appendix 1 all locations - mbi › wp-content › uploads › 2017 › ...promotes private sector...

11. Glossary 1

Appendix 1

All Locations

22 June 2016 Supported by

Provided under contract to The Asian Development Bank

By

Brian Barge (Individual Consultant)

with support of

The Evidence Network Inc.

www.theevidencenetwork.com

Report Structure We have created this report as the appendix to the main report, provided separately. In this report we present

our in-depth assessment of the venture support programs in Da Nang, Ho Chi Minh City, and Phnom Penh including the detailed analyses and diagrams.

The Mekong Business Initiative (MBI)

The Evidence Network would like to thank the Australian Government’s Mekong Business Initiative for supporting the preparation of this report. MBI is an advisory facility that

promotes private sector development in Cambodia, the Lao People’s Democratic Republic (Lao PDR), Myanmar, and Vietnam. MBI fosters development of the innovation ecosystem by

supporting business advocacy, alternative finance and innovation. It is supported by the Government of Australia and the Asian Development Bank.

2

The views expressed in this report are those of the authors and do not necessarily reflect the views and policies of the Government of Australia, or of the Asian Development Bank (ADB) and its Board of Governors and the governments represented by ADB.

ADB and the Government of Australia do not guarantee the accuracy of the data in this report and accept no responsibility for any consequence of their use. The mention of specific companies or products of manufacturers does not imply that they are endorsed or recommended by ADB or the Government of Australia in preference to others of a similar nature that are not mentioned.

By making any designation of, or reference to, a particular territory or geographic area, or by using the term “country” in this document, ADB and the Government of Australia do not intend to make any judgments about the legal or other status of any territory or area.

TableofContents 3

Contents

1.TEN’sMethodology. ..........................................................................................4

2.DescriptionofSample. ......................................................................................5

3.DemographicsoftheRandomSampleofYoungCompanies. .............................8

4.DemographicsoftheParticipantsintheVentureSupportProgramsinAllLocations. ............................................................................................................23

5.UseofServicesbyVentureSupportProgramParticipants. ..............................38

6.ImpactonResourcesandCapabilitiesofVentureSupportProgramParticipants. ..........................................................................................................................44

7.ImpactonPerformanceofVentureSupportProgramParticipants. ..................52

8.PredictorsofSelectionforSupport. .................................................................58

9.PredictorsofCompanyGrowth. .......................................................................62

10.PredictorsofImpact. .....................................................................................71

11.GlossaryofTerms. .........................................................................................93

4 VietnamandCambodiaImpactAssessment

Figure'8.1'Innova0on'Intermediary'Logic'Model'

©"The"Evidence"Network"

IMPACT'ON'CHANGE"''''ACTIVITIES"

OBJECTIVE'

Na0onal'Compe00veness''''''''''Community,'Regional,'Economic'Development''''''''''Industry'Strength''''''''''Viable'New'Ventures'

INPUTS"

•  Knowledgeable'people'•  Rela0onships'•  Equipment,'facili0es'

•  Funding'

•  Business,'science'and'technology'knowledge,''

equipment,'facili0es'

•  Business,'research,'''technology'rela0onships'

•  Design,'tes0ng,'prototyping,'scaleSup,'IP'management,'

licensing'services'

•  Plans,'proposals,'projects'•  Events,'conferences,'seminars,'mee0ngs'

•  Websites,'blogs,'reports,''

directories,'newsleVers'

•'Financing'

Impact'on'Change:''•  Products,'services'•  Time'to'market'

•  Market'share'

•  Employment'•  Environmental'impact'

•  Revenues'•  Valua0on'•  Investment'

Impact'on'Change:''•  Sustainable'wealth'and'jobs'•  Environmental'and'health'

care'improvement'

•  Increased'community,'''regional,'na0onal,'economic'

and'social'wellness'

RESOURCES'&'CAPABILITIES' PERFORMANCE' SOCIOSECONOMIC'

Impact'on'Change:''•  Knowledge,'exper0se'•  Opportuni0es'for''''promo0on,'influence'

•  Business'and'research''''linkages'

•  Access'to'technology''''services'

•  Access'to'financing'•  Access'to'complementary'''

business'inputs'

1.TEN’sMethodology ThemethodologyemployedbyTheEvidenceNetworkInc.(TEN)isrepresentedinthelogicmodelforinnovationintermediariesshownbelowinFigure1.1.Thelogicmodelillustrateshowinnovationintermediariesworktofulfilltheirmissions,andhowTENmeasurestheirimpact.Theterminnovationintermediarybroadlyencompassesbusinesssupportprogramsthatoperatetofurtherthedevelopmentofbusiness,andincludesexportandinternationalisationsupport.

Asshownatthetopofthefigure,innovationintermediariesexpresstheirobjectivesintermsofenhancingnationalcompetitiveness,advancingregionaleconomicdevelopment,bolsteringindustrystrength,orsupportingviablenewventures.

TEN’slogicmodelexpressestheexpectationthatinnovationintermediaryactivitiescreateshorter-termimpactsoncompanies’resourcesandcapabilities,whichleadtosubsequentimpactsoncompanyperformance,andultimatelyleadtolonger-termimpactsintheformofsocio-economicbenefits,anexpectationthatholdsacrossalltypesofinnovationintermediaries.Detailsofhowinnovationintermediariesachievetheirdesiredimpactsareshowninthelowerpartofthefigure.Knowledge-basedandtangibleinputsleadtoawiderangeofactivitiessuchasprovisionofknowledge,relationships,events,publications,prototypes,equipment,andfacilities.Theactivitiesareexpectedtolead,inturn,totheshorter,medium,andlonger-termimpactsdescribedabove.

Statisticalexaminationsoftherelationshipsbetweentheuseofservicesoffered,impactonresourcesandcapabilities,andimpactsoncompanyperformancemakeitpossibletoassesswhichservicesandimpactsonresourcesandcapabilitiesaresignificantlyrelatedtotheimpactoftheintermediaryoncompanies’performanceinthemarket.

Figure1.1:TEN’sInnovationIntermediaryLogicModel

2.DescriptionofSample 5

Table2.1ResponseRatebyVentureSupportProgram

2.DescriptionofSample

InJuneof2016,206outof274companiesthathadparticipatedinventuresupportprogramsrespondedtoaweb-basedsurvey.Table2.1providesfurtherdetailsontheresponseratebyprogram.Duringthesameperiodthe309entrepreneursrepresentingarandomsampleofyoungcompaniesoperatinginDaNangwereinterviewed.

WehavebeenaskedtoexcludethosecompaniessupportedbytheDaNangSMEAssociationfromtheanalysissampleonthebasisthatprogrammingoftheDaNangSMEAssociationdifferssubstantivelyfromtheotherventuresupportprograms,andthereforecannotbeconsideredinthesameanalysiscontext.AllanalysesofimpactthereforeexcludetheDaNangSMEAssociationincludingthedescriptionsinAppendix1(AllLocations),andAppendix2(DaNang).Wehave,however,includedthe

1TheBusinessStartupSupportCentre(BSSC)hasapproximately100clients.However,wedonothaveinformationontheresponseratefortheBSSC,astheyprovidedthesurveylinktotheirclientsdirectly.Asaresult,wedonotincludetheBSSCintheoverallresponseratecalculation.

Location Program ProgramSize Invitations Respondents ResponseRate

PhnomPenh

EmergingMarkets 8 7 6 86%

MinistryofCommerce101program 87 83 58 70%

NationalBusinessPlanCompetition 20 14 70%

WeCreate 18 17 14 82%

NOMINetwork 20 10 8 80%

Total 133 137 100 73%

HoChiMinhCity

BusinessStartupSupportCentre 100 10 ArgiBusinessIncubator 17 6 35%

BusinessIncubationandInnovationCentre–NguyenTatThanhUniversity 15 8 5 63%

NongLamUniversity–CenterforTechnologyBusinessIncubation 9 3 33%

SaigonHi-TechPark–IncubationCenter ~20 16 8 50%

InformationTechnologyPark–VietnamNationalUniversityinHCMC ~10 16 11 69%

QuangTrungSoftwareBusinessIncubationCenter 10 9 7 78%

HoChiMinhCityUniversityofTechnology–TechnologicalBusinessIncubator 6 5 83%

Total 141 81 551 56%

DaNang

DaNangBusinessIncubator 8 8 8 100%

CollegeofInformationIncubator 15 15 13 87%

DaNangSMEAssociation 500 33 20 61%

Total 523 56 41 73%

6 VietnamandCambodiaAppendix

SMEAssociationinouraggregatepresentationsofcompanyandentrepreneurcharacteristics,onthepremisethattheSMEAssociationsupportscompanies(albeitindifferentwaysthantheincubators),andthenumberofrespondentsthatengagedwiththeDaNangSMEAssociationconstitutearelativelysmallportionofthetotalpopulation(20of206respondents).

3.DemographicsoftheRandomSample 7 7

TheRandomSampleofYoungCompanies


3.DemographicsoftheRandomSampleofYoungCompaniesThissectionofthereportprovidesinformationonthe309entrepreneurs,andcompanyrespondentsfromtherandomsampleofyoungcompaniesoperatinginDaNang.FirmCharacteristicsoftheRandomSampleofYoungCompanies

Theanalysisofthedemographicsoftherandomsampleofyoungcompaniesrevealedthat:• 71%ofcompaniesgeneratelessthan5BVNDinannualrevenues• 74%havenotreceivedfunding• 75%haveoneortwofull-time,paidfounders;80%donothaveanyfull-timeunpaid

founders• 44%havethreetofivefull-timepaidemployees;54%donothaveanyfull-timeunpaid

employees• 75%havesomepercentageofthefoundersoremployeesintheircompanywithdomestic

displacementexperience;88%havenofoundersoremployeesthathavestudiedorworkedoutsideVietnam

• 34%havemorethan65%oftheirfoundersoremployeeseducatedwithcollegeoruniversitydegrees

• 46%donothaveanyfoundersoremployeesthatarefamilymembers• 72%havemodestgrowthplans;15%havehighgrowthplans• 79%donothaveawebsite• 23%operateintheconstructionsector;17%operateintheretailorwholesalesector• 35%werefoundedin2015;13%werefoundedin2016

Webeginbyprovidinginformationaboutthecompanies’annualrevenues,financialsupportreceived,employeedemographics,internationalanddomesticdisplacementexperience,growthplans,yearfounded,industrialsector,andcompanywebsite.Figuresdescribingthesurveyedcompaniesfollow,accompaniedbythecorrespondingsurveyquestions,numberofrespondents(n),andanalysisfindings.


Figure3.1

Whatareyourcompany’sannualrevenues?n=300

Findings:

• 71%ofrespondentsreportedthattheircompanygenerateslessthan5BVNDinannualrevenues.• 21%ofrespondentsreportedthattheircompanyispre-revenue.

Figure3.2

Hasyourcompanyreceivedfunding?n=301

Finding:

• 74%ofrespondentsreportedthattheircompanyhasnotreceivedfunding.

0

20

40Pe

rcen

tageofR

espo

nden

ts

Pre-revenue

<500MVND

500-999MVND

1-4.9BVND

5-9.9BVND

10-19.9BVND

20-49.9BVND

50BVNDormore

AnnualRevenues

0

20

40

60

80

Percen

tageofR

espo

nden

ts

Grant Loan EquityInvestment

CompetitionPrize

NoFunding

FinancialSupport


Figure3.3

Howmanyfull-timepaidfoundersarethereinyourcompany?n=283

Finding:

• 75%ofrespondentsreportedthattheircompanyhasoneortwofull-timepaidfounders.

Figure3.4

Howmanyfull-timeunpaidfoundersarethereinyourcompany?n=173

Finding:

• 80%ofrespondentsreportedthattheircompanydoesnothaveanyfull-timeunpaidfounders.

0

20

40

60

80Pe

rcen

tageofR

espo

nden

ts

0 1-2 3-5 6-10 >10

Full-timePaidFounders

0

20

40

60

80

100

Percen

tageofR

espo

nden

ts

0 1-2 3-5 6-10 >10

Full-timeUnpaidFounders


Figure3.5

Howmanyfull-timepaidemployeesarethereinyourcompany?n=290

Findings:

• 44%ofrespondentsreportedthattheircompanyhasthreetofivefull-timepaidemployees.• 35%ofrespondentsreportedthattheircompanyhassixormorefull-timepaidemployees.

Figure3.6

Howmanyfull-timeunpaidemployeesarethereinyourcompany?n=200

Finding:

• 54%ofrespondentsreportedthattheircompanydoesnothaveanyfull-timeunpaidemployees.

0

20

40

60Pe

rcen

tageofR

espo

nden

ts

0 1-2 3-5 6-10 >10

Full-timePaidEmployees

0

20

40

60

Percen

tageofR

espo

nden

ts

0 1-2 3-5 6-10 >10

Full-timeUnpaidEmployees


Figure3.7

Howmanyfoundersoremployeesinyourcompanyarefamilymembers?n=305

Finding:

• 46%ofrespondentsreportedthattheircompanydoesnothaveanyfoundersoremployeesthatarefamilymembers.

Figure3.8

Howmanyfoundersoremployeeswithcollegeoruniversitydegreesarethereinyourcompany?n=307

Finding:

• 34%ofrespondentsreportedthatmorethan65%ofthefoundersoremployeesintheircompanyhavecollegeoruniversitydegrees.

0

20

40

60Pe

rcen

tageofR

espo

nden

ts

0% <35% 35-65% >65%

FoundersorEmployeesthatareFamilyMembers

0

20

40

Percen

tageofR

espo

nden

ts

0% <35% 35-65% >65%

FoundersorEmployeeswithUniversityDegrees


Figure3.9

Howmanyfoundersoremployeesinyourcompanyhaveworkedoutsidethetownorcitywheretheygrewup?n=301

Finding:

• 75%ofrespondentsreportedthatsomepercentageofthefoundersoremployeesintheircompanyhaveworkedoutsidethetownorcitywheretheygrewup.

Figure3.10

HowmanyfoundersoremployeesinyourcompanyhavestudiedorworkedoutsideVietnam?n=292

Finding:

• 88%ofrespondentsreportedthatnoneofthefoundersoremployeesintheircompanyhavestudiedorworkedoutsideVietnam.

0

20

40

60Pe

rcen

tageofR

espo

nden

ts

0% <35% 35-65% >65%

FoundersorEmployeeswithDomesticDisplacementExperience

0

20

40

60

80

100

Percen

tageofR

espo

nden

ts

0% <35% 35-65% >65%

FoundersorEmployeeswithInternationalDisplacementExperience


Figure3.11

Whatareyourcompany’srevenuegrowthplans?n=307

Findings:

• 72%ofrespondentsreportedthattheircompanyhasmodestgrowthplans.• 15%ofrespondentsreportedthattheircompanyhashighgrowthplans.

Figure3.12

Whenwasyourcompanyfounded?n=301

Findings:

• 35%ofrespondentsreportedthattheircompanywasfoundedin2015.• 13%ofrespondentsreportedthattheircompanywasfoundedin2016.

0

20

40

60

80Pe

rcen

tageofR

espo

nden

ts

Lowergrowth Nogrowth Modestgrowth Highgrowth

CompanyGrowthPlans

0

20

40

Percen

tageofR

espo

nden

ts

2011orearlier 2012 2013 2014 2015 2016

YearFounded


Figure3.13

Inwhatindustrialsectordoesyourcompanybelong?n=309

Findings:

• 23%ofrespondentsreportedthattheircompanyoperatesintheconstructionsector.• 17%ofrespondentsreportedthattheircompanyoperatesintheretailorwholesalesector.

Figure3.14

Doesyourcompanyhaveawebsite?n=308

Finding:

• 79%ofrespondentsreportedthattheircompanydoesnothaveawebsite.

0 20 40PercentageofResponses

ConstructionRetailorWholesale

OtherProfessionalorBusinessServices

TourismHoteloraccommodation

MaterialsorManufacturingTransportationorLogistics

EducationalServicesInformationorCommunicationsHardware

SoftwareFoodProcessing

AgricultureHealth,Medical,orBiotechnology

EntertainmentRestaurantorFoodandBeverageServices

Energy,Mining,orForestryEnvironment

FisheriesorAquaculture

IndustrialSector

0

20

40

60

80

100

Percen

tageofR

espo

nden

ts

Yes No

Website


EntrepreneurCharacteristicsoftheRandomSampleofYoungCompanies

Theanalysisoftheentrepreneursoftherandomsampleofyoungcompaniesrevealedthat:• 61%ofentrepreneursare36orolder• 65%aremale• 55%haveacollegeoruniversitycertificate• 92%havenotstudiedorworkedoutsideofVietnam• 88%didnothaveafamilybusiness• 90%hadaninternshipasastudent;ofthosewhohadaninternship,72%wereemployed

byaprivatecompany• 62%hadfiveormoreyearsofworkexperiencepriortofoundingtheircompany• 34%donothaveaFacebookaccount;32%have100–499Facebookfriends

Thissectionprovidesinformationabouttheentrepreneurs’age,gender,levelofeducation,internationalexperience,priorexperienceinfamilybusiness,internshipexperience,priorworkexperience,andFacebooknetwork.Figuresdescribingthesurveyedentrepreneursfollow,accompaniedbythecorrespondingsurveyquestions,numberofrespondents(n),andanalysisfindings.

