E readnesse valuation at medium and large enterprises in thai nguyen province vietnam

E-READINESS EVALUATION AT MEDIUM AND LARGE ENTERPRISES IN THAI NGUYEN PROVINCE, VIETNAM A DISSERTATION PAPER Presented to the Faculty of the Graduate Program of the College of Business and Accountancy Central Philippine University, Philippines in Collaboration with Thai Nguyen University, Vietnam In Partial Fulfillment of the Requirements for the Degree Doctor in Business Administration By TRAN CONG NGHIEP DECEMBER 2020 i ACKNOWLEDGEMENTS I would like to take this opportunity to express my thanks to those who helped me with various aspects of conducting research and the writing of this thesis First and foremost, Dr Lee Song Kun for his guidance, patience and support throughout this research and the writing of this thesis His insights and words of encouragement have often inspired me and renewed my hopes for completing my graduate education I would also like to thank my family for encouraging and supporting me to finish my dissertation I would additionally like to thank my friends and staff at TUEBA who help and continuously encouraged me to complete the work ii DECLARATION OF AUTHORSHIP I, TRAN CONG NGHIEP, declare that this dissertation titled, “E-Readiness Evaluation at Medium and Large Enterprises in Thai Nguyen Province, Vietnam” and the work presented in it are my own I confirm that: • This work was done wholly or mainly while in candidature for a research degree at this University • Where any part of this thesis has previously been submitted for a degree or any other qualification at this University or any other institution, this has been clearly stated • Where I have consulted the published work of others, this is always clearly attributed • Where I have quoted from the work of others, the source is always given With the exception of such quotations, this thesis is entirely my own work • I have acknowledged all main sources of help • Where the thesis is based on work done by myself jointly with others, I have made clear exactly what was done by others and what I have contributed myself iii ABSTRACT The main objectives of the study are to measure the e-readiness level at medium and large enterprises, and analyze factors affecting e-readiness and propose solutions for those enterprises to develop their strategy to enhance the level of e-readiness of medium and large enterprises in Thai Nguyen province To accomplish the research objectives of the thesis, the researcher focused on the following specific objectives Firstly, by systematizing the tools for measuring e-readiness, a simple tool modified from the Verify End-user e- Readiness using a Diagnostic Tool (VERDICT) was proposed Secondly, the profile of the large and medium enterprises in Thai Nguyen province as well as the e-readiness of the surveyed firms using the proposed tool were described The significant difference of e-readiness level, Perceived Organization E-Readiness and Perceived Environmental E-Readiness based on profile of the enterprises such as size, industry, years in business, ownership