equation model (SEM) to confirm the validity of the social capital model Dependent variable is measured by sales per employee, which represent the productivity Using fix-effect model 69 The calculations the relative score of social capital for each firm shows that firms gain different benefits from social capital Some firms can change their strategy to gain different benefits from each dimensions of social capital The change of entire network to reach more information richness and information diversity rather than information volume The dimensions of social capital can influence firm performance differently and may be negatively Lechner, Dowling & Welpe (2006) Watson (2007) Examine the impact of the different networks on firm development Examine the relationship between SME performance (survival, growth and ROE) and networking 60 venture capitalfinanced firms in Germany, Austria and Switzerland in 2003 Forming a model of relational mix: (1) social networks: personal relationship (2) reputational networks: relationship with market leaders or highly respected firms (3) marketing information networks: relationship with firms providing market information (4) co-petition networks: relationship with competitors (5) co-operative technology networks: relationship with firm providing technology solution Network size is defined as the total number of network ties Therefore, the independent variables include: (1) Overall network size; (2) Network size of each types Two types of performance measurement: (1) time-to-break-even (2) sales (sales volume and sales growth) Different types of networks are more crucial to firm development In details: (1) Reputational network: moderate role in star-up phase (2) Co-operative technology network: weak significant negative relationship with time-to-breakeven (3) Social network: no direct effect on time-to-break-even, significant negative relationship with sales (4) Positive effects: marketing information networks, competitornetworks, technology partner Strength of the research: forming a relational mix that can measure many types of business network Weaknesses: (1) the paper is based on ownresearched data in a limited range of companies; (2) the reversed causality such as high growth firms can gain more relationships is not covered; (3) the network variables only proxy for network quantity rather than network quality 5014 Australian SMEs from 19951998 (three accounting years) Measurements of formal networks: bank, business consultant, accountants, associations, lawyers, tax offices Informal networks: family and friends, local businesses, others Measurements of network: network size (total number of each network types), network range and network intensity Firm demographic variables including: age, industry and size Dependent variables: Dummy for firm survival, Dummy for high/low growth and ROE (profitability) Logistic regression (firm survival is a dichotomous variable) is employed This paper showed the positive relationship between networking (particularly with formal network such as external accountants) and both firm survival and growth, but not ROE Network can have impact on both new venture firms and established firms Both informal network and formal 70 network influence firm survival, whereas only formal networks are associated with growth However, neither formal network nor informal network has relationship with ROE Parker (2008) Schoonjans et al (2011) Investigate the economics of formal business network including its benefit and cost to the firm performance and social welfare Identify the impact of formal business networking on SMEs' growth Range: 1992-2008 sub-samples Using mathematic formula under some assumption to examine the economics of formal business network Formal network enhance the efficiency of not only joining firms (efficiency means minimizing the waste), but also welfare of wider economy Formal business network influence consumer surplus in a weakly way Network could be collapse if firm attempt to grow its network over the equilibrium size The "free riders" can affect network negatively however they can also be punished by being eliminated from the alliance Formal networking is measured by PLATO, a dummy for joining in a government supported program Firm growth is measured by three proxies: net asset growth, added value growth and employment growth Regression using fixed effects estimation: Growth = f(size, age, size^2, age^2, size*age, Debt/Equity, Industry growth, PLATO) Formal networking is significantly positively correlated with net asset (2.5%) and added value growth (3.07%), while has no impact on employment growth Some limitations: (1) Ignore other networking (su lie s, e l yees…) (2) Dummy of networking cannot measure the quality of networking (3) The causality problem: business networking causes growth or growth helps to promote networking? 71 Appendix 3: Empirical studies on the technical efficiency measurements of manufacturing firms in Vietnam No References Data/Data level Chu & Kalirajan (2011) 1312 observations of manufacturing firms from 2000-2003 Data is extracted from Enterprise Data set (GSO) Key method TE results SFA - using Translog production function (1) Full-industry sample: 60.5% (2) Food & Beverages: 59.1% (3) Wood & Wooden products: 64.2% (4) Fabricated metal products: 55.7% Le (2010) 5204 observations of manufacturing SMEs in three surveys carried out in 2002-2007 SFA - using Cobb-Douglas production function and Translog production function (1) Full-industry sample: TE in 2002: 84.25% TE in 2005: 92.55% TE in 2007: 92.34% (2) Food & Beverages: TE in 2002: 80.43% TE in 2005: 90.87% Pham, Dao & Reilly (2010) 10.759 manufacturing firms in the Enterprise Data survey conducted in 2003 SFA - using Cobb-Douglas production function and Translog production function TE: 62% Minh & Vinh (2007) 1000 manufacturing firms in 23 industries in the period of 2000-03 DEA (1) Full-industry sample: TE (CRS): 43.8% TE (VRS): 47.5% (2) Food & Beverages: TE (CRS): 41.9% TE (VRS): 47.7% (3) Basic metals & fabricated metal products: TE (CRS): 44.7% TE (VRS): 46.6% Vu (2003) 164 State Owned Enterprises in the period of 1997 - 1998 SFA – using Cobb-Douglass production function TE in 1997: 78.8% TE in 1998: 78.9% 72