A STUDY OF PERFORMANCE AND EFFORT EXPECTANCY FACTORS … · 4/1/2013 · A STUDY OF PERFORMANCE...

ASTUDYOFPERFORMANCEANDEFFORTEXPECTANCYFACTORSAMONG

GENERATIONALANDGENDERGROUPSTOPREDICTENTERPRISESOCIALSOFTWARE

TECHNOLOGYADOPTION

SunilS.Patel,B.S,M.S.

DissertationPreparedfortheDegreeof

DOCTOROFPHILOSOPHY

UNIVERSITYOFNORTHTEXAS

May2013

APPROVED:

JeffM.Allen,MajorProfessor,Director,CenterforKnowledgeSolutions

HermanL.Totten,MinorProfessor,DeanoftheCollegeofInformation

JerryWircenski,CommitteeMemberandProgramCoordinator

Patel,SunilS.AStudyofPerformanceandEffortExpectancyFactorsAmong

GenerationalandGenderGroupstoPredictEnterpriseSocialSoftwareTechnology

Adoption.DoctorofPhilosophy,(AppliedTechnologyandPerformanceImprovement),

May2013,109pp.,36tables,6figures,references,97titles.

Socialsoftwaretechnologyhasgainedconsiderablepopularityoverthelastdecade

andhashadagreatimpactonhundredsofmillionsofpeopleacrosstheglobe.Businesses

havealsoexpressedtheirinterestinleveragingitsuseinbusinesscontexts.Asaresult,

softwarevendorsandbusinessconsumershaveinvestedbillionsofdollarstousesocial

softwaretoimprovebusinessandemployeeproductivity.

Thepurposeofthisstudywastoprovideinsightstobusinessleadersanddecision

makersastheyshapedtheirenterprisesocialsoftware(ESS)deliveryplans.Avastbodyof

informationexistsonthebenefitsofESSanditstechnicalimplementation,butlittle

empiricalresearchisavailableonemployees'perceptionsofESSexpectancyfactors(i.e.

usefulnessandeaseofuse).ThisstudyfocusedonITmanagers'perceptionsofESS

expectancyfactorstounderstandtheirbehavioralintenttoadoptESStechnology.

AdditionalresearchwasperformedtouncoverrelationshipsanddifferencesbetweenIT

Managers'adoptionintentionsandemployeeage,gender,andgenerationalgroups.

Surveyresultswereanalyzedusingacorrelationresearchdesignanddemonstrated

significantrelationshipswerefoundbetweenITmanagers'expectancyfactorsandtheir

behavioralintenttoadoptESStechnology.DifferenceswerealsodemonstratedbetweenIT

managers'age,gender,andgenerationalcohortgroups.Theresultsofthisresearchshould

helpbusinessleadersgaininsightsintotechnologyadoptionfactorsamongITmanagers.

Lastly,thepracticalapplicabilityandopportunitiesforfutureresearcharediscussed.

SunilS.Patel

ACKNOWLEDGEMENTS

Thewritingofthisdissertationwouldnothavebeenpossiblewithoutthesupportof

manypeople.Itistheproductofnearly8yearsofstudy,countlessreamsofarticles,and

manyweekends,non-vacations,latenights,andearlymornings.Thefollowingpeople,and

more,havehelpedmeseeitthroughtotheend.

Iwouldfirstliketoexpressmymostsinceregratitudetomyadvisor,Dr.JeffAllen,

forhiscommitmentandunendingencouragementthroughouttheprogram.WithoutDr.

Allen’sremindersto“getthisfinished"Imighthavenevercompletedthisjourneyand

foreverremainedSunilPatel,ABD.Iamindebtedandthankful.Ialsooweagreatdealof

thankstomycommitteemembersDr.JerryWircenskiandDr.HermanTottenfortheir

guidanceandadvice.

Tomyeditor,KathleenSmith,Isubmitmyhumblethanks.Notonlydidyousettlea

weeklongargumentbetweenmywifeandmeontheuseoftheOxfordcomma,youredits

broughtthisworktoitsmorerefinedstate.I'msureallwhocontinuereadingarethankful.

Iwouldliketothankmyfriends,whoseloyalty,humor,andsupporthaveprovided

theencouragementIneeded…oftenwithshenanigansatGharPatel.

TomyMomandDad,thankyou.YoushapedmetobethepersonIamtodayand

showedmethetrueworthofhardwork.Yourworkethicandearlyemphasisonthe

importanceofeducationinspiredmethroughoutalmost30yearsofschooling.

Finallyandmostimportantly,Iwouldliketothankmywifeandsoulmate,Betsy.Her

support,encouragement,patience,tolerance,andunconditionalloveprovidedthefoundation

uponwhichthisdissertationwasbuilt.Shewasasoundingboardformyideas,asecondpair

ofeyes,andcomfortforthemanywearydays.Icouldnothavedonethiswithoutyou,Betsy.

TABLEOFCONTENTS

ACKNOWLEDGEMENTS......................................................................................................................................III

LISTOFTABLES......................................................................................................................................................VI

LISTOFFIGURES.................................................................................................................................................VIII

CHAPTER1:INTRODUCTION.............................................................................................................................1

Background..................................................................................................................................................1

NeedfortheStudy.....................................................................................................................................4

TheoreticalFramework..........................................................................................................................8

PurposeoftheStudy.............................................................................................................................11

ResearchHypotheses............................................................................................................................12

Limitations.................................................................................................................................................13

Delimitations............................................................................................................................................14

DefinitionsofTerms..............................................................................................................................14

Summary....................................................................................................................................................15

CHAPTER2:INTRODUCTION..........................................................................................................................16

Introduction..............................................................................................................................................16

ResearchQuestions................................................................................................................................17

TechnologyAcceptanceFactors.......................................................................................................18

GenerationalDifferencesinTechnologyAcceptance..............................................................21

GenderDifferencesandTechnologyAcceptance......................................................................23

Summary....................................................................................................................................................24

Introduction..............................................................................................................................................25

ResearchQuestions................................................................................................................................25

ResearchDesign......................................................................................................................................26

Sampling.....................................................................................................................................................28

Instrumentation......................................................................................................................................29

DataCollection.........................................................................................................................................32

DataAnalysis............................................................................................................................................33

Summary....................................................................................................................................................43

Overview....................................................................................................................................................44

DataValidationandDescriptiveStatistics...................................................................................45

InstrumentAnalysis..............................................................................................................................48

HypothesesAnalysis..............................................................................................................................51

Summary....................................................................................................................................................66

Overview....................................................................................................................................................67

SummaryofFindings............................................................................................................................67

DiscussionandConclusionsFromFindings................................................................................69

Implications...............................................................................................................................................75

RecommendationsforFutureResearch.......................................................................................79

Summary....................................................................................................................................................81

APPENDICES............................................................................................................................................................83

APPENDIXA:INSTRUMENTS............................................................................................................83

APPENDIXB:IRBAPPROVALANDINFORMEDCONSENTNOTICE.................................88

REFERENCES...........................................................................................................................................................91

LISTOFTABLES

Table:

1. EnterpriseSocialSoftwareTechnologyExamplesandCoreFrameworkofFeatures.....6

2. AreasofApplicationandImplicationsforUsingSocialSoftwareinanOrganization....7

3. ComparisonofGenerations..................................................................................................................22

4. ResearchHypothesesAnalysis,VariableTypes,andMeasurements...................................35

5. DescriptiveStatistics:GenderandGenerationGroups.............................................................46

6. DescriptiveStatistics:VariableNormality....................................................................................47

7. ComparisonofCronbach’sAlpha.......................................................................................................49

8. ConvergentValidityAnalysis(1of2)...............................................................................................50

9. ConvergentValidityAnalysis(2of2)...............................................................................................50

10. DiscriminantValidityAnalysis...........................................................................................................51

11. ResearchHypothesesAnalyses,Results...........................................................................................52

12. PearsonCorrelationResults................................................................................................................52

13. Ho1aAnalysisofVariance.....................................................................................................................53

14. Ho1aRegressionModelSummary.....................................................................................................53

15. Ho1aCoefficients......................................................................................................................................53

16. Ho1bMediationDirectandTotalEffects........................................................................................54

17. Ho1bMediationIndirectEffectandSignificanceUsingNormalDistribution..................54

21. Ho2bGenerationalMultivariateAnalysis.......................................................................................56

22. Ho2bTestsofBetween-SubjectsEffects...........................................................................................56

23. Ho2bPairwiseComparisons.................................................................................................................57

27. Ho3bGenderMultivariateAnalysis...................................................................................................59

28. Ho3bGenderTestsofBetween-SubjectsEffects...........................................................................60

33. Ho4bGenerationandGenderInteractionMultivariateAnalysis..........................................63

34. Ho4bGenerationandGenderTestsofBetween-SubjectsEffects...........................................64

LISTOFFIGURES

Figure:

1. Technologyacceptancemodel.............................................................................................................8

2. Theoreticalframework...........................................................................................................................9

3. Mediationmodel.....................................................................................................................................19

4. Modifiedtechnologyacceptancemodel........................................................................................34

5. Mediationprocessmethodology......................................................................................................37

6. Correlationresults.................................................................................................................................68

CHAPTER1

INTRODUCTION

Socialsoftwaretechnologyhashadagreatimpactonhundredsofmillionsofpeople

acrosstheglobe.Websitesbasedonsocialsoftwaretechnology,suchasFacebookand

Wikipedia,provideamediumforuserstointeractwitheachotherandwithgroupsof

individuals.Whilesocialsoftwaretechnologyisnotnew,ithasgainedconsiderable

popularityinthelastdecade.Businesseshavealsoexpressedtheirinterestinleveraging

socialsoftwaretosupportemployeeandorganizationalproductivity.Asaresult,software

vendorsandbusinessleadershaveinvestedbillionsofdollarsindevelopingtheirsocial

softwareapplications,infrastructure,andpresenceaimedatenhancingbusinessand

employeeproductivity.

Marketdemandforsocialsoftwaredevelopersandvendorsisexpectedtoincrease

atacompoundedrateof13.7%through2014(Gartner,2010).Thisindicatesincreased

marketpotentialforitssalesandthevalueitcanbringtobusinessproductivityand

organizationalresults.Giventhatsocialsoftwaretechnologyisrelativelyyoungand

rapidlyevolvingatthetimeofthisstudy,littleresearchliteratureexistsonitsadoption

factors.

Background

Socialsoftwaretechnologyhasattractedhundredsofmillionsofpeopleacrossthe

globetothetechnologybyfacilitatingcollaborationamongpeopleandgroups.Whileitcan

bearguedthattheconcepthasexistedsincethefirsttwomodern-daycomputerswere

networked,itsimplementationinmajorWebformatsbeganappearingjustoveradecade

ago,in1997(Boyd&Ellison,2008),andhassincegainedconsiderablepopularity.For

example,Facebookwaslaunchedin2003,andasofDecember2011,thesitehadmorethan

800millionactiveusers,50%ofwhomwereloggedinonanygivenday(Facebook,2011).

Wikipediawaslaunchedin2001,andjustoveradecadelaterithadover16million

registereduserswithover53,000Web-requests,onaverage,perday(Wikipedia,2011).

Modern-daypoliticalmovements–the2011ArabSpringrevolutionsandOccupyWall

Streetdemonstrations–leveragedsocialsoftwaretechnologiessuchasTwitterand

Facebooktofurthercommunicationsamongdemonstratorsandprotesters(Howardetal.,

2011).

ThisstudyfocusedonInformationTechnology(IT)managers'perceptionsofsocial

softwareusageintheenterprise;thatis,theuseofsocialsoftwareinbusinesscontexts.

Regardlessofthecontextofitsuse,personalorbusiness,socialsoftwareisanenablingtool

orsetoftoolsthatfacilitatescollaborationthrough“thecreationandexchangeofuser

generatedcontent”(Kaplan&Haenlein,2010,p.61)builtonWeb2.0patterns(Boyd&

Ellison,2008).TheseWeb2.0patternsprovideatechnologyframeworkuponwhich

collaborativeapplicationscanbebuiltforInternetandIntranetcommunicationamong

businesses,employees,businesspartners,vendors,families,friends,andothergroupsand

individuals.

Socialsoftwareprovidesanetwork-basedapplicationplatformenablingusersto

interactwitheachotherandwithgroupsofindividuals.Itallowsindividualstoinvite

friendsandcolleaguestojointheirpersonalorgroupnetworksandshareinformation

profileswithothers(Boyd&Ellison,2008).Byprovidingthemeanstointeractand

collaborate,thesoftwareitselffurtherscollaborationtowarduser-generatedcontent

(Kaplan&Haenlein,2010;Shirkey,2003).Forexample,Wikipediahadover26million

wikipagesin2011;over3.8millionofthosewikiswerealmostcompletelywritten/edited

byitsusersandvolunteersontheInternetwhocontributedtheirintellectualcapital

withoutpayment.

AndrewMcAfeeofHarvardBusinessSchoolcoinedthetermEnterprise2.0in2006,

whichisessentiallybuiltontheWeb2.0technologyframework.McAfeedefined

Enterprise2.0asthe“useofemergentsocialsoftwareplatformsbyorganizationsin

pursuitoftheirgoals”(A.McAfee,2009;A.P.McAfee,2006).Ithassincegained

considerableacceptancebyindustryexpertsandresearchers(A.P.McAfee,2006;Cook,

2008;vanZyl,2009;Warr,2008)andisnowcommonlyreferredtoasthebusiness

platformforcollaborationovertheIntra/Inter-net.WiththeshiftandtrendtowardWeb

2.0-enabledtechnology,industryleadersandresearchershavesoughttoidentify

applicationsofsocialsoftwareinbusiness(Gartner,2010;Traudt&Vancil,2011).

Enterprise2.0hasmanynames–E2.0,EnterpriseWeb2.0andSocialBusiness–

amongothervariations.TheterminologythisstudyusedtodescribeEnterprise2.0

softwaretechnologywasEnterpriseSocialSoftware(ESS).Thatis,softwareapplication(s)

usedinbusinesscontextswhosecapabilitiesincludethecollaborativenatureinherentto

Web2.0consumer-basedsocialsoftwaresuchasFacebook,Blogger,andWikipedia,butare

usedbycompaniesandtheiremployeestowardimprovingbusinessresultsormeeting

goalssetbytheorganization(A.McAfee,2009).SeveralexamplesofESStoolsinclude

wikis,socialbookmarking,virtualcommunities,blogs,forums,mashups,andsocialprofiles

(Cook,2008;A.McAfee,2009).ESStechnologiescanbeleveragedinorganizationsacross

anyindustrytowardimprovingthesharingandvisibilityofideas,expertise,andcontent

acrossanorganization(Cook,2008;A.McAfee,2009;A.P.McAfee,2006).

Withinthefirewallsofbusiness,usercomputingandspendingonIThassteadily

increased,promptingbusinessestoinvestigatetheimpactofinnovationsinITand

employeeacceptancetowardincreasesinproductivityandeffectiveness(Igbaria&Tan,

1997;Klaus,Wingreen,&Blanton,2007).Asaresult,manyESSvendorshavedeveloped

packagedand/orcustomizedofferingscomprisedofoneormoreESStechnologies.

NeedfortheStudy

In2011,InternationalDataCorporationreported,“Theriseinconsumer-oriented

socialnetworkingapplicationsandplatformsoverrecentyearshasdrawncuriosityfrom

enterprisesbothlargeandsmall”(Traudt&Vancil,2011,p.1).Thetrendhadeffectively

blurredthelinesbetweenconsumeruseofsocialsoftwareandbusinessuse.Business

professionalsandexecutivesnoticedthepotentialforharvestingtheknowledgeofthe

masseswithintheirorganizationstocreatebusinessvalue,andin2007,Gartner

recommendedthatbusinessesdevelopandevolvetheirsocialsoftwarebusinessplans.

ResearchperformedbySkeelsandGrudin(2009)foundthattheuseofsocialnetworking

softwarebyprofessionalsinthecorporateenvironmenthadincreaseddramatically.This

trendwasreiteratedin2011whenGartnerstatedthatsocialsoftware“willreplacee-mail

astheprimaryvehicleforinterpersonalcommunicationsfor20percentofbusinessusers

by2014.”

