Valuing Social Capital: Shifting Strategies for Export Success of Vietnamese Small- and Medium-Sized Enterprises

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Valuing Social Capital: Shifting Strategies for Export Success of Vietnamese Small- and Medium-Sized Enterprises Huong Thi Xuan NGUYEN Victoria Institute of Strategic Economic Studies Victoria University, Melbourne, Australia Submitted in fulfilment of the requirement of the degree of Doctor of Philosophy February 2018 ABSTRACT This study examines the impact of social capital on the export success of domestic SMEs in Vietnam, looking at both export propensity (whether they export at all) and export performance (how much they export) Two stylized facts inspire the research The first is the relatively modest performance of Vietnamese SMEs in export activities to date, despite their potential and increasing importance in the economy The second is the widespread perception within Vietnam about the power of social relationships of firms in the Vietnamese business environment This study uses a mixed methods approach, combining a qualitative thematic analysis of semi-structured interviews of SME owners/managers with quantitative testing of hypotheses on a secondary panel dataset from 2007 to 2015 Logistic regression models were employed to test hypotheses related to export propensity and multiple linear regressions were used to test those related to export performance The results indicate that social capital is positively related to export propensity of domestic SMEs, but that its impacts on their export performance are not consistent Each type of network impacts differently on export performance indicators of the studied SMEs (positive, negative and sometimes non-significant) Similarly, the qualitative study provides evidence that social capital supports SMEs in the initial stage of their export venture, but not their continuous export sustainability One of the significant findings to emerge from the qualitative study is that the impact channels of social capital on SMEs’ export success may have changed from relying on rent-seeking opportunities provided by close connections with authorities towards being a tool for improving credibility and building capability for SMEs This study highlights the importance of establishing an appropriate networking strategy for SMEs who wish to pursue export ventures It suggests that, if SMEs aim at long-term export success, they may need to diversify their networks, including business networks and social networks, both domestically and internationally, rather than focus on connections with politicians and authorities This is because the expected benefits from rent appropriation in export activities will eventually diminish as a more transparent system is put in place Hence, SMEs should utilize their networks to improve their knowledge, credibility and capability, which help in enhancing their long-term competitiveness The present study also implies that the government and relevant trade ii associations, in supporting SMEs to participate successfully in the international market, may need to focus on long-term network building and capacity-building activities iii PUBLICATIONS INTEGRATED IN THE THESIS Some sections of this thesis have been presented at a conference and a research symposium: Referred International Conference Presentation Nguyen, TXH., Le, V 2017, ‘Network Ties, Informal Fees and Export Propensity of Vietnamese SMEs’, paper presented at Australia and New Zealand International Business Academy (ANZIBA) Annual Conference, 17-19 February 2017, University