and the e-readiness level were determined and the factors affecting the e-readiness level of the large and medium enterprises also were identified Data were collected at 102 large and medium enterprises randomly chosen from 132 large and medium enterprises in Thai Nguyen province Pearson correlation analysis shows that Perceived Organizational E-Readiness has positive relationship to ereadiness of enterprise and Perceived External E-Readiness has positive relationship to e-readiness of enterprise and Linear regression analysis shows that both internal factors and external factors are significant affecting the e-readiness of enterprises Other factors like firm size, firm age, ownership, industry sector is not significant iv Contents Acknowledgements Declaration of Authorship Abstract i ii iii PROBLEM AND ITS SETTING 1.1 Background and Rationale of the Study 1.2 Objectives 1.2.1 General Objective 1.2.2 Specific Objectives 1.3 Hypotheses 1.4 Theoretical Framework 1.5 Conceptual Framework 12 1.6 The Operational Definitions 14 1.6.1 Dependent Variable: E-readiness 14 1.6.2 Independent Variables 22 1.6.3 Antecedent Variables 24 1.7 Significance of the Study 25 1.8 Scope and Delimitation 26 1.9 Organization of the Dissertation 27 REVIEW OF RELATED LITERATURE AND STUDIES 29 2.1 Literature Review 29 2.1.1 Macro e-readiness Assessment 29 2.1.2 Micro e-readiness Assessment 30 v 2.2 Overview of e-readiness Assessment tools 35 2.1.4 Vietnam ICT Index 42 2.1.5 Factors Influencing e-readiness 43 Empirical related studies 45 RESEARCH METHODOLOGY 51 3.1 Research Design 51 3.2 Population, Sample Size and Sampling Technique 52 3.3 Research Instruments 53 3.3.1 Evaluation the Scale Reliability 54 3.3.2 Explore Factor Analysis (EFA) 59 Data Gathering Procedures 62 3.4.1 Secondary Data 62 3.4.2 Primary Data 62 Data Processing and Data Analysis 63 3.5.1 Descriptive Statistics 63 3.5.2 Factor Naming and Model Modification 63 3.5.3 Verbal Interpretation 64 3.5.4 Testing the Hypothesizes 64 3.5.5 Regression Analysis 65 3.4 3.5 2.1.3 DATA PRESENTATION, ANALYSIS AND INTERPRETATION 68 4.1 Characteristics of Survey Sample 68 4.2 Results of e-readiness Assessment of Enterprises in Thai Nguyen Province 70 4.3 The Perceived E-readiness 74 4.4 Hypothesis Testing 77 4.5 Factors Affecting E-readiness Level of Large and Medium Enterprises 84 4.5.1 The Basic Model 85 4.5.2 The Extended Model with Control Variables as Dummy Variables 86 vi 4.5.3 The Final Model 88 SUMMARY, CONCLUSION AND POLICY RECOMMENDATIONS 90 5.1 Summary of Findings 90 5.2 Conclusions 93 5.3 Recommendation 94 5.4 Limitations of the Study and Scope for Future Research 96 REFERENCES A PREPARED QUESTIONNAIRE 97 102 B E-READINESS IN DIMENSIONS AND OVERALL E-READINESS AT SURVEYED ENTERPRISES 117 C CRONBACH’S ALPHA 122 D ROTATED COMPONENTS MATRIX 125 E POST-HOC ANALYSIS FOR SIGNIFICANT DIFFERENCES 127 vii List of Figures 1.1 Model of E-Commerce by Molla et all 2005a 12 1.2 Conceptual framework for determining factors affecting e-readiness 13 4.1 Surveyed firms by Business Industry 69 4.2 Surveyed firms by Ownership 70 