Thisshiftwasunlikelytooccurautomatically.Akeyingredientnecessaryforthis

changewascenteredonemployeeadoptionandusageoftheenterprisesocialsoftware

systems.TechnologyadoptionwasacriticalsuccessfactorforsuccessfulIT

implementationandrollout(Saleem,1996),andthiswasespeciallytrueinthecaseofESS

(A.P.McAfee,2006).Decreasedadoption,inturn,hadthepotentialtoalsodecreasethe

levelofsuccesssoughtfromagivenESSimplementation.

Oftheresearchandmaterialavailable,muchoftheinformationfocusedprimarily

ondescribingsocialnetworking,itsworkingsandrelevance(vanZyl,2009).Whilethe

benefitandvaluestatementsconcerningsocialnetworkingandsocialsoftwareoften

appealedtobusinessleaders,manycompaniesexpressedskepticismonthecollaborative

impactthatESSmighthaveintheirorganization.Numerousstudieshaveshownthat

merelymakingtechnologyavailablewillnotnecessarilyproducechangesinestablished

employeecollaborationpracticesunlessemployeesfindittobeausefultoolintheirjobs

(Davis,1989;Mithas,Costello,&Tafti,2011;Traudt&Vancil,2011).Inessence,ifa

technologyisnotusefuloreasytouseasperceivedbyusers,adoption(actualusage)will

bereduced(Davis,1989).

ManystudiesandindustryarticlesarefocusedonESSbenefits,itsinternalsoftware

workings,oritstechnicalimplementation,butlittleinformationexistsonemployee

perceptionsofESStechnologyadoptionfactors.Thisstudyaddsinformationtothefieldon

ITmanagers'perceptionsofESStechnologyacceptance.Itparallelsabodyofexisting

researchrelatedtosoftwareandsystemstechnologyacceptanceintheconsumerand

businesscontexts;however,researchrelatedtomanagers'perceptionsofsocialsoftware

andESStechnologyadoptionforuseinthecontextofbusinesswasstilllacking.Thisstudy

canalsoprovideinsightintomanagers'perceptionsofESStechnologyacceptancefactors

basedondifferingemployeegenerationalgroupsandgendertypes.

McAfeeidentifiedsixkeyfeaturesthatcompriseESStechnologyandcenteron

search,links,authoring,tags,extensions,andsignals(A.McAfee,2009).Thesefeatures

(seeTable1)weretenetsofEnterprise2.0andformedthefoundationalcharacteristicsof

ESStechnologiesasidentifiedbyMcAfee(2006,2009).Theysupportedreciprocal

informationexchangesamongemployeesinthedirectionofachievingcommongoals

(Ferreira&DuPlessis,2009;Green&Pearson,2005).

Table1

EnterpriseSocialSoftwareTechnologyExamplesandCoreFrameworkofFeatures

ESSexamples Feature DescriptionBlog,Wikis,RSS,Mashups,SocialBookmarking,CollaborativeFiltering,SocialNetworking,SocialNetworkAnalysis

Search Findinginformationthroughkeywordsearch.Links Connectsinformationtogetherintoameaningful

informationecosystemusingthemodeloftheWebAuthoring Theabilitytocreateandupdatecontentleadstothe

collaborativeworkofmanyratherthanjustafewwebauthors.Inwikis,usersmayextend,undoandredoeachother'swork.Inblogs,postsandthecommentsofindividualsbuildupovertime.

Tagging Categorizationofcontentbyusersaddingsemantictagstofacilitatesearching,withoutdependenceonpre-madecategories

Extensions Softwarethatisextensibleandallowingthenetworktoactasanapplicationplatformandadocumentserver

Signals TheuseofsyndicationtechnologysuchasRichSiteSummary(RSS)tonotifyusersofcontentchanges

Note.AdaptedfromA.P.McAfee(2006),A.McAfee(2009).

ThesinglemostimportantanddistinctivefeatureofallWeb2.0andESSswasthat

valuewasderivedandcontrolledthroughend-user-generatedcontentandtheirbehavioral

actionofusingthesoftware.Thatis,themoreanESSsystemwasused,themorevaluable

itbecame,commonlyreferredtoasthewisdomofthecrowdsorknowledgeofthemasses.

InthecontextofESS,thissharingandreciprocalinformationexchangeassistedemployees

inachievingcommongoals(Ferreira&duPlessis,2009;Green&Pearson,2005).

Asnotedearlier,technologyadoptionwasacriticalsuccessfactorinmaximizingthe

intendedsuccesssoughtfromatechnologyimplementation.Butwhatmotivated

employeestoadoptanduseESStechnology?Kaiser,Müller-Seitz,PereiraLopes,andPinae

Cunha(2007)arguedthat“individualmotivationisapreconditionfortheactive

participationinpractice”(p.393)suggestingthatemployeemotivationstemsfromthe

needtoa)havingaproblem,b)solvingtheproblem,andc)communicatingtheresults.

Gherardi(2003),ontheotherhand,believedthatknowledgeinitselfmotivatesindividuals

tocommunicatetheircontributions,precludingtheneedforaproblem.Accordingto

Ryyppo(2007),bothoftheseeffectscanbeamplifiedwithESSanditsinherent

characteristicsofemployee-driven,bottom-updynamics(seeTable2).Thesemotivations

accountedforemployeeinvolvementincommunities,collaboration,andknowledge

distributionandacquisition.

Table2

AreasofApplicationandImplicationsforUsingSocialSoftwareinanOrganization

Areaofapplication

Implications

Humannetworksandcommunities

BettersupportforrelationshipsandjointactivitiesImprovedinformationsharingIncreasedaccessibilitytoandavailabilityofpeopleSupportandfacilitationofinformalnetworksandcommunitiesofpractice

Communicationandinteraction

AcceleratedandamplifiedcommunicationflowSupportforinteractionprocessesImprovedinformationsharingandlearningIncreasedaccesstoandawarenessofastrongcommunityIncreasedawarenessandunderstandingoftheimportanceofsharinginnetworkingIncreasedunderstandingofuseofinformationtechnologyforinteraction

Knowledge IncreasedabilitytoeffectivelyapplyexistingknowledgetocreatenewknowledgeandtotakeactionRapidmobilizationofknowledge

Note.AdaptedfromRyyppo(2007).

TheoreticalFramework

Thetheoreticalframeworkwasbasedonthetechnologyacceptancemodel(TAM),

asshowninFigure1.TAM,asamodel,wasintendedtoprovidepredictivemodelofend-

useruptake(acceptance)ofinformationtechnologythroughthreecoreconstructs:(a)

performanceexpectancy(usefulness),thedegreetowhichanindividualbelievesthatusing

thesystemwillhelponeattaingainsinjobperformance;(b)effortexpectancy(easeofuse),

thedegreeofeaseassociatedwiththeuseofthesystem;and(c)behavioralintentiontouse,

thedegreetowhichanindividualhasformulatedconsciousplanstoperformornot

performsomespecifiedfuturebehavior.

Perceived Usefulness (PU)

Perceived Ease of Use (PEOU)

Behavioral Intention to Use

External Factors

Actual System Use

Figure1.Technologyacceptancemodel.Adaptedfrom“ACriticalAssessmentofPotentialMeasurementBiasesintheTechnologyAcceptanceModel:ThreeExperiments.”byF.D.DavisandV.Venkatesh,1996,InternationalJournalofHuman-ComputerStudies,45,p.20. TheoverallframeworkforthisstudyextendedTAM,asillustratedinFigure2,and

describedtherelationshipbetweenTAMconstructs,generationalgroups,andgendertypes.

Theproposedframeworktheorizedthatthetechnologyacceptancefactorsdiffered

betweenemployeegenerationalgroupsandgendertypes.TheconstructsofTAMreflected

inthisstudyincludedperceivedusefulness(PU),perceivedeaseofuse(PEOU),and

behavioralintention(BI)touseESStechnology.

TAMwasdesignedforthecontextofITtomeasureemployees'perceptionsofa

technology'susefulness,easeofuse,andbehavioralintentiontousethetechnologyas

determinantsofpredictingactualsystem/technologyadoption.Ithasbeenusedtogain

insightsintoemployees'effectiveness(usefulnessofthetechnology)resultingfromthe

introductionofITtoolingintheirjobs.Ithasalsoassistedbusinessleaderstobetter

determinewhetherornottheconsequencesofITacceptanceaddedvaluetothebusiness

(Igbaria&Tan,1997)throughenhancementsinemployeeeffectiveness(Yi&Hwang,

2003).

Generational Groups

Gender

External Factors

Actual System Use

Figure2.Theoreticalframework–modifiedtechnologyacceptancemodel.Adaptedfrom“ACriticalAssessmentofPotentialMeasurementBiasesintheTechnologyAcceptanceModel:ThreeExperiments.”byF.D.Davis,andV.Venkatesh,1996,InternationalJournalofHuman-ComputerStudies,45,p.20.

TechnologyAcceptanceFactors(ResearchQuestion1)

Thetheoreticalframeworkofthisstudywasbasedontheconstructsofperceived

usefulness(PU),perceivedeaseofuse(PEOU),andbehavioralintention(BI)tousea

system.Davis(1989)describedPUandPEOUasdeterminantsimpactingBItousea

systemtowardpredictingactualsystemuse.Actualsystemusewasadirectfunctionof

perceivedBI,whereBIwasaweightedfunctionofPUandPEOU.Additionally,Davis

suggestedthatPUwasinfluencedbyPEOUandthatPUandPEOUwerejointlyinfluenced

byexternalfactors(antecedents).

FishbeinandAjzen's(1975)theoryofreasonedaction(TRA)alsosupported

“predictinginformationtechnologyacceptanceandusageonthejob”(Venkatesh,Morris,

Davis,&Davis,2003,p.428)althoughTAMconstructswere“bettersuitedtoInternet

technology”(C.Yang,Hsu,&Tan,2010,p.142).ThekeydifferencesbetweenTAMandTRA

werethatTAMdidnotincludeTRA'ssubjectivenormcomponentasadeterminantofBI

becauseitwasdifficulttodecoupledirecteffectsofthesubjectivenorm(SN)onBItousea

giveninformationtechnologysystem(Davis,Bagozzi,&Warshaw,1989).

Age,Generation,GenderFactors(ResearchQuestions2,3,and4)

TheTAMframeworkprovidedthebasisformeasuringotherexternalvariablesas

well.Forexample,experience,educationlevel,income,andsocialinfluencecouldbeadded

asantecedentsimpactingPUandPEOU.Thisstudyincludedtheantecedentsofemployee

ageandgender.MorrisandVenkatesh(2000)suggestedthattherewasa“cleardifference

withageintheimportanceofvariousfactorsintechnologyadoptionandusageinthe

workplace”(p.392).Chung,Park,Wang,Fulk,andMcLaughlin(2010)suggestedthatwhile

PU,PEOU,andBIhavebeenwidelytestedandacceptedtowarddeterminingtechnology

acceptance,moderators,suchasageandgender,haveremainedlargelyuntested.

Moreover,itcouldbetheorizedthatrapidenhancementsanddevelopmentsinITledto

increaseddisparitybetweengenerations,aspurportedbyChungetal.(2010).

BothageandgenderhaveshowntobemoderatorstoPU,PEOU,andBIasper

previousstudiesasrelatedtooveralltechnologyacceptance(Gefen&Straub,1997;Gilroy

&Desai,1986;Jones&Fox,2009;Morris&Venkatesh,2000;Venkatesh&Morris,2000).

GenderdifferencesindicatedthatPUhadhighersalienceformalesthanfemales(Minton&

Schneider,1980),whereasPEOUhadhighersalienceforfemalesthanmales(Venkatesh&

Morris,2000).Morris,Venkatesh,andAckerman(2005)foundageandgendertobe

significantmoderatorsofPU,PEOU,andBIwhiletheChungetal.(2010)findingsindicated

thepotentialdangerofanincreaseddigitaldividebetweengenerationsgiventheincreased

rateoftechnologicalevolution.

PurposeoftheStudy

ThisstudyexaminedITmanagers'perceptionsofESStechnologyacceptancefactors

asdeterminantstopredictESStechnologyadoption.Theresearchanalysisadded

informationtothefieldonmanagers'perceptionsofESStechnology'sperceivedusefulness

andeaseofusestratifiedbydifferinggenerationalgroupsandgendertypes.Italso

providedinsightstobusinessleaders/executivesastheyshapeESSdeliveryplansbased

onfindingsfromthisstudyconcerningpotentialdifferencesingenerationalgroupsand

gendertypes.ThetargetpopulationincludedITmanagersintheUnitedStateswhereESS

technologywasavailabletouseormayhavebecomeavailableforuseintheirjobs.

ResearchHypotheses

Thisstudyaimedtoexaminethefollowingresearchquestionsandhypotheses:

TechnologyAcceptanceFactors(i.e.,Usefulness,EaseofUse,andBehavioralIntent)

1. IstherearelationshipbetweenvariablesofITmanagers'behavioralintentiontouse

ESStechnology,perceivedusefulness,andperceivedeaseofuse?

Ho1a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'perceivedbehavioralintentiontouseESStechnologyandvariablesofperceivedusefulnessandperceivedeaseofuse.Ho1b:ITmanagers'perceivedeaseofuseisnotpositivelyrelatedtoperceivedusefulness.

GenerationalGroups

2. IstherearelationshipordifferencebetweenITmanagers'ageandgenerationalgroups

andthevariablesofperceivedusefulness,perceivedeaseofuse,andbehavioral

intentiontouseESStechnology?

Ho2a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,andage.Ho2b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'generationalgroupsandthevariablesofperceivedeaseofuse,perceivedusefulness,andbehavioralintentiontouseESStechnology.

GenderGroups

3. IstherearelationshipordifferencebetweenITmanagers'genderandthevariablesof

perceivedusefulness,perceivedeaseofuse,andbehavioralintentiontouseESS

technology?

Ho3a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,andgender.

Ho3b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'genderandthevariablesofperceivedeaseofuse,perceivedusefulness,andbehavioralintentiontouseESStechnology.

AllConstructsandMediators

4. IstherearelationshipordifferencebetweenITmanagers'behavioralintentiontouse

ESStechnologyandthevariablesofage,gender,perceivedusefulness,andperceived

easeofuse?

Ho4a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,age,andgender.Ho4b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'generationalgroupsandgendertypesandthevariablesofperceivedusefulness,perceivedeaseofuse,andbehavioralintentiontouseESStechnology.

Limitations

1. ESStechnologythatcontainsbugsimpactingemployeeexperiencemaydiffer

betweenmanagers.

2. CompanypoliciesregardingappropriateuseandrestrictionsonusageofEnterprise

SocialSoftwaremaydifferbetweencompaniesandorganizations.

3. Companyculture,employeeattitudes,andothersubjectivenormsmaydiffer

betweenparticipantsinthisstudy.

4. TheamountoffunctionalityandcapabilitiesarelikelytodifferamongESSvendor

solutions.

5. ManycombinationsofESStechnologiescanbeimplementedinanorganization.

ThisstudyfocusesonITmanagerswhohaveaccesstoormayinthefuturehave

accesstoESStechnologyintheirjob.

6. Themanagersmayormaynotbetech-savvy.

7. ManagementperceptionsofESStechnologyacceptance,usefulness(on-the-job

performance),oreaseofusemayormaynotbeactualeffectsofESStechnology.

8. Participantvoluntary-useversusmandatory-useofESStechnologymaydiffer

betweencompaniesandparticipants.

Delimitations

1. Vendorsoftwareofferingsmaybecustombuiltandvaryamongthecompanies.This

studywasbasedonemployeeperceptionsofESStechnologiesbeingused(orwould

beavailabletouse)inbusinesscontexts.

2. ThisstudyexaminedgenerationaldifferencesbetweenBabyBoomers,GenerationX,

andGenerationY.TheSilentGenerationandNewBoomersarenotcoveredinthis

study.

DefinitionsofTerms

BabyBoomers:Agenerationofindividualscategorizedashavingbeenbornbetween

1943-1960(Strauss&Howe,1997).

EffortExpectancy:The“degreeofeaseassociatedwiththeuseofthesystem”(Venkatesh

etal.,2003,p.450).

Enterprise2.0:The“useofemergentsocialsoftwareplatformsbyorganizationsinpursuit

oftheirgoals”andobjectives(A.McAfee,2009;A.P.McAfee,2006).