of South Australia, Adelaide, Australia International Research Symposiums Nguyen, TXH 2017, ‘The changing impacts of social capital on export propensity of Vietnamese SMEs – an exploratory study from an institutional approach’, extended abstract presented at the Academy International Business -Australia and New Zealand Chapter (AIB-ANZ) Research Symposium, 10-11 November 2017, the University of Otago, Dunedin, New Zealand v ACKNOWLEDGEMENTS For the completion of this research, I have received tremendous support and continuous encouragement from many special people Hence, I would like to take this opportunity to sincerely express my gratitude to those who have assisted me in this most challenging yet rewarding journey I wish to firstly express my deepest gratitude to my principal supervisor, Professor Adam Fforde, for his dedicated supervision, invaluable guidance, detailed comments, practical suggestions, constructive feedback and most importantly his continuous encouragement Several times during my journey, when I lost my confidence, and my own belief almost failed to carry me, he lifted me up by his strong determination that my work does matter, and that academic scholarship should be a journey to extend one’s own limit This was one of the major empowerments for me to complete my thesis I especially wish to show my heartfelt thanks to my co-supervisor, Professor Peter Sheehan, for his scholarly advice, invaluable encouragement, and continuous feedback He has provided me with his generous assistance, always prepared to listen to my troubles He persuasively taught me the discipline of doing academic research I could not have asked for more supportive and considerate supervision My greatest appreciation also goes to the Australian Government, Department of Education and Training for granting me the Prime Minister’s Australia Asia Scholarship This prestigious scholarship has provided me with a wonderful opportunity to undertake my PhD study and to pursue my academic endeavour I am thankful for the generous financial sponsorship of the Endeavour Postgraduate Award and the dedicated support from the scholarship administrative team I would like to extend my appreciation to Dr Viet Le, Swinburne University, for his valuable input at the early stage of my research; to Dr Sidney Lung and Dr Masha Fridman of Victoria University for their statistics advice; and to Dr Bradley Smith for his meticulous and professional editing services, according to the university-endorsed ‘Guidelines for Thesis Editing’ I would like to send my sincere thanks to all the research participants, without whose voluntary participation this research would not have been possible My appreciation also goes to the Victoria Institute of Strategic Economic Studies, Victoria University for administrative and technical support throughout my research work vi I am indebted to the unconditional love and support of my parents, who instilled in me the eternal appreciation for knowledge and the attitude of trying and thriving This thesis would not have been completed without my parents’ strongest belief in my success Deep gratitude also goes to my parents-in-law and my siblings for their emotional support during my study away from home My sincerest thanks are extended to my friends and their families for helping me and my family in all aspects of our life in Australia, so that I can concentrate and complete my study Last but not least, I owe my loving thanks to my husband, Minh Hieu Tran, who made enormous sacrifices in his career to support my study in Australia I am fortunate to have the