4.3 Overall Level of e-readiness at Enterprises in Thai Nguyen Province 71 4.4 Average e-readiness Level of Firms Grouped by Industries 73 4.5 Average E-readiness of Enterprises Grouped by Business Age 74 viii List of Tables 3.1 List of indicators to measure e-readiness 55 3.2 List of indicators to assess factors affecting e-commerce 56 3.3 Cronbach’s Alpha of the questionnaire to measure e-readiness 58 3.4 KMO and Bartlett’s Test 61 3.5 Verbal Interpretation of POER anf PEER 64 4.1 Profile of surveyed companies 69 4.2 Average e-readiness of Enterprises Grouped by Ownership 71 4.3 Average e-readiness of Enterprises grouped by industries 72 4.4 Average e-readiness of enterprises grouped by size 73 4.5 Average e-readiness of Enterprises grouped by years in business 73 4.6 Arevage perceived e-readiness grouped by industries 75 4.7 Perceived e-readiness grouped by years in business 75 4.8 Perceived e-readiness grouped by ownership and by size 76 4.9 T-test and ANOVA for significant difference e-readiness interms of firm profile 77 4.10 Post-hoc analysis of significant difference of e-readiness in terms of profile 78 4.11 Test for significant difference of POER, PEER interms of profile 80 4.12 Post-hoc Analysis difference of POER, PEER in terms of profile 81 4.13 Pearson corelation of e-readiness and PEOR, PEER 84 4.14 The ANOVA of the basic regression model 85 4.15 Coefficients of basic model 85 4.16 Coefficients of regression with firm size as dummy 86 4.17 Coefficiens of regression with business age as dummy variable 87 4.18 Coefficients of regression with industry as dummy 88 ix 4.19 Regression coefficients with types of ownership as dummy variables 89 B.1 E-Readiness in dimensions of the surveyed enterprises 117 C.1 Cronbach’s Alpha of Tool to Measure E-Readiness (27 items) 122 D.1 Rotated Component Matrix For E-Readiness Measure 125 D.2 Rotated Component Matrix For POER And PEER 126 E.1 Post-Hoc Alanisys for significant Differences of E-readiness and POER PEER based on firm porfile 127 119 Continuation of Table B.1 Indus Age Own Size Tech Manag Peop Proc s Eread Level Man M10 Col M 2.86 3 3.2 3.02 Con M10 Col M 1.6 3.2 2.8 2.4 Man M10 Col M 3 2.5 Man M10 Col L 3.4 3.2 3.6 3.3 Tra M10 Col M 2.6 2.4 2.8 2.45 Man M10 Col M 3.2 3.4 3.6 3.83 3.51 Min 5-10 Col L 2.2 2.6 2.8 2.4 Ret M10 Col M 2.6 2.8 2.85 Con M10 Col M 2.4 1.8 2.3 Con M10 Col L 2.8 2.2 2.2 2.55 Con 5-10 Col L 2.7 2.6 2.2 2.63 Min M10 Col L 2.6 2.8 2.8 2.8 Man 5-10 Col L 3.5 2.5 3.33 3.08 Man 5-10 Sta L 2.8 2.75 3.2 2.94 Ret 2-5 Pri M 2.2 2.8 2.5 Man 2-5 Col M 3.88 3.63 4.17 3.67 Man 2-5 Col M 2.25 2.2 2.8 2.4 2.41 Man 2-5 FDI L 4.25 3.75 4.3 4.08 Man S2 FDI M 4.88 4.25 4.5 4.41 Man S2 FDI L 4.5 4.63 4.2 4.33 Who M10 Col M 2.13 3 2.8 2.73 Con 5-10 FDI L 3.63 3.6 3.67 3.72 Con 5-10 Sta L 4.25 4.75 4.8 4.67 4.62 Con 2-5 Sta M 2.5 2.13 2.6 2.83 2.51 Man M10 Col M 4.25 4.5 4.2 4.33 4.32 Con 2-5 Sta M 2.38 2.5 2.8 2.67 120 Continuation of Table B.1 Indus Age Own Size Tech Manag Peop Proc s Eread Level Man M10 Col L 4.75 4.8 4.67 4.8 Man 5-10 Col L 3.13 3.83 3.49 Man M10 Pri L 3.13 3.25 3.6 3.49 