EnterpriseSocialSoftware(ESS):TheterminologyusedtodescribeEnterprise2.0-based

socialsoftwaretechnology.

Generation:Definedas“acohort-groupwhoselengthapproximatesthespanofaphaseof

lifeandwhoseboundariesarefixedbypeerpersonality”(Strauss&Howe,1994,p.

60;Strauss&Howe,1997).

GenerationX:Agenerationofindividualscategorizedashavingbeenbornbetween1961-

1981(Strauss&Howe,1997).

GenerationY:Agenerationofindividualscategorizedashavingbeenbornbetween1982-

2004(Strauss&Howe,1997).

PeerPersonality:Definedas“agenerationalpersonarecognizedanddeterminedby(1)

commonagelocation;(2)commonbeliefsandbehavior;and(3)perceived

membershipinacommongeneration”(Howe,2012).

PerformanceExpectancy:The“degreetowhichanindividualbelievesthatusingthe

systemwillhelphimorherattaingainsinjobperformance”(Venkateshetal.,2003,

p.447).

TheoryofReasonedAction:Fishbein'stheoryofreasonedaction(TRA),amodel“designed

toexplainvirtuallyanyhumanbehavior”(Ajzen&Fishbein,1980,p.4).

Summary

Thischapterprovidedbackground,significanceofthestudy,andthetheoretical

frameworkdescribinghowthisstudycontributestotheexistingbodyofknowledge.This

studyexaminedITmanagers'perceptionsofESStechnologyacceptancefactorsas

determinantsinpredictingESStechnologyadoption.Thestudyalsoexamined

relationshipsanddifferencesbetweentechnologyacceptancefactorsandITmanagerage,

generationalgroups,andgendertypes.Chapter1inthisstudyidentifiedtheresearch

questionsandhypothesesinvestigatedandincludedlimitations,delimitations,and

definitionsofimportanttermsusedthroughout.Chapter2providesareviewofresearch

literaturerelevanttothisstudy.

CHAPTER2

LITERATUREREVIEW

Introduction

asdeterminantsinpredictingESStechnologyadoption.Thestudyalsoexamined

relationshipsanddifferencesbetweentechnologyacceptancefactorsandITmanagers'age,

generationalgroups,andgendertypes.Theliteraturereviewfocusedontechnology

acceptancefactorsofperceivedusefulness(PU),perceivedeaseofuse(PEOU),and

behavioralintention(BI)touseasrelevanttoESStechnologyadoption.Additionally,the

reviewexamineddifferencesbetweenthesefactorsanddifferingemployeegenerational

groupsandgendertypes.Inthesectionstofollow,thereviewofexistingresearchis

presentedtosupporttheproposedframeworkfactorsasrelatedtosocialsoftwareandESS

technology(seeFigure2).

ThestudyofITacceptancebeganin1975withtheworkofRobeyandwasrefined

byDavis(1989).Robey(1979)theorizedthat“asystemthatdoesnothelppeopleperform

theirjobsisnotlikelytobereceivedfavorablyinspiteofcarefulimplementationefforts”(p.

537)andwasmorelikelytoresultindecreasedemployeeon-the-jobperformanceand

systemusefulness.Thiswasreferredtoasperformanceexpectancy,otherwisestatedas

usefulness,orPU.Incontrast,“thedegreetowhichapersonbelievesthatusingaparticular

systemwouldbefreeofeffort”(Davis,1989,p.320)referredtoeffortexpectancy,

otherwisestatedaseaseofuse,orPEOU.

Davis(1989)andDavisandVenkatesh(1996)suggestedthatindividualsaremore

apttouseornotusetechnologytotheextentthatitwould(a)beuseful,therebyhelping

themperformtheirjobmoreeffectively,and(b)beeasytouse.Researchershavelong

arguedthattechnologyacceptancefactors,PUandPEOU,whenrelatedtoBI,performas

strongpredictorsofactualtechnologyadoption(Davis,1989;Davis&Venkatesh,1996;

Venkateshetal.,2003).

ResearchQuestions

ThepaceatwhichESSevolvedinthefirstyearsofthe21stcenturywasprofound.

SeveralindustryresearchandadvisoryfirmsemphasizedtheimportanceofESS

technologyinsupportingstrategicbusinessgoals(Gartner,2010;Koplowitz,2011;Traudt

&Vancil,2011).Giventhisshiftandsoftwareevolution,howdoemployeesperceiveESS's

usefulnessandeaseofuse,anddoemployeesintendtouseitifESSis(orweremade)

availableintheirjobs?Furthermore,howdotheseperceptionsdifferbetweenemployee

ageandgenderwhencomparedwithESStechnologyusage?Thisstudyprovidesinsights

intotheseareasbyexaminingandansweringthefollowingresearchquestions.

TechnologyAcceptanceFactors

GenerationalGroups

GenderGroups

technology?

easeofuse?

SeveralstudiesdocumentedtheuseofPU,PEOU,andBIasfactorsmeasuring

technologyacceptanceanditsvalidityinthecontextofITandsocialsoftware(Adams,

Nelson,&Todd,1992;Davis,1989,1993;Davisetal.,1989;Davis&Venkatesh,1996;Lou,

Luo,&Strong,2000;Mathieson,1991;Szajna,1994,1996;Taylor&Todd,1995a,1995b;

Venkatesh&Davis,2000).Inonesuchstudy,LaneandColeman(2011)assessedthe

perceivedusefulnessandeaseofuseofsocialsoftwaretechnologyinauniversitysetting.

Thisstudyfoundforvalidationofthetechnologyacceptancefactors(PU,PEOU,BI),andthe

authorsfoundthat“higherperceivedeaseofuseleadstohigherperceivedusefulnessand

moreintensityintheuseofthesocialmedia”(p.7).Thatis,theeasieritwasforstudents

tousethesocialsoftware,themoreusefulitbecametoperformtasks/activities,suggesting

usefulnesswasamediatorasillustratedinFigure3.

b, b’

Figure3.Mediationmodel.Adaptedfrom“TheModerator-MediatorVariableDistinctioninSocialPsychologicalResearch:Conceptual,Strategic,andStatisticalConsiderations”byR.M.Baron,andD.A.Kenny,1986,JournalofPersonalityandSocialPsychology,51,p.1176. Involuntary-usesettings,asimilarmediatorrelationshipwasfoundwhenPEOU

wastheprimarydeterminantofanindividual'sbehavioralintentiontoadoptasystem,

withPUasasignificantsecondarydeterminant.Thiswasalignedwithmanyfindingsfrom

priorresearchinvoluntary-usesettingswhereusefulnessofITemergedastheprimary

antecedenttoBI(Davis,1993;Venkatesh,1999).Inanotherstudy,conductedbyBrown,

Massey,Montoya-Weiss,andBurkman(2002)inthecaseofamandatory-usesetting,the

researchersstudiedthemandatoryadoptionofnewtechnologytoreplaceanoldersystem

ata$5billionmulti-bankholding.TheBrownetal.studyalsoresultedinsupportofthe

relationshipsofPUandPEOUasdeterminantsofBI.SimilartothestudybyDavis(1993)

andVenkatesh(1999),PEOUwastheprimarydeterminantofBI,withPUasasignificant

secondarydeterminant.

Thereispotential,however,forareverserelationshipbetweenPEOUandPU,

contradictingPUasamediatingvariable.Additionally,whenindividualsmustperform

specificbehaviors,theimportanceofusers'beliefsaboutanIT'seaseofuseandusefulness

wasmorelikelytobeminimized,whilethebehavioralintentiontousethesystemwas

inflated,indicatingthatusersmaynothavewantedtoperformthemandatedbehaviorbut

diditanyway(Brownetal.,2002).Thisfurthersuggestedthatusefulnessandeaseofuse

measurementsremainedintactforbothmandatory-useandvoluntary-useenvironments,

althoughthemediatingfactormayhavedifferedbetweenthetwoenvironments.

employeeeffectivenessandESS

Employees'effectivenessremainsakeyconcernforbusinessesandisunlikelyto

decreaseinimportance.Inthecontextofcomputingtechnology,ifbusinessvalueisnot

derivedfromasystem,whyinvestinacquiringit?ThiswasadrivingfactorinLehrand

Lichtenberg's(1999)studytoaddressITanditsimpactonbusinessandemployee

productivity.ThedatasetanalyzedconsistedofU.S.firm-levelcomputerassetsand

financialdatafornon-agriculturalfirmsduringtheperiod1977-1993.Theirfindings

showedthatpersonalcomputerscontributedpositivelytoproductivitygrowthand

“yieldedexcessreturns”(p.335)relativetoothertypesofcapitalinvestmentoverthe16-

yearperiod.AreportreleasedbyForresterresearch,Koplowitz(2011,p.2)statedthe

following:

[Sixty-fourpercent]ofseniorbusinessleaderssaythatgrowingoverallcompanyrevenueistheirtoppriorityin2011.Howdotheyintendtodoit?Morethanhalfpointtonewcustomeracquisition;acquiringandretainingtoptalentranksthirdontheirlist;andoneinthreelooktoimproveoverallcustomerrelationships.TheseloftybusinessgoalsoftentrickledowntoITinitiativesthatuseenterprisesocialtechnologies.Infact,Forrester’stechnologyadoptionsurveyspointtoashiftinsoftwareinvestmentgrowthfrommorematuresoftwarecategories—likeenterpriseresourceplanning(ERP),humancapitalmanagement(HCM),andsupplychainmanagement(SCM)—tomorepeople-ornetwork-centricsoftware.Considerthat37%ofITdecision-makersplantoimplementorexpandtheuseofcollaborationtoolsin2011comparedwith25%orlesswhoareplanninginvestmentsinERP,HCM,productlife-cyclemanagement(PLM),andSCMappcategories.Theclientinterestinsocialplatformsisfueledbythreefactors:

• Thedesiretocaptureandre-useknowledge.

• Theneedtomaintainhumanconnectionsacrossadisparateworkforce.• Thepressuretomodernizesystemstomeetnewworkforcedemands.(p.2)

InthecaseofESS,InternationalDataCorporation(Traudt&Vancil,2011)andGartner

(2010)alsoconductedmarketresearch,findingthatsocialsoftwaretechnologyhadthe

potentialtocreatesignificantbusinessreturnsthroughapositiveimpactonemployee

productivity.

GenerationalDifferencesinTechnologyAcceptance

Generationaldifferencesintheworkplacehavebeenstudiedfordecades.Strauss

andHowe(1994)theorizedthattherearepatternstoeachnewgeneration.Theydefineda

generationas“acohort-groupwhoselengthapproximatesthespanofaphaseoflifeand

whoseboundariesarefixedbypeerpersonality”(p.60).Theyalsodefinedapeer

personalityas“agenerationalpersonarecognizedanddeterminedby(1)commonage

location;(2)commonbeliefsandbehavior;and(3)perceivedmembershipinacommon

generation”(p.64).ThegenerationsincludedinthisstudyareshowninTable3alongside

theirassociatedcharacteristics.Thesestrataindicatedthatemployeescanbegrouped

accordingtocharacteristicsofgenerationandthatmotivationsonusageofITdiffered

amonggenerationalgroups.

Researchcontinuedinanattempttodeterminehowbusinessesandindividuals

respondedtodifferentgenerations.BasedonastudyconductedbyMorrisandVenkatesh

(2000)onagedifferenceintechnologyadoptiondecisions,thereisa“cleardifferencewith

ageintheimportanceofvariousfactorsintechnologyadoptionandusageintheworkplace”

(p.392).Thissuggestedthatwhenintroducingnewtechnology,trainingprogramsshould

bestructuredwithgenerationalgroupsinmindbecauseeachgroup'straitsweredifferent.

Thatis,aone-size-fits-allapproachtomarketingthenewapplicationneededtobetailored

basedondifferinggenerationalaudiences.

Table3

ComparisonofGenerations

Generation Birthyear Identifyingtraitsandvalues

Influentialworldlysituations

SilentGeneration

1925-1942 Security(highpriority)Riskavoidant,responsibleHardworking,dependableFiscallyconservative

GreatDepressionWorldWarII

BabyBoomer

1943-1960

Valueteamwork,groupworkCompanycommitment,loyaltyIndividualistic,competitiveHighworkethicNeedtosucceed

“PeriodofunprecedentedprosperityandaffluencethatfollowedWWII”(ParkerandChusmir,1990)

GenerationX

1961-1981

ValueautonomyIndependenceOpencommunicationBalancedwork/lifePersonalgoalsandvaluesratherthancareerSkeptical,reluctanttotakeonleadershiproles

“Periodsofeconomicprosperityanddistress(early1980'srecessionanddownsizings)andfamilydisruption(highdivorcerateforparents)duringformativeyears”(Kupperschmidt,2000)

GenerationY

1982-2004

TechsavvyEmbraceschangeCollaborativeStrongworkethicEntrepreneurialspirit

September11/warinIraqandAfghanistanEconomicrecession

Note.AdaptedfromWhitman(2010).

Innumeroussurveysandstudies,theagingworkforceremainedakeytopicof

discussion.In2003,WorkforceManagementincluded3ofits25keyforecastedtrends

whichweredirectlyrelatedtotheretirementofBabyBoomers.TheSocietyforHuman

ResourceManagement'sSHRMWorkplaceForecast(2008)reportstatedasitsnumber2

trend:“largenumbersofBabyBoomers(1943-1960)retiringataroundthesametime”(p.

6).Thistrendwasatthecoreofnumerousforecastsandreports.Forexample,theU.S.

CensusBureauestimatedBabyBoomerstobealmost83millionindividuals(L’Allier&

Kolosh,2007).Asthisshiftofretirementoccurs,businessesneedtoconsiderthediffering

needsand/orrequirementsofthenewdemographic(s)enteringtheworkforce,as

suggestedbyMorrisandVenkatesh(2000);forexample:(a)increaseduseoftechnology

fornewgenerationofworkforce;(b)morehands-onperformancesimulations,and(c)

coaching/mentoringasaformofemployeedevelopmentandcareergrowth.

GenderDifferencesandTechnologyAcceptance

Severalstudieshaveexaminedgenderdifferenceasrelatedtotechnology

acceptancefactors(Chungetal.,2010;Gefen&Straub,1997;Morrisetal.,2005;Terzis&

Economides,2011;Venkatesh&Morris,2000;Wattal,Racherla,&Mandviwalla,2009).

Littleempiricalevidenceexisted,however,onthetopicofgenderdifferencesinthecontext

ofITorinformationsystemstechnologyadoption.Oneofthefirststudiesconductedonthe

influenceofgenderontechnologyacceptancewasperformedbyGefenandStraubin1997.

Theysuggestedthattheeffectsofgenderdifferencesonusefulnessandeaseofusewere

wellestablishedinareasotherthanIT,andtheythereforehypothesizedthatgendercould

havesimilardifferencesinthecaseofe-mailtechnologyadoption.Theresultsoftheir

1997studysuggestedthatgenderdifferencesexistedontheacceptanceofe-mail

technology.

AlongitudinalstudyconductedbyVenkateshandMorris(2000)exploredtheroleof

genderininitialtechnologyacceptancedecisions.Theypositedthatgenderdifferences

existedandthateven“duringtheearlieststagesoftechnologyintroduction,usersare

makinganacceptancedecision”(p.117),whichhasbeenknowntodifferfromusage

decisionsoveralongerperiodoftime(Davisetal.,1989).Theirfindingsalsosupported

previousliteratureindicatingthatmenaremoretaskorientedthanwomen(Minton&

Schneider,1980)andthereforeusefulnessofatechnologyhasgreatersaliencetomenthan

towomen(Venkatesh&Morris,2000;Venkateshetal.,2000;Wattaletal.,2009).Onthe

otherhand,easeofusewasfoundtobemoresalienttowomen.MintonandSchneider

(1980)alsofoundthatmen'sassessmentofeaseofuseofthesystemwentupsomewhat

withtime/experienceandfurtherhighlightedthatusefulnessismoresalienttomen;

however,women'seaseofuseoftechnologywentdownwithmoretime/experience.The

samepatternheldtrueforlong-termtechnologyacceptancedecisionsaswell,thus

providing“compellingevidenceforthenotionthatgenderplaysavitalroleinshaping

initialandsustainedtechnologyadoptiondecisions”(Venkatesh&Morris,2000,p.129).