endless inspiration from my sweetest daughter, Linh Tran, who insists that I ‘read as a writer and write as a reader’ Her positive attitude, enjoyment and happiness help me fight the stress I also have an untold amount of empowerment from my cutest little son, Xuan Son Tran, who expresses curiosity about everything around him, including the question of when can mum become an alumnus of Victoria University I am wholeheartedly thankful to your love, your tolerance and your continuous encouragement during my journey vii TABLE OF CONTENTS ABSTRACT II DECLARATION OF AUTHENTICITY IV PUBLICATIONS INTEGRATED IN THE THESIS V ACKNOWLEDGEMENTS VI TABLE OF CONTENTS VIII LIST OF TABLES XVI LIST OF FIGURES XVIII LIST OF ACRONYMS XIX EXECUTIVE SUMMARY XX CHAPTER 1: INTRODUCTION 1.1 Motivation and background of the research 1.2 Research objectives and research questions 1.3 Positioning of the study 1.4 Theory, practice and policy contributions 1.5 Research design and methodology 11 1.5.1 Research paradigm 11 1.5.2 Research method 11 1.6 Structure of the research 12 CHAPTER 2: VIETNAMESE SMES IN THE ERA OF REFORMS 16 2.1 Introduction 16 2.2 Economic reforms and international integration 16 2.2.1 Country overview 16 2.2.2 Trade liberalization process 18 2.2.2.1 Trading right reform 20 2.2.2.2 International economic integration 20 2.2.2.3 Trade policies reform 24 2.3 Overview of the institutional context 26 2.3.1 Formal institutional context 26 viii 2.3.2 Informal institutional context 26 2.3.2.1 Collectivism culture in the rise of individualism 27 2.3.2.2 The emergence of the new crony capitalism 28 2.3.2.3 Bribery and corruption 29 2.4 Vietnamese SMEs in the trade reform era 29 2.4.1 The development of SMEs in Vietnam 29 2.4.2 Overview of SME exports 32 2.5 Chapter summary 34 CHAPTER 3: LITERATURE REVIEW 35 3.1 Introduction 35 3.2 Export performance 36 3.2.1 Overview of research on export performance 36 3.2.2 Export performance concept 37 3.2.3 Export performance determinants 38 3.2.4 Export performance measurements 40 3.2.4.1 The consolidated measurement model 40 3.2.4.2 The contingency approach 42 3.3 Export propensity 44 3.4 Social capital 46 3.4.1 Social capital concept 46 3.4.1.1 The development of the social capital concept 46 3.4.1.2 The three “pillars” of SC: Bourdieu, Coleman and Putnam 47 3.4.1.3 Nahapiet and Ghoshal’s formulation of SC 51 3.4.2 Social capital dimensions 51 3.4.2.1 Structural dimension 52 3.4.2.2 Relational dimension 53 3.4.2.3 Cognitive dimension 54 3.4.3 Social capital measurement 55 3.4.4 Operationalization of social capital: Social network ties 56 3.5 Social networks theory and the resource-based view 58 3.6 Social capital and internationalization process 58 3.7 Social capital and export performance 62 3.8 Chapter summary 64 ix CHAPTER 4: RESEARCH METHODOLOGY 67 4.1 Introduction 67 4.2 Mixed methods research design 68 4.3 Research setting and variable selection 69 4.3.1 Research setting 69 4.3.2 Variables, constructs and concepts 71 4.4 Qualitative methodology approach 72 4.4.1 Rationale for the selection of research setting and data source 73 4.4.2 Data collection method 74 4.4.3 Data collection procedure 75 4.4.3.1 Development of questionnaire and interview protocol 76 4.4.3.2 Data collection 78 4.4.4 Data analysis procedure 80 4.4.5 Ethical considerations 82 4.4.5.1 Ethics application 82 4.4.5.2 Anonymization and pseudonymization 83 4.4.6 Validity and reliability issues 83 4.5 Quantitative methodology approach: general features 84 4.5.1 Data source 85 4.5.2 Sampling and data collection methodology 86 4.6 Export propensity model 87 