Figure3.15

Whatisyourage?n=302

Finding:

• 61%ofrespondentsreportedthattheyare36yearsoldorolder.

0

20

40

Percen

tageofR

espo

nden

ts

25orunder 26-30 31-35 36-40 41-45 46-50 Over50

Age


Figure3.16

Whatisyourgender?n=308

Finding:

• 65%ofrespondentsreportedthattheyaremale.

Figure3.17

Whatisyourhighestlevelofeducation?n=305

Finding:

• 55%ofrespondentsreportedthattheyhaveacollegeoruniversitycertificate.

0

20

40

60

80Pe

rcen

tageofR

espo

nden

ts

Male Female Prefertonotrespond

Gender

0 20 40 60PercentageofRespondents

Primaryschoolcertificate

Juniorhighschoolcertificate

Somehighschool

Highschoolcertificate

Somecollegeoruniversity

Collegeoruniversitycertificate

Master'sorPhD

LevelofEducation


Figure3.18

HaveyoustudiedorworkedoutsideofVietnam?Pleaseselectallthatapply.n=286

Finding:

• 92%ofrespondentsreportedthattheyhavenotstudiedorworkedoutsideofVietnam.

Figure3.19

Didyourparentsownabusiness?n=304

Finding:

• 88%ofrespondentsreportedthattheirparentsdidnotownabusiness.

0

20

40

60

80

100Pe

rcen

tageofR

espo

nses

NotstudiedorworkedoutsideVietnam Studiedorworkedinaforeigncountry

InternationalExperience

0

20

40

60

80

100

Percen

tageofR

espo

nden

ts

Yes No

FamilyBusiness


Figure3.20

Didyouworkinyourparents’business?n=36

Finding:

• Ofthosethatreportedtheirparentsownedabusiness,50%reportedthattheyworkedinthatbusiness.

Figure3.21

Didyouhaveaninternshipasastudent?n=249

Finding:

• 90%ofrespondentsreportedthattheyhadaninternshipasastudent.

0

20

40

60Pe

rcen

tageofR

espo

nden

ts

Yes No

WorkinFamilyBusiness

0

20

40

60

80

100

Percen

tageofR

espo

nden

ts

Yes No

Internships


Figure3.22

Pleasespecifyyourinternshipemployer?n=225

Finding:

• Ofthoserespondentsthathadaninternship,72%reportedthattheirinternshipemployerwasaprivatecompany.

Figure3.23

Beforefoundingyourcompany,howmuchworkexperiencedidyouhave?n=303

Finding:

• 62%ofrespondentsreportedthattheyhadfiveormoreyearsofworkexperiencepriortofoundingtheircompany.

0 20 40 60 80PercentageofRespondents

Aprivatecompany

Anagencyorgovernmentorganization

Anon-governmentalorganization(NGO)

Auniversity,hospital,orotherNPO

Other

InternshipEmployer

0

20

40

Percen

tageofR

espo

nden

ts

Noworkexperience <5years 5-10years >10years

WorkExperiencePriortoFoundingCompany


Figure3.24

HowmanyFacebookfriendsdoyouhave?n=293

Findings:

• 34%ofrespondentsreportedthattheydonothaveaFacebookaccount.• 32%ofrespondentsreportedthattheyhave100–499Facebookfriends.

0

20

40Pe

rcen

tageofR

espo

nden

ts

NoFacebookaccount

<100friends 100-499friends

500-999friends

1,000ormorefriends

Facebook


VentureSupportProgramParticipants–AllLocations

4.DemographicsofProgramParticipants 23 23

4.DemographicsoftheParticipantsintheVentureSupportProgramsinAllLocationsThissectionofthereportprovidesinformationonthe196entrepreneurs,andcompanyrespondentsfromthosecompaniesparticipatingintheventuresupportprogramsoperatinginDaNang,HoChiMinhCity,andPhnomPenh.

FirmCharacteristicsoftheVentureSupportProgramParticipants

Theanalysisofthedemographicsofcompaniesparticipatingintheventuresupportprogramsacrossalllocationsrevealedthat:• 34%companiesarepre-revenue;38%generatelessthan$50KUSDinannualrevenues• 35%receivedfinancialsupport• 53%haveoneortwofull-time,paidfounders• 25%havemorethantenfull-time,paidemployees;25%havethreetofive• 37%haveoneortwofull-time,unpaidemployees• 53%donothaveanyfoundersoremployeesthatarefamilymembers• 61%havemorethan65%ofthefoundersoremployeesintheircompanywithuniversity

degrees• 48%havemorethan65%ofthefoundersoremployeeswithdomesticdisplacement

experience• 54%ofrespondentshavefoundersoremployeeswithinternationaldisplacement

experience• 34%havehighgrowthplans;55%havemodestgrowthplans• 57%werefoundedin2014–2016• 25%operateinthesoftwaresector;18%operateintheinformationorcommunications

hardwaresector• 60%haveawebsite• 71%joinedtheprogramtonetworkwithotherentrepreneurs• 53%firstengagedwiththeirprogramin2015;23%ofrespondentsfirstengagedwith

theirprogramin2016

Webeginbyprovidinginformationaboutthecompanies’annualrevenues,financialsupportreceived,employeedemographics,internationalanddomesticdisplacementexperience,growthplans,yearfounded,industrialsector,companywebsite,reasonsforjoiningtheprogram,andyearoffirstengagement.Figuresdescribingthesurveyedcompaniesfollow,accompaniedbythecorrespondingsurveyquestions,numberofrespondents(n),andanalysisfindings.Foreachmeasurewefirstpresentthefindingsforthewholesampleoftheventuresupportprogramparticipants,followedbythechartsandfindingsforeachoftheparticipatingcities.


Figure4.1

Whatareyourcompany’sannualrevenues?n=170

Findings:

• 34%ofrespondentsreportedthattheircompanyispre-revenue.• 38%ofrespondentsreportedthattheircompanygenerateslessthan$50Kinannualrevenues.

Figure4.2

Hasyourcompanyreceivedfunding?Pleaseselectallthatapply.2n=172

Finding:

• 35%ofresponsesindicatereceiptofoneormoretypesoffinancialsupport.

2Respondentswereaskedtoselectalltypesofapplicablefinancing,thereforethepercentagesadduptoavaluegreaterthan100%.

0

20

40Pe

rcen

tageofR

espo

nden

ts

Pre-revenue <$5K $5-9.9K $10-49.9K $50-249.9K

$250-999.9K

$1millionormore

AnnualRevenues

0

20

40

60

80

Percen

tageofR

espo

nden

ts

Grant Loan EquityInvestment

CompetitionPrize

NoFunding

FinancialSupport


Figure4.3

Howmanyfull-timepaidfoundersarethereinyourcompany?n=134

Findings:

• 53%ofrespondentsreportedthattheircompanyhasoneortwofull-time,paidfounders.• 27%ofrespondentsreportedthattheircompanyhasthreetofivefull-time,paidfounders.

Figure4.4

Howmanyfull-timeunpaidfoundersarethereinyourcompany?n=106

Findings:

• 49%ofrespondentsreportedthattheircompanyhasoneortwofull-time,unpaidfounders.• 21%ofrespondentsreportedthattheircompanyhasthreetofivefull-time,unpaidfounders.

0

20

40

60Pe

rcen

tageofR

espo

nden

ts

0 1-2 3-5 6-10 >10

Full-timePaidFounders

0

20

40

60

Percen

tageofR

espo

nden

ts

0 1-2 3-5 6-10 >10

Full-timeUnpaidFounders


Figure4.5

Howmanyfull-timepaidemployeesarethereinyourcompany?n=157

Findings:

• 25%ofrespondentsreportedthattheircompanyhasmorethantenfull-time,paidemployees.• 25%ofrespondentsreportedthattheircompanyhasthreetofivefull-timeemployees.

Figure4.6

Howmanyfull-timeunpaidemployeesarethereinyourcompany?n=90

Findings:

• 37%ofrespondentsreportedthattheircompanyhasoneortwofull-time,unpaidemployees.• 19%ofrespondentsreportedthattheircompanyhasthreetofivefull-time,unpaidemployees.

0

20

40Pe

rcen

tageofR

espo

nden

ts

0 1-2 3-5 6-10 >10

Full-timePaidEmployees

0

20

40

60

Percen

tageofR

espo

nden

ts

0 1-2 3-5 6-10 >10

Full-timeUnpaidEmployees


Figure4.7

Howmanyfoundersoremployeesinyourcompanyarefamilymembers?n=176

Finding:

• 53%ofrespondentsreportedthatnoneofthefoundersoremployeesintheircompanyarefamilymembers.

Figure4.8

Howmanyfoundersoremployeeswithcollegeoruniversitydegreesarethereinyourcompany?n=176

Finding:

• 61%ofrespondentsreportedthatmorethan65%ofthefoundersoremployeesintheircompanyhaveuniversitydegrees.

0

20

40

60Pe

rcen

tageofR

espo

nden

ts

0% <35% 35-65% >65%

FoundersorEmployeesthatareFamilyMembers

0

20

40

60

80

Percen

tageofR

espo

nden

ts

0% <35% 35-65% >65%

FoundersorEmployeeswithUniversityDegrees


Figure4.9

Howmanyfoundersoremployeesinyourcompanyhaveworkedoutsidethetownorcitywheretheygrewup?n=176

Finding:

• 48%ofrespondentsreportedthatmorethan65%ofthefoundersoremployeesintheircompanyhavedomesticdisplacementexperience.

Figure4.10

Howmanyfoundersoremployeesinyourcompanyhavestudiedorworkedoutsideyourcountry?n=179

Finding:

• 54%ofrespondentsreportedthattheircompanyhasfoundersoremployeeswithinternationaldisplacementexperience.

0

20

40

60Pe

rcen

tageofR

espo

nden

ts

0% <35% 35-65% >65%

FoundersorEmployeeswithDomesticDisplacementExperience

0

20

40

60

Percen

tageofR

espo

nden

ts

0% <35% 35-65% >65%

FoundersorEmployeeswithInternationalDisplacementExperience


Figure4.11

Whatareyourcompany’srevenuegrowthplans?n=173

Findings:

• 34%ofrespondentsreportedthattheircompanyhashighgrowthplans.• 55%ofrespondentsreportedthattheircompanyhasmodestgrowthplans.

Figure4.12

Whenwasyourcompanyfounded?n=178

Findings:

• 57%ofrespondentsreportedthattheircompanywasfoundedin2014–2016.• 35%ofrespondentsreportedthattheircompanywasfoundedin2012orearlier.

0

20

40

60Pe

rcen

tageofR

espo

nden

ts

Lowergrowth Nogrowth Modestgrowth Highgrowth

CompanyGrowthPlans

0

20

40

Percen

tageofR

espo

nden

ts

2012orearlier 2013 2014 2015 2016

YearFounded


Figure4.13

Inwhatindustrialsectordoesyourcompanybelong?Pleaseselectallthatapply.3n=186

Findings:

• 25%ofresponsesindicatecompaniesbelonginthesoftwaresector.• 18%ofresponsesindicatecompaniesbelongintheinformationorcommunications

hardwaresector

Figure4.14

Doesyourcompanyhaveawebsite?n=178

Finding:

• 60%ofrespondentsreportedthattheircompanyhasawebsite.

3Respondentswereaskedtoselectalltypesofapplicablesectors,thereforethepercentagesadduptoavaluegreaterthan100%.

0 20 40PercentageofResponses

SoftwareInformationorCommunicationsHardware

MaterialsorManufacturingRetailorWholesale

OtherAgriculture

ProfessionalorBusinessServicesEducationalServices

TourismRestaurantorFoodandBeverageServices

Health,Medical,orBiotechnologyEnvironment

FoodProcessingConstruction

EntertainmentEnergy,Mining,orForestry

FisheriesorAquacultureHoteloraccommodation

TransportationorLogistics

IndustrialSector

0

20

40

60

80

Percen

tageofR

espo

nden

ts

Yes No

Website


Figure4.15

Whydidyoujoin[Program]?Pleaseselectallthatapply.4n=174

Finding:

• 71%ofresponsesindicatecompaniesjoinedtheprogramtonetworkwithotherentrepreneurs.

Figure4.16

Whatwastheyearofyourcompany’sfirstengagementwith[Program]?n=161

Findings:

• 53%ofrespondentsreportedthattheircompanyfirstengagedwiththeirprogramin2015.• 23%ofrespondentsreportedthattheircompanyfirstengagedwiththeirprogramin2016.

4Respondentswereaskedtoselectalltypesofapplicablereasonsforjoiningtheprogram,thereforethepercentagesadduptoavaluegreaterthan100%.

0 20 40 60 80PercentageofResponses

TonetworkwithotherentrepreneursToaccessbusinesssupportservices

TolearnfromknowledgeablementorsToaccessfunding

ToattendinstructionalsessionsGoodformycompany’sreputation

Toaccessofficeorco-workingspaceGoodformypersonalreputation

Forotherreasons

ReasonsforJoiningtheProgram

0

20

40

60

Percen

tageofR

espo

nden

ts

2009orearlier

2010 2011 2012 2013 2014 2015 2016

YearofFirstEngagement


EntrepreneurCharacteristicsoftheVentureSupportProgramParticipantsinAllLocations

The analysis of the demographics of the entrepreneurs of companies in all locationsparticipatingintheventuresupportprogramsrevealedthat:• 62%ofentrepreneursareinthe26–40yearagecategory;23%are25orunder• 71%aremale• 53%haveacollegeoruniversitycertificate;29%haveamaster’sorPhD• 60%havenotstudiedorworkedinaforeigncountry• 82%didnothaveafamilybusiness• 66%hadaninternshipasastudent;58%ofthosethathadaninternshipwereemployed

byaprivatecompany• 28%hadmorethan10yearsofworkexperiencepriortofoundingtheircompany;21%

hadnone• 35%have1,000ormoreFacebookfriends;53%have100–999Facebookfriends

Thissectionprovidesinformationabouttheentrepreneurs’age,gender,levelofeducation,internationalexperience,priorexperienceinfamilybusiness,internshipexperience,priorworkexperience,andFacebooknetwork.Figuresdescribingthesurveyedentrepreneursfollow,accompaniedbythecorrespondingsurveyquestions,numberofrespondents(n),andanalysisfindings.Foreachmeasurewefirstpresentthefindingsforthewholesampleoftheventuresupportprogramparticipants,followedbythechartsandfindingsforeachoftheparticipatingcities.

Figure4.17

Whatisyourage?n=174

Findings:

• 62%ofrespondentsreportedthattheyareinthe26–40yearagecategory.• 23%ofrespondentsreportedthattheyareinthe25orunderagecategory.

0

20

40

Percen

tageofR

espo

nden

ts

25orunder 26-30 31-35 36-40 41-45 46-50 Over50

Age


Figure4.18

Whatisyourgender?n=174

Finding:

• 71%ofrespondentsreportedthattheyaremale.

Figure4.19

Whatisyourhighestlevelofeducation?n=174

Findings:

• 53%ofrespondentsreportedthattheyhaveacollegeoruniversitycertificate.• 29%ofrespondentsreportedthattheyhaveamaster’sorPhD.

0

20

40

60

80Pe

rcen

tageofR

espo

nden

ts

Male Female Prefertonotrespond

Gender

0 20 40 60PercentageofRespondents

Primaryschoolcertificate

Juniorhighschoolcertificate

Somehighschool

Highschoolcertificate

Somecollegeoruniversity

Collegeoruniversitycertificate

Master'sorPhD

LevelofEducation


Figure4.20

HaveyoustudiedorworkedoutsideofVietnam?Pleaseselectallthatapply.5n=174

Finding:

• 60%ofrespondentsreportedthattheyhavenotstudiedorworkedinaforeigncountry.

Figure4.21

Didyourparentsownabusiness?n=173

Finding:

• 82%ofrespondentsreportedthattheirparentsdidnotownabusiness.

5Respondentswereaskedtoselectallapplicableresponses,thereforethepercentagesadduptoavaluegreaterthan100%.

0

20

40

60

80Pe

rcen

tageofR

espo

nden

ts

Notstudiedorworkedinaforeigncountry

Studiedinaforeigncountry Workedinaforeigncountry

InternationalExperience

0

20

40

60

80

100

Percen

tageofR

espo

nden

ts

Yes No

FamilyBusiness


Figure4.22

Didyouworkinyourparents’business?n=30

Finding:

• 70%ofrespondentsthatreportedthattheirparentsdoownabusinessdonotworkinthatbusiness.