Summary

Theaimofthisstudywastoexamineemployees'perceptionsoftechnology

acceptanceofESStechnologyasadeterminanttotechnologyadoption.Italsoexamines

howemployeesofdifferinggenerationalandgendergroupsperceivetheimpactontheir

on-the-jobperformance.Thischapterprovidedareviewoftheliteraturetogaingreater

insightinto(a)informationtechnologyacceptance,adoption,andimpactonemployee

effectiveness,and(b)generationalandgenderdifferencesasrelatedtotechnology

acceptancefactors.Chapter3providestheresearchmethodologyofthisstudy.

CHAPTER3

METHODOLOGY

Introduction

Thepurposeofthisstudywastoexamineemployees'perceptionsofESStechnology

acceptancefactors(PU,PEOU,andBI)asdeterminantsinpredictingESStechnology

adoption.Thestudyalsoexaminedhowemployeesofdifferinggenerationalgroupsand

gendergroupsperceivedESSusefulness,easeofuse,andthebehavioralintentiontouse

ESStechnology.Thischapterprovidestheresearchquestions,researchdesign,target

population,instrumentation,datacollectionprocedures,anddataanalysisprocess.

ResearchQuestions

Thisstudyexaminedthefollowingresearchquestionsandhypotheses:

Ho1a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'perceivedbehavioralintentiontouseESStechnologyandvariablesofperceivedusefulness,andperceivedeaseofuse.Ho1b:ITmanagerperceivedeaseofuseisnotpositivelyrelatedtoperceivedusefulness.

GenerationalGroups

Ho2a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,andage.

Ho2b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'generationalgroupsandthevariablesofperceivedeaseofuse,perceivedusefulness,andbehavioralintentiontouseESStechnology.

GenderGroups

technology?

Ho3a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,andgender.Ho3b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'genderandthevariablesofperceivedeaseofuse,perceivedusefulness,andbehavioralintentiontouseESStechnology.

easeofuse?

Ho4a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,age,andgender.Ho4b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'generationalgroupsandgendertypesandthevariablesofperceivedusefulness,perceivedeaseofuse,andbehavioralintentiontouseESStechnology.

ResearchDesign

Thisstudyusedacorrelationresearchdesignandgatheredinformationfromthe

targetpopulationoverasingleperiodoftime.Thesurveymethodologydescribedviewsof

employeesacrossgenerationalgroupsandgendertypesontheirperceptionsofESS

technologyusefulness,easeofuse,andbehavioralintentiontouseESStechnologyas

determinantsinpredictingadoption,oractualsystemuse.Thesurveyinstrument

gathereddataonvariablesofperceivedusefulness(PU),perceivedeaseofuse(PEOU),

behavioralintention(BI)tousethesystem,employeeage,andgender.

Thisresearchstudyincludedsixvariablesduringanalysis.Giventheresearch

designandmodelselectedinthisstudy,PUandPEOUwereperceiveddeterminantsofBI.

Twoadditionalvariablesincludedageandgender,whichactedascontrolvariables.The

sixthvariablewasgenerationalgroup,acategoricalvariablemadeupofBabyBoomers,

GenerationX,andGenerationY,whichwascalculatedusingtheagevariableduringdata

analysis.Table4identifiedthevariabletypes,measurements,andhypothesismapping.

Theresearchdesignusedinthisstudywassimilartoacross-sectionalresearch

designwhichalloweddatatobecollectedinashorterperiodoftimeversusalongitudinal

study(Gall,Gall,&Borg,2003).ThisdesignfitwellbycontributingasnapshotofIT

managers'perceptionsofESStechnologyacceptance.Giventhatthedatacollection

occurredwithinashorttimeframe,sampleattritionwasnotanissue.However,therewere

otherthreatstoconsider,suchasthreatstointernalandexternalvalidity.Internalvalidity

referstotheextenttowhichextraneousvariablesarecontrolledsuchthatanychangesto

thedependentvariableareattributedsolelybytheindependentvariableortreatment(Gall

etal.,2003).Externalvalidityreferstothegeneralizabilityofresearchfindingstoother

settingsandpopulations.Campbell,Stanley,andGage(1963)provided12factorsaffecting

internalvalidityand10factorsaffectingexternalvalidity.

Thecorrelationresearchdesignusedinthisstudywasanticipatedtohavemore

successthanotherresearchdesignstowardachievinggreatergeneralizabilitygiventhe

study'ssimilaritytothecross-sectionaldesign(DeVaus,2001).DeVaus(2001)statedthe

following:

Experimentsencounterproblemswithrepresentativenessfortwomainreasons.Theyoftenaskmoreofpeoplethandoone-offcross-sectionalstudies.Theyalsoinvolveactiveinterventionsandthereforehavetorelyonvolunteersandavailabilitysamples.Theyconsequentlylackrepresentativeness.Evenwhererepresentativesamplesareobtainedinitiallythiscanbelostaspeopledropoutoverthecourseoftheexperiment.(p.184).

AccordingtoGalletal.(2003),onemajorproblemistheeffectofchangesthatoccurinthe

populationoveraperiodoftime.However,thiswasnotanissueforthisstudygiventhat

alldatawerecollectedwithin7days.Despitethecorrelationresearchdesign'sadvantages,

ithadexposureoninternalvalidity(Babbie,1973)duetothepotentialconfoundingeffects

ofextraneousvariables.However,thisriskwascontrolledandminimizedbyhaving

selectedahomogeneouspopulation(Reynolds,Simintiras,&Diamantopoulos,2003).

Thecorrelationresearchdesignwasselectedforthisstudybecauseitwasthemost

effectivewaytoobtaindescriptivedatainashorttimeframe.Withthesimilaritytoacross-

sectionaldesign,itwasalsothebestwaytodetermineprevalence(Mann,2003,p.57).

Experimentalresearchdesignswereconsideredbutnotselectedgiventhat(a)thisstudy

didnotintendtoperformcausalanalysisand(b)generalizabilitymightbedecreaseddue

tothehighlycontrollednatureofexperimentalresearchdesigns.

Sampling

ThetargetpopulationforthisstudyincludedITmanagersintheUnitedStates.

AccordingtotheU.S.DepartmentofLabor,BureauofLaborStatistics(2011),thetotal

estimatedpopulationofworkersinmanagementoccupationsexceeds6millionworkers

acrossallindustrysectorsineverystateandtheDistrictofColumbia.Oftheseworkers,

288,660areclassifiedascomputerandinformationsystems(CIS)managersandaccount

foralmost5%ofallmanagementoccupationsintheUnitedStates.Chiefexecutivesas

definedbytheBureauofLaborStatisticswerenotincludedinthe288,660workercount

giventhefollowing:(a)TheyweretrackedseparatelyfromCISmanagers,and(b)a

breakdownofchiefexecutivesinITversuschiefexecutivesinotherindustrysegmentswas

unavailable.

Thisstudy'stargetpopulationincludedITmanagers(andexecutives)intheUnited

StateswhereESStechnologywasavailabletouseorhadthepotentialtobecomeavailable

foruse.Giventhattherewereover288,660CISjobsalone(notincludingchiefexecutives),

theminimumsamplesize,accordingtoKrejcieandMorgan(1970),was384,basedon

factorsofalphasetto.05;powersetto.80.Thesamplewasobtainedthroughanonline

panelresearchsurveyfirmviaaWebsurvey.Thestudyrequiredaresponserateofless

than1%ofthepopulation,whichwaslikelyattainablethroughmethodsoutlinedinthe

DataCollectionsection.

Instrumentation

ThisstudywasbasedonacorrelationresearchdesignandutilizedthePerceived

UsefulnessandEaseofUseinstrumentoriginallydevelopedbyDavis(1989)andlater

revisedbyDavisandVenkatesh(1996).Theinstrumentwasdesignedtopredictand

explainuseracceptanceofITandwaswidelyusedbyresearchersandpractitionersfor

manyareasofsoftware,hardware,andWeb(network)technologies.Itincludedthree

constructs/variables,perceivedusefulness(PU),perceivedeaseofuse(PEOU),and

behavioralintention(BI)touse.PUandPEOUweresignificantlycorrelatedwithBIand

actedasdeterminantsinactualtechnologyacceptance(Davis&Venkatesh,1996).This

studyusedamodifiedversionofthisinstrument(seeAppendixA).

TheoriginalscalewasdevelopedbyDavisin1989throughaprocessthatincluded

twostudiesconsistingof(a)pretestingandscalerefinement,(b)retestinginastudywith

furtherrefinement,(c)pretestingandscalerefinement,and(d)retestinginanotherstudy.

Thesamepatternofcorrelationswasfoundinbothstudieswheredifferenttechnologies

weretestedforuseracceptance.TheinstrumentwasfurtherrevisedbyDavisetal.(1989),

resultingina10-iteminstrument.Reliabilityandvalidityremainedconsistentcompared

toDavis'soriginalinstrument(1989)asevidencedthroughnumerousreplicationstudies

(Adamsetal.,1992;Davisetal.,1989;Igbaria&Iivari,1995;Hendrickson,Massey,&

Cronan,1993;Segars&Grover,1993;Subramanian,1994;Szajna,1994).Therevisionsto

theinstrumentpreservedreliabilityandvalidityaswasevidentintheoriginalinstrument.

Furtherresearchwasperformedontheinstrumenttodeterminewhetheritemgrouping

hadaneffectonreliabilityandvalidity,andresultsshowedthatitemgroupingdidnot

artificiallyinflateordeflatereliabilityorvalidity(Davisetal.,1989;Davis&Venkatesh,

1996).ThisstudyusedtheinstrumentaspublishedbyDavisandVenkateshin1996,with

minormodificationstoreflectthetechnology(i.e.ESS)surveyedbythisstudy.

Reliability

NumerousreplicationstudieshaveshownthePerceivedUsefulnessandEaseofUse

scaletohavehighreliability(Adamsetal.,1992;Davis,1989;Davisetal.,1989;Davis&

Venkatesh,1996;Hendricksonetal.,1993;Igbaria&Livari,1995;Segars&Grover1993;

Subramanian,1994;Szajna,1994).Cronbach'salphainthesestudieshasremainedat

over.90,indicatingthehighreliabilityoftheinstrument.Davisetal.(1989)performeda

studytoassessdifferencesingroupedversusintermixedorderingofitemsandfoundthat

Cronbach'salphaexceeded.95inbothgroupsforbothscales.Inthe1996studyperformed

byDavisandVenkatesh,reliabilityofintermixedversusgroupedconstructsbasedon

threeseparateexperimentsalsoresultedinhighCronbachalpha'sof.95,.90,and.90,

respectively.Inthisstudy,reliabilityoftheinstrumentwasmeasuredwithCronbach's

alpha.

Validity

ThePerceivedUsefulnessandEaseofUsescalealsoexhibitedhighdiscriminantand

factorialvalidity(Adamsetal.,1992;Davis,1989;Davisetal.,1989;Davis&Venkatesh,

1996;Hendricksonetal.,1993;Igbaria&Livari,1995;Segars&Grover1993;Subramanian,

1994;Szajna,1994).BasedonDavis's1989study,PUwassignificantlycorrelatedwith

bothself-reportedcurrentusageandself-predictedfutureusage(r=.85),andPEOUwas

alsosignificantlycorrelatedwithcurrentusageandfutureusage(r=.59)atp<.01.

InstrumentDescriptionandUsage

Thesurveyinstrumentdatausedinthisstudywerecomprisedof12items.Thefirst

10itemsmeasuredPU,PEOU,andBI.Theremaining2itemscapturedageandgender.

Additionalitemswereincludedintheinstrumentanddatacollectedforfutureuse.

Constructitemswerekeptintacttopreserveinstrumentreliabilityalthoughmultiple

studieshavepreviouslyindicatedthatgroupeditemsversusintermixeditemsdidnotaffect

thePU,PEOU,andBIconstructsvalidity/reliability(Davisetal.,1989;Davis&Venkatesh,

1996).ConstructitemsforPU,PEOU,andBIweremeasuredwitha7-pointLikertscale

rangingfrom+3(StronglyAgree)to-3(StronglyDisagree).SeeAppendixAfor

instrumentationandscales.TheinstrumentwasaccessibleontheInternetforthesample

populationwhoparticipatedinthestudy.

DataCollection

Datawerecollectedusingasecureonlinesurveyapplication,Qualtrics.com.The

onlinepanelresearchservicefirmusedwasResearchNow.ResearchNowwasprovidedthe

requirementcriterionofselectingonlyrespondentscurrentlyemployedasITmanagersin

theUnitedStateswhoseeducationminimallyincludedahighschooldiplomaorGED.Once

thesurveyandrespondentlistwereprepared,ResearchNowsentaninvitationemailto

theirpanelmemberparticipantsrequestingvoluntaryparticipationinthisstudy.The

emailscontainedalinktothesurveyhostedonQualtrics.combytheUniversityofNorth

Texas.Participantswhoaccessedthelinkwerepresentedwiththeinformationonthe

studyandtheInformedConsentNotice(seeAppendixB).Thosewhoconsentedand

agreedtoparticipateinthestudyclickedthroughwiththeiragreement,allowing

respondentstocontinuetothesurveyitems.

Manyonlinepanelresearchservicesindicatedturnaroundtimesof10daysfor

approximately400validresponses.Thedatainthisstudywerecollectedin6days,after

whichthesurveywasclosedandthedataweredownloadedforanalysis.Intotal,402valid

responseswerereceivedandusedtocontinuethestudy.

Overthepriordecade,therewasmuchdiscussionanddebateontheuseand

advantages/disadvantagesofonlinepanelresearchasameansofdatacollection

(Ayyagari,Grover,&Purvis,2011;Braunsberger,Wybenga,&Gates,2007;Duffy,Smith,

Terhanian,&Bremer,2005;Evans&Mathur,2005;Spijkerman,Knibbe,Knoops,VanDe

Mheen,&VanDenEijnden,2009).Onthekeyaspectofrepresentativeness,Scholl,Mulders,

andDrent(2002)statedthatwhenmostofasocietyhasInternetaccessandiscapableof

usingrelevanttechnology(i.e.,theInternet)thedrawbackofthelackofrepresentativeness

ofonlinepanelresearchdisappears.ThisappearedtoholdtrueforITmanagerssincethe

targetpopulationofthisstudyhadreceivedanadequateamountofexposuretocomputer

andsoftwaretechnology.

DataAnalysis

ThisstudyusedacorrelationresearchdesignandcollecteddatatoexamineIT

managerperceptionsofPU,PEOU,andBItopredictESStechnologyadoption.Thisstudy

wasbasedonthetheoreticalunderpinningsofTAM.Externalvariablesrefertovariables

thatmayhavepotentialimpactonPUandPEOU,suchasexperience,jobrelevance,social

imageofusingsystem,andsoon.ActualSystemUsereferstoactualtechnologyadoption

(seeFigure4).ThisstudyfocusedonPU,PEOU,BI,age(generationalgroup),andgender

types.

Generational Groups

Gender

H1a, H2a, H3a, H4a

H3a, H3c, H4a

H2a, H2b, H4a

H5a H3a, H3b, H4a

H3a, H3d, H4a

H2a, H2d, H4a

H2a, H2c, H4a

External Factors

Actual System Use

Oncethedatawerecollected,analysiswasperformedusingStatisticalPackagefor

theSocialSciences(SPSS)version15.0.Basedontheresearchdesignandhypothesesin

thisstudy,dataanalysisincludedmultipleregressionandMANOVA.SeeTable4fora

detailedmappingoftheresearchhypotheses,dataanalysis,variables,andrelatedconstruct

items.

Figure4:Modifiedtechnologyacceptancemodel.Adaptedfrom“ACriticalAssessmentofPotentialMeasurementBiasesintheTechnologyAcceptanceModel:ThreeExperiments.”byF.D.Davis,andV.Venkatesh,InternationalJournalofHuman-ComputerStudies,45,p.20.