4.6.1 Data screening 87 4.6.1.1 Exclusion of big firms from the sample 87 4.6.1.2 Treatment of missing data 87 4.6.2 Method of estimation 88 4.6.2.1 Binary logistic regression 88 4.6.2.2 Binary logistic regression for panel data 89 4.7 Export performance 90 4.7.1 Data source 90 4.7.2 Data screening 91 4.7.3 Method of estimation 92 4.7.3.1 Multiple linear regression 92 4.7.3.2 Pooled OLS regression models 93 x Appendix 6: Robustness check for export propensity models Appendix 6.1: Regression results of Probit model VARIABLES 2.Firm_size3a 3.Firm_size3a 4.Firm_size3a Location Ownership D_sector1 D_sector2 D_sector3 D_sector4 (1) Coefficients (2) Coefficients (3) Coefficients (4) Coefficients 1.042*** (0.0775) 1.860*** (0.102) 1.896*** (0.367) 0.0968 (0.0722) 0.527*** (0.0728) 0.271*** (0.0922) 0.442*** (0.102) 0.441*** (0.109) 1.075*** (0.104) 1.063*** (0.0806) 1.839*** (0.106) 1.889*** (0.364) 0.154** (0.0742) 0.520*** (0.0747) 0.331*** (0.0946) 0.427*** (0.104) 0.456*** (0.110) 1.064*** (0.105) -0.285*** (0.0789) 0.0865 (0.0786) 0.207*** (0.0540) 0.0369 (0.0496) 0.0403** (0.0190) 0.937*** (0.0827) 1.661*** (0.109) 1.595*** (0.367) 0.0765 (0.0756) 0.390*** (0.0768) 0.368*** (0.0961) 0.452*** (0.105) 0.459*** (0.112) 1.099*** (0.108) -0.330*** (0.0819) 0.0999 (0.0810) 0.219*** (0.0550) 0.00318 (0.0510) 0.0436** (0.0195) 0.417*** (0.113) 0.544*** (0.114) 0.874*** (0.124) -2.617*** (0.0854) -2.343*** (0.154) -2.569*** (0.170) 0.924*** (0.0837) 1.621*** (0.111) 1.489*** (0.369) 0.0863 (0.0772) 0.390*** (0.0772) 0.380*** (0.0969) 0.459*** (0.105) 0.441*** (0.113) 1.098*** (0.108) -0.332*** (0.0829) 0.0873 (0.0819) 0.218*** (0.0581) -0.00429 (0.0515) 0.0533*** (0.0200) 0.401*** (0.114) 0.530*** (0.115) 0.827*** (0.125) 0.228** (0.107) 0.150** (0.0709) 0.0852 (0.0886) -0.00632 (0.0746) -2.630*** (0.178) ln_business ln_social ln_bank ln_pol ln_res_all 2.Ex_kngedge3 3.Ex_kngedge3 4.Ex_kngedge3 D_newprod D_improd D_tech D_RDinvest Constant Observations 5,791 5,791 5,791 Standard errors in parentheses; *** p chi2 0.0000 0.0000 0.0000 0.0000 0.0000 Pseudo R2 0.4073 0.3622 0.3373 0.2992 0.4298 VARIABLES Firm_size (2) Firm_size (3) Firm_size (4) Ownership Sector (1) Sector (2) Sector (3) Sector (4) Business networks Social networks Bank networks Political networks Firm knowledge (2) Firm knowledge (3) Firm knowledge (4) Note: Standard errors in parentheses; *, ** and *** denotes significance at 10%, 5% and 1% levels, respectively Page 253 MODEL (2007) Marginal effects (2009) Marginal effects (2011) Marginal effects (2013) Marginal effects (2015) Marginal effects 0.0676*** (0.0161) 0.169*** (0.0489) 0.0533 (0.0651) 0.0368*** (0.0124) 0.0270 (0.0166) 0.0204 (0.0186) 0.0559*** (0.0190) 0.0639*** (0.0161) 0.0674*** (0.0165) 0.171*** (0.0403) 0.0967*** (0.0232) 0.201*** (0.0554) 0.0158 (0.0139) 0.0315* (0.0179) 0.0118 (0.0195) 0.0184 (0.0201) 0.0989*** (0.0193) 0.109*** (0.0252) 0.299*** (0.0637) 0.0882 (0.121) 0.0224 (0.0151) 0.0417** (0.0181) 0.0364* (0.0211) 0.0377* (0.0197) 0.123*** (0.0217) 0.0294* (0.0158) 0.0398* (0.0205) 0.0462** (0.0207) 0.0188 (0.0254) 0.104*** (0.0203) 0.135*** (0.0293) 0.394*** (0.0926) 0.538* (0.275) 0.0425*** (0.0142) 0.0214 (0.0190) 0.0669*** (0.0185) 0.0421* (0.0215) 0.0753*** (0.0211) Observations 0.00521 (0.0124) -0.00953 (0.0126) 0.00317 (0.00251) 0.000170 (0.00137) -0.00626 (0.0155) 0.0304* (0.0174) 0.0501** (0.0214) 0.0168 (0.0183) 0.0288** (0.0139) -0.0132 (0.0130) 0.0224* (0.0119) 1,166 -0.0220 (0.0163) -0.000990 (0.0165) 0.00263* (0.00156) 0.000233 (0.00105) 0.0601*** (0.0151) 0.0574*** (0.0124) 0.106*** (0.0220) 0.0342 (0.0273) 