Figure4.23

Didyouhaveaninternshipasastudent?n=169

Finding:

• 66%ofrespondentsreportedthattheyhadaninternshipasastudent.

0

20

40

60

80Pe

rcen

tageofR

espo

nden

ts

Yes No

WorkinFamilyBusiness

0

20

40

60

80

Percen

tageofR

espo

nden

ts

Yes No

Internships


Figure4.24

Pleasespecifyyourinternshipemployer.n=110

Finding:

• 58%ofrespondentsthatreportedtheyhadaninternshipasastudentspecifiedaprivatecompanyastheiremployer.

Figure4.25

Beforefoundingyourcompany,howmuchworkexperiencedidyouhave?n=169

Findings:

• 28%ofrespondentsreportedthattheyhadmorethan10yearsofworkexperiencepriortofoundingtheircompany.

• 21%ofrespondentsreportedthattheyhadnoworkexperiencepriortofoundingtheircompany.

0 20 40 60 80PercentageofResponses

Aprivatecompany

Anagencyorgovernmentorganization

Anon-governmentalorganization(NGO)

Auniversity,hospital,orotherNPO

Other

InternshipEmployer

0

20

40

Percen

tageofR

espo

nden

ts

Noworkexperience <5years 5-10years >10years

WorkExperiencePriortoFoundingCompany


Figure4.26

HowmanyFacebookfriendsdoyouhave?n=171

Findings:

• 35%ofrespondentsreportedthattheyhave1,000ormoreFacebookfriends.• 53%ofrespondentsreportedthattheyhave100–999Facebookfriends.

0

20

40Pe

rcen

tageofR

espo

nden

ts

NoFacebookaccount

<100friends

100-499friends

500-999friends

1000ormorefriends

Facebook


5.UseofServicesbyVentureSupportProgramParticipants

94%ofcompaniesacrossall locationsusedtheprogramsupportservices (27%high intensityofuse,38%moderateintensityofuse,29%lowintensityofuse).

Theventuresupportprogramsprovidecompanieswithasetofsupportservicesintendedtoenablecompaniestogrowandsucceed.ThesesupportservicesaredescribedingreaterdetailinTable5.1.

Table5.1SupportServicesOffered

Location Program SupportServicesOffered

PhnomPenh

EmergingMarkets

- Provisionofmanagement,financial,andlegaladvice

- Mentorshipandguidance- Facilitationoffinancing- Analyticalsupport

MinistryofCommerce101program

- Mentorshipandcoaching- Bootcampentranceprogram- Facilitationoffinancing- Networkingandevents- Promotionopportunities

NationalBusinessPlanCompetition

- Workshopsandtrainingprograms- Leadershipcamp- Networkingandevents- Onlinelearningforum

WECREATE

- Businessbuildingprograms- Facilitationoffinancing- Networkingandevents(workshops,training,etc.)

- Mentorshipandcoaching

NOMINetwork - Technicaltraining- Improvedmarketaccess

DaNang

DaNangBusinessIncubator

- Mentorshipandcoaching- Facilitationoffinancing- Prototypedevelopmentsupport- Networkingandevents

CollegeofInformationIncubator

- Mentorshipandcoaching- Facilitationoffinancing- Provisionoftraining- Networkingandevents

DaNangSMEAssociation

- Regulatoryguidance- Assistancewithbusinessregistration,taxprocedures,andcontractdisputeresolution

- Networkingandevents

5.UseofServices 39 39

Table5.1(Continued) Location Program SupportServicesOffered

HoChiMinhCity

AgriBusinessIncubator - AccesstofacilitiesandequipmentBusinessIncubationandInnovationCenter–NguyenTatThanhUniversity

- Networkingandevents- Workshops- Mentorshipandcoaching

NongLamUniversity–CenterforTechnologyBusinessIncubation

- Researchandtransferadvancedtechnologyonagriculture,forestryandfishery,environmentandnaturalresources

- Organizingtechnicaltrainingcoursesforstudents,techniciansandextensionstaff

- Demonstrationprojects- Scientificandtechnologyservices(e.g.,information,consultancy,technologytransfer)

SaigonHi-TechPark–IncubationCenter

- Mentorshipandcoaching- Facilitationoffinancing

InformationTechnologyPark–VietnamNationalUniversityinHCMC

- Mentorshipandcoaching- Facilitationoffinancing- Networkingandevents

QuangTrungSoftwareBusinessIncubationCenter

- Accesstofacilitiesandequipment- Businessdevelopmentservices

HoChiMinhCityUniversityofTechnology–TechnologicalBusinessIncubator

- Networkingandevents- Trainingprogram- Facilitationoffinancing

BusinessStartupSupportCentre

- Mentorshipandcoaching- Training- Provisionandfacilitationoffinancing- Tradepromotion- Networkingandevents- Accesstofacilitiesandequipment

Respondentswereaskedtoratethesupportservicesoftheventuresupportprogramsintermsoftheirintensityofuse.Alloftheventuresupportprogramsarecategorizedaseitherfull-time,(thatis,provisionoffull-timeMentoring,Networking,Instruction,Workingspace,Accesstofunding,andBusinesssupportservices),ortrainingprograms.Intermsoffull-timeprograms,respondentswereaskedtoindicatetheirintensityofuseofservicesonafour-pointscalefrom‘didnotuse’(codedas1)to‘highintensity’(codedas4).Intermsoftrainingprograms,respondentswereaskedtoindicatetheirdegreeofparticipationintrainingsessionsonafour-pointscalefrom‘didnotparticipate’(codedas1)to‘fullyparticipated’(codedas4).Forfull-timeprograms,thecombinedintensityofuseofsupportservicesvariableiscalculatedastheaverageofMentoring,Networking,Instruction,Workingspace,Accesstofunding,andBusinesssupportservices.Fortrainingprograms,thecombinedintensityofuseofsupportservicesvariableiscalculatedbasedonthedegreeofparticipationintrainingsessions.Figure5.1showsacomparisonoftheintensityofuseresponsesgivenforfull-timeandtrainingprograms,foreachoftheventuresupportprograms.


0" 20" 40" 60" 80" 100"

Other&Programs&

Ministry&of&Commerce&101&

Da&Nang&SME&Associa;on&

WeCreate&

College&of&Informa;on&Incubator&

Informa;on&Technology&Park&

Business&Startup&Support&Centre&

Na;onal&Business&Plan&Compe;;on&

Da&Nang&Business&Incubator&

Saigon&HiFTech&Park&

NOMI&Network&

Percentage&of&Respondents&

High&Intensity&of&Use& Moderate&Intensity&of&Use& Low&Intensity&of&Use& Did&Not&Use&

Figure5.1Figure5.2showstheresponsesgiven,andthenumberofresponses(‘n’)fortheuseoffull-timesupportservices,andtrainingprograms.

Finding:

• 94%ofrespondentsreportedthattheircompanyusedtheprogramsupportservices(27%highintensityofuse,38%moderateintensityofuse,29%lowintensityofuse).

Figure5.2

Pleaseassessyourcompany’sintensityofuseoftheprogramsupportservices.n=173;Average=7.2

0

20

40

60

Percen

tageofR

espo

nden

ts

Didnotuse Lowintensity Moderateintensity Highintensity

IntensityofUseofServices


SatisfactionwithSupportServices

93% of companies across all locations were satisfied with the program support services (30%highlysatisfied,63%somewhatsatisfied).

Respondentswhocompletedthesurveywereaskedabouttheirdegreeofsatisfactionwiththesupportservicesprovidedbytheventuresupportprograms.Figure5.3showstheresponsesgiven,andthenumberofresponses(‘n’)forthequestion.

Finding:

• 93%ofrespondentsreportedthattheyweresatisfiedwiththeprogramsupportservices(30%highlysatisfied,63%somewhatsatisfied).

Figure5.3

Towhatdegreeareyousatisfiedwiththeservicesprovided?n=101:Average=6.1

0

20

40

60

80

Percen

tageofR

espo

nden

ts

Notsatisfied Somewhatsatisfied Highlysatisfied

SatisfactionwithServices


PredictorsofIntensityofUseofSupportServices

Tobetterunderstandthecharacteristicsandbehavioursofsupportedcompaniesthatusedsupportservicesinallcities,weconductedstatisticalexaminationsoftherelationshipsbetweenintensityofuseofservices,andpredictorsoftheintensityofuse6.

Weconsiderfourtypesofpredictorsoftheintensityofuseofservicesasindependentvariables:

• Companyattributes(C)

• Entrepreneurattributes(E)

• Satisfactionwithservicesprovidedbyventuresupportprograms7

• Reasonstojoinintheventuresupportprograms(R)8

Fromthisanalysiswefindthatcompaniesthatjoinedintheventuresupportprogramsforthepurposeofnetworkingandlearning(e.g.mentors,instructionalsessions)aremorelikelytousethesupportserviceswithahigherintensity,whilecompaniesthatjoinedintheprogramsforaccesstoofficespacearelesslikelytousethesupportservicesorusetheserviceswithalowerintensity.Thisfindingindicatesthattheventuresupportprogramsaremoreattractivetoentrepreneursandcompaniesforthepurposesofnetworkingandlearningcomparedtoaccesstoofficespace.

Additionally,intheparagraphbelow,wepresentdetailsthatshowcompanieswithplansformodestgrowthorsustainedoperations,companiesthathavelessemployeeswithinternationaldisplacementexperience,andcompaniesthatfoundedbyentrepreneurswithmoreFacebookfriendsaremorelikelyusethesupportserviceswithhigherintensity.

Model1belowregressescompanyattributes,entrepreneurattributes,satisfactionvariables,andreasonstojoinintheprogramsagainstintensityofuseservices.DetailsonModel1maybefoundinTable2below.

Specifically,Model1explains25%ofthevarianceinthedependentvariable,Intensityofuseofservices.Model1showsthatNetworking,andLearningaresignificantlyassociatedwithIntensityofuseofservices(significantatthe95%confidencelevel,andatthe90%confidencelevelrespectively),indicatingthatcompaniesthatjoinedintheprogramsfornetworkingandlearningpurposeandmorelikelytousesupportserviceswithahigherintensity.Moreover,AccesstoofficespaceissignificantandnegativelyassociatedwithIntensityofuseofservices(significantatthe95%confidencelevel),indicatingthatcompaniesthatjoinedintheprogramsforaccesstoofficespacearelesslikelytousesupportservicesoruseserviceswithalowerintensity.Ofthecompanyattributesvariables,Growthplan,andInternationaldisplacementexperienceofemployeearesignificantlyandnegativelyassociatedwithIntensityofuseofservices(bothsignificantatthe90%confidencelevel),indicatingthatcompanieswithplansformodestgrowthorsustainedoperations,andcompaniesthathavelessemployeesthathaveinternationaldisplacementexperiencearemorelikelytousetheserviceswithahigherintensity.Oftheentrepreneurattributesvariables,FacebookfriendsissignificantlyassociatedwithIntensityofuseofservices(significantatthe95%confidencelevel),indicatingthatcompaniesthatfoundedbyentrepreneurswithmoreFacebookfriendsaremorelikelytouseserviceswithahigherintensity.

6CompaniesthatengagedwiththeDaNangSMEAssociationareexcludedfromtheanalysissample.7Respondentswereaskedtoindicatetheirsatisfactionwiththeservicesprovidedbytheventuresupportprogramsonathree-pointscalefrom‘Notsatisfied’(codedas0)to‘Highlysatisfied’(codedas2).8Weincludefivedummyvariablesasreasonstojoinintheprograms:1)Networking,2)Accesstoofficespace,3)Learning,4)Accesstofunding,and5)Businesssupportservices.


Table5.2PredictorsofIntensityofUseofSupportServices9

VariableModel1

IntensityofUseofServicesC:Age C:Size C:Growthplan -αC:Fundingreceived($) C:Internationaldisplacementofemployees -αC:Domesticdisplacementofemployees C:Employeesthatarefamilymembers C:Employeesthathaveuniversitydegrees C:Website E:Age E:Gender(male) E:Parentsthatownabusiness E:Levelofeducation E:Workexperience(years) E:Internationalexperience E:Facebookfriends +αSatisfactionwithservices R:NetworkingR:AccesstoofficespaceR:LearningR:AccesstofundingR:Businesssupportservices

+*-*+α

ModelCharacteristics TotalN 138AdjustedR2 .25F(dof) ***(20)

dof=Degreesoffreedomα=p<.1,*=p<.05,**=p<.01,***=p<.001

9CompaniesthatengagedwiththeDaNangSMEAssociationareexcludedfromtheanalysissample.


6.ImpactonResourcesandCapabilitiesofVentureSupportProgramParticipantsANoteAboutStatisticalSignificance

Instatistics,confidencelevelstellushowlikelyitisthatapatterninthedataisduetochance.Forexample,inouranalysis,whenwepresentfindingsthatare‘significantatthe95%confidencelevel’,weareexplainingthatthepatternweseeinthedata(thefindingwepresent)hasa95%likelihoodofbeingtrue,andonlya5%(100%-95%)likelihoodofbeingduetochance.Higherconfidencelevels(e.g.,99%)meanthepatterninthedataismorelikelytrue,andnotduetochance.

Overallcompanyrespondentsacrossall locationsattributedthegreatestaverage impacttotheventuresupportprogramsonimprovementstotheircompanies’BusinessexpertiseandBusinessnetworkexpansion,andlowerimpactonimprovementstoKnowledgeofcustomerneeds.The average impact of the venture support programs on the resources and capabilities ofcompaniesacrossalllocationsisgreaterforcompaniesthatwerefoundedin2016,have35%ormoreoftheirfoundersandemployeeswithdomesticdisplacementexperience,havelessthan35%oftheirfoundersoremployeesthatarefamilymembers,havemorethan65%oftheir foundersoremployeeswithuniversitydegrees,arepre-revenue,orhaverevenuesoflessthan$50KUSD,andhavereceivedfinancing.The average impact of the venture support programs on the resources and capabilities ofcompanies across all locations is also greater for companies with entrepreneurs that areyoungerthan25,hadan internship,aremale,haveahighschoolcertificate,orhavetakensome,orcompletedcollegeoruniversity,anddidnothaveanyworkexperience,orhadlessthanfiveyearsofworkexperiencepriortofoundingthecompany.Further, theaverage impactoncompanies’ resourcesandcapabilitiesacrossall locations isgreaterforcompaniesthatusedthesupportserviceswith‘moderate’or‘high’intensity.

Followingourlogicmodelapproachforassessmentofimpact,theventuresupportprogramsachieveimpactoncompanyperformancebyhelpingtoimprovecompanies’resourcesandcapabilities.Thisimprovementtotheresourcesandcapabilitiesofcompaniesisthedirectimpactoftheventuresupportprograms,achievedthroughthevarioussupportservicesavailabletocompanies.Table6.1showsthethreeresourcesandcapabilitiesimpactmeasuresthatwereselectedusingTEN’smethodologytoassesstheventuresupportprograms’impactonimprovementstocompanies’resourcesandcapabilities10.Forconvenience,explanatoryexamplesmayalsobefoundinTable6.1. 10CompaniesthatengagedwiththeDaNangSMEAssociationwereexcludedfromtheanalysissample.

6.ImpactonCapabilities 45 45

Table6.1ResourcesandCapabilitiesImpactMeasuresandAssociatedExamples

ImpactMeasure ExamplesBusinessexpertise • Businessmodels,orbusinessplans,marketingand

salesstrategies,stakeholderrelations,financingstrategies,orcorporategrowthstrategies

• Newmarketingororganizationalmethodsinbusinesspractices,workplaceorganization,orexternalrelations

• Expansionofthescaleofoperations,diversificationintonewproductlines,orexpansionofindustrialorgeographicmarkets

Businessnetworkexpansion • Accesstocustomers,suppliers,manufacturers,businesspartners,serviceproviders,channeltomarketpartners,orotherrelevantbusinessesinFinlandorabroad

• Accessto,orbetterunderstandingof,industrialknowledge,newdevices,products,orservices

• Accesstokeypersonsinlargecompanies

Knowledgeofcustomerneeds • Knowledgeofcustomerneeds• Knowledgeofhowtoaccesscustomers,domestically,

orabroadFigure6.1showstheaverageimpactresponsesforthethreeresourcesandcapabilitiesimpactmeasures.11Readingclockwise,wecanseethattheaverageimpactsonresourcesandcapabilitiesareattheupperendofthe‘alittle’impactrangeonimprovementstoallthreeresourcesandcapabilitiesmeasures.Thissuggeststhattheventuresupportprogramshavesimilaraverageimpactonimprovementstocompanies’abilitytomakegainbusinessexpertise,expandtheirbusinessnetworks,andlearnabouttheircustomers.

11Forresourcesandcapabilities,impactismeasuredonascaleof0to10usingthefollowingweights:‘Noimpact’0,‘alittle’impact5.0,‘alot’ofimpact10.