Table4

ResearchHypothesesAnalysis,VariableTypes,andMeasurements

Hypothesis Dataanalysis Variable Type Items

Ho1a Multipleregression PU IV 1,2,3,4PEOU IV 5,6,7,8BI DV 9,10

Ho1b MediationAnalysis PEOU IV 5,6,7,8PU Mediator 1,2,3,4BI DV 9,10

Ho2a Multipleregression PU IV 1,2,3,4PEOU IV 5,6,7,8Age(continuous) IV 15BI DV 9,10

Ho2b One-wayMANOVA GenerationalGroups IV 15*BI DV 9,10PU DV 1,2,3,4PEOU DV 5,6,7,8

Ho3a Multipleregression PU IV 1,2,3,4PEOU IV 5,6,7,8Gender IV 16BI DV 9,10

Ho3b One-wayMANOVA Gender IV 16BI DV 9,10PU DV 1,2,3,4PEOU DV 5,6,7,8

Multipleregression

PU IV 1,2,3,4PEOU IV 5,6,7,8Age(continuous) IV 15Gender IV 16BI DV 9,10

Ho4b Two-wayMANOVA

GenerationalGroups IV 15*Gender IV 16BI DV 9,10PU DV 1,2,3,4PEOU DV 5,6,7,8

Note.*GenerationalgroupsarecomputedbasedonAge(item15).

Thefollowingresearchquestionsprovideadescriptionoftheanalysisperformedin

Chapter4.

ResearchHypothesisHo1a

Ho1a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'perceivedbehavioralintentiontouseESStechnologyandvariablesofperceivedusefulnessandperceivedeaseofuse.

HypothesisHo1aexaminedwhetherastatisticallysignificantrelationshipexisted

betweenITmanagers'perceivedbehavioralintention(BIitems9,10)touseESS

technologyandvariablesofperceivedusefulness(PUitems1,2,3,4)andperceivedeaseof

use(PEOUitems5,6,7,8).Multipleregressionanalysiswasperformedtotestwhether

therewasarelationshipbetweenindependentanddependentvariables.Variablestotest

included1DV(BehavioralIntention)and2IVs(PerceivedUsefulnessandPerceivedEase

ofUse).Sinceeachofthevariables'constructscontainedmultipleitems,compositemeans

werecomputedforeachofthevariables'constructs.

Thenullhypothesiswouldberejectediftheregressionanalysisresultsinap-value

significantatthep<.05levelforPUandPEOUonBI.Nullhypothesisrejectionwould

indicateITmanagers'perceivedintentionstouse/adoptESStechnologyifitwas(orwould

be)availabletouseinhisorherjob.Retainingthenullhypothesiswouldindicatethata

strongenoughrelationshipdoesnotexisttostatisticallyindicateITmanagers'behavioral

intentionstouse/adoptESStechnology.

ResearchHypothesisHo1b

Ho1b:ITmanagers'perceivedeaseofuseisnotpositivelyrelatedtoperceivedusefulness.

HypothesisHo1aexaminedwhetheraperceivedeaseofuse(PEOUitems5,6,7,8)

hadastatisticallysignificantpositiverelationshiptoperceivedusefulness(PUitems

1,2,3,4)todeterminewhetherPUperformedasamediatortobehavioralintention(BI

items9,10)touseESStechnology.Testingformediationusedbothsimpleandmultiple

regressionthroughthefollowingfourstepsandasillustratedinFigure5).

1. ConductasimpleregressionanalysiswithPEOUpredictingBItodeterminethe

directeffectof(a).Ifasignificantrelationshipexists,proceedtostep2.

2. ConductasimpleregressionanalysiswithPEOUpredictingPUtodeterminethe

directeffectof(b).Ifasignificantrelationshipexists,proceedtostep3.

3. ConductasimpleregressionanalysiswithPUpredictingBItodeterminethe

directeffectof(c).Ifasignificantrelationshipexists,proceedtostep4.

4. ConductamultipleregressionanalysiswithPUandPEOUpredictingBI.IfPEOU

(b')andPU(c)bothsignificantlypredictBI,thereispartialmediation.However,

ifPEOU(b')nolongersignificantlypredictsBIaftercontrollingforPU(c),full

mediationexists.Additionally,someformofmediationexistsiftheeffectofPU

(b)remainssignificantaftercontrollingforPEOU(b').

Behavioral Intention (BI) to

use ESS technology

b, b’

Figure5.Mediationprocessmethodology.Adaptedfrom“TheModerator-MediatorVariableDistinctioninSocialPsychologicalResearch:Conceptual,Strategic,andStatisticalConsiderations”byR.M.Baron,andD.A.Kenny,1986,JournalofPersonalityandSocialPsychology,51,p.1176.

Thenullhypothesiswouldberejectediftheregressionanalysisresultedinap-value

significantatthep<.05levelforPUonBI.Thiswouldindicatethatsomeformof

mediationexists.IfbothPUandPEOUaresignificantatp<.05level,partialmediation

exists.Furthermore,ifPEOUisnolongersignificantaftercontrollingforPU,fullmediation

exists,althoughthisscenariowasnotexpectedbasedonresearchliteraturefindings.

Anullhypothesesrejectionwouldindicatethatperceivedeaseofusedoesnot

significantlyinfluenceperceivedusefulness.However,iftherewereastatistically

significantnegativerelationshipofPEOUtoPU,itwouldhaveindicatedthatPEOUisthe

potentialmoderator.

betweenITmanagers'perceivedbehavioralintention(BIitems9,10)touseESStechnology

andvariablesofperceivedusefulness(PUitems1,2,3,4)andperceivedeaseofuse(PEOU

items5,6,7,8)wheninteractedwithage.Multipleregressionanalysiswasusedtotestthe

relationships.Variablestotestincluded1DV(BI)and3IVs(PU,PEOU,andage).Sincethe

PU,PEOU,andBIvariables'constructscontainedmultipleitems,compositemeanswere

computedforeachofthevariables'constructs.

significantatthep<.05levelforPU,PEOU,andageonBI.Nullhypothesisrejectionwould

indicatethatastatisticallysignificantrelationshipexistsbetweenITmanagers'perceived

intentionstouse/adoptESStechnologyandthevariablesofPEOU,PU,andage.Retaining

thenullhypothesiswouldindicatethatoneormoreoftheIVswasnotsignificanttoBI.

Additionally,toavoidthepossibilityofaTypeIorTypeIIerror,theresultsofthistest

requiredacomparisonwithHo1avalidatingthatbothPUandPEOUweresignificanttoBI

regardlessofageinvolvedasaninteractingvariable.

Ho2b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'generationalgroupsandthevariablesofperceivedeaseofuse,perceivedusefulness,andbehavioralintentiontouseESStechnology.

HypothesisHo2bexaminedwhetherastatisticallysignificantrelationshipexisted

items5,6,7,8)forITmanagergenerationalgroups.AMANOVAanalysiswasusedtotest

therelationshipstodeterminewhethertherewereanydifferencesbetweengenerational

groupsonvariablesofBI,PU,andPEOU.Variablestotestincluded3DVs(PU,PEOU,BI)

and1IV(generationalgroups).BecausePU,PEOU,andBIvariables'constructscontain

multipleitems,compositemeanswerecomputedforeachofthevariables'constructs.

significantatthep<.05levelforgenerationalgroupsusingWilks'sLambdateststatistic.

NullhypothesisrejectionwouldindicatethatITmanagers'perceivedintentionsto

use/adoptESStechnologydifferbetweengenerationalgroups.Ifthenullhypothesiswas

retained,itwouldindicatethattherewasnodifferencebetweengenerationalgroupson

variablesofPU,PEOU,andBI.Additionally,toavoidthepossibilityofaTypeIorTypeII

error,theresultsofthistestrequiredacomparisonwithHo1atovalidatethatbothPUand

PEOUweresignificanttoBI.

items5,6,7,8)wheninteractedwithgender.Multipleregressionanalysiswasusedtotest

therelationships.Variablestotestinclude1DV(BI)and3IVs(PU,PEOU,andgender).

SincethePU,PEOU,andBIvariables'constructscontainedmultipleitems,composite

meanswerecomputedforeachofthevariables'constructs.

significantatthep<.05levelforPU,PEOU,andgenderonBI.Nullhypothesisrejection

wouldindicatethatastatisticallysignificantrelationshipexistedbetweenITmanagers'

perceivedintentionstouse/adoptESStechnologyandthevariablesofPEOU,PU,and

gender.Ifthenullhypothesiswasretained,oneormoreoftheIVswasnotsignificanttoBI.

requiredacomparisonwithHo1avalidatingthatbothPUandPEOUweresignificanttoBI

regardlessofgenderinvolvedasaninteractingvariable.

Ho3b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'genderandthevariablesofperceivedeaseofuse,perceivedusefulness,andbehavioralintentiontouseESStechnology.

items5,6,7,8)forITmanagergenerationalgroups.AMANOVAanalysiswasusedtotest

therelationshipstodeterminewhethertherewereanydifferencesbetweengenderson

variablesofBI,PU,andPEOU.Variablestotestincluded3DVs(PU,PEOU,BI)and1IV

(gender).SincePU,PEOUandBIvariables'constructscontainedmultipleitems,composite

meanswerecomputedforeachofthevariables'constructs.

significantatthep<.05levelforgenerationalgroupsusingWilks'sLambdateststatistic.

NullhypothesisrejectionwouldindicatethatITmanagers'perceivedintentionsto

use/adoptESStechnologydifferedbetweengendertypes.Ifthenullhypothesiswas

retained,itwouldindicatethatthetherewasnodifferencebetweengendertypeson

variablesofPU,PEOU,andBI.Additionally,toavoidthepossibilityofaTypeIorTypeII

error,theresultsofthistestrequiredacomparisonwithHo1avalidatingthatbothPUand

PEOUweresignificanttoBI.

Ho4a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,age,andgender.

items5,6,7,8)wheninteractedwithageandgendertypes.Multipleregressionanalysiswas

usedtotesttherelationships.Variablestotestinclude1DV(BI)and4IVs(PU,PEOU,age,

andgender).BecausethePU,PEOU,andBIvariables'constructscontainedmultipleitems,

compositemeanswerecomputedforeachofthevariables'constructs.

significantatthep<.05levelforPU,PEOU,age,andgenderonBI.Nullhypothesis

rejectionwouldindicatethatastatisticallysignificantrelationshipexistedbetweenIT

managers'perceivedintentionstouse/adoptESStechnologyandthevariablesofPEOU,PU,

age,andgender.Ifthenullhypothesiswasretained,oneormoreoftheIVswasnot

significanttoBI.Additionally,toavoidthepossibilityofaTypeIorTypeIIerror,the

resultsofthistestrequiredacomparisonwithHo2aandHo3avalidatingthatbothPUand

PEOUweresignificanttoBIregardlessofageandgenderinvolvedasinteractingvariables.

Ho4b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'generationalgroupsandgendertypesandthevariablesofperceivedusefulness,perceivedeaseofuse,andbehavioralintentiontouseESStechnology.

items5,6,7,8)forITmanagergenerationalgroupsandgendertypes.Atwo-wayMANOVA

analysiswasusedtotesttherelationshipstodeterminewhethertherewereany

differencesbetweengenerationalgroupsandgendertypesonvariablesofBI,PU,and

PEOU.VariablestotestincludedthreeDV's(PU,PEOU,BI)and2IVs(generationalgroups

andgendertypes).SincePU,PEOU,andBIvariables'constructscontainedmultipleitems,

compositemeanswerecomputedforeachofthevariables'constructs.

significantatthep<.05levelforgenerationalgroupsandgendertypesusingtheWilks's

Lambdateststatistic.NullhypothesisrejectionwouldindicatethatITmanagers'perceived

intentionstouse/adoptESStechnologydiffersbetweengenerationalgroupsandgender

types.Ifthenullhypothesiswasretained,itwouldindicatethattherewasnodifference

betweengenerationalgroupsandgendertypesonvariablesofPU,PEOU,andBI.

requiredacomparisonwithHo2bandHo3bvalidatingthatbothPUandPEOUwere

significanttoBI.

Summary

Thischapterdiscussedthestudy'sresearchdesign,sampling,instrumentation,data

collectionprocedures,andthedataanalysis.Theresearchcarriedoutwasbasedonthe

proceduresoutlinedinthischapter.Chapter4discussesthefindingsofthestudy.

CHAPTER4

FINDINGS

Overview

asdeterminantstopredictESStechnologyadoption.Theresearchanalysisintendedtoadd

informationtothefieldonmanagers'perceptionsofESStechnology'susefulness,easeof

use,andtheirbehavioralintentiontouseESS.Thestudyalsointendedtoprovide

informationontechnologyadoptionfactorsacrossages,generationalgroups,andgender

typestoprovideinsightstobusinessleaders/executivesastheyshapeESSdeliveryplans.

Thischapterdocumentsthefindingsofthestudythroughtheexaminationand

analysisoffourresearchquestionsasoutlinedinChapter3.Thefirstresearchquestion

askedwhethertherewererelationshipsbetweenthevariablesofITmanagers'behavioral

intention(BI)touseESStechnology,perceivedusefulness(PU),andperceivedeaseofuse

(PEOU).ItalsoidentifiedwhethereaseofusewasamoderatingfactortousefulnessofESS

technology.Thesecondresearchquestionaskedwhethertherewererelationshipsor

differencesbetweenITmanagers'ageandgenerationalgroupsandthevariablesofPU,

PEOU,andBI.Thethirdresearchquestionconcernedtherelationshipsordifferences

betweenITmanagers'genderandthevariablesofPU,PEOU,andBI,andthefourth

researchquestionaskedwhethertherewererelationshipsordifferencesbetweenIT

managers'behavioralintentiontouseESStechnologywhenrelatedwithallvariables(i.e.,

PU,PEOU,Age,Generation,andGender).

Inthesectionstofollow,descriptivestatisticsanalysiswasperformedtoreport

samplecharacteristics;testsofnormalitytoensurenormalityandhomoscedasticity;

instrumentanalysistoreportthereliabilityandvalidityofthesurveyinstrument;and

hypothesesanalysisusingmultipleregression,mediationanalysis,analysisofvariance,and

multivariateanalysisofvariancetoreporttheresultsoftheresearchquestionsandnull

hypotheses.

DataValidationandDescriptiveStatistics

SampleSize

Surveyquestions/itemdatawerecollectedbytheonlinesurveytool(Qualtrics)

andstoredimmediatelyuponindividualrespondents’surveysubmissions.Respondent

datawerecollectedfor647totalsurveysubmissions.Therespondentswereselectedand

identifiedbyResearchNowasITmanagersintheUnitedStatesusingresearcher-identified

filters.ThesefiltersrestrictedstudyparticipantstothosewhowereemployedasanIT

managersatthetimeofthesurveyandwhoseeducationminimallyincludedhavingahigh

schooldiplomaorequivalent.Thesefiltersresultedineliminating131responsesforthose

whodidnotself-identifyasITmanagers.Furthermore,110responsesweredetermined

invalidbecausetherespondentsselected/bubbled-inastraight-ticketresponseforthePU,

PEOU,andBIquestions.Finally,responsesfromthoseintheSilentGenerationwere

removedfromthestudyduetohavingreceivedonlyfourvalidresponses.Theresulting

samplesizetotaled402validresponsesfromITmanagers,whichexceededtheminimum

requiredsamplesizeof384.

DescriptiveStatistics

Ofthevalidsurveycompletions,approximately75%oftherespondentsweremale

andtheremainderwerefemaleacrossthethreegenerationsofITmanagers:Baby

Boomers,GenerationX,andGenerationY(seeTable5).TheSilentGenerationcohort

groupwasremovedbecauseonlyfourresponseswerereceived,allofwhomweremale.

Therefore,onlythreegenerationalgroupingswereusedforanalysis.Aspreviouslystated,

agewasusedtodeterminearespondent’sgenerationalcohortgroup.

DataDistributionandNormality

Theassumptionsofnormalityweredeemedacceptabletocontinuewithparametric

analysis.Bothquantitativeandvisual(observational)methodswereusedtoevaluate

normality.Ruleofthumbhasheldthatavariableisreasonablynormalifitsskewnessand

kurtosishavevaluesbetween–1.0and+1.0.Inthisstudy,skewnessforPU,PEOU,BI,Age,

andGenerationrangedfrom-.10to.59;kurtosisrangedfrom-.76to.14.Genderkurtosis

wasalsowithinparametersat-.64althoughskewnesswas1.17;theskewwasexpected

giventheratioofmentowomenwhoparticipatedinthestudy(seeTables5and6).Q-Q

plotsalsosupportedtheassumptionofnormaldata.Thatis,theobservationdatawere

distributedcloselyaroundtheresultinglinearregressionline.