0.0286** (0.0133) 0.00691 (0.0140) -0.00890 (0.0132) 1,164 -0.0632*** (0.0175) 0.0324* (0.0175) 0.00261 (0.00176) 0.00310* (0.00185) 0.0267 (0.0178) 0.0417** (0.0186) 0.0640** (0.0252) -0.0522 (0.0369) 0.0154 (0.0137) 0.0331** (0.0162) -0.0121 (0.0149) 1,162 -0.0517** (0.0201) 0.0205 (0.0196) 0.00493* (0.00298) -0.00216 (0.00331) 0.0182 (0.0169) 0.0567*** (0.0192) 0.118*** (0.0296) 0.0857 (0.0593) 0.0241 (0.0158) -0.00633 (0.0241) -0.0238 (0.0150) 1,165 -0.0151 (0.0161) 0.00997 (0.0152) 0.000999 (0.00320) -0.000305 (0.00269) 0.0369* (0.0210) 0.0309 (0.0208) 0.0481* (0.0264) 0.0164 (0.0137) -0.0151 (0.0184) 0.0523** (0.0205) 0.00339 (0.0145) 1,131 Log likelihood LR chi2 Prob> chi2 Pseudo R2 -133.983 199.08 0.0000 0.4263 -164.369 200.52 0.0000 0.3789 -186.563 204.15 0.0000 0.3536 -218.004 197.77 0.0000 0.3121 -167.978 267.48 0.0000 0.4433 VARIABLES Firm_size (2) Firm_size (3) Firm_size (4) Ownership Sector (1) Sector (2) Sector (3) Sector (4) Business networks Social networks Bank networks Public officials network Firm knowledge (2) Firm knowledge (3) Firm knowledge (4) Newproduct Product improvement New technology R&Dinvestment Note: Standard errors in parentheses; *, ** and *** denotes significance at 10%, 5% and 1% levels, respectively Page 254 Appendix 6.4: Models specification Model Hosmer-Lemeshow chi2 Prob> chi2 AIC BIC 2007 5.57 0.5912 306.6873 357.3007 2009 5.22 0.6337 392.4153 437.9518 2011 3.47 0.9012 425.4236 476.0025 2013 3.41 0.8447 495.5681 541.1124 2015 5.13 0.6440 379.6751 430.2627 Avg 4.56 0.72296 399.95388 448.52602 2007 5.06 0.7507 308.3481 384.2682 2009 7.42 0.4925 359.2946 428.2804 2011 9.93 0.2700 400.8347 475.6747 2013 4.93 0.7645 440.9811 510.5318 2015 4.89 0.7690 322.1936 395.6137 Avg 6.446 0.60934 366.33042 438.87376 2007 12.36 0.1358 308.8318 389.8132 2009 4.45 0.8147 367.5772 443.4715 2011 4.90 0.7680 414.5625 495.4889 2013 8.26 0.4089 474.1656 550.0728 2015 8.38 0.3968 376.0651 456.5588 Avg 7.67 0.50484 388.24044 467.08104 2007 10.09 0.2587 309.4945 405.6599 2009 3.38 0.9086 365.1978 456.2709 2011 5.93 0.6546 411.795 507.8951 2013 8.33 0.4017 474.5572 565.6458 2015 5.38 0.7167 374.0108 469.5971 Avg 6.622 0.58806 387.01106 481.01376 Model Hosmer-Lemeshow chi2 Prob> chi2 AIC BIC Model Hosmer-Lemeshow chi2 Prob> chi2 AIC BIC Model Hosmer-Lemeshow chi2 Prob> chi2 AIC BIC Page 255 Appendix 6.5: Estimation results of logit model on individual datasets BASELINE MODEL VARIABLES 2.Firm_size3a 3.Firm_size3a 4.Firm_size3a Location Ownership D_sector1 D_sector2 D_sector3 D_sector4 (2007) Marginal effects (2009 Marginal effects (2011) Marginal effects (2013) Marginal effects (2015) Marginal effects 0.0690*** (0.0108) 0.251*** (0.0362) 0.286** (0.130) 0.0257*** (0.00897) 0.0409*** (0.00842) 0.0335*** (0.0112) 0.0485*** (0.0104) 0.0320** (0.0146) 0.0781*** (0.0135) 0.0862*** (0.0125) 0.254*** (0.0364) 0.290** (0.145) 0.0154* (0.00904) 0.0422*** (0.00886) 0.0294** (0.0121) 0.0460*** (0.0109) 0.0291* (0.0155) 0.102*** (0.0137) 0.0830*** (0.0132) 0.281*** (0.0398) 0.338** (0.137) 0.0187** (0.00940) 0.0459*** (0.00937) 0.0413*** (0.0117) 0.0528*** (0.0115) 0.0412*** (0.0149) 0.110*** (0.0159) 0.0983*** (0.0151) 0.251*** (0.0393) 0.154 (0.127) 0.00878 (0.00961) 0.0394*** (0.00990) 0.0135 (0.0128) 0.0527*** (0.0121) 0.0190 (0.0166) 0.107*** (0.0146) 0.123*** (0.0169) 0.371*** (0.0461) 0.311** (0.121) 0.00382 (0.00924) 0.0383*** (0.00953) 0.0196 (0.0125) 0.0642*** (0.0112) 0.0205 (0.0170) 0.0818*** (0.0146) 2,657 2,550 2,573 2,643 Observations 2,622 Standard errors in parentheses *** p
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