Figure6.1AverageImpactoftheVentureSupportProgramsonCompanies’Resourcesand

Capabilities

Wealsoseektounderstandthedistributionofscoresaroundtheaveragesreportedabovetovalidatetheimportanceofthethreeresourcesandcapabilitiesimpactmeasures.Wedeterminedthepercentageofrespondentswhoreportedpositiveimpactontheircompany’sresourcesandcapabilities(i.e.,‘ALot’ofimpact,or‘ALittle’impact).Figure6.2showsthepercentageofcompaniesthatattributedpositiveimpactforthethreeresourcesandcapabilitiesimpactmeasures.WeseeinFigure6.2thatwhilethetotalpercentageofpositiveimpactisapproximatelyequal,moreclientsattributed‘ALot’ofimpactontheirKnowledgeofcustomerneedsmeasure.

BusinessExper,se

KnowledgeofCustomerNeeds

BusinessNetworkExpansion

AllLoca,ons

NoImpact ALi>leImpact ALotofImpact


Figure6.2PercentageofCompaniesAttributingPositiveImpact

ontheirResourcesandCapabilitiesRespondentsfromalllocationsreportedthefollowingimpactsonimprovementstotheircompanies’resourcesandcapabilitiestobe‘ALot’,or‘ALittle’:

• Knowledgeofcustomerneeds(85%positiveimpact)(29%‘ALot’,56%‘ALittle)

• Businessexpertise(84%positiveimpact)(34%‘ALot’,50%‘ALittle)

• Businessnetworkexpansion(84%positiveimpact)(26%‘ALot’,58%‘ALittle)

Thefrequencydistributionsthatfollow,Figures6.3to6.5showimpactresponsesforthethreeresourcesandcapabilitiesimpactmeasures,togetherwiththecorrespondingsurveyquestions,numberofrespondents,andaverageimpactscores(outof10).

n=133

n=132

n=134

0 20 40 60 80 100

BusinessExper1se



PercentageofRespondents

"ALiEle" "ALot"


Figure6.3

Asaconsequenceof[Program],hasyourcompany’sbusinessexpertiseincreased?n=157;Average=6.0

Figure6.4

Asaconsequenceof[Program],hasyourcompany’sbusinessnetworkincreased?n=156;Average=5.5

Figure6.5

Asaconsequenceof[Program],hasyourcompany’sknowledgeofcustomerneedsincreased?n=157;Average=5.7

0

20

40

60Pe

rcen

tageofR

espo

nden

ts

Noimpact Alittleimpact Alotofimpact

BusinessExpertise

0

20

40

60

Percen

tageofR

espo

nden

ts



0

20

40

60

Percen

tageofR

espo

nden

ts




Impactoftheventuresupportprogramsoncompanyresourcesandcapabilitieswasfurtheranalyzedwithrespecttocompanyinformation,entrepreneurinformation,andintensityofuseofsupportservices.TheVentureSupportPrograms’Impact:CompanyAttributesAllLocationsFromtheinformationsegmentedbycompanyattributes,foralllocations,wefindthat:

• Theaverageimpactoncompanies’resourcesandcapabilitiesisgreaterforthosethatwerefoundedin2016,comparedtothosethatwerefoundedin2015orearlier(significantatthe95%confidencelevel).

• Theaverageimpactoncompanies’resourcesandcapabilitiesisgreaterforthosewith35%or

moreoftheirfoundersoremployeeswithdomesticdisplacementexperience,comparedtothosewithlessthan35%(significantatthe95%confidencelevel).

• Theaverageimpactoncompanies’resourcesandcapabilitiesisgreaterforcompanieswithless

than35%oftheirfoundersoremployeesthatarefamilymembers,comparedtothosewith35%ormore(significantatthe95%confidencelevel).

• Theaverageimpactoncompanies’resourcesandcapabilitiesisgreaterforcompanieswith

morethan65%oftheirfoundersoremployeesthathaveuniversitydegrees,comparedtothosewith65%orless(significantatthe99%confidencelevel).

• Theaverageimpactoncompanies’resourcesandcapabilitiesisgreaterforcompaniesthatare

pre-revenue,orhaverevenuesoflessthan$50K,comparedtothosewithrevenuesof$50Kormore(significantatthe95%confidencelevel).

• Theaverageimpactoncompanies’resourcesandcapabilitiesisgreaterforcompaniesthat

receivedfinancing,comparedtothosethatdidnotreceivefinancing(significantatthe99%confidencelevel).

TheVentureSupportPrograms’Impact:EntrepreneurAttributesFromtheinformationsegmentedbyentrepreneurattributes,foralllocations,wefindthat:

• Theaverageimpactoncompanies’resourcesandcapabilitiesisgreaterforcompanieswithentrepreneursthatare25orunder,comparedtothosewithentrepreneursthatareolderthan25(significantatthe99%confidencelevel).

• Theaverageimpactoncompanies’resourcesandcapabilitiesisgreaterforcompanieswithentrepreneursthataremale,comparedtothosewithentrepreneursthatarefemale(significantatthe99%confidencelevel).


entrepreneursthathaveahighschoolcertificate,orhavetakensome,orcompleted,collegeoruniversity,comparedtothosewithentrepreneursthathaveamaster’sorPhD(significantatthe99%confidencelevel).



entrepreneursthathadaninternship,comparedtothosewithentrepreneursthatdidnothaveaninternship(significantatthe99%confidencelevel).


entrepreneursthatdidnothaveanyworkexperience,orhadlessthanfiveyearsofworkexperiencepriortofoundingthecompany,comparedtothosewithentrepreneursthathadfivetotenyearsofexperience(significantatthe95%confidencelevel).


entrepreneursthathavenotstudiedorworkedinaforeigncountry,comparedtothosewithentrepreneursthathavestudiedorworkedinaforeigncountry(significantatleastatthe95%confidencelevel).

TheVentureSupportPrograms’Impact:IntensityofUseofServicesFromtheinformationsegmentedbyintensityofuseofthesupportservicesprovidedbytheventuresupportprograms,foralllocations,wefindthat:

• Theaverageimpactoncompanies’resourcesandcapabilitiesisgreaterforcompaniesthatusedthesupportserviceswithmoderateorhighintensitycomparedtocompaniesthatusedtheserviceswithlowintensity,ordidnotusetheservices(significantatthe99%confidencelevel).

Figure6.6comparestheaverageimpactattributedbyrespondentsontheircompanies’resourcesandcapabilitiesagainstintensityofuseofeachoftheindividualsupportservices.Wecanseethatingeneral,averageimpactonresourcesandcapabilitiesincreaseswiththeintensityofuseofeachsupportservice.


Figure6.6

2"

4"

6"

8"

10"

Low"Intensity"of"Use"or"Did"Not"Use" Moderate"or"High"Intensity"of"Use"

Weighted"Av

erage"Im

pact"

Mentoring) Networking) Instruc1onal)Sessions)

Working)Space) Access)to)Funding) Business)Support)Services)


7.ImpactonPerformanceofVentureSupportProgramParticipants

Overall,theanalysisindicatesthattheaverageimpactoftheventuresupportprogramswashighest in terms of improvements to companies’ Change in employment, and Change inannual revenues measures, and lower in terms of improvements to companies’ Fundingreceivedmeasure.The average impact of the venture support programs on improvements to companyperformanceishigherforcompaniesthatfirstengagedwiththeprogramsin2014orearlier,werefoundedin2014orearlier,generaterevenues,havemodestorhighgrowthplans,andhavereceivedfinancing.The average impact of the venture support programs on improvements to companyperformance isalsohigher for companieswithentrepreneurs thatare25orunder,donothaveafamilybusiness,didnothaveanyworkexperiencepriortofoundingthecompany,andhavenotstudiedorworkedinaforeigncountry.Further, theaverage impactoncompanies’performance is greater for those thatused thesupportserviceswithanyintensity,comparedtothosethatdidnotusethesupportservices.

Followingourlogicmodelapproachforassessmentofinnovationimpacts,theventuresupportprogramsachievelong-termimpactsintheformofsocio-economicbenefitsbyhelpingcompaniestoimprovetheirperformance.Measuringimpactoncompanies’performanceisimportantbecauseitcorrespondstotheventuresupportprograms’missionandprovidesthehardevidencethatstakeholdersseek.However,companyperformancedependsonanumberoffactorsandsotoassessimpactonperformanceweconsiderboththechangeincompanyperformanceandthedegreetowhichthechangeisattributabletotheventuresupportprograms.Companyperformanceimprovementsoccurasaconsequenceoftheimpactthattheventuresupportprogramshaveonimprovingcompanies’resourcesandcapabilities.Table9.1showsthethreeperformanceimpactmeasuresthatwereselectedtoassesstheventuresupportprograms’impactoncompanyperformance.12

Table7.1PerformanceMeasures

• Changeinannualrevenues• Changeinemployment• Fundingreceived

12CompaniesthatengagedwiththeDaNangSMEAssociationwereexcludedfromtheanalysissample.

7.ImpactonPerformance 53 53

Figure7.1showstheaverageimpactresponsesforthethreeperformanceimpactmeasures.13Readingclockwise,wecanseethattheaverageimpactsonperformanceareatthelowendofthe‘alittle’impactrangeonimprovementstotheChangeinannualrevenuesmeasure,atthetopendofthe‘noimpact’rangeonimprovementstotheChangeinemploymentmeasure,andatthemiddleofthenoimpactmeasurefortheFundingreceivedmeasure.Thissuggeststhatamongthethreecompanyperformanceimpactmeasures,theventuresupportprogramshavethegreatestaverageimpactonimprovementstocompanies’abilitytoincreasetheirannualrevenues.

Figure7.1AverageImpactoftheVentureSupportProgramsonCompanies’Performance

Wetestedforsignificantdifferencesamongthemeasures,andfoundthatcompaniesattributedhigherimpactstotheventuresupportprogramsintermsoftheChangeinannualrevenues,andChangeinemploymentmeasures,comparedtotheFundingreceivedperformancemeasure(significantatthe99%confidencelevel).Wealsoseektounderstandthedistributionofscoresaroundtheaveragesreportedabovetovalidatetheimportanceofthethreeperformanceimpactmeasures.Wedeterminedthepercentageofrespondentswhoreportedpositiveimpactontheircompany’sperformance(i.e.,‘ALot’ofimpact,or‘ALittle’impact).

13Forperformance,impactismeasuredonascaleof0to10usingthefollowingweights:‘Noimpact’0,‘alittle’impact5.0,‘alot’ofimpact10.

ChangeinAnnualRevenues

ChangeinEmployment

FundingReceived

AllLoca9ons

NoImpact ALi<leImpact ALotofImpact


Figure7.2showsthepercentageofcompaniesthatattributedpositiveimpactforthethreeperformanceimpactmeasures.WeseeinFigure7.2thatagreaterpercentageofcompaniesattributepositiveimpactontheirChangeinannualrevenuesmeasure.

Figure7.2PercentageofCompaniesAttributingPositiveImpactontheirPerformance

Respondentsfromalllocationsreportedthefollowingimpactsonimprovementstotheircompanies’performancetobe‘ALot’,or‘ALittle’,andinthecaseofFundingreceived,‘Asignificantamount’,or‘Asmallamount’:

• Changeinannualrevenues(57%positiveimpact)(9%‘ALot’,48%‘ALittle)

• Changeinemployment(46%positiveimpact)(10%‘ALot’,36%‘ALittle)

• Fundingreceived(20%positiveimpact)(6%‘Asignificantamount’,14%‘Asmallamount’)

Thefrequencydistributionsthatfollow,Figures7.3to7.5showimpactresponsesforthethreeperformanceimpactmeasures,togetherwiththecorrespondingsurveyquestions,numberofrespondents,andaverageimpactscores(outof10).

n=72

n=88

n=30

0 20 40 60

FundingReceived

ChangeinEmployment


PercentageofRespondents

"ALiCle" "ALot" "ASmallAmount" "ASignificantAmount"


Figure7.3

Asaconsequenceof[Program],haveyourcompany’sannualrevenuesincreased?n=155;Average=3.3

Figure7.4

Asaconsequenceof[Program],hasyourcompany’snumberofemployeesincreased?n=154;Average=2.9

Figure7.5

Asaconsequenceof[Program],hasyourcompanyreceivedfunding?n=155;Average=1.3

0

20

40

60Pe

rcen

tageofR

espo

nden

ts



0

20

40

60

Percen

tageofR

espo

nden

ts


ChangeinEmployment

0

20

40

60

80

100

Percen

tageofR

espo

nden

ts

Noimpact Asmallamount Asignificantamount

FundingReceived


Impactoftheventuresupportprogramsoncompanyperformancewasfurtheranalyzedwithrespecttocompanyinformation,entrepreneurinformation,andintensityofuseofsupportservices.TheVentureSupportPrograms’Impact:CompanyAttributesFromtheinformationsegmentedbycompanyattributes,foralllocations,wefindthat:

• Theaverageimpactoncompanies’performanceisgreaterforcompaniesthatfirstengagedin2014orearlier,comparedtothosethatfirstengagedin2015or2016(significantatthe99%confidencelevel).

• Theaverageimpactoncompanies’performanceisgreaterforcompaniesthatwerefoundedin2014orearlier,comparedtothosethatwerefoundedin2015or2016(significantatthe99%confidencelevel).

• Theaverageimpactoncompanies’performanceisgreaterforcompaniesthatgenerate

revenues,comparedtothosethatdonotgeneraterevenues(significantatthe99%confidencelevel).

• Theaverageimpactoncompanies’performanceisgreaterforcompanieswithmodestorhigh

growthplans,comparedtothosethathavelowerornogrowthplans(significantatthe99%confidencelevel).

• Theaverageimpactoncompanies’performanceisgreaterforcompaniesthathavereceivedfinancing,comparedtothosethathavenotreceivedfinancing(significantatthe95%confidencelevel).

TheVentureSupportPrograms’Impact:EntrepreneurAttributesFromtheinformationsegmentedbyentrepreneurattributes,foralllocations,wefindthat:

• Theaverageimpactoncompanies’performanceisgreaterforcompanieswithentrepreneursthatare25orunder,comparedtothosewithentrepreneursthatare36orolder(significantatleastatthe95%confidencelevel).

• Theaverageimpactoncompanies’performanceisgreaterforcompanieswithentrepreneurs

thatdonothaveafamilybusiness,comparedtothosewithentrepreneursthathaveafamilybusiness(significantatthe95%confidencelevel).

• Theaverageimpactoncompanies’performanceisgreaterforcompanieswithentrepreneursthatdidnothaveanyworkexperiencepriortofoundingthecompany,comparedtothosewithentrepreneursthathadtenyearsorlessofexperience(significantatthe95%confidencelevel).

• Theaverageimpactoncompanies’performanceisgreaterforcompanieswithentrepreneurs

thathavenotstudiedorworkedinaforeigncountry,comparedtothosewithentrepreneursthathavestudiedinaforeigncountry(significantatthe99%confidencelevel).


Figure7.6comparestheaverageimpactattributedbyrespondentsontheircompanies’performanceagainstintensityofuseofeachoftheindividualsupportservices.Wecanseethatingeneral,averageimpactonperformanceincreaseswiththeintensityofuseofeachsupportservice.

Figure7.6

0"

2"

4"

6"

Low"Intensity"of"Use"or"Did"Not"Use" Moderate"or"High"Intensity"of"Use"

Weighted"Av

erage"Im

pact"

Mentoring) Networking) Instruc1onal)Sessions)

Working)Space) Access)to)Funding) Business)Support)Services)


8.PredictorsofSelectionforSupport

Smallercompanies,companiesthathavemoreemployeeswithdisplacementexperience,andcompaniesthathaveawebsitearemorelikelytobeselectedforprogramsupport.

Additionally,companiesthatfoundedbyyoungerentrepreneurs,thatfoundedbyentrepreneurswithahigherlevelofeducation,andthatfoundedbyentrepreneurswithmoreFacebookfriendsaremorelikelytobeselectedforprogramsupport.

WhichCompaniesAreMoreLikelytobeSupported?

ThissectionconsidersthequestionofwhichcompaniesaremorelikelytobeselectedforADBsupportprograms.Therefore,logisticregressionwasusedtoexaminetherelationshipsbetweenprogramsupportandpredictorsofselectionforsupport.14

Forstatisticalanalysis,weincludeprogramsupportasthedependentvariable:

• Programsupport(y/n):indicateswhetherornotacompanyhasbeenselectedforADBsupportprograms.

Weconsidertwokindsofpredictorsasindependentvariables:

• Companyattributes,and• Entrepreneurattributes.

Inthefollowingsectionswedescribeourmeasures,andshowtheresultsoflogisticregressions.

14NotethatforthisanalysistheDaNangSMEAssociationwasincludedwiththerandomsampleofyoungcompanies.Intheinterestofcompleteness,wehavererunthisparticularanalysis,excludingtheDaNangSMEAssociationentirely.Theresultswereinconsequentialanddidnotyieldanysignificantchanges.

8.PredictorsofSelectionforSupport 59

DependentVariables

Programsupportisincludedasthedependentvariable:

• Programsupport(y/n):IndicateswhetherornotacompanyhasbeensupportedbyADBprograms.Itisabinaryvariablewithavalueof1ifacompanyhasbeensupportedand0otherwise.

IndependentVariables

Weincludecompanyattributes,andentrepreneurattributesasindependentvariables:

Companyattributes

• Companyage:Indicatesthecompany’sageinyearsasof2016(allcompaniesformedpriorto2012wereconsideredtobeformedin2012).

• Size:Respondentswereaskedtoindicatetheirannualrevenuesonaseven-pointscalefrom‘pre-revenue’(codedaszero)to‘$1millionormore’(codedas$1.5million).Themid-pointvalueswereusedfortheannualrevenuesresponses.Respondentswerealsoaskedtoindicatethenumberoffounders,andnumberofemployees,includingfull-timeandpart-timefoundersandemployees(actualnumber).Thenumberoffoundersandemployeesandthemid-pointvaluesfortheannualrevenuesresponsesweremultipliedtogetanindicatorofcompanysize.

• Growthplan:Companiesthathaveaclearandambitiousgrowthplanareexpectedtohavegreatermotivationandconfidenceinimprovedcompanyperformance.Respondentswereaskedtoindicatetheircompany’sgrowthplanaseither‘lowergrowthplan’(codedaszero),‘nogrowthplan’(codedas1),‘modestgrowthplan’(codedas2),or‘highgrowthplan’(codedas3).

• Fundingreceived($):Respondentswereaskedtoindicatetheamountoffunding(e.g.grant,loan,equityinvestments,orcompetitionprize)thattheircompaniesreceivedinactualvalues($).

• Displacementexperienceofemployees:Respondentswereaskedtoindicatehowmanyfoundersandemployeeshaveworkedorstudiedoutsidethetownorcitywheretheygrewuponafour-pointscalefrom‘none’(codedas1)to‘morethan65%’(codedas4).Respondentswerealsoaskedtoindicatehowmanyfoundersandemployeesintheircompanieshaveworkedorstudiedoutsidehomecountryonafour-pointscalefrom‘none’(codedas2)to‘morethan65%’(codedas8).DomesticdisplacementexperienceandinternationaldisplacementexperiencewereaddedtogethertocalculatetheDisplacementexperienceofemployeesvariable.

• Employeesthatarefamilymembers:Respondentswereaskedtoindicatehowmanyfoundersandemployeesintheircompaniesarefamilymembersonafour-pointscalefrom‘none’(codedas1)to‘morethan65%’(codedas4).

• Employeesthathaveuniversitydegrees:Respondentswereaskedtoindicatehowmanyfoundersandemployeesintheircompanieshavecollegeoruniversitydegreesonafour-pointscalefrom‘none’(codedas1)to‘morethan65%’(codedas4).

• Website:Respondentswereaskedtoindicateiftheircompanieshaveawebsite.Ifitabinaryvariablewithavalueof1ifthecompanyhasawebsiteand0otherwise.


EntrepreneurAttributes

• Entrepreneurage:Entrepreneurswereaskedtoindicatetheirageonaseven-pointscalefrom‘25orunder’(codedas1)to‘over50’(codedas7).

• Gender(male):Entrepreneurswereaskedtoindicatetheirgender.Itisabinaryvariablewithavalueof1iftheentrepreneurismaleand0otherwise.

• Parentsthatownabusiness:Entrepreneurswereaskedtoindicateiftheirparentsownabusiness.Itisabinaryvariablewithavalueof1iftheentrepreneur’sparentsownabusinessand0otherwise.

• Levelofeducation:Entrepreneurswereaskedtoindicatetheirhighestlevelofeducationonaseven-pointscalefrom‘primaryschoolcertificate’(codedas1)to‘MastersorPhD’(codedas7).

• Workexperience:Entrepreneurswereaskedtoindicatetheirworkexperiencebeforefoundingacompanyonafour-pointscalefrom‘noexperience’(codedas0)to‘morethan10years’(codedas15).

• Internationalexperience:Entrepreneurswereaskedtoindicateiftheyhaveworkedorstudiedinaforeigncountry.Itisabinaryvariablewithavalueof1iftheentrepreneurhadinternationalexperienceand0otherwise.

• Website:Entrepreneurswereaskedtoindicateiftheircompanieshaveawebsite.Itisabinaryvariablewithavalueof1ifthecompanyhasawebsiteand0otherwise.

• Facebookfriends:EntrepreneurswereaskedtoindicatetheirFacebookfriendsonafive-pointscalefrom‘noFacebookaccount’(codedas0)to‘morethan1,000friends’(codedas1,500).

8.PredictorsofSelectionforSupport 61

LogisticRegressionModelResults

Logisticregressionwasusedtoassesswhichcompanyattributes,andentrepreneurattributesarepredictorsofselectionforsupport.Table6.1presentslogisticregressionagainstprogramsupport,basedonthesampleofallcompanies.InTable8.1Model1,whichincludesthecompanyattributesandentrepreneurattributes,explains40%ofthevarianceinthedependentvariable,Programsupport.Ofthecompanyattributesvariables,SizeissignificantlyandnegativelyassociatedwithProgramsupport(significantatthe90%confidencelevel),indicatingthatsmallercompaniesaremorelikelytobesupportedbyprograms.DisplacementexperienceofemployeesissignificantlyassociatedwithProgramsupport(significantatthe99.9%confidencelevel),indicatingthatcompaniesthathavemoreemployeeswithdomesticandinternationaldisplacementexperiencearemorelikelytobesupportedbyprograms.WebsiteissignificantlyassociatedwithProgramsupport(significantatthe99%confidencelevel),indicatingthatcompaniesthathaveawebsitearemorelikelytobesupportedbyprograms.Oftheentrepreneurattributesvariables,EntrepreneurageissignificantlyandnegativelyassociatedwithProgramsupport(significantatthe99.9%confidencelevel),indicatingthatcompaniesthatfoundedbyyoungerentrepreneursaremorelikelytobesupportedbyprograms.Moreover,Levelofeducation,andFacebookfriendsaresignificantlyassociatedwithProgramsupport(significantatthe90%confidencelevel,andatthe99.9%confidencelevel),indicatingthatcompaniesthatfoundedbyentrepreneurswithahigherlevelofeducationandhavemoreFacebookfriendsaremorelikelytobesupportedbyprograms.

Table8.1:LogisticRegressionAgainstProgramSupport

VariableModel1

ProgramSupport(y/n)C:Age C:Size -αC:GrowthPlan C:Fundingreceived($) C:Displacementexperienceofemployees +***C:Employeesthatarefamilymembers C:Employeesthathaveuniversitydegrees C:Website +**E:Age -***E:Gender(male) E:Parentsthatownabusiness E:Levelofeducation +αE:Workexperience(years) E:Internationalexperience E:Facebookfriends +***ModelCharacteristics TotalN 393AdjustedR2 .40Chi2(dof) ***(15)dof=Degreesoffreedomα=p<.1,*=p<.05,**=p<.01,***=p<.001


9.PredictorsofCompanyGrowth

Withregardtounsupportedcompanies,oldercompanies,companiesthathaveawebsite,companiesfoundedbyolderentrepreneurs,andcompaniesfoundedbyentrepreneurswithahigherlevelofeducationaremorelikelytogrowintermsofannualrevenues.Additionally,companiesthathaveanambitiousgrowthplan,companiesthatreceivedmorefinancialsupport,companiesthathaveawebsite,andcompaniesfoundedbyentrepreneurswhoseparentsownabusinessaremorelikelytogrowintermsofnumberoffoundersandemployees.

Withregardtosupportedcompanies,oldercompanies,companiesthatfoundedbyolderentrepreneurs,andcompaniesthatfoundedbymaleentrepreneursaremorelikelytogrowintermsofannualrevenues.Additionally,oldercompanies,companiesthathavemoreemployeesthatarefamilymembers,companiesthatfoundedbyolderentrepreneurs,andcompaniesthatfoundedbymaleentrepreneursaremorelikelytogrowintermsofnumberoffoundersandemployees.

WhichCompaniesAreMoreLikelytoGrowinSize?

Thissectionconsidersthequestionofwhichcompaniesaremorelikelytogrowintermsofannualrevenuesandemployment.Therefore,weconductstatisticalexaminationsoftherelationshipsbetweenthecompanygrowthinsizeandpredictorsofthisgrowthinsize.15

Forthestatisticalexaminations,weincludetwosizeindicatorsasdependentvariables:

• Annualrevenues,and• Numberoffoundersandemployees.

Weconsiderthreekindsofpredictorsasindependentvariables:

• Companyattributes,• Entrepreneurattributes,and• Whetherornotthecompanyhasbeensupportedbyprograms.

Inthefollowingsectionswedescribeourmeasures,andshowtheresultsoflinearregressionsagainstAnnualrevenues,andNumberoffoundersandemployees.Specifically,wereportregressionresultsonsupportedcompanies,youngcompaniesfromarandomsample,andall16companiesrespectively.

15CompaniesthatengagedwiththeDaNangSMEAssociationwereexcludedfromtheanalysissample.16Allcompaniesincludebothsupportedcompanies,andtherandomsampleofyoungcompanies.

9.PredictorsofCompanyGrowth 63

DependentVariables

WeincludeAnnualrevenues,andNumberoffoundersandemployeesasdependentvariables:

• Annualrevenues17:Respondentswereaskedtoindicatetheirannualrevenuesonaseven-pointscalefrom‘pre-revenue’(codedaszero)to‘$1millionormore’(codedas$1.5million).Themid-pointvalueswereusedfortheannualrevenuesresponses.

• Numberoffoundersandemployees:Respondentswereaskedtoindicatethenumberoffounders,andnumberofemployees,includingfull-timeandpart-timefoundersandemployees(actualnumber).Thetotalnumberoffounders(bothfull-timeandpart-time)andemployees(bothfull-timeandpart-time)weresummedupasNumberoffoundersandemployeesvariable.

IndependentVariables

Weincludecompanyattributes,entrepreneurattributes,andprogramsupportasindependentvariables:

Companyattributes

• Age:Indicatesthecompany’sageinyearsasof2016(allcompaniesformedpriorto2012wereconsideredtobeformedin2012).

• Growthplan:Companiesthathaveaclearandambitiousgrowthplanareexpectedtohavegreatermotivationandconfidenceinimprovedcompanyperformance.Respondentswereaskedtoindicatetheircompany’sgrowthplanaseither‘lowergrowthplan’(codedaszero),‘nogrowthplan’(codedas1),‘modestgrowthplan’(codedas2),or‘highgrowthplan’(codedas3).

• Fundingreceived($):Respondentswereaskedtoindicatetheamountoffunding(e.g.grant,loan,equityinvestments,orcompetitionprize)thattheircompaniesreceivedinactualvalues($).

• Displacementexperienceofemployees:Respondentswereaskedtoindicatehowmanyfoundersandemployeeshaveworkedorstudiedoutsidethetownorcitywheretheygrewuponafour-pointscalefrom‘none’(codedas1)to‘morethan65%’(codedas4).Respondentswerealsoaskedtoindicatehowmanyfoundersandemployeesintheircompanieshaveworkedorstudiedoutsidehomecountryonafour-pointscalefrom‘none’(codedas2)to‘morethan65%’(codedas8).DomesticdisplacementexperienceandinternationaldisplacementexperiencewereaddedtogethertocalculatetheDisplacementexperienceofemployeesvariable.

• Employeesthatarefamilymembers:Respondentswereaskedtoindicatehowmanyfoundersandemployeesintheircompaniesarefamilymembersonafour-pointscalefrom‘none’(codedas1)to‘morethan65%’(codedas4).

• Employeesthathaveuniversitydegrees:Respondentswereaskedtoindicatehowmanyfoundersandemployeesintheircompanieshavecollegeoruniversitydegreesonafour-pointscalefrom‘none’(codedas1)to‘morethan65%’(codedas4).

• Website:Respondentswereaskedtoindicateiftheircompanieshaveawebsite.Ifitabinaryvariablewithavalueof1ifthecompanyhasawebsiteand0otherwise.

17AnnualrevenuesinVNDhavebeenconvertedtoUSD.



• Entrepreneurage:Entrepreneurswereaskedtoindicatetheirageonaseven-pointscalefrom‘25orunder’(codedas1)to‘over50’(codedas7).


• Parentsthatownabusiness:Entrepreneurswereaskedtoindicateiftheirparentsownabusiness.Itisabinaryvariablewithavalueof1iftheentrepreneur’sparentsownabusinessand0otherwise.

• Levelofeducation:Entrepreneurswereaskedtoindicatetheirhighestlevelofeducationonaseven-pointscalefrom‘primaryschoolcertificate’(codedas1)to‘MastersorPhD’(codedas7).

• Workexperience:Entrepreneurswereaskedtoindicatetheirworkexperiencebeforefoundingacompanyonafour-pointscalefrom‘noexperience’(codedas0)to‘morethan10years’(codedas15).

• Internationalexperience:Entrepreneurswereaskedtoindicateiftheyhaveworkedorstudiedinaforeigncountry.Itisabinaryvariablewithavalueof1iftheentrepreneurhadinternationalexperienceand0otherwise.

• Website:Entrepreneurswereaskedtoindicateiftheircompanieshaveawebsite.Itisabinaryvariablewithavalueof1ifthecompanyhasawebsiteand0otherwise.


Programsupport

• Programsupport(y/n):IndicatedwhetherornotacompanywassupportedbyADBprograms.Itisabinaryvariablewithavalueof1ifthecompanywassupportedand0otherwise.

LinearRegressionModelResults

Linearregressionwasusedtoassesswhichcompanyattributes,andentrepreneurattributesaresignificantlyrelatedtothecompanies’annualrevenuesandnumberoffoundersandemployees.Regressionwasalsousedtoexaminetherelationshipsbetweenprogramsupportandcompanies’growthinsize.


ModelResultsofaRandomSampleofYoungCompanies

Models1and2showninTable9.1belowregresscompanyattributesandentrepreneurattributesagainstAnnualrevenuesandNumberoffoundersandemployeesrespectively,basedonthesampleofunsupportedcompanies.Model1,whichincludesboththecompanyattributesandentrepreneurattributes,explains15%ofthevarianceinthedependentvariable,Annualrevenues.InModel1,CompanyageandWebsitearesignificantlyassociatedwithAnnualrevenues(significantatthe99%confidencelevel,andatthe99.9%confidencelevelrespectively),indicatingthatoldercompanies,andcompaniesthathaveawebsitearemorelikelytoachievegrowthintermsofannualrevenues.Oftheentrepreneurattributesvariables,EntrepreneurageandLevelofeducationaresignificantlyassociatedwithAnnualrevenues(significantatthe90%confidencelevel,andatthe95%confidencelevel),indicatingthatcompaniesthatfoundedbyolderentrepreneurs,andentrepreneurswithahigherlevelofeducationaremorelikelytogrowintermsofannualrevenues.Model2,whichincludesboththecompanyattributesandentrepreneurattributes,explains12%ofthevarianceinthedependentvariable,Numberoffoundersandemployees.InModel2,Growthplan,Financialsupport,andWebsitearesignificantlyassociatedwithNumberoffoundersandemployees(significantatthe90%confidencelevel,atthe99%confidencelevel,andatthe99%confidencelevelrespectively),indicatingthatcompanieswithanambitiousgrowthplan,companiesthathavereceivedmorefinancialsupport,andcompaniesthathaveawebsitearemorelikelytogrowintermsofnumberoffoundersandemployees.Oftheentrepreneurattributesvariables,ParentsownabusinessissignificantlyassociatedwithNumberoffoundersandemployees(significantatthe99%confidencelevel),indicatingthatcompaniesthatfoundedbyentrepreneursthattheirparentsownabusinessaremorelikelytogrowintermsofnumberoffoundersandemployees.AdjustedR2arelessthan.25,suggestingtheseresultsbeinterpretedandusedwithcaution.


Table9.1ModelResultsofaRandomSampleofYoungCompanies

Variables

Model1AnnualRevenues

Model2FoundersandEmployees

C:Age +** C:Growthplan +αC:Fundingreceived($) +**C:Displacementexperienceofemployees C:Employeesthatarefamilymembers C:Employeesthathaveuniversitydegrees C:Website +*** +**E:Age +α E:Gender(male) E:Parentsthatownabusiness +**E:Levelofeducation +* E:Workexperience(years) E:Internationalexperience E:Facebookfriends ModelCharacteristics TotalN 270 268AdjustedR2 .15 .12F(dof) ***(14) ***(14)

dof=Degreesoffreedomα=p<.1,*=p<.05,**=p<.01,***=p<.001Tofurtherexplorethecompanyattributesandentrepreneurattributesoftherandomsampleofyoungcompanies,wepresentthedescriptivestatisticsandcorrelationstableinTable9.2.Foreachvariable,thetableprovides:correlationwithothervariables,thenumberofobservations(N),itsmean,standarddeviation,minimumvalue,andmaximumvalue.Herewereportthepertinentcorrelationresults:

• Oldercompaniesaremorelikelytohaveawebsite,andtobefoundedbyolderentrepreneurs.• Largercompaniesaremorelikelytobefoundedbyentrepreneursthattheirparentsowea

business.• Companieswithanambitiousgrowthplanaremorelikelytohaveawebsite,andfoundedby

youngerentrepreneurs.• Companiesthathavemoreemployeeswithinternationaldisplacementexperiencearemorelikely

tohavemoreemployeeswithdomesticdisplacementexperience,andtobefoundedbyentrepreneursthathavestudiedorworkedinaforeigncountry.