Table5

DescriptiveStatistics:GenderandGenerationGroups

Generationalgroups

TotalBabyBoomers GenerationX GenerationYMaleFemale

155 129 18 30243 47 10 100

Total 198 176 28 402Note.Silentgenerationexcludedfromsample.

Table6

DescriptiveStatistics:VariableNormality

Variable Mean Std.deviation Variance Skewness KurtosisPU 3.797 1.685 2.840 .453 -.764PEOU 3.199 1.313 1.724 .547 .137BI 3.418 1.772 3.140 .553 -.665Age 46.880 9.505 90.338 -.101 -.886Generation 2.580 .620 .384 .588 -.583Gender 1.250 .433 .187 1.167 -.642

Itshouldbenoted,however,thatdeviationfromnormalitywasindicatedbutgiven

theskewness,kurtosis,andvisualQQ-Plots,itwasdeterminedthatthelevelofnormality

wasacceptableforcontinuingwithparametrictestsasoutlinedinthestudy'smethodology.

Deviationfromnormality,includingadditionaldataanalysisandsupportfromprevious

researchliteraturesupportingcontinuancewithparametrictestingarediscussedbelow.

DeviationfromnormalitywasindicatedbytheShapiro-Wilksstatistic.Analysis

performedbetweengenerationalgroupsandthevariablesofPUandBIindicatedviolations

ontheassumptionofequalvariance.Asaresultofthepotentialthreatsofnon-normality,

additionaltestswereperformedtodemonstrateequalvariance(i.e.,homoscedasticity)

betweenGenderandGenerationGroups.Evidenceofnormalitywasdemonstratedby

Levene'stestsindicatingnonsignificancetounequalvariances,demonstratingsupportfor

continuingwithparametrictesting.Thisalsoprecludedtheneedtoperformlog

transformationofthedata.

PreviousresearchliteraturehasalsolongheldthetandFtest’srobustnessto

certainviolationsofnormality.Boneau(1960)statedthatttestsmaintainrobustnessto

certainviolationsofnon-normalityandfurtherstatedthat,“sincethetandFtestsof

analysisofvarianceareintimatelyrelated,itcanbeshownthatmanyofthestatements

referringtothettestcanbegeneralizedquitereadilytotheFtest”(p.63).Box(1953),and

Boneau(1960)havealsoinvestigatedtheeffectsofnormalityviolations,andthegeneral

conclusiondrawnfromthestudiesisthat“forequalsamplesizes,violatingtheassumption

ofhomogeneityofvarianceproducesverysmalleffects”(Howell,2007,p.203).Additional

researchsupportingcontinuingtouseparametricanalysiswithoutperforminglog

transformationwasdiscussedinthereliabilityanalysissectiontofollow.

InstrumentAnalysis

ThesurveyinstrumentgathereddataonvariablesofPU,PEOU,BI,Gender,Age,and

Generation.TheGenerationvariablewascalculatedwithAgeandgroupedasoneofeither

BabyBoomers,GenerationX,orGenerationY.Compositemeanswerecomputedforeach

ofthethreeconstructs:PUandPEOUconstructscontainedfouritemseach,andBI

containedtwoitems-eachweremeasuredbasedona7-pointLikertscale.Reliability,

convergentvalidity,anddiscriminantvaliditywerealsoevaluated.

Reliability

Reliabilityanalysiswasconsistentwithpreviousresearchstudies,showinghigh

reliabilityasmeasuredbyCronbach'salpha.Specifically,Cronbach'salphascoresforPU,

PEOU,andBIwere.98,.92,and.97,respectively.PriorstudieshavereportedCronbach

alphascoresgreaterthan.90forPU,PEOU,andBI.Inonecase,Davisetal.(1989)

performedastudytoassessdifferencesingroupedversusintermixedorderingofitems

andfoundthatCronbach'salphaexceeded.95inbothgroupsforbothscales.Inanother

study,performedbyDavisandVenkatesh(1996),reliabilityofintermixedversusgrouped

constructsbasedonthreeseparateexperimentsalsoresultedinhighCronbachalpha's

of.95,.90,and.90,respectively.Inaddition,eachofthefollowingstudiesalsoshowed

similar,highCronbachalphascores:Adamsetal.(1992),Davisetal.(1989),Hendrickson

etal.(1993),IgbariaandLivari(1995),SegarsandGrover(1993),Subramanian(1994),and

Szajna(1994).

Additionally,NorrisandAroian(2004)positedthatdatatransformationisnot

alwaysneededoradvisablewhentheCronbachalphaorPearsonproduct-moment

correlationiscalculatedforinstrumentswithskewedornon-normalitemresponses.

NorrisandAroian(2004)furtherstated:

Regardlessofsamplesize,neithertheCronbachalphanorthePearsonproduct-momentcorrelationshowedadifferencebetweenoriginalandtransformeddata,withoneexception.WhenitemsweretransformedfirstbeforebeingsummedinthecalculationofthePearsonproduct-momentcorrelation,inconsistentlyhigher(+.05)orslightlylowervalues(-.01)wereobservedrelativetothosecreatedwiththenontransformeddataacrossthedifferentsamplesizes.[p.1].

ThesecommentswereconsistentwithDunlap,Chen,andGreer(1994),suggestingthat

whenskewnessisenhancedorminimizedthroughlogtransformation,thereispotentialfor

introductionofartificiallyinflatedreliabilitycoefficients.

Table7

ComparisonofCronbach’sAlpha

Variable Cronbach’salpha NofitemsPU .98 4PEOU .92 4BI .97 2 ConvergentValidity

TheextenttowhichdataconvergedonthemselveswithintheconstructsofPU,

PEOU,andBIwasexaminedtodemonstrateevidenceofconvergentvalidity.Theresulting

analysisindicatedstrongcorrelationsbetweenitemsintheirrespectiveconstructs.All

constructsanditemshadcorrelationssignificantatthep<.01level.Correlationsforeach

oftheconstructsareprovidedinTable8.

Table8

ConvergentValidityAnalysis(1of2)

Measure PU1 PU2 PU3 PU4 PEOU1 PEOU2 PEOU3 PEOU4PU1 1 PU2 .93** 1 PU3 .94** .93** 1 PU4 .92** .90** .94** 1 PEOU1 .58** .58** .59** .60** 1 PEOU2 .42** .42** .45** .45** .70** 1 PEOU3 .47** .44** .49** .48** .71** .77** 1 PEOU4 .52** .53** .55** .54** .71** .73** .82** 1BI1 .80** .81** .82** .82** .58** .48** .53** .59**BI2 .80** .80** .81** .83** .56** .48** .53** .56**Note.**Correlationissignificantatthe0.01level(2-tailed).Table9

ConvergentValidityAnalysis(2of2)

Measure BI1 BI2 BI1 1 BI2 .94** 1 Note.**Correlationissignificantatthe0.01level(2-tailed).

Convergentvalidityexhibitedgoodinter-itemcorrelations,withrangesbetween.92

to.94forPU;and.70to.82forPEOU.BIwas.94sinceitsconstructconsistedoftwoitems.

DiscriminantValidity

Evidenceofdiscriminantvaliditywasdemonstratedbyexaminingcorrelations

amongtheconstructs,thusensuringthattheconstructsmeasureduniquedimensions.Asa

ruleofthumb,a.85correlationorlargerindicatespoordiscriminantvalidity(Davis,1998),

whereasacorrelationlowerthan.85indicatesanadequatevalidity.Thecorrelation

betweenPU,PEOU,andBIconstructsareshowninTable10.

ThecorrelationwithPUandBIat.85,p<.01,indicatedpossiblemulticollinearity.

Furtheranalysiswithcollinearitydiagnosticsresultedinatolerancefactorof.66anda

varianceinflationfactor(VIF)of1.51.AccordingtoGarson(2012),itisacceptabletohave

ahighcorrelationsolongasthetolerancefactorisgreaterthan.20.Furthermore,a

generalruleofthumbisthatVIFvalueslessthan10areacceptablelevelsofproceeding

withoutanyseriousthreatofcollinearityinthedata.SincethetolerancefactorandVIF

scoreswerewellwithintheirrespectivethresholds,itwasdeterminedthata

multicollinearityproblemdidnotexistinthedata.

Table10

DiscriminantValidityAnalysis

PU PEOU BIPU 1 PEOU .58** 1 BI .85** .61** 1Note.**Correlationissignificantatthe0.01level(2-tailed).

HypothesesAnalysis

ThisstudyusedacorrelationresearchdesigntoexamineITmanagerperceptionsof

PU,PEOU,andBItopredictESStechnologyadoption.Datawereexaminedforeight

hypotheses;resultsaresummarizedinTables11and12.

Table11

ResearchHypothesesAnalyses,Results

Hypothesis Result Measure Coefficient Value Sig.Ho1a Rejected MultipleRegression F 566.19 p<.01Ho1b Rejected SobelSimpleMediation Z 12.23 p<.01Ho2a Rejected MultipleRegression F 376.58 p<.01Ho2b Rejected MANOVA Wilks'sΛ .97 p<.05Ho3a Rejected MultipleRegression F 378.48 p<.01Ho3b Rejected MANOVA Wilks’sΛ .97 p<.01Ho4a Rejected MultipleRegression F 283.16 p<.01Ho4b Retained MANOVA-Generation Wilks’sΛ .97 p>.05

Gender Wilks’sΛ .98 p>.05Generation*Gender Wilks’sΛ .99 p>.05

Table12

PearsonCorrelationResults

Variable BI PU PEOU Age GenderBI 1.00 PU .85** 1.00 PEOU .61** .58** 1.00 Age .17** .18** .17** 1.00 Gender -.13** -.09* -.17** -.11* 1.00Note.*=p<.05,**=p<.01.N=402forallanalyses.Ho1a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'perceivedbehavioralintentiontouseESStechnologyandvariablesofperceivedusefulness,andperceivedeaseofuse.

MultipleregressionanalysisresultedinanFstatisticof566.19,p<.01.Therefore,

nullhypothesisHo1awasrejected.Resultsindicatedstatisticallysignificantcorrelationsof

PUandBI(r=.85,p<.01);andPEOUandBI(r=.61,p<.01),asreferencedinTables12,13,

14,and15.

Table13

Ho1aAnalysisofVariance

Sumofsquares df Meansquare F Sig.Regression 931.18 2 465.59 566.19 p<.01Residual 328.11 399 .82 Total 1259.29 401 Note.Predictors(Constant):PEOU,PU;Dependent:BI.

Table14

Ho1aRegressionModelSummary

R Rsquare AdjustedRsquare Std.erroroftheestimate.86 .74 .74 .98

Note.Predictors(Constant):PEOU,PU;Dependent:BI.Table15

Ho1aCoefficients

Unstandardizedcoefficients

Standardizedcoefficients

B Std.Error Beta t Sig.(Constant) -.32 .13 -2.51 p<.05PU .78 .03 .74 23.70 p<.01PEOU .24 .04 .18 5.66 p<.01Note.DependentVariable:BI.

Ho1b:ITmanagerperceivedeaseofuseisnotpositivelyrelatedtoperceivedusefulness.

NullhypothesisHo1bwasrejectedasresultsfoundforpartialmediation.The

regressionprocesstotestmediationexaminedwhetherperceivedeaseofuse(PEOU)hada

statisticallysignificantpositiverelationshiptoPUtodetermineifPUwasamediatortoBI.

ResultsindicatedstatisticallysignificantcorrelationsofPEOUandBI(r=.61,p<.01);

PEOUandPU(r=.58,p<.01);PUandBI(r=.85,p<.01)asoutlinedinTable12.Further

analysisindicatedthatPUremainedsignificantlyrelatedtoBIaftercontrollingforPEOU,

therebydemonstratingevidenceofpartialmediation(Z=12.23,p<.01).

TheanalysisalsoincludedanalysistheindirecteffectofPEOUonBIwhenPUwas

controlled.Theindirecteffectwascalculatedbymultiplyingthetworegression

coefficientsobtainedbytworegressionmodelsidentifiedbySobel(1982)andanalyzed

usingthePreacherandHayes(2004)SPSSadd-in.CompleteresultsareprovidedinTables

16and17.

Table16

Ho1bMediationDirectandTotalEffects

Method Coefficient Std.error t Sig(two-tailed)b(YX) .82 .05 15.42 p<.01b(MX) .75 .05 14.29 p<.01b(YM.X) .78 .30 23.70 p<.01b(YX.M) .24 .04 5.66 p<.01Note.Variables:Y=BI,X=PEOU,M=PU.Table17

Ho1bMediationIndirectEffectandSignificanceUsingNormalDistribution

ValueStd.

errorLL95CI UL95CI Z Sig(two-

tailed)Effect .58 .05 .49 .68 12.23 p<.01

MultipleregressionanalysisresultedinanFstatisticof376.58,p<.01.Therefore,

nullhypothesisHo2awasrejected.Resultsindicatedstatisticallysignificantcorrelationsof

PUandBI(r=.85,p<.01)andPEOUandBI(r=.61,p<.01).AgeandBIwerenotfoundto

besignificantlycorrelated(r=.17,p>.05)althoughtheoverallregressionmodeldidfind

forrejectionofthenullhypothesis.SeeTables18,19,and20.

Table18

Sumofsquares df Meansquare F Sig.Regression 931.22 3 310.41 376.58 p<.01Residual 328.07 398 .82 Total 1259.29 401 Note.Predictors:(Constant),Age,PEOU,PU;DependentVariable:BI.

Table19

Note.Predictors:(Constant),Age,PEOU,PU;DependentVariable:BI.

Table20

Ho2aCoefficients

B Std.error Beta t Sig.(Constant) -.37 .24 -1.52 p>.05PU .78 .03 .74 23.51 p<.01PEOU .24 .04 .18 5.62 p<.01Age .001 .01 .01 .22 p>.05Note.DependentVariable:BI.Ho2b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'generationalgroupsandthevariablesofperceivedeaseofuse,perceivedusefulness,andbehavioralintentiontouseESStechnology.

MANOVAresultedinWilks’sLambdavalueof.97,p<.05.Therefore,null

hypothesisHo2bwasrejected.PU,PEOU,andBIhadalsoeachcontributedtothe

significanceoftheoveralleffect.Partialetasquaredvaluewas.02;dependentvariablesof

PU,PEOU,andBIhadvaluesof.03,.02,.03,respectively,asfoundinTables21and22.

Table21

Ho2bGenerationalMultivariateAnalysis

Value F Hypothesisdf Errordf Sig.

PartialEtasquared

Observedpower

Wilks'sLambda

.97 2.32 6 794 p<.05 .02 .81

Note.Observedpowercalculatedusingalpha=.05.

Table22

Ho2bTestsofBetween-SubjectsEffects

SourceDependentvariable

TypeIIIsumofsquares df

Meansquare F Sig.

Partialeta

squaredObservedpowerb

CorrectedModel

PU 33.11a 2 16.56 5.97 p<.01 .03 .88PEOU 10.55c 2 5.27 3.09 p<.05 .02 .59BI 32.64d 2 16.32 5.30 p<.01 .03 .84

Intercept PU 2590.71 1 2590.71 934.77 p<.01 .70 1.00PEOU 1888.00 1 1888.00 1106.33 p<.01 .74 1.00BI 2153.57 1 2153.57 700.50 p<.01 .64 1.00

Generation PU 33.11 2 16.56 5.97 p<.01 .03 .88PEOU 10.55 2 5.27 3.09 p<.05 .02 .60BI 32.64 2 16.32 5.30 p<.01 .03 .84

Error PU 1105.83 399 2.77 PEOU 680.91 399 1.71 BI 1226.65 399 3.07

Total PU 6933.56 402 PEOU 4805.38 402 BI 5955.50 402

CorrectedTotal

PU 1138.94 401 PEOU 691.46 401 BI 1259.29 401

Note.a.Rsquared=.03(AdjustedRsquared=.02);b.Computedusingalpha=.05;c.Rsquared=.02(AdjustedRsquared=.01);d.Rsquared=.03(AdjustedRsquared=.02).

Pairwisecomparisonswerealsoperformedtodeterminethespecificdependent

variablesthatcontributedtothesignificanceoftheoveralleffectsbetweengenerational

groups.ForPU,resultsfoundforsignificancebetweengenerationalgroupsofBaby

BoomersandGenerationX(p<.05)andBabyBoomersandGenerationY(p<.05).There

wasnofindingofsignificancebetweenGenerationXandGenerationY.ForPEOU,results

foundforsignificancebetweengenerationalgroupsofBabyBoomersandGenerationX

only.ForBI,resultsalsofoundforsignificancebetweengenerationalgroupsofBaby

BoomersandGenerationXonly(p<.05).CompleteresultsareprovidedinTable23.