Tra M10 Pri M 2.3 2.6 2.2 2.53 Man 5-10 Col L 3.2 2.8 Man 5-10 Col L 2.5 2.3 3 2.7 Con 5-10 Col M 1.8 2.2 2.17 2.29 Man 5-10 Col L 2.88 3.13 3.2 3.17 3.09 Ret 5-10 Pri M 3.13 2.8 2.17 2.77 Con M10 Col M 1.8 2.88 2.17 2.46 Man M10 Sta L 2.68 2.2 3.2 2.77 Man M10 Pri M 2.3 2.38 2.8 2.62 Who M10 Col L 2.8 2.63 2.4 2.71 Con M10 Col M 2.4 2.5 1.8 2.43 Who M10 Pri L 2.8 2.75 2.6 2.79 Con M10 Col M 1.8 2.6 3.2 2.33 2.48 Min M10 Col L 2.6 2.63 1.83 2.51 Man 5-10 Col L 2.8 2.75 3 2.89 Man M10 Col M 2.25 2.5 3 2.69 Man 2-5 Col M 2.4 2.63 2.83 2.71 Man 5-10 Pri L 2.13 2.4 2.5 2.26 Man 5-10 Col M 2.34 2.88 3.83 3.01 Who 5-10 Sta L 2.75 2.69 Ser 2-5 Col M 2.2 2.05 Man 5-10 FDI M 3.75 3.75 3.2 4.17 3.72 Man 2-5 FDI M 4.25 4.75 4.2 4.55 121 Continuation of Table B.1 Indus Age Own Size Tech Manag Peop Proc s Eread Level Man 2-5 FDI M 4.25 4.75 4.8 4.67 4.62 Man 2-5 FDI L 2.5 2.13 2.6 2.83 2.51 Man 2-5 FDI M 4.25 4.5 4.2 4.33 4.32 Man 2-5 FDI L 2.38 2.5 2.8 2.67 Man 2-5 FDI L 4.8 4.75 4.8 4.67 4.75 Man 2-5 FDI L 2.75 2.8 2.89 Man 2-5 FDI L 3.88 4.13 4.2 4.4 4.15 Ser S2 FDI M 2.63 3.6 2.81 Man 2-5 FDI L 4.88 4.88 3.2 4.83 4.45 Man S2 FDI M 3.13 4.83 3.74 Man M10 Col M 3 3.6 3.2 3.2 Man 2-5 Col L 3.2 3.4 3.2 3.2 End of Table 122 Appendix C CRONBACH’S ALPHA CRONBACH’S ALPHA FOR THE TOOLS TO MEASURE E-READINESS AND PERCEPTION TABLE C.1: Cronbach’s Alpha of Tool to Measure E-Readiness (27 items) Variables Corre r Alph T1 Kind of business application installed 0.792 0.897 T2 Portion of digitized data 0.663 0.908 T3 Type of network 0.751 0.9 T4 Frequency of website update 0.791 0.897 T5 Online advertising 0.766 0.899 T6 Market information seeking 0.496 0.919 T7 Customer order online 0.718 0.904 T8 Security solution 0.793 0.897 M1 Management has IT policy 0.841 0.945 M2 Management planning to implement e-commerce 0.897 0.941 M3 Management awareness on benefit of network 0.864 0.943 M4 Management vision on e-commerce 0.899 0.942 M5 Management vision on ICT equipment 0.911 0.941 M6 Management support on e-commerce 0.859 0.945 0.86 0.944 Technology Management M7 Frequency of Hardware upgrade 123 Continuation of Table B.1 Variables Corre r Alph 0.575 0.967 Pe1 Availability of professional labors 0.688 0.807 Pe2 Employees’ ICT literacy 0.723 0.798 Pe3 Staff Experience with web-based application 0.598 0.831 Pe4 Organizational culture support e-commerce 0.637 0.821 Pe5 Human Resource supportive to e-commerce 0.637 0.821 Pr1 Level of process analysis 0.893 0.935 Pr2 Level of organization adapt to change 0.815 0.942 Pr3 Flexibility encouragement at work 0.783 0.946 Pr4 Identification of bottleneck in business process 0.853 0.938 Pr5 Identification of inefficient in business process 0.867 0.937 Pr6 Business process flexibility to accommodate with 0.881 B1 Believe website promote firm 0.815 0.967 B2 Believe network help doing business effectively 0.841 0.966 B3 Believe investment in computer application is effective 0.821 0.967 B4 Believe e-commerce helps cut cost 0.827 0.967 B5 Believe e-commerce helps expand market share 0.809 0.967 0.78 0.967 0.788 0.967 0.85 0.966 B9 Believe power supply effect on implementing e-commerce 0.647 0.969 B10 Quality of Network effect on implementing e-commerce 0.829 0.967 B11 Believe Internet cost effect on implementing e-commerce 0.689 0.968 M8 Frequency of Software upgrade People process Perceived E-readiness B6 ICT staff contribute effectively to implementing e-commerce B7 Believe management interest in e-commerce B8 Believe organization has strong relationship with customers 124 Continuation of Table B.1 Variables Corre r Alph B12 Believe software cost effect on implementing e-commerce 0.695 0.968 B13 Believe Organization understanding e-commerce model 0.772 0.967 B14 has necessary technical and human resource for e-commerce 0.832 0.967 B15 Believe Customers ready for e-commerce 0.812 0.856 B16 Believe Partners ready for e-commerce 0.753 0.86 B17 Local technology infrastructure ready for e-commerce 0.758 0.862 B18 Law environment enough for e-commerce 0.773 0.861 B19 Government encourage e-commerce 0.621 0.881 B20 Government support ICT and e-commerce implementation 0.597 0.884 125 Appendix D ROTATED COMPONENTS MATRIX TABLE D.1: Rotated Component Matrix For E-Readiness Measure Item T1 Kind of busin App installed T2 Portion of digitized data T3 Type of network T4 Frequ of web update T5 Online advertising T6 Market information seeking T7 Customer order online T8 Security solution M1 Management has IT policy M2 Planning to impl e-com M3 Aware on benefit of network M4 Vision on e-commerce M5 Vision on ICT equip M6 Support on e-commerce M7 Frequ of Hardware upgrade M8 Frequ of Software upgrade Pe1 Avail of profess labors Pe2 Employees’ ICT literacy Pe3 Staff Exper with web-based app Pe4 Organiz culture support e-com Pe5 Human Resour Support to e-com Pr1 Level of process analysis Pr2 Level of organiz adapt to change Pr3 Flex Encourag at work Pr4 Identif of bot’neck in busi process Pr5 Identi of ineffic in busin process Pr6 Busin Proc Flex to accom with Comp 0.706 0.794 0.778 0.784 0.738 0.667 0.785 0.763 Comp Comp Comp 0.729 0.733 0.686 0.704 0.671 0.596 0.794 0.803 0.872 0.88 0.859 0.855 0.713 876 0.852 0.828 0.849 0.831 0.803 126 TABLE D.2: Rotated Component Matrix For POER And PEER Item B1 Believe website promote firm B2 Believe net help doing busin effectively B3 Believe invest in computer app is effective B4 Believe e-commerce helps cut cost B5 Believe e-com helps expand market share B6 ICT staff contrib effec to implemen e-com B7 Believe manag.t interest in e-commerce B8 Believe organiz has strong relat with customers B9 Believe power supply effect on impl e-com B10 Quality of Net effect on impl e-com B11 Believe Internet cost effect on impl e-com B12 Believe software cost effect on impl e-com B13 Believe Organi Underst e-com model B14 Has necessary tech and human resour for e-com B15 Believe Customers ready for e-com B16 Believe Partners ready for e-commerce B17 Local tech.infras.e ready for e-com B18 Law environ.enough for e-com B19 Government encourage e-com B20 Government support ICT and e-com Impl Comp 0.788 0.811 0.886 0.84 0.83 0.831 0.866 0.861 0.875 0.846 0.841 0.841 0.811 0.725 0.491 Comp 0.486 0.786 0.85 0.882 0.885 0.903 0.891 127 Appendix E POST-HOC ANALYSIS FOR SIGNIFICANT DIFFERENCES TABLE E.1: Post-Hoc Alanisys for significant Differences of E-readiness and POER PEER based on firm porfile Variable I Variable J Mean dif Std Err Sig Lower B Upp B test of POER different on profile Mining Construction -0.253 0.336 0.454 -0.92 0.415 Mining Transportation -0.014 0.492 0.978 -0.991 0.963 Mining Manufacturing -.96245* 0.304 0.002 -1.567 -0.358 Mining Service -0.403 0.492 0.415 -1.38 0.574 Mining Wholesale -0.508 0.391 0.197 -1.283 0.268 Mining Retail -0.162 0.544 0.767 -1.242 0.918 Construction Mining 0.253 0.336 0.454 -0.415 0.92 Construction Transportation 0.239 0.44 0.589 -0.635 1.113 Construction Manufacturing -.70967* 0.21 0.001 -1.127 -0.292 Construction Service -0.15 0.44 0.734 -1.024 0.724 Construction Wholesale -0.255 0.323 0.431 -0.895 0.386 Construction Retail 0.091 0.498 0.856 -0.897 1.079 Transportation Mining 0.014 0.492 0.978 -0.963 0.991 Transportation Construction -0.239 0.44 0.589 -1.113 0.635 Transportation Manufacturing -.94856* 0.416 0.025 -1.775 -0.122 Transportation Service -0.389 0.568 0.495 -1.517 0.739 Transportation Wholesale -0.494 0.483 0.309 -1.453 0.465 128 Continuation of Table E.1 Variable I Variable J Mean dif Std Err Sig Lower B Upp B Transportation Retail -0.148 0.614 0.81 -1.367 1.071 Manufacturing Mining 96245* 0.304 0.002 0.358 1.567 Manufacturing Construction 70967* 0.21 0.001 0.292 1.127 Manufacturing Transportation 94856* 0.416 0.025 0.122 1.775 Manufacturing Service 0.56 0.416 0.182 -0.267 1.386 Manufacturing Wholesale 0.455 0.289 0.119 -0.12 1.029 Manufacturing Retail 0.8 0.477 0.096 -0.146 1.747 Service Mining 0.403 0.492 0.415 -0.574 1.38 Service Construction 0.15 0.44 0.734 -0.724 1.024 Service Transportation 0.389 0.568 0.495 -0.739 1.517 Service Manufacturing -0.56 0.416 0.182 -1.386 0.267 Service Wholesale -0.105 0.483 0.828 -1.064 0.854 Service Retail 0.241 0.614 0.696 -0.978 1.459 Wholesale Mining 0.508 0.391 0.197 -0.268 1.283 Wholesale Construction 0.255 0.323 0.431 -0.386 0.895 Wholesale Transportation 0.494 0.483 0.309 -0.465 1.453 Wholesale Manufacturing -0.455 0.289 0.119 -1.029 0.12 Wholesale Service 0.105 0.483 0.828 -0.854 1.064 Wholesale Retail 0.346 0.536 0.52 -0.718 1.409 Retail Mining 0.162 0.544 0.767 -0.918 1.242 Retail Construction -0.091 0.498 0.856 -1.079 0.897 Retail Transportation 0.148 0.614 0.81 -1.071 1.367 Retail Manufacturing -0.8 0.477 0.096 -1.747 0.146 Retail Service -0.241 0.614 0.696 -1.459 0.978 Retail Wholesale -0.346 0.536 0.52 -1.409 0.718 129 Continuation of Table E.1 Variable I Variable J Mean dif Std Err Sig Lower B Upp B Test of PEER difference on profile Mining Construction -0.175 0.27 0.519 -0.711 0.361 Mining Transportation 0.534 0.395 0.18 -0.251 1.319 Mining Manufacturing -.73022* 0.245 0.004 -1.216 -0.245 Mining Service -.80682* 0.395 0.044 -1.592 -0.022 Mining Wholesale -0.471 0.314 0.137 -1.094 0.152 Mining Retail -0.027 0.437 0.952 -0.894 0.841 Construction Mining 0.175 0.27 0.519 -0.361 0.711 Construction Transportation 70909* 0.354 0.048 0.007 1.411 Construction Manufacturing -.55522* 0.169 0.001 -0.891 -0.22 Construction Service -0.632 0.354 0.077 -1.334 0.07 Construction Wholesale -0.296 0.259 0.256 -0.811 0.219 Construction Retail 0.148 0.4 0.711 -0.645 0.942 Transportation Mining -0.534 0.395 0.18 -1.319 0.251 Transportation Construction -.70909* 0.354 0.048 -1.411 -0.007 Transportation Manufacturing -1.26431* 0.335 -1.929 -0.6 Transportation Service -1.34091* 0.457 0.004 -2.247 -0.434 Transportation Wholesale -1.00505* 0.388 0.011 -1.775 -0.235 Transportation Retail -0.561 0.493 0.259 -1.54 0.419 Manufacturing Mining 73022* 0.245 0.004 0.245 1.216 Manufacturing Construction 55522* 0.169 0.001 0.22 0.891 Manufacturing Transportation 1.26431* 0.335 0.6 1.929 Manufacturing Service -0.077 0.335 0.819 -0.741 0.588 Manufacturing Wholesale 0.259 0.233 0.268 -0.202 0.721 Manufacturing Retail 0.704 0.383 0.069 -0.057 1.464 Service Mining 80682* 0.395 0.044 0.022 1.592 130 Continuation of Table E.1 Variable I Variable J Mean dif Std Err Sig Lower B Upp B Service Construction 0.632 0.354 0.077 -0.07 1.334 Service Transportation 1.34091* 0.457 0.004 0.434 2.247 Service Manufacturing 0.077 0.335 0.819 -0.588 0.741 Service Wholesale 0.336 0.388 0.389 -0.435 1.106 Service Retail 0.78 0.493 0.117 -0.199 1.759 Wholesale Mining 0.471 0.314 0.137 -0.152 1.094 Wholesale Construction 0.296 0.259 0.256 -0.219 0.811 Wholesale Transportation 1.00505* 0.388 0.011 0.235 1.775 Wholesale Manufacturing -0.259 0.233 0.268 -0.721 0.202 Wholesale Service -0.336 0.388 0.389 -1.106 0.435 Wholesale Retail 0.444 0.431 0.305 -0.41 1.299 Retail Mining 0.027 0.437 0.952 -0.841 0.894 Retail Construction -0.148 0.4 0.711 -0.942 0.645 Retail Transportation 0.561 0.493 0.259 -0.419 1.54 Retail Manufacturing -0.704 0.383 0.069 -1.464 0.057 Retail Service -0.78 0.493 0.117 -1.759 0.199 Retail Wholesale -0.444 0.431 0.305 -1.299 0.41 test of E-readiness different on profile Mining Construction -0.426 0.293 0.149 -1.008 0.156 Mining Transportation 0.271 0.429 0.53 -0.581 1.123 Mining Manufacturing -1.40259* 0.265 -1.93 -0.876 Mining Service -0.664 0.429 0.125 -1.516 0.188 Mining Wholesale -0.639 0.341 0.064 -1.315 0.037 Mining Retail -0.248 0.474 0.602 -1.19 0.694 Construction Mining 0.426 0.293 0.149 -0.156 1.008 Construction Transportation 0.697 0.384 0.073 -0.065 1.459 131 Continuation of Table E.1 Variable I Variable J Mean dif Std Err Sig Lower B Upp B Construction Manufacturing -.97619* 0.183 -1.34 -0.612 Construction Service -0.238 0.384 0.537 -1 0.524 Construction Wholesale -0.212 0.281 0.452 -0.771 0.346 Construction Retail 0.178 0.434 0.682 -0.683 1.039 Transportation Mining -0.271 0.429 0.53 -1.123 0.581 Transportation Construction -0.697 0.384 0.073 -1.459 0.065 Transportation Manufacturing -1.67317* 0.363 -2.394 -0.952 Transportation Service -0.935 0.496 0.062 -1.919 0.049 Transportation Wholesale -.90944* 0.421 0.033 -1.745 -0.073 Transportation Retail -0.519 0.535 0.335 -1.582 0.544 Manufacturing Mining 1.40259* 0.265 0.876 1.93 Manufacturing Construction 97619* 0.183 0.612 1.34 Manufacturing Transportation 1.67317* 0.363 0.952 2.394 Manufacturing Service 73827* 0.363 0.045 0.017 1.459 Manufacturing Wholesale 76373* 0.252 0.003 0.263 1.265 Manufacturing Retail 1.15424* 0.416 0.007 0.329 1.98 Service Mining 0.664 0.429 0.125 -0.188 1.516 Service Construction 0.238 0.384 0.537 -0.524 Service Transportation 0.935 0.496 0.062 -0.049 1.919 Service Manufacturing -.73827* 0.363 0.045 -1.459 -0.017 Service Wholesale 0.025 0.421 0.952 -0.811 0.862 Service Retail 0.416 0.535 0.439 -0.647 1.479 Wholesale Mining 0.639 0.341 0.064 -0.037 1.315 Wholesale Construction 0.212 0.281 0.452 -0.346 0.771 Wholesale Transportation 90944* 0.421 0.033 0.073 1.745 Wholesale Manufacturing -.76373* 0.252 0.003 -1.265 -0.263 132 Continuation of Table E.1 Variable I Variable J Mean dif Std Err Sig Lower B Upp B Wholesale Service -0.025 0.421 0.952 -0.862 0.811 Wholesale Retail 0.391 0.467 0.405 -0.537 1.318 Retail Mining 0.248 0.474 0.602 -0.694 1.19 Retail Construction -0.178 0.434 0.682 -1.039 0.683 Retail Transportation 0.519 0.535 0.335 -0.544 1.582 Retail Manufacturing -1.15424* 0.416 0.007 -1.98 -0.329 Retail Service -0.416 0.535 0.439 -1.479 0.647 Retail Wholesale -0.391 0.467 0.405 -1.318 0.537 FDI State owned 94530* 0.312 0.003 0.326 1.564 FDI Collective 1.33336* 0.182 0.971 1.695 FDI Private 1.47868* 0.259 0.964 1.994 State owned FDI -.94530* 0.312 0.003 -1.564 -0.326 State owned Collective 0.388 0.283 0.174 -0.174 0.95 State owned Private 0.533 0.338 0.118 -0.137 1.204 Collective FDI -1.33336* 0.182 -1.695 -0.971 Collective State owned -0.388 0.283 0.174 -0.95 0.174 Private FDI -1.47868* 0.259 -1.994 -0.964 Private State owned -0.533 0.338 0.118 -1.204 0.137 Private Collective -0.145 0.224 0.518 -0.589 0.299 years less to years 0.526 0.44 0.235 -0.346 1.398 years less to 10 years 1.15586* 0.432 0.009 0.299 2.012 years less 10 years more 1.30106* 0.426 0.003 0.456 2.146 to years years Less -0.526 0.44 0.235 -1.398 0.346 to years to 10 years 63008* 0.22 0.005 0.194 1.066 to years 10 years More 77528* 0.208 0.362 1.189 to 10 years years Less -1.15586* 0.432 0.009 -2.012 -0.299 133 Continuation of Table E.1 Variable I Variable J Mean dif Std Err Sig Lower B Upp B to 10 years to years -.63008* 0.22 0.005 -1.066 -0.194 to 10 years 10 years More 0.145 0.191 0.449 -0.234 0.524 10 years more years Less -1.30106* 0.426 0.003 -2.146 -0.456 11 years more to years -.77528* 0.208 -1.189 -0.362 12 years more to 10 years -0.145 0.191 0.449 -0.524 0.234 End of Table ... between the e- readiness of the enterprises and the Perceived Environment E- Readiness, and • To determine the factors that are main affecting the e- readiness of medium and large enterprises in. .. appropriate tool to evaluate e- readiness of medium and large enterprises in Thai Nguyen province The results of the study can be used to help medium and large enterprises in Thai Nguyen province as... participated enterprises and to test the hypothesizes Linear regression was used to determine factors affecting the e- readiness of medium and large enterprises in Thai Nguyen province Data were collected