• Companiesthathavemoreemployeeswithdomesticdisplacementexperiencearemorelikelytohavemoreemployeeswithuniversitydegrees,andtobefoundedbyentrepreneurswithmoreyearsofworkingexperience.

• Companiesthathavemoreemployeesthatarefamilymembersaremorelikelytobefoundedbyentrepreneurswithalowerlevelofeducation.

• Companiesthathavefeweremployeeswithdomesticdisplacementexperiencearemorelikelytousesupportserviceswithahigherintensity.

• Companiesthathavemoreemployeeswithuniversitydegreesaremorelikelytohaveawebsite,andtobefoundedbyentrepreneurswithahigherlevelofeducation.


• Youngerentrepreneursaremorelikelytohaveahigherlevelofeducation,andtohavemoreFacebookfriends.

• EntrepreneursthathaveahigherlevelofeducationaremorelikelytohavemoreFacebookfriends.

• EntrepreneurswithmoreyearsofworkexperiencearemorelikelytohavefewerFacebookfriends.

Allcorrelationfindingsreportedabovearesignificantatthe99%confidencelevel.

68

VietnamandCam

bodiaAppendix

Table9.2DescriptiveStatisticsandCorrelationsTableoftheRandom

SampleofYoungCom

panies

1.

2.3.

4.5.

6.7.

8.9.

10.11.

12.13.

14.15.

16.

1.C:Age

2.C:Size

3.C:Grow

thplan

4.C:Fundingreceived($)

5.C:Internationaldisplacementofem

ployees

6.C:Dom

esticdisplacementofem

ployees

+**

7.C:Em

ployeesthatarefamilym

embers

+*

8.C:Employeesthathaveuniversitydegrees

+*

+**+**

9.C:Website

+**+*

+**+*

+**

10.E:Age+**

-**

+*

-*

11.E:G

ender(male)

+*

-*-*

12.E:Parentsthatownabusiness

+**

13.E:Levelofeducation

+*

+*-**

+**

-**

+*

14.E:W

orkexperience(years)

+*

+*

+**

+**

+*+*

15.E:Internationalexperience

+**

16.E:Facebookfriends

+*

+*+*

-**

+**

-**

N

301289

307309

292301

305307

308309

306304

305303

309293

Mean

3.033.2M

2.99

9.8K2.34

2.321.81

2.82.21

4.09.66

.125.40

7.39.15

316.7Standarddeviation

1.5321.5M

.61

41.4K1.03

1.01.93

1.02.41

1.56.48

.331.00

5.20.36

418.2Minim

um

10

10

11

11

01

00

20

00

Maxim

um

63.3x10

84450.0K4

44

41

81

17

151

1500

*=p<.05,**=p<.01


ModelResultsofSupportedCompanies

Models3and4showninTable9.3belowregresscompanyattributesandentrepreneurattributesagainstAnnualrevenuesandNumberoffoundersandemployeesrespectively,basedonthesampleofsupportedcompanies.Model3,whichincludesboththecompanyattributesandentrepreneurattributes,explains21%ofthevarianceinthedependentvariable,Annualrevenues.InModel3,CompanyageissignificantlyassociatedwithAnnualrevenues(significantatthe99%confidencelevel),indicatingthatoldercompaniesaremorelikelytoachievegrowthintermsofannualrevenues.Oftheentrepreneurattributesvariables,EntrepreneurageandGender(male)aresignificantlyassociatedwithAnnualrevenues(significantatthe99.9%confidencelevel,andatthe95%confidencelevel),indicatingthatcompaniesthatfoundedbyolderandmaleentrepreneursaremorelikelytoachievegrowthintermsofannualrevenues.Model4,whichincludesboththecompanyattributesandentrepreneurattributes,explains18%ofthevarianceinthedependentvariable,Numberoffoundersandemployees.InModel4,Companyage,andEmployeesthatarefamilymembersaresignificantlyassociatedwithNumberoffoundersandemployees(bothsignificantatthe95%confidencelevel),indicatingthatoldercompanies,andcompaniesthathaveahigherproportionofemployeesthatarefamilymembersaremorelikelytogrowintermsofnumberoffoundersandemployees.Oftheentrepreneurattributesvariables,EntrepreneurageandGender(male)aresignificantlyassociatedwithNumberoffoundersandemployees(bothsignificantatthe95%confidencelevel),indicatingthatcompaniesthatfoundedbyolderandmaleentrepreneursaremorelikelytogrowintermsofnumberoffoundersandemployees.


Table9.3ModelResultsofSupportedCompanies

Variable

Model3AnnualRevenues

Model4FoundersandEmployees

C:Age +** +*C:Growthplan C:Fundingreceived($) C:Displacementexperienceofemployees C:Employeesthatarefamilymembers +*C:Employeesthathaveuniversitydegrees C:Website E:Age +*** +*E:Gender(male) +* +*E:Parentsthatownabusiness E:Levelofeducation E:Workexperience(years) E:Internationalexperience E:Facebookfriends ModelCharacteristics TotalN 147 146AdjustedR2 .21 .18F(dof) ***(14) ***(14)

dof=Degreesoffreedomα=p<.1,*=p<.05,**=p<.01,***=p<.001

10.PredictorsofImpact 71

10.PredictorsofImpact

Theimpactoncompanies’resourcesandcapabilitiesismoststronglyassociatedwithimpactoncompanyperformance.

Additionally,oldercompanies,companiesthatwithanambitiousgrowthplan,companiesthathavemoreemployeeswithinternationaldisplacementexperiencearemorelikelytoattributetheSupportProgramswithimpactoncompanyperformance.

Moreover,companiesthatfoundedbyentrepreneurswithahigherlevelofeducation,andcompaniesthatfoundedbyentrepreneurswhoseparentsdidnotownabusinessaremorelikelytoattributetheSupportProgramswithimpactoncompanyperformance.

HowSupportProgramsAchieveImpactonCompanyPerformance

InthissectionweconsiderthequestionofhowSupportProgramshaveachievedthisimpact.Itisexpected,asindicatedbyTEN’slogicmodel,thatinnovationintermediaryactivitiesandservicescreateshorter-termimpactsoncompanies’resourcesandcapabilities,whichleadtosubsequentimpactsoncompanyperformance.Therefore,weconductstatisticalexaminationsoftherelationshipsbetweentheSupportProgram’simpactoncompanyperformanceandpredictorsofthatimpact.

Forthestatisticalexaminations,weselectedfourmeasuresofimpactoncompanyperformanceasdependentvariables:

• ImpactonAnnualrevenues,• ImpactonEmployment,• ImpactonFundingreceived,and• Indirectimpactfactor18.

Weconsideredthreekindsofpredictorsofimpactonthedependentvariables:

• Companyattributesandentrepreneurattributesthatweincludedascontrolvariables,• ThedegreeofuseofsupportservicesofferedbytheSupportPrograms,and• Thenatureanddegreeofimpactoncompanies’resourcesandcapabilities.

Inthefollowingsectionswedescribeourmeasures,andshowtheresultsoflinearregressionsagainstimpactonAnnualrevenues,impactonEmployment,impactonFundingreceived,andindirectimpactfactorrespectively.Additionally,wealsopresentregressionresultsagainstimpactoncompanies’resourcesandcapabilities.19

18Indirectimpactfactorvariablewascalculatedusingfactoranalysisbasedonimpactonannualrevenues,

impactonemployment,andimpactonfundingreceived.19CompaniesthatengagedwiththeDaNangSMEAssociationwereexcludedfromtheanalysissample.


ControlVariables

Wecontrolledforsixteencompanyattributesandentrepreneurattributesthatmayaffecta

respondent’sassessmentoftheimpactoftheSupportProgramsontheirAnnualrevenues,Employment,andFundingreceivedperformance:

CompanyAttributes

• Companyage:Indicatesthecompany’sageinyearsasof2016(allcompaniesformedpriorto

2012wereconsideredtobeformedin2012).

• Size:Respondentswereaskedtoindicatetheirannualrevenuesonaseven-pointscalefrom

‘pre-revenue’(codedaszero)to‘$1millionormore’(codedas$1.5million).Themid-point

valueswereusedfortheannualrevenuesresponses.Respondentswerealsoaskedto

indicatethenumberoffounders,andnumberofemployees,includingfull-timeandpart-time

foundersandemployees(actualnumber).Thenumberoffoundersandemployeesandthe

mid-pointvaluesfortheannualrevenuesresponsesweremultipliedtogetanindicatorof

companysize.

• Growthplan:Companiesthathaveaclearandambitiousgrowthplanareexpectedtohave

greatermotivationandconfidenceinimprovedcompanyperformance.Respondentswere

askedtoindicatetheircompany’sgrowthplanaseither‘lowergrowthplan’(codedaszero),

‘nogrowthplan’(codedas1),‘modestgrowthplan’(codedas2),or‘highgrowthplan’(coded

as3).

• Fundingreceived($):Respondentswereaskedtoindicatetheamountoffunding(e.g.grant,

loan,equityinvestments,orcompetitionprize)thattheircompaniesreceivedinactualvalues

($).

• Internationaldisplacementexperienceofemployees:Respondentswerealsoaskedtoindicatehowmanyfoundersandemployeesintheircompanieshaveworkedorstudied

outsidehomecountryonafour-pointscalefrom‘none’(codedas2)to‘morethan65%’

(codedas8).

• Domesticdisplacementexperienceofemployees:Respondentswereaskedtoindicatehowmanyfoundersandemployeesintheircompanieshaveworkedorstudiedoutsidethetownor

citywheretheygrewuponafour-pointscalefrom‘none’(codedas1)to‘morethan65%’

(codedas4).

• Employeesthatarefamilymembers:Respondentswereaskedtoindicatehowmany

foundersandemployeesintheircompaniesarefamilymembersonafour-pointscalefrom

‘none’(codedas1)to‘morethan65%’(codedas4).

• Employeesthathaveuniversitydegrees:Respondentswereaskedtoindicatehowmany

foundersandemployeesintheircompanieshavecollegeoruniversitydegreesonafour-point

scalefrom‘none’(codedas1)to‘morethan65%’(codedas4).

• Website:Respondentswereaskedtoindicateiftheircompanieshaveawebsite.Ifitabinary

variablewithavalueof1ifthecompanyhasawebsiteand0otherwise.



• Entrepreneurage:Entrepreneurswereaskedtoindicatetheirageonaseven-pointscalefrom

‘25orunder’(codedas1)to‘over50’(codedas7).


• Parentsthatownabusiness:Entrepreneurswereaskedtoindicateiftheirparentsownabusiness.Itisabinaryvariablewithavalueof1iftheentrepreneur’sparentsownabusiness

and0otherwise.

• Levelofeducation:Entrepreneurswereaskedtoindicatetheirhighestlevelofeducationonaseven-pointscalefrom‘primaryschoolcertificate’(codedas1)to‘MastersorPhD’(codedas

7).

• Workexperience:Entrepreneurswereaskedtoindicatetheirworkexperiencebeforefoundingacompanyonafour-pointscalefrom‘noexperience’(codedas0)to‘morethan10

years’(codedas15).

• Internationalexperience:Entrepreneurswereaskedtoindicateiftheyhaveworkedorstudiedinaforeigncountry.Itisabinaryvariablewithavalueof1iftheentrepreneurhad

internationalexperienceand0otherwise.

• Website:Entrepreneurswereaskedtoindicateiftheircompanieshaveawebsite.Itisa

binaryvariablewithavalueof1ifthecompanyhasawebsiteand0otherwise.


IntensityofUseofSupportServices

Useofsupportservices:Respondentswereaskedtoratethesupportservicesoftheventuresupportprogramsintermsoftheirintensityofuse.Alloftheventuresupportprogramsare

categorizedaseitherfull-time(thatis,provisionoffull-timeMentoring,Networking,Instruction,

Workingspace,Accesstofunding,andBusinesssupportservices),ortrainingprograms.Intermsof

full-timeprograms,respondentswereaskedtoindicatetheirintensityofuseofservicesonafour-

pointscalefrom‘didnotuse’(codedas1)to‘highintensity’(codedas4).Intermsoftraining

programs,respondentswereaskedtoindicatetheirdegreeofparticipationintrainingsessionsona

four-pointscalefrom‘didnotparticipate’(codedas1)to‘fullyparticipated’(codedas4).

RegressionVariablesWeconductedcorrelationsandlinearregressionanalysestoexplainhowimpactoncompany

resourcesandcapabilities,andperformanceisachieved.Table10.1showsallthevariablesincluded

intheregressions.Toreducecomplexity,afactoranalysiswasusedtoconsolidatemeasuresof

impact.Asshowninthetablebelow,thethreeimpactonresourcesandcapabilitiesmeasureswere

reducedtoonefactor:Directimpact.Thethreeimpactoncompanyperformancemeasureswere


reducedtoonefactor:Indirectimpact.AllcompositefactorsofimpactmeasuresarereliableasindicatedbytheCronbachalphas.20

Table10.1RegressionVariables

TypeofMeasures Measures RegressionVariables

IntensityofUse

• Mentoring• Networking• Instruction• Workingspace• Accesstofunding• Businesssupportservices• Degreeofparticipationintraining

sessions

Useofsupportservices21

Independentimpactmeasures

Impacton:• Businessexpertise• Businessnetworkexpansion• Knowledgeofcustomerneeds

Directimpact(Cronbach’sAlpha=.91)

Impactonperformancemeasures

Impacton:

• ChangeinannualrevenuesImpacton:

• ChangeinemploymentImpacton:

• Fundingreceived

ImpactonannualrevenuesImpactonemploymentImpactonfundingreceived

Impacton:• Changeinannualrevenues• Changeinemployment• Fundingreceived

Indirectimpactfactor(Cronbach’sAlpha=.93)

Controls

• Yearfounded• Annualrevenues• Numberofemployees• Companygrowthplan• Financialsupport($)

CompanyageSizeGrowthplanFundingreceived

20Cronbach’salphaisameasureofinternalconsistency.21Forfull-timeprograms,UseofsupportservicevariableiscalculatedastheaverageofMentoring,Networking,Instruction,Workingspace,Accesstofunding,andBusinesssupportservices.Fortrainingprograms,Useofsupportservicevariableiscalculatedbasedonthedegreeofparticipationintrainingsessions.


Table10.1(Continued)TypeofMeasures Measures RegressionVariables

Controls • Proportionoffoundersandemployeeswithint’ldisplacementexperience

Internationaldisplacement

experienceofemployees

• Proportionoffounderandemployees

withdomesticdisplacementexperience Domesticdisplacement

experienceofemployees

• Proportionoffoundersandemployees

thatarefamilymembers Employeesthatarefamily

members

• Proportionoffoundersandemployees

thathaveuniversitydegrees Employeesthathaveuniversity

degrees

• Website Website

• Entrepreneurage Entrepreneurage

• Gender Gender(male)

• Familybusiness Parentsthatownabusiness

• Highestlevelofeducation Levelofeducation

• Workexperiencebeforefoundingthe

company Workexperience(years)

• Entrepreneurshaveworkedorstudiedin

aforeigncountry Internationalexperience

• NumberofFacebookfriends Facebookfriends

DescriptiveStatistics

Table10.2presentsadescriptivestatisticsandcorrelationstable.Foreachvariable,thetable

provides:correlationwithothervariables,thenumberofobservations(N),itsmean,standard

deviation,minimumvalue,andmaximumvalue.Herewereportthepertinentcorrelationresults:

DirectImpact• Younger,maleentrepreneurs,withalowerlevelofeducation,andcompaniesthathaveused

theventuresupportprogramserviceswithgreaterintensityaremorelikelytoattribute

impactontheirresourcesandcapabilities.

IndirectImpact• Youngerentrepreneurs,andcompaniesthathaveattributedgreaterimpactontheirresources

andcapabilitiesaremorelikelytoattributeimpactontheirperformance.

CompanyandEntrepreneurAttributes• Oldercompaniesaremorelikelytobelargerinsize,tohaveawebsite,andtobefoundedby

olderentrepreneurs.

• Youngercompaniesaremorelikelytousesupportserviceswithahigherintensity.

• Companieswithoutgrowthplan,orwithalowergrowthplanaremorelikelytousesupport

serviceswithahigherintensity.

• Companiesthatfoundedbyolderentrepreneursaremorelikelytoreceiveagreateramount

offinancialsupport.

• Companiesthathavemoreemployeeswithinternationaldisplacementexperiencearemore

likelytohavemoreemployeeswithdomesticdisplacementexperience,tobefoundedby


entrepreneurswithahigherlevelofeducation,andtobefoundedbyentrepreneurswithinternationalexperience.

• Companiesthathavemoreemployeeswithdomesticdisplacementexperiencearemorelikelytohaveawebsite,tobefoundedbymaleentrepreneurs,andtobefoundedbyentrepreneursthathavestudiedorworkedinaforeigncountry.

• Companiesthathavefeweremployeeswithdomesticdisplacementexperiencearemorelikelytousesupportserviceswithahigherintensity.

• Companiesthathavemoreemployeeswithuniversitydegreesaremorelikelytobefoundedbymaleentrepreneurs.

• Olderentrepreneursaremorelikelytohaveahigherlevelofeducation,tohavemoreyearsofworkexperience,andtohavestudiedorworkedinaforeigncountry.

Allcorrelationfindingsreportedabovearesignificantatthe99%confidencelevel.


77

Table10.2DescriptiveStatisticsandCorrelationsTableofSupportedCompanies 22

1.

2.3.

4.5.

6.7.

8.9.

10.11.

12.13.

14.15.

16.17.

18.19.

1.C:Age

2.C:Size+**

3.C:Grow

thplan

4.C:Fundingreceived($)

5.C:Internationaldisplacement

6.C:Dom

esticdisplacement

+*+*

+**

7.C:Fam

ilymem

bers+*

8.C:Universitydegrees

-*

+*

-*

9.C:W

ebsite+**

+*

+**

10.E:Age+**

+**+*

+*

11.E:Gender(m

ale)

+**

+**

12.E:Parentsthatownabusiness

-**

13.E:Levelofeducation

+**

+*+**

14.E:W

orkexperience(years)

+*+*

+*

+*

+**

+**

15.E:Internationalexperience

+**

+**

+**

+**

+**

16.E:Facebookfriends

-*

-*

17.Supportservices-**

-**

-**

+*

+*

18.Directimpactfactor

-**

+*

-**

+**

19.Indirectim

pactfactor

-*

+**

N

162157

157176

163160

160160

162158

155157

158153

158157

157156

152Mean

2.991.1M

3.23

7.5K1.90

3.021.65

3.41.56

2.66.70

.186.10

6.38.39

812.12.99

.08.05

Standarddeviation1.54

3.3M

.6922.9K

1.001.15

.89.84

.501.43

.46.38

.725.83

.49566.9

.81.98

1.01Minim

um

10

10

11

11

01

00

40

00

1-2.03

-1.02Maxim

um

527.0M

4

200.0K44

44

17

11

715

11500

41.64

3.34*=p<.05,**=p<.01

22Com

paniesthatengagedwiththeDaN

angSMEAssociationareexcludedfrom

theanalysissample.

78VietnamandCambodiaAppendix

LinearRegressionModelResults

AsindicatedbyTEN’slogicmodelforinnovationintermediaries,theachievementofimpactsoncompanyperformancedependsontheachievementofshorter-termimpactsoncompanies’resourcesandcapabilities,whichinturn,dependsontheSupportProgram’sactivities.LinearregressionwasusedtoexaminetherelationshipsbetweentheuseofsupportservicesofferedbytheSupportPrograms,impactonresourcesandcapabilities,andimpactoncompanyperformance.RegressionwasalsousedtoassesswhichservicesandimpactonresourcesandcapabilitiesaresignificantlyrelatedtotheimpactoftheSupportProgramsoncompanies’performance.Wealsocontrolledforcompanyattributesandentrepreneurattributesthatmayaffectcompanies’assessmentoftheimpactoftheSupportProgramsontheirperformance.DetailsonfivemodelsmaybefoundinTable10.3below.Model1regressescontrolvariables,andintensityofuseofsupportservicesagainstdirectimpactoncompanies’resourcesandcapabilities.Models2,3,4,and5regresscontrolvariables,intensityofuseofsupportservices,andtheindependentimpactfactoragainstimpactoncompanyperformancemeasures.Model1,whichincludescontrolvariablesandintensityofuseofsupportservices,explains37%ofthevarianceinthedependentvariable,Directimpactfactor.Model1showsthatUseofservicesissignificantlyassociatedwithDirectimpactfactor(significantatthe99.9%confidencelevel),indicatingthatcompaniesthatusedsupportserviceswithahigherintensityaremorelikelytoattributetheSupportProgramswithimpactonresourcesandcapabilities.Ofthecompanyattributesvariables,Domesticdisplacementexperienceofemployees,andEmployeesthathaveuniversitydegreesaresignificantlyassociatedwithDirectimpactfactor(significantatthe95%confidencelevel,andatthe90%confidencelevelrespectively),indicatingthatcompaniesthathavemoreemployeeswithdomesticdisplacementexperience,andcompaniesthathavemoreemployeeswithuniversitydegreesaremorelikelytoattributetheSupportProgramswithimpactonresourcesandcapabilities.Oftheentrepreneurattributesvariables,EntrepreneurageissignificantlyandnegativelyassociatedwithDirectimpactfactor(significantatthe99%confidencelevel),indicatingthatcompaniesthatwerefoundedbyyoungerentrepreneursaremorelikelytoattributetheSupportProgramswithimpactonresourcesandcapabilities.Inaddition,LevelofeducationissignificantlyandnegativelyassociatedwithDirectimpactfactor(significantatthe99%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswithalowerlevelofeducationaremorelikelytoattributetheSupportProgramswithimpactonresourcesandcapabilities.Moreover,WorkexperienceissignificantlyassociatedwithDirectimpactfactor(significantatthe95%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswithmoreyearsofworkexperiencearemorelikelytoattributetheSupportProgramswithimpactonresourcesandcapabilities.Model2,depictedinFigure10.1,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains27%ofthevarianceinthedependentvariable,Indirectimpactfactor.Model2showsthatDirectimpactfactorissignificantlyassociatedwithIndirectimpactfactor(significantatthe99.9%confidencelevel),indicatingthatcompaniesreportedhigherimpactonresourcesandcapabilitiesaremorelikelytoattributetheSupportProgramswithimpactoncompanyperformance.Ofthecompanyattributesvariables,Companyage,Growthplan,andInternationaldisplacementexperienceofemployeesaresignificantlyassociatedwithIndirect


impactfactor(significantatthe95%confidencelevel,atthe99%confidencelevel,andatthe95%confidencelevelrespectively),indicatingthatoldercompanies,companieswithanambitiousgrowthplan,andcompaniesthathavemoreemployeesthathaveinternationaldisplacementexperiencearemorelikelytoattributetheSupportProgramswithimpactoncompanyperformance.Oftheentrepreneurattributesvariables,ParentsthatownabusinessissignificantlyandnegativelyassociatedwithIndirectimpactfactor(significantatthe99%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswhoseparentsdonotownabusinessaremorelikelytoattributetheSupportProgramswithimpactoncompanyperformance.Inaddition,LevelofeducationissignificantlyassociatedwithIndirectimpactfactor(significantatthe90%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswithahigherlevelofeducationaremorelikelytoattributetheSupportProgramswithimpactoncompanyperformance.

Figure10.1Model2–ImpactonIndirectImpact

Model3,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains34%ofthevarianceinthedependentvariable,impactonAnnualrevenues.Model3showsthatDirectimpactfactorissignificantlyassociatedwithimpactonAnnualrevenues(significantatthe99.9%confidencelevel),indicatingthatcompaniesthatreportedhigherimpactonresourcesandcapabilitiesaremorelikelytoattributetheSupportProgramswithimpactontheirannualrevenues.Ofthecompanyattributesvariables,Companyage,Growthplan,andInternationaldisplacementexperienceofemployeesaresignificantlyassociatedwithimpactonAnnualrevenues(significantatthe99.9%confidencelevel,atthe99%confidencelevel,andatthe95%confidencelevelrespectively),indicatingthatoldercompanies,companieswithanambitiousgrowthplan,andcompaniesthathavemoreemployeesthathaveinternationaldisplacementexperiencearemorelikelytoattributetheSupportProgramswithimpactonAnnualrevenues.Additionally,EmployeesthathaveuniversitydegreesissignificantlyandnegativelyassociatedwithimpactonAnnualrevenues(significantatthe95%confidencelevel),indicatingthatcompaniesthathavefeweremployeeswithauniversitydegreearemorelikelytoattributetheSupportProgramswithimpactontheirannualrevenues.Oftheentrepreneurattributesvariables,Parentsthatowna


businessissignificantlyandnegativelyassociatedwithimpactonannualrevenues(significantatthe90%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswhoseparentsdonotownabusinessaremorelikelytoattributetheSupportProgramswithimpactonAnnualrevenues.Inaddition,LevelofeducationissignificantlyassociatedwithimpactonAnnualrevenues(significantatthe99%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswithahigherlevelofeducationaremorelikelytoattributetheSupportProgramswithimpactonAnnualrevenues.Moreover,InternationalexperienceissignificantlyassociatedwithimpactonAnnualrevenues(significantatthe95%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneursthatstudiedorworkedinaforeigncountryaremorelikelytoattributetheSupportProgramswithimpactonAnnualrevenues.Model4,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains13%ofthevarianceinthedependentvariable,impactonEmployment.Model3showsthatDirectimpactfactorissignificantlyassociatedwithimpactonEmployment(significantatthe99.9%confidencelevel),indicatingthatcompaniesthatreportedhigherimpactonresourcesandcapabilitiesaremorelikelytoattributetheSupportProgramswithimpactontheiremployment.Ofthecompanyattributesvariables,Growthplan,andEmployeesthatarefamilymembersaresignificantlyassociatedwithimpactonEmployment(significantatthe99%confidencelevel,andatthe90%confidencelevelrespectively),indicatingthatcompanieswithanambitiousgrowthplan,andcompaniesthathavemoreemployeesthatarefamilymembersaremorelikelytoattributetheSupportProgramswithimpactontheiremployment.Model5,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains13%ofthevarianceinthedependentvariable,impactonFundingreceived.Model3showsthatDirectimpactfactorissignificantlyassociatedwithimpactonFundingreceived(significantatthe99.9%confidencelevel),indicatingthatcompaniesthatreportedhigherimpactonresourcesandcapabilitiesaremorelikelytoattributetheSupportProgramswithimpactontheirfundingreceived.Ofthecompanyattributesvariables,DomesticdisplacementexperienceofemployeesissignificantlyandnegativelyassociatedwithimpactonFundingreceived(significantatthe99%confidencelevel),indicatingthatcompaniesthathavefeweremployeeswithdomesticdisplacementexperiencearemorelikelytoattributetheSupportProgramswithimpactontheirfundingreceived.Oftheentrepreneurattributesvariables,Parentsthatownabusiness,andWorkexperiencearesignificantlyandnegativelyassociatedwithimpactonFundingreceived(significantatthe99%confidencelevel,andatthe95%confidencelevelrespectively),indicatingthatcompaniesthatwerefoundedbyentrepreneurswhoseparentsdonotownabusiness,andbyentrepreneurswithlessworkexperiencearemorelikelytoattributetheSupportProgramswithimpactontheirfundingreceived.


81

Table10.3LinearRegressionsofAllSupportPrograms

Variable

Model1

Directim

pactfactor

Model2

Indirectimpact

factor

Model3

Impactonannualrevenues

Model4

Impacton

employm

ent

Model5

Impactonfundingreceived

C:Age

+*+***

C:Size

C:Grow

thplan

+**+**

+**

C:Fundingreceived($)

C:Internationaldisplacementofem

ployees

+*+*

C:Domesticdisplacem

entofemployees

+*

-**C:Em


embers

+ α

C:Em

ployeesthathaveuniversitydegrees+ α

-*

C:Website

E:Age

-**

E:G

ender(male)

E:Parentsthatow

nabusiness

-**- α

-**

E:Levelofeducation-**

+ α+**

E:Workexperience(years)

+*

-*E:Internationalexperience

-*

E:Facebookfriends

UseofServices

+***

DirectIm

pactFactor

+***+***

+***+***

ModelCharacteristics

TotalN

147

149147

149149

AdjustedR2

.37.27

.34.13

.20F(dof)

***(17)***(18)

***(18)**(18)

***(18)dof=Degreesoffreedom

α=p<.1,*=p<.05,**=p<.01,***=p<.001


LinearRegressionModelResultsbyCityofSupportPrograms

Wealsoconductindividualregressionanalysesbydifferentcitiesofsupportprograms.Specifically,thefollowingsectionwillreportregressionmodelresultsonMinistryofCommerce101(MOC)program,PhnomPenhprograms,andHoChiMingCityprogramsrespectively23.Model6regressescontrolvariables,andintensityofuseofsupportservicesagainstdirectimpactoncompanies’resourcesandcapabilities,basedonsampleofMinistryofCommerce101program.Models7,8,9,and10regresscontrolvariables,intensityofuseofsupportservices,andthedirectimpactfactoragainstimpactoncompanyperformancemeasures,basedonsampleofMinistryofCommerce101program.DetailsonthefivemodelsmaybefoundinTable10.4.Model6,whichincludescontrolvariablesandintensityofuseofsupportservices,explains31%ofthevarianceinthedependentvariable,Directimpactfactor.Model6showsthatUseofservicesissignificantlyassociatedwithDirectimpactfactor(significantatthe95%confidencelevel),indicatingthatcompaniesthatusedsupportserviceswithahigherintensityaremorelikelytoattributetheMOCprogramwithimpactonresourcesandcapabilities.Ofthecompanyattributesvariables,FundingreceivedissignificantlyassociatedwithDirectimpactfactor(significantatthe95%confidencelevel),indicatingthatcompaniesthatreceivedmorefinancialsupportaremorelikelytoattributetheMOCprogramwithimpactontheirresourcesandcapabilities.Oftheentrepreneurattributesvariables,Entrepreneurage,andLevelofeducationaresignificantlyandnegativelyassociatedwithDirectimpactfactor(significantatthe90%confidencelevel,andatthe99%confidencelevelrespectively),indicatingthatcompaniesthatwerefoundedbyyoungerentrepreneurs,andthatfoundedbyentrepreneurswithalowerlevelofeducationaremorelikelytoattributetheMOCprogramwithimpactontheirresourcesandcapabilities.Moreover,Workexperience,andFacebookfriendsaresignificantlyassociatedwithDirectimpactfactor(significantatthe99%confidencelevel,andatthe95%confidencelevelrespectively),indicatingthatcompaniesthatwerefoundedbyentrepreneurswithmoreyearsofworkexperience,andthatfoundedbyentrepreneursthathavemoreFacebookfriendsaremorelikelytoattributetheMOCprogramwithimpactonresourcesandcapabilities.Model7,depictedinFigure10.2,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains27%ofthevarianceinthedependentvariable,Indirectimpactfactor.Ofthecompanyattributesvariables,FundingreceivedissignificantlyassociatedwithIndirectimpactfactor(significantatthe95%confidencelevel),indicatingthatcompaniesthatreceivedmorefinancialsupportaremorelikelytoattributetheMOCprogramwithimpactoncompanyperformance.Oftheentrepreneurattributesvariables,EntrepreneurageissignificantlyandnegativelyassociatedwithIndirectimpactfactor(significantatthe90%confidencelevel),indicatingthat

23WedidnotconductindividualregressionanalysisforDaNangprogramsduetodatapaucity.


companiesthatfoundedbyyoungerentrepreneursaremorelikelytoattributetheMOCprogramwithimpactoncompanyperformance.


Model8,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains30%ofthevarianceinthedependentvariable,impactonAnnualrevenues.Model8showsthatDirectimpactfactorissignificantlyassociatedwithimpactonAnnualrevenues(significantatthe95%confidencelevel),indicatingthatcompaniesreportedhigherimpactonresourcesandcapabilitiesaremorelikelytoattributetheMOCprogramwithimpactontheirannualrevenues.Ofthecompanyattributesvariables,FundingreceivedissignificantlyassociatedwithimpactonAnnualrevenues(significantatthe95%confidencelevel),indicatingthatcompaniesthatreceivedmorefinancialsupportaremorelikelytoattributetheMOCprogramwithimpactonAnnualrevenues.Oftheentrepreneurattributesvariables,Entrepreneurageissignificantlyandnegativelyassociatedwithimpactonannualrevenues(significantatthe90%confidencelevel),indicatingthatcompaniesthatfoundedbyyoungerentrepreneursaremorelikelytoattributetheMOCprogramwithimpactonAnnualrevenues.Model9,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains21%ofthevarianceinthedependentvariable,impactonEmployment.Ofthecompanyattributesvariables,FundingreceivedissignificantlyassociatedwithimpactonEmployment(significantatthe90%confidencelevel),indicatingthatcompaniesthatreceivedmorefinancialsupportaremorelikelytoattributetheMOCprogramwithimpactontheiremployment.Oftheentrepreneurattributesvariables,WorkexperienceandFacebookfriendsaresignificantlyassociatedwithimpactonemployment


(bothsignificantatthe90%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswithmoreworkexperience,andthatwerefoundedbyentrepreneurswithmoreFacebookfriendsaremorelikelytoattributetheMOCprogramwithimpactontheiremployment.Model10,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,doesnotexplainthevarianceinthedependentvariable,impactonFundingreceived.NoneoftheindependentvariablesissignificantinModel10.


85

Table10.4LinearRegressionsoftheMinistryofCom

merce101Program

Variable

Model6

Directim

pactfactor

Model7

Indirectimpact

factor

Model8


Model9

Impacton

employm

ent

Model10


C:Age

C:Size

C:Grow

thplan

C:Fundingreceived($)+*

+*+*

+ α


ployees

C:Domesticdisplacem

entofemployees

C:Em


embers

C:Em

ployeesthathaveuniversitydegrees

C:Website

E:Age

- α- α

- α

E:G

ender(male)

E:Parentsthatow

nabusiness

E:Levelofeducation-**


+**

+ α

E:Internationalexperience

E:Facebookfriends

+*

+ α

UseofServices

+*

DirectIm

pactFactor

+*


TotalN

43

4747

4848

AdjustedR2

.31.27

.30.21

-.06F(dof)

**(8)**(8)

**(8)**(8)

(8)dof=Degreesoffreedom

α=p<.1,*=p<.05,**=p<.01,***=p<.001


DetailsonthefivemodelsbasedonsampleofthePhnomPenhprogramsmaybefoundinTable10.5below.Model11regressescontrolvariables,andintensityofuseofsupportservicesagainstdirectimpactoncompanies’resourcesandcapabilities,.Models12,13,14,and15regresscontrolvariables,intensityofuseofsupportservices,andthedirectimpactfactoragainstimpactoncompanyperformancemeasures,basedonsampleofthePhnomPenhprograms.Model11,whichincludescontrolvariablesandintensityofuseofsupportservices,explains43%ofthevarianceinthedependentvariable,Directimpactfactor.Model11showsthatUseofservicesissignificantlyassociatedwithDirectimpactfactor(significantatthe95%confidencelevel),indicatingthatcompaniesthatusedsupportserviceswithahigherintensityaremorelikelytoattributethePhnomPenhprogramswithimpactonresourcesandcapabilities.Ofthecompanyattributesvariables,GrowthplanissignificantlyandnegativelyassociatedwithDirectimpactfactor(significantatthe95%confidencelevel),indicatingthatcompanieswithamodestgrowthplanorwithnogrowthplanaremorelikelytoattributethePhnomPenhprogramswithimpactontheirresourcesandcapabilities.Oftheentrepreneurattributesvariables,LevelofeducationissignificantlyandnegativelyassociatedwithDirectimpactfactor(significantatthe95%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswithalowerlevelofeducationaremorelikelytoattributethePhnomPenhprogramswithimpactontheirresourcesandcapabilities.Moreover,WorkexperienceissignificantlyassociatedwithDirectimpactfactor(significantatthe90%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswithmoreyearsofworkexperiencearemorelikelytoattributethePhnomPenhprogramswithimpactonresourcesandcapabilities.Model12,depictedinFigure10.3,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains26%ofthevarianceinthedependentvariable,Indirectimpactfactor.Model12showsthatDirectimpactfactorissignificantlyassociatedwithIndirectimpactfactor(significantatthe99.9%confidencelevel),indicatingthatcompaniesthatreportedhigherimpactonresourcesandcapabilitiesaremorelikelytoattributethePhnomPenhprogramswithimpactoncompanyperformance.Ofthecompanyattributesvariables,Companyage,andInternationaldisplacementexperienceofemployeesaresignificantlyassociatedwithIndirectimpactfactor(significantatthe90%confidencelevel,andatthe99%confidencelevel),indicatingthatoldercompanies,andcompaniesthathavemoreemployeeswithinternationaldisplacementexperiencearemorelikelytoattributethePhnomPenhprogramswithimpactoncompanyperformance.Moreover,EmployeesthathaveuniversitydegreesissignificantlyandnegativelyassociatedwithIndirectimpactfactor(significantatthe95%confidencelevel),indicatingthatcompaniesthathavelessemployeeswithauniversitydegreearemorelikelytoattributethePhnomPenhprogramswithimpactoncompanyperformance.



Model13,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains30%ofthevarianceinthedependentvariable,impactonAnnualrevenues.Model13showsthatDirectimpactfactorissignificantlyassociatedwithimpactonAnnualrevenues(significantatthe99.9%confidencelevel),indicatingthatcompaniesreportedhigherimpactonresourcesandcapabilitiesaremorelikelytoattributethePhnomPenhprogramswithimpactontheirannualrevenues.Ofthecompanyattributesvariables,Companyage,Growthplan,andInternationaldisplacementexperienceofemployeesaresignificantlyassociatedwithimpactonAnnualrevenues(allsignificantatthe90%confidencelevel),indicatingthatoldercompanies,companieswithanambitiousgrowthplan,andcompaniesthathavemoreemployeeswithinternationaldisplacementexperiencearemorelikelytoattributethePhnomPenhprogramswithimpactonAnnualrevenues.Moreover,EmployeesthathaveuniversitydegreesissignificantlyandnegativelyassociatedwithimpactonAnnualrevenues,indicatingthatcompaniesthathavefeweremployeeswithauniversitydegreearemorelikelytoattributethePhnomPenhprogramswithimpactonAnnualrevenues.Oftheentrepreneurattributesvariables,Levelofeducationissignificantlyassociatedwithimpactonannualrevenues(significantatthe95%confidencelevel),indicatingthatcompaniesthatfoundedbyentrepreneurswithahigherlevelofeducationaremorelikelytoattributethePhnomPenhprogramswithimpactonAnnualrevenues.Model14,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains18%ofthevarianceinthedependentvariable,impactonEmployment.Model14showsthatDirectimpactfactorissignificantlyassociatedwithimpactonEmployment(significantatthe99%confidencelevel),indicatingthatcompaniesreportedhigherimpactonresourcesandcapabilitiesaremorelikelytoattributethePhnomPenhprogramswithimpactontheiremployment.Ofthecompanyattributesvariables,Companyage,andInternational


displacementexperienceofemployeesaresignificantlyassociatedwithimpactonEmployment(significantatthe95%confidencelevel,andatthe99%confidencelevelrespectively),indicatingthatoldercompanies,andcompaniesthathavemoreemployeeswithinternationaldisplacementexperiencearemorelikelytoattributethePhnomPenhprogramswithimpactontheiremployment.Oftheentrepreneurattributesvariables,Levelofeducationissignificantlyassociatedwithimpactonemployment(significantatthe90%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswithahigherlevelofeducationaremorelikelytoattributethePhnomPenhprogramswithimpactontheiremployment.Model15,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains35%ofthevarianceinthedependentvariable,impactonFundingreceived.Model15showsthatDirectimpactfactorissignificantlyassociatedwithimpactonFundingreceived(significantatthe95%confidencelevel),indicatingthatcompaniesthatreportedhigherimpactonresourcesandcapabilitiesaremorelikelytoattributethePhnomPenhprogramswithimpactontheirfundingreceived.Ofthecompanyattributesvariables,Size,andInternationaldisplacementexperienceofemployeesaresignificantlyassociatedwithimpactonFundingreceived(significantatthe99.9%confidencelevel,andatthe90%confidencelevelrespectively),indicatingthatoldercompanies,andcompaniesthathavemoreemployeeswithinternationaldisplacementexperiencearemorelikelytoattributethePhnomPenhprogramswithimpactontheirfundingreceived.


89

Table10.5LinearRegressionsofthePhnomPenhProgram

sVariable

Model11

Directim

pactfactor

Model12

Indirectimpact

factor

Model13


Model14

Impacton

employm

ent

Model15


C:Age

+ α+ α

+*

C:Size

+***

C:Grow

thplan-*

+ α

C:Fundingreceived($)


ployees

+**+ α

+**+ α

C:Domesticdisplacem

entofemployees

C:Em


embers

C:Em

ployeesthathaveuniversitydegrees

-*-*

C:Website

E:Age

E:G

ender(male)

E:Parentsthatow

nabusiness

E:Levelofeducation-*

+*

+ α


+ α

E:Internationalexperience

-*

E:Facebookfriends

UseofServices

+*

DirectIm

pactFactor

+***+***

+**+*


TotalN

65

6969

7271

AdjustedR2

.43.26

.30.18

.35F(dof)

***(13)**(13)

**(13)*(13)

***(13)dof=Degreesoffreedom

α=p<.1,*=p<.05,**=p<.01,***=p<.001


DetailsoffivemodelsbasedonsampleoftheHCMCprogramsmaybefoundinTable10.6.Model16regressescontrolvariables,andintensityofuseofsupportservicesagainstdirectimpactoncompanies’resourcesandcapabilities.Models17,18,19,and20regresscontrolvariables,intensityofuseofsupportservices,andthedirectimpactfactoragainstimpactoncompanyperformancemeasures,.Model16,whichincludescontrolvariablesandintensityofuseofsupportservices,explains39%ofthevarianceinthedependentvariable,Directimpactfactor.Model16showsthatUseofservicesissignificantlyassociatedwithDirectimpactfactor(significantatthe99%confidencelevel),indicatingthatcompaniesthatusedsupportserviceswithahigherintensityaremorelikelytoattributetheHCMCprogramswithimpactonresourcesandcapabilities.Ofthecompanyattributesvariables,SizeissignificantlyandnegativelyassociatedwithDirectimpactfactor(significantatthe95%confidencelevel),indicatingthatsmallercompaniesaremorelikelytoattributetheHCMCprogramswithimpactontheirresourcesandcapabilities.Oftheentrepreneurattributesvariables,EntrepreneurageissignificantlyandnegativelyassociatedwithDirectimpactfactor(significantatthe99%confidencelevel),indicatingthatcompaniesthatwerefoundedbyyoungerentrepreneursaremorelikelytoattributetheHCMCprogramswithimpactontheirresourcesandcapabilities.Moreover,WorkexperienceissignificantlyassociatedwithDirectimpactfactor(significantatthe90%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswithmoreyearsofworkexperiencearemorelikelytoattributetheHCMCprogramswithimpactonresourcesandcapabilities.Model17,depictedinFigure10.4,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains29%ofthevarianceinthedependentvariable,Indirectimpactfactor.Model17showsthatDirectimpactfactorissignificantlyassociatedwithIndirectimpactfactor(significantatthe99%confidencelevel),indicatingthatcompaniesthatreportedhigherimpactonresourcesandcapabilitiesaremorelikelytoattributetheHCMCprogramswithimpactoncompanyperformance.



Model18,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,explains46%ofthevarianceinthedependentvariable,impactonAnnualrevenues.Model18showsthatDirectimpactfactorissignificantlyassociatedwithimpactonAnnualrevenues(significantatthe99%confidencelevel),indicatingthatcompaniesthatreportedhigherimpactonresourcesandcapabilitiesaremorelikelytoattributetheHCMCprogramswithimpactontheirannualrevenues.Ofthecompanyattributesvariables,CompanyageissignificantlyassociatedwithimpactonAnnualrevenues(significantatthe95%confidencelevel),indicatingthatoldercompaniesaremorelikelytoattributetheHCMCprogramswithimpactonAnnualrevenues.Oftheentrepreneurattributesvariables,Levelofeducation,andWorkexperiencearesignificantlyassociatedwithimpactonannualrevenues(bothsignificantatthe90%confidencelevel),indicatingthatcompaniesthatwerefoundedbyentrepreneurswithahigherlevelofeducation,andthatwerefoundedbyentrepreneurswithmoreyearsofworkexperiencearemorelikelytoattributetheHCMCprogramswithimpactonAnnualrevenues.Moreover,InternationalexperienceissignificantlyandnegativelyassociatedwithimpactonAnnualrevenues(significantatthe95%confidencelevel),indicatingthatcompaniesthatwerefoundedentrepreneursthathavenotstudiedorworkedinaforeigncountryaremorelikelytoattributetheHCMCprogramswithimpactontheirannualrevenues.Model19,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,onlyexplains7%ofthevarianceinthedependentvariable,impactonEmployment.NoneoftheindependentvariablesaresignificantinModel19.Model20,whichincludescontrolvariables,intensityofuseofsupportservices,anddirectimpactfactorvariable,onlyexplains5%ofthevarianceinthedependentvariable,impactonFundingreceived.NoneoftheindependentvariablesaresignificantinModel20.


Table10.6LinearRegressionsoftheHCMCProgramsVariable

Model16Directimpactfactor

Model17Indirectimpactfactor

Model18Impactonannualrevenues

Model19Impacton

employment

Model20Impactonfundingreceived

C:Age +* C:Size -* C:Growthplan C:Fundingreceived($) C:Internationaldisplacementofemployees

C:Domesticdisplacementofemployees

C:Employeesthatarefamilymembers

C:Employeesthathaveuniversitydegrees

C:Website E:Age -** E:Gender(male) E: Parents that own abusiness

E:Levelofeducation +α E:Workexperience(years) +α +α E:Internationalexperience -α E:Facebookfriends Useofservices +** Directimpactfactor +** +** ModelCharacteristics TotalN 48 48 48 48 48AdjustedR2 .39 .29 .46 .07 .05F(dof) **(9) **(9) ***(9) (9) (9)dof=Degreesoffreedomα=p<.1,*=p<.05,**=p<.01,***=p<.00

11.Glossary 93

11.GlossaryofTerms

Term DescriptionConfidencelevel Usedtodescribethereliabilityofacalculationorestimate.Ahigherconfidencelevel

indicatesamorereliableestimate.

Impactonresourcesandcapabilities

Improvements,withinashorttimeframe,toresourcesandcapabilities.TENexaminesimprovementstoresourcesandcapabilitiesasoutcomesofserviceofferingsfrominnovationintermediaries,suchasimprovedbusinesslinkages.

Distribution Thearrangementofthefrequencyofoccurrencearoundaparticularvalue.

Frequencydistribution Agraphicalrepresentationoftheoccurrenceofeachvaluewithinarangeofvalues.TENoftenusesthistooltorepresentthefrequencyofdifferentanswersinresponsetoaparticularsurveyquestion.

Impactonperformance Achangeinperformanceresultingfromchangesinresourcesandcapabilities.TENinvestigateschangesincompanyperformancemetricsattributabletoservicesprovidedbyinnovationintermediariesthatincreasecompanies’capacitytoperform.Forexample,changeinemployment.

Innovationintermediary Amemberofaclassoforganizationswithcommongoalsincludingthesupportofinnovation.TENworkswithinnovationintermediaries,rangingfromsmalleconomicdevelopmentorganizationstolargeandsophisticatedresearchinstitutes,whoseektomaketheirclientsmoreinnovative,intheinterestsoffacilitatingincreasesintheirviability,profitability,internationalpresence,orothermanifestationsoftheirsuccess.

Logicmodel Arepresentationoftherelationshipsbetweentheinputs,outputsandoutcomesofaprogram.TEN'sinnovationintermediarylogicmodelillustrateshowinnovationintermediariesworktofulfilltheirmissions,andhowTENmeasurestheirimpact.

Primarydata Datacollecteddirectlyfromasourcebythepersonororganizationconductingtheresearch.TENcollectsprimarydatafrominnovationintermediariesandtheirclientcompaniesthroughanestablishedsurveymethodology.

Privatefinancing Financingfromanindividualoraprivateinstitutionsuchasloansorangelinvestment.

R&D Researchanddevelopment.Companiesmayinvestinresearchanddevelopmentactivitieswiththegoalofimprovingordevelopingproductsorprocedures.


Resourcesandcapabilities Factorsdescribingacompany’scapacitytoperform,forexample,strategicandoperationalknowledge.

Significance Thelikelihoodthataresultorrelationshipiscausedbysomethingotherthanmererandomchance.Thestatisticalsignificancerepresentstheprobabilitythatrandomchancecouldexplaintheresult.Ingeneral,a5%orlowerp-valueisconsideredtobestatisticallysignificant.

SME Smallandmediumsizedenterprises,asdefinedbytheCanadianTradeCommissionerService,arecategorizedbysize.Smallenterpriseshavelessthan$10millioninannualsalesandlessthan50employeesintheservicesectororlessthan100employeesinthemanufacturingsector.Medium-sizedenterpriseshavelessthan$50millioninannualsalesand101to500employees.

TEN TheEvidenceNetworkInc.isanindependentthirdpartycompanythatspecializesinimpactassessmentfororganizationsthatsupportinnovation.

Timetomarket Theelapsedtimebetweentheinitialconceptstageofproductdevelopmentandwhentheproductisavailableforsale.

appendix 1 all locations - mbi › wp-content › uploads › 2017 › ...promotes private sector...

Documents