Table23

Ho2bPairwiseComparisons

Dependentvariable (I)Generation (J)Generation

Meandifference(I-

J) Std.error Sig.PU BB GenX .54 .17 p<.01

GenY .75 .34 p<.05GenX BB -.54 .17 p<.01

GenY .21 .34 p>.05GenY BB -.74 .34 p<.05

GenX -.21 .34 p>.05PEOU BB GenX .30 .14 p<.05

GenY .42 .26 p>.05GenX BB -.30 .14 p<.05

GenY .11 .27 p>.05GenY BB -.42 .26 p>.05

GenX -.11 .27 p>.05BI BB GenX .57 .18 p<.01

GenY .55 .35 p>.05GenX BB -.57 .18 p<.01

GenY -.03 .36 p>.05GenY BB -.55 .35 p>.05

GenX .03 .36 p>.05

MultipleregressionanalysisresultedinanFstatisticof378.48withap-value

significantatthep<.01level.Therefore,nullhypothesisHo3awasrejected.Results

indicatedstatisticallysignificantcorrelationsofPUandBI(r=.85,p<.01);PEOUandBI(r

=.61,p<.01);andgenderandBI(r=-.13,p>.05),asreferencedinTables12,24,25,and

Table24

Sumofsquares df Meansquare F Sig.Regression 932.45 3 310.82 378.48 p<.01Residual 326.84 398 .82 Total 1259.29 401 Note.Predictors:(Constant),Gender,PU,PEOU;DependentVariable:BI.Table25

Note.Predictors:(Constant),Gender,PU,PEOU;DependentVariable:BI.

Table26

Ho3aCoefficients

Unstandardizedcoefficients Standardizedcoefficients

B Std.error Beta t Sig.(Constant) -.13 .20 -.68 p>.05PU .78 .03 .75 23.73 p<.01PEOU .23 .03 .17 5.43 p<.01Gender -.13 .11 -.03 -1.24 p>.05Note.DependentVariable:BI.Ho3b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'genderandthevariablesofperceivedeaseofuse,perceivedusefulness,andbehavioralintentiontouseESStechnology.

MANOVAresultedinWilks’sLambdavalueof.97,p<.01.Therefore,null

hypothesisHo3bwasrejected.PU,PEOU,andBIhadalsoeachcontributedtothe

significanceoftheoveralleffect.Partialetasquaredvaluewas.03;dependentvariablesof

PU,PEOU,andBIhadvaluesof.01,.03,.02,respectively.Completeresultsareprovidedin

Tables27and28.

Table27

Ho3bGenderMultivariateAnalysis

Value F Hypothesisdf Errordf Sig.

PartialEtasquared

Observedpower

Wilks'sLambda .97 4.42 3 398 p<.01 .03 .87Note.Observedpowercalculatedusingalpha=.05.

Table28

Ho3bGenderTestsofBetween-SubjectsEffects

Meansquare F Sig.

Partialeta

squaredObservedpowerb

CorrectedModel

PU 9.11a 1 9.11 3.23 p>.05 .01 .43PEOU 19.63c 1 19.63 11.69 p<.01 .03 .93BI 20.55d 1 20.55 6.64 p<.05 .02 .73

Gender PU 9.11 1 9.11 3.23 p>.05 .01 .43PEOU 19.63 1 19.63 11.69 p<.01 .03 .93BI 20.55 1 20.55 6.64 p<.05 .02 .73

Error PU 1129.83 400 2.83 PEOU 671.83 400 1.68 BI 1238.74 400 3.10

Total PU 6933.56 402 PEOU 4805.38 402 BI 5955.50 402

CorrectedTotal

PU 1138.94 401 PEOU 691.46 401 BI 1259.29 401

Note.a.RSquared=.01(AdjustedRsquared=.01);b.Computedusingalpha=.05;c.Rsquared=.03(AdjustedRsquared=.02);d.RSquared=.02(AdjustedRsquared=.01).

Pairwisecomparisonswerealsoperformedtodeterminethespecificdependent

variablesthatcontributedtothesignificanceoftheoveralleffectsbetweengendergroups

(seeTable29).

Table29

Ho4a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,age,andgender.

MultipleregressionanalysisresultedinanFstatisticof283.16withap-value

significantatthep<.01level.Therefore,nullhypothesisHo4awasrejected.Results

indicatedstatisticallysignificantcorrelationsofPUandBI(r=.85,p<.01);PEOUandBI(r

=.61,p<.01);AgeandBI(r=.17,p<.01);andGenderandBI(r=-.13,p<.01)as

referencedinTable12.ANOVA,modelsummary,andcoefficientdetailsareprovidedin

Tables30,31,and32.

Table30

Sumofsquares df Meansquare F Sig.Regression 932.46 4 233.12 283.16 p<.01Residual 326.83 397 .82 Total 1259.29 401 Note.Predictors:(Constant),Gender,PU,Age,PEOU;DependentVariable:BI.

Dependentvariable (I)Generation (J)Generation

Meandifference(I-J) Std.error Sig.

PU MaleFemale

Female .35 .194 p>.05Male -.35 .194 p>.05

PEOU MaleFemale

Female .51 .150 p<.01Male -.51 .150 p<.01

BI MaleFemale

Female .52 .203 p<.01Male -.52 .203 p<.01

Table31

Note.Predictors:(Constant),Gender,PU,Age,PEOU;DependentVariable:BI.Table32

Ho4aCoefficients

B Std.error Beta t Sig.(Constant) -.16 .29 -.54 p>.05PU .78 .03 .74 23.54 p<.01PEOU .23 .04 .17 5.40 p<.01Age .00 .01 .00 .12 p>.05Gender -.13 .11 -.03 -1.23 p>.05Note.DependentVariable:BI.Ho4b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'generationalgroupsandgendertypesandthevariablesofperceivedusefulness,perceivedeaseofuse,andbehavioralintentiontouseESStechnology.

MANOVAresultedinWilks’sLambdavaluesof.98,p>.05forGeneration;.98,p

>.05forGender;andWilks'sLambdavalueof.99,p>.05forGenerationandGender

correlation.Therefore,nullhypothesisHo4bwasretained(seeTable33).

Fortestsofbetweensubjectseffects,onlyPUcontributedtothesignificanceofthe

effect,p<.05forGeneration;andhadapartialetasquaredof.02.ForGender,PEOUandBI

contributedtothesignificanceoftheeffect,p<.05;andbothhadpartialetasquaredvalues

of.01.TheGenerationandGenderinteractionresultedinPUandPEOUhavingno

contributiontothesignificanceoftheeffect,p>.05(seeTable34).

Table33

Ho4bGenerationandGenderInteractionMultivariateAnalysis

Effect WilksLambda F

Hypothesisdf

Errordf Sig.

Partialetasquared

Observedpower

Intercept .27 364.76 3 394 p<.01 .74 1.00Generation .98 1.51 6 788 p>.05 .01 .59Gender .98 2.33 3 394 p>.05 .02 .58Generation*Gender

.99 .45 6 788 p>.05 .00 .19

Note.Observedpowercalculatedusingalpha=.05.ReportedstatisticisWilks'sLambda.

Pairwisecomparisonswereperformedtodeterminethespecificdependent

variablesthatcontributedtothesignificanceoftheoveralleffectsbetweengenerational

andgendergroupsasaresultoftheinteraction.ForPU,resultsfoundforsignificance

betweengenerationalgroupsofBabyBoomersandGenerationX(p<.05);BabyBoomers

andGenerationY(p<.05);andnofindingofsignificancebetweenGenerationsXandY.

TherewerenofindingsofsignificancebetweenPUandgendergroups.ForPEOU,there

werenofindingsofsignificancebetweengenerationalgroupsalthoughgendergroupswere

foundtobesignificant,p<.01.ForBI,resultsfoundforsignificancebetweenBaby

BoomersandGenerationX(p<.05);andfindingsforsignificancebetweengendergroups,

p<.05.CompleteresultsareprovidedinTables34,35,and36.

Table34

Ho4bGenerationandGenderTestsofBetween-SubjectsEffects

Meansquare F Sig.

Partialeta

squaredObservedpower

CorrectedModel

PU 39.89a 5 7.98 2.88 p<.05 .04 .84PEOU 28.25c 5 5.65 3.37 p<.01 .04 .90BI 52.46d 5 10.49 3.44 p<.01 .04 .91

Generation PU 23.04 2 11.52 4.15 p<.05 .02 .73PEOU 5.81 2 2.92 1.73 p>.05 .01 .36BI 16.13 2 8.06 2.65 p>.05 .01 .53

GenderCode PU 4.82 1 4.84 1.74 p>.05 .00 .26PEOU 8.40 1 8.40 5.02 p<.05 .01 .61BI 13.47 1 13.44 4.42 p<.05 .01 .56

Generation*GenderCode

PU .18 2 .01 .03 p>.05 .00 .06PEOU .15 2 .08 .05 p>.05 .00 .06BI 2.63 2 1.32 .43 p>.05 .00 .12

Error PU 1099.05 396 2.78 PEOU 663.21 396 1.68 BI 1206.84 396 3.06

Total PU 6933.56 402 PEOU 4805.38 402 BI 5955.50 402

CorrectedTotal

PU 1138.94 401 PEOU 691.46 401 BI 1259.29 401

Note.a.Rsquared=.04(AdjustedRsquared=.02);b.Computedusingalpha=.05;c.Rsquared=.04(AdjustedRsquared=.03);d.Rsquared=.04(AdjustedRsquared=.03).

Table35

Dependentvariable

(I)Generation (J)Generation

Meandifference

(I-J) Std.error Sig.aPU BB GenX .51 .20 p<.05

GenY .72 .36 p<.05GenX BB -.51 .20 p<.05

GenY .21 .36 p>.05GenY BB -.72 .36 p<.05

GenX -.21 .36 p>.05PEOU BB GenX .27 .16 p>.05

GenY .32 .28 p>.05GenX BB -.27 .16 p>.05

GenY .05 .28 p>.05GenY BB -.32 .28 p>.05

GenX -.05 .28 p>.05BI BB GenX .46 .21 p<.05

GenY .48 .38 p>.05GenX BB -.46 .21 p<.05

GenY .02 .38 p>.05GenY BB -.48 .38 p>.05

GenX -.02 .38 p>.05Table36

Dependentvariable

(I)Generation (J)Generation

Meandifference(I-

J) Std.error Sig.PU Male

FemaleFemale .35 .19 p>.05Male -.35 .19 p>.05

PEOU MaleFemale

Female .51 .15 p<.01Male -.51 .15 p<.01

BI MaleFemale

Female .52 .20 p<.05Male -.52 .20 p<.05

Summary

Thischapterprovidedtheresultsfromthedatacollectedandthestatisticaltests

performed.Theanalysesvalidatedtheinstrumentation,data,andmethodologyusedto

answerthestudy’sresearchquestionstoacceptorrejectthenullhypotheses.Methods

includedreliabilityandvalidityanalysis,correlationanalysis,multipleregression,and

MANOVA.Findingsresultedintherejectionofsevenofeighthypothesesoutlinedin

previouschapters.Chapter5providesasummaryofthestudy,discussionofitsfindings,

andrecommendationsforfutureresearch.

CHAPTER5

SUMMARY,IMPLICATIONS,AND,RECOMMENDATIONS

Overview

Thischapterprovidesthesummaryoffindings,implicationsforthefieldand

inferencesdrawnfromtheresults,andrecommendationsforfutureresearch.The

summaryprovidesanoverviewofthefindingsthathelpedanswerthestudy'sresearch

questionsandhypotheses.Next,implicationsforthefieldarediscussedandinferencesare

drawnthathavepractical,research,andtheoreticalsignificance.Lastly,recommendations

areprovidedforfutureresearchopportunities.

SummaryofFindings

Adrivingpremiseforthisstudywastheresultofthesteepriseinconsumeruseof

socialnetworkingsoftwaretechnologyforpersonaluse(e.g.FacebookandTwitter)and

thecorrespondingincreaseininterestfrombusinessleadersinadoptingsocialsoftware

fortheiremployeestoimprovebusinessproductivity.Thepurposeofthisstudywasto

examineITmanagers’perceptionsofEnterpriseSocialSoftware(ESS)acceptancefactors

topredictwhetherornotITmanagerswouldadoptanduseESSintheirownjobs.The

studyfurtherexaminedtheacceptancefactorsacrossITmanagers’age/generational

groups,andgendertypes.

Thestudywascomprisedof402ITmanagersintheUnitedStates.Datawere

collectedwithanonlinequestionnaireinareasofperceivedusefulness,easeofuse,and

behavioralintentiontouse/adoptESStechnology.Oftheparticipants,24.9%werefemale,

indicatingarepresentativesampleofmale/femaleITmanagementoccupationswhen

comparedtotheU.S.DepartmentofLabor(2011),whichstatedthat25.3%ofITmanagers

werefemale.Thedatawerethenanalyzedusingmultipleregression,mediationanalysis,

andmultivariateanalysis.

TheresultsindicatedthatasignificantrelationshipexistedbetweenanITmanager's

behavioralintentiontouseenterprisesocialsoftwarebasedontheirperceptionsofthe

technology'susefulnessandeaseofuse.Mediationanalysisalsofoundthatusefulnesswas

apartialmediatortowardITmanagers'intentiontoadoptESStechnology.Thatis,the

usefulnessofESSwastheleadingfactortowardanITmanagers'decisionontheintentto

use/adoptthesystem.Easeofusealsoremainedsignificantlycorrelatedtointentionsof

adoption(seeFigure6).

Perceived Usefulness (PU)Partial Mediator

(BI)Age

Gender

r = .58, p < .01

r = -.17, p < .01

External Factors

Actual System Use

r = .18, p < .01 r = -.09, p < .05

r = .85, p < .01

r = .61, p < .01

r = .17, p < .01

Figure6:Correlationresults.Note.Dependentvariable:BI.

ResultsalsofoundasignificantdifferencebetweenITmanagergenerationalcohort

groupsanddifferencesbetweenITmanagergendertype.Multivariateanalysissuggested

thatITmanagerageandgenerationalcohortgroupsdemonstratedhavingdiffering

perceptionsontheirintenttouse/adoptESStechnology.Evidencewasalsodemonstrated

ongenderdifferenceshavinganimpactontheintentofESStechnologyadoption.

Theseresultswereconsistentwithpreviousresearchliteratureusingtheconstructs

identifiedinthetechnologyacceptancemodel(TAM)andsupportpreviousresearch

performedbyAdamsetal.(1992),Davis(1989),Davisetal.(1989),DavisandVenkatesh

(1996),Hendricksonetal.(1993),IgbariaandLivari(1995),SegarsandGrover(1993),

Subramanian(1994),andSzajna(1994).Reliabilityanalysisindicatedhighinternal

consistency,havingCronbachalphascoreslargerthan.90(seeTable7).AccordingtoKline

(1999),alphascoreslargerthan.90areconsideredexcellent.Evidenceofinstrument

validitywasalsodemonstrated,indicatingconsistencywithpriorresearchliteraturethat

leveragedtheTAMconstructs.

DiscussionandConclusionsFromFindings

ThisstudyexaminedfourresearchquestionsaimedatexaminingITmanagers'

perceptionsofESStechnologyacceptancewiththeintentofprovidinginsightstobusiness

leadersandexecutivesastheyshapetheirESSbusinessplans.Theresearchquestionsand

findingsarefocusedonthetechnologyacceptancefactorsandITmanagers'age,

generationalgroups,andgendertypes.Additionaldiscussionincludesthepractical

significanceofthefindings.

ConclusionsFromFindings

Thefirstquestionaddressedthefoundationalcomponentsofthetechnology

acceptancemodel.Ashypothesized,astatisticallysignificantrelationshipwas

demonstratedbetweenITmanagers'behavioralintentiontouseenterprisesocialsoftware

technologyandvariablesofperceivedusefulnessandperceivedeaseofuse.Thedata

suggestedthatbothperceivedusefulnessandeaseofusecontributedsignificantlytoanIT

managers’intentionofadoptingandusingESStechnology.Theregressionequation

explained73.9%ofthevarianceinITmanagers’intentiontouseESStechnology,

suggestingtheimportanceofusefulnessasaleadingfactorintechnologyadoption

decisions.Both,perceivedusefulnessandeaseofusehadsignificantcorrelationstoBI,

whichwasnotsurprising;researchershavelongarguedthattechnologyacceptancefactors

(i.e.PUandPEOUrelatedtoBI),performasstrongpredictorsofactualtechnologyadoption.

ThissupportspriorresearchconductedbyDavis(1989),DavisandVenkatesh(1996),and

Venkateshetal.(2003).Thefindingsalsoassertthatthefactorsofusefulnessandeaseof

usecanbeextendedtoenterprisesocialsoftwaretopredictitsadoption.

ThisstudyaddstothebodyofknowledgeinthecontextofbusinessuseofESSto

predicttechnologyacceptance.Thatis,thefindingsextendpreviousresearchonthe

applicabilityofTAMconstructsusedinnonbusinesscontextstoitsuseinbusinesscontexts

(Adamsetal.,1992;Davis,1989;Davisetal.,1989;Davis&Venkatesh,1996;Hendrickson

etal.,1993;Igbaria&Livari,1995;Segars&Grover,1993;Subramanian,1994;Szajna,

1994).

Mediationanalysisresultsfoundthatperceivedusefulnesswasapartialmediating

factortowardITmanagers’behavioralintentiontouseESStechnology.Thefindingalso

supportspreviousresearchconductedbyDavis(1989,1996),andDavisandVenkatesh

(1996),whofoundthatusefulnessisinfluencedbyeaseofuse.Giventhehighcorrelation

betweenperceivedusefulnessandeaseofuse(r=.58,p<.01),inadditiontousefulness

actingasamoderatortoeaseofuse,itcanbefurthersuggestedthateaseofuseamplified

theeffectofusefulnessontheintenttoadoptESStechnologyinthisstudy.Thisfinding

alsosupportstheLaneandColeman(2011)study,whichassessedtheperceivedusefulness

andeaseofuseofsocialsoftwaretechnologyinauniversitysettingandfoundthat“higher

perceivedeaseofuseledtoincreasedperceivedusefulnessandmoreintensityintheuseof

thesocialmedia”(p.7).Thatis,theeasieritwastousethesocialsoftware,themoreuseful

itbecametoperformtasks/activities.

Incontrast,theChungetal.(2010)studyonperceptionsofonlinecommunity

participationamongnon-usersfoundthateaseofusedidnotinfluenceusefulness.This

studydidnotsupportorrefutetheChungetal.studyalthoughthecontrastmightbemore

readilyexplainedgiventhatnon-usersofonlinecommunitiesarenotaslikelytohavehad

theknowledgeofonlinecommunities.ItcouldbepurportedthatITmanagerswouldhave

astrongerawarenessandunderstandingofsocialsoftware,regardlessoftheiractiveuseof

itthuspotentiallyexplainingthedifferenceinfindingsfromChungetal.(2010).

ThesecondresearchquestionintroducedITmanagers'ageandgenerationalcohort

groupsandfoundastatisticallysignificantrelationshipbetweenITmanagers'behavioral

intentiontouseESStechnologyandvariablesofperceivedusefulness,perceivedeaseof

use,andtheITmanagers'age.Thedatarevealedthatperceivedusefulness,easeofuse,

andagecontributedsignificantlyITmanagers’intentionsofusingESStechnology.The

resultantregressionmodelalsodemonstratedsignificance(F=376.58,p<.01).Agewas

foundtohaveasignificantrelationshipwithbehavioralintentiontouseESSalthoughthe

correlationwaslow(r=.17,p<.01).Theregressionmodelchangedminimallywiththe

additionofage(B<.01,standardized).

ThesefindingssupportstudiesconductedbyMorrisandVenkatesh(2000),and

Morrisetal.(2005)onagedifferenceintechnologyadoptiondecisions,suggestinga“clear

differencewithageintheimportanceofvariousfactorsintechnologyadoptionandusage

intheworkplace”(p.392).Whiletheanalysisresultedinasmalleffectsize,itdoesnot

discounttheimportanceofgenerationalgroupcharacteristics.Infact,manyresearchers

believethatregressioninterpretationshouldnotbebasedsolelyonbetaweights(Kraha,

Turner,Nimon,Zientek,&Henson,2012).Therewerealsofindingsofdifferencesbetween

ITmanagers’generationalgroups.Partialetasquaredvaluewas.02,suggestinganoverall

smalleffect,whichexplains2%ofthedifferencebetweengenerationalgroups.

Pairwisecomparisonsidentifiedthegenerationalgroupsthatdifferedwhen

comparedtovariablesofPU,PEOU,andBI(seeTable23).TheseresultssuggestthatBaby

Boomers’perceptionsofusefulnessofEnterpriseSocialSoftwaredifferssignificantlyfrom

howGenerationsXandYperceiveitsusefulness.Also,BabyBoomers’perceptionsofease

ofusedifferonlywithGenerationX.TheresultsalsosuggestthatGenerationXandYare

similargiventhatbothGenerationXandYwereexposedforalargerpercentageoftheir

livestotheboominITandtheInternetthanwereBabyBoomers,whichisconsistentwith

researchperformedbyMorrisandVenkatesh(2000),Morrisetal.(2005),L’Allierand

Kolosh(2007),StraussandHowe(1994),andWhitman(2010).

ThethirdresearchquestionfocusedonITmanagersgender.Thestudyfounda

statisticallysignificantrelationshipbetweenITmanagers'perceivedbehavioralintention

touseESStechnologyandvariablesofperceivedusefulness,perceivedeaseofuse,and

gender(F=378.48,p<.01).Resultssuggestthatperceivedusefulnesshadthegreatest

impactonthepredictivemodel(B=.75,standardized),althougheaseofusewasalsoan

importantcontributor(B=.17,standardized).Genderhadanegativecontributiontothe

predictivemodel(B=-.03,standardized).

ThefindingssuggestthatforeveryunitincreaseofafemaleITmanager,the

aggregateBIscorewoulddecreaseby.03,therebysuggestingthatmaleITmanagersare

slightlymorelikelytoadoptanduseESStechnologythantheirfemalecounterpartsinthe

study.Additionally,correlationanalysisindicatedanegativerelationshipwithPU(r=-.09),

PEOU(r=-.17).ThisisconsistentwithresearchconductedbyVenkateshandMorris

(2000)andMintonandSchneider(1980),whosuggestedthatmenaremoretaskoriented

andthereforetheusefulnessofthetechnologyhasgreatersaliencetomenthantowomen

(Venkatesh&Morris,2000;Venkateshetal.,2000;Wattaletal.,2009).However,as

relatedtoeaseofusebeingmoresalienttowomen,thisstudydoesnotsupportorrefute

priorresearchconductedVenkateshandMorris(2000),andMintonandSchneider(1980)

becausethisstudydidnotincludeadditionalfactorssuchastimeandexperience.

EvidencealsodemonstratedfindingsofstatisticaldifferencesbetweenITmanagers’

gendergroups.Theresultssupportedpreviousresearchstudieswhichexaminedgender

asrelatedtotechnologyacceptancefactors.Inparticular,theresultssupportGefenand

Straub’s(1997)study,whichfoundforexistenceofgenderdifferencesonusefulnessand

easeofuseinthecaseofe-mailtechnologyadoption.Thisstudyalsosupportsother

technologyacceptancestudiesinwhichgenderwasfoundtobeasignificantcontributing

factor,whichincludes:Chungetal.(2010),Morrisetal.(2005),TerzisandEconomides

(2011),VenkateshandMorris(2000),Wattaletal.(2009).Thepartialetasquaredvaluein

thisstudywas.03,suggestinggenderhadanoverallsmalleffect.

Thefourthresearchquestionintendedtodeterminewhetherrelationshipsexisted

andwhetherdifferenceswereidentifiedwhenincludingageandgenderintheregression

andmultivariateanalyses.Asexpected,minimalchangeswerenoticedintheregression

modelcomparedtotheanalysesperformedtoanswerthefirstthreeresearchquestions;

thatis,thestudyfoundthatastatisticallysignificantrelationshipexistsbetweenIT

managers'perceivedbehavioralintentiontouseESStechnologyandvariablesofperceived

usefulness,perceivedeaseofuse,age,andgender(F=283.16,p<.01).Resultsindicated

thatPUhadthegreatestimpactonthepredictivemodel(B=.74),followedbyPEOU(B

=.17),andage(B<.01).Gendermaintainedanegativecontributiontothepredictive

model(B=-.03).

Thefindingssuggestthatageasacontinuousmeasurementvariablehasminimal

impact/contributiontothepredictivemodel.Genderhasasimilarminimalimpact

althougheveryunitincreaseoffemaleITmanagerswouldresultinadecreasedaggregate

BIscore,suggestingthatmaleITmanagersareslightlymorelikelytoadoptanduseESS

technologythantheirfemalecohorts.Italsosupportspreviousresearchconductedby

MorrisandVenkatesh(2000),andMorrisetal.(2005)onagedifferenceintechnology

adoptiondecisions;resultssuggestedthattherewasa“cleardifferencewithageinthe

importanceofvariousfactorsintechnologyadoptionandusageintheworkplace”(p.392).

However,itshouldbenotedthatwhiletheregressionmodelfoundevidenceof

statisticalsignificance(F=283.16,p<.01),themultivariateanalysesresultedinretaining

nullhypothesesHo4b(Wilks'sLambda=.99,p>.05).Thiswastheresultofgenerational

groupsandgendertypesbeingofdifferenttypesofvariables.Thenullhypotheses,if

consideredindependently,wouldhaveresultedinrejectionasperformedinbothHo2band

Implications

PracticalApplication

Successfuldeploymentofenterprisesocialsoftwareislikelytorelyonthesuccessof

itsadoption.Ifitisnotusefultoenhancingbusiness/jobproductivity,itisunlikelyto

exhibitthelevelofadoptiondesired.Ifthetechnologyisnoteasytouse,usefulnesswillbe

reduced,therebyfurtherreducingtheoveralldesiredlevelofadoptionasdemonstratedby

thisstudy.Asanenterprisesocialsoftwaretechnologydeveloperorvendor,itisnecessary

tohelpclientsunderstand(directlyorindirectly)howtheirsocialsoftwaretechnologyis

usefulandeasytouse.Incontrast,acompanyconsideringprovidingemployeeswithsocial

softwaretechnologycanusetheresultsofthisstudytounderstandhowemployee

perceptionssupporttechnologyadoptionandaddressareasofpotentialissues,suchas

helpingemployeesunderstandhowthesoftwareisuseful,includingtrainingtofacilitate

easeofusefornon-intuitivecapabilities.

ThisstudycanbegeneralizedtoITmanagersandleaders,andperhapstheoverall

ITorganizationasrelatedtoITmanagers'perceptionsontheirintentiontoadoptESS

technology.ItcanfurtherbeassertedthatthestudycouldbegeneralizedtotheoverallIT

organization.However,Theresearchconductedinthisstudycanalsobeusedasatoolto

sell,market,anddeployESSsoftwarebeyondITmanagers/organization.Considerthe

socialandbehavioralsciencepresentedinthisstudy(andsupportedbypriorresearch)

highlightingthatanemployees'propensitytoadopttechnologyisdirectlyrelatedtoa

technology'susefulnessandeaseofuse(i.e.performanceandeffortexpectancies).These

expectancyfactorsaretiedtoimprovementsinemployeeproductivityasoutlinedin

Chapters1and2.Examplesareprovidedbelowonhowthisstudycansupportthesales

andsoftwaredeploymentgoals.

Itisalongheldbeliefthatimprovedemployeeproductivitydrivesgreaterbusiness

valueandoverallnetresults;andfromtheperspectiveofabusinessconsumerconsidering

thepurchaseofESS,productivitygainsremainacriticalsuccessfactorsoughtfromits

implementation.Often,ESSsalesopportunitieshavedifficultyofachievingsuccessfuldeal

closureduetothecomplexityofquantifyingtheimpacttoemployeeproductivity;and

employeeproductivitygainisakeyfactorincustomers'purchasingdecisions.However,

ESSisoftendeliveredtoemployeesinavoluntary-useenvironment,introducingan

unknownfactorthatoftennegativelyimpactssuccessfuldealclosure.Ifthepopulationof

ESSusersareITmanagersthefindingsinthisstudycanbegeneralizedtothatpopulation

andpossiblyalsogeneralizedtotheoverallITorganization.Ifthepopulationdiffers(e.g.

Marketinganalysts),pre-salesactivitiesmightconsideraddingaquantificationofthe

customers'employees'intentionsofESStechnologyadoptionbasedonthemethodologyin

thisstudypresentedinthisstudytosupportthesalesprocess.Forexample,thecustomers'

employeedatarevealedemployeeadoptionintentis80%explainedbytheleveloftheESS

solutionusefulness,thenthevendors'salesteamcouldbetterdetermineareastofocusto

maximizeexpectancyfactorsresultinginincreasedadoptionintentions.

CompaniesthathavealreadyinvestedinESStechnology(post-sales)canusethe

datainthisstudytogeneralizeunderstandingofITmanagers(andquitepossiblythe

overallITorganization'semployees).Itcanbefurtherassertedthatthesurvey

methodologyandinstrumentcanbeusedtodevelopapredictivemodelofadoptionintent

foranygivenbusinessunit(e.g.Marketing,Sales,Development).Italsogivesusinsight

abouttheendusers(includingbarriers)toadoptionintent.Thiscanhelpusidentifywhich

toolsonourbeltwouldmakethebestsenseasanextstep(e.g.PoT,adoptionsession).

However,additionalfactorsshouldbeconsideredasstatedintheRecommendations

sectionwherefurtherresearchisnecessary.

AgeandGenderImplicationsofITManagers

AgeandgenderareimportantinthebusinesscontextofESStechnologyadoption.

Thisstudyindicatedstatisticallysignificantdifferencesinage,generationalcohortgroups,

andgenderasimpactingITmanagers'intentiontoadoptESStechnology.However,itis

importanttonotethatthedifferenceswereminimalanddonotnecessarilywarrantan

inspectionorcustomizationtotheusefulnessoreaseofuseofatechnologybasedonage

and/orgendergiventheuniquenessofdemographicsoftheITorganization.Forexample,

whileITmanagers'perceptionsdifferedbetweenGenerationXandGenerationY,the

resultsofthisstudydonotsuggestnorvalidateaneedtodevelopatrainingprogram(or

othertreatment)fordifferinggenerationalgroups,whichconsequentlymightalsohave

legalimplications.

ResearchOpportunities/Implications

Thisstudyprovidedquantitativeresearchforthetechnologyadoptionfactorsof

perceivedusefulnessandperceivedeaseofuse,whicharedeterminantsofone'sbehavioral

intentiontoadoptenterprisesocialsoftware.Inthecaseofthisstudy,whichincludedIT

managers,easeofusehadapositiveinfluencetoperceivedusefulness.Theresearch

implicationspresentedincludessupportforthesefactorsinthecontextofenterprisesocial

softwareusedinthecontextofbusiness.

Additionally,ageandgenderhavetraditionallybeendeemedascriticalfactorsin

technologyadoptiondecisions.Thisstudysupportsfindingsinpreviousliteraturewhen

comparedwithfindingsofstatisticalsignificance.Theeffectsizes,whenconsideringage

andgender,however,wereminimal.Forexample,previousliteratureontechnology

adoptionhasindicatedtheneedforprovidingemployeesgreateraccesstodifferentiated

trainingmaterialstobridgetheskillsgapand/orgenerationaldivides.Asaresultofthe

effectsizesnotedinthisstudy,includingthegenerationalsimilaritiesanddifferenceswith

respecttofactorsinthisstudy,theremightbereasontoindicateubiquityofenterprise

socialsoftwareamongITmanagers,andcanbepurportedtobegeneralizedtoother

informationandknowledgeworkersrequiringregularaccesstoinformationtechnology,

theInternet,andnetwork-basedapplications.

Theamountoffeaturesandcapabilitiesrelevanttoenterprisesocialsoftwarehas

risendramatically.Collaborativecapabilitieshavegonefromone-waycommunicationto

multi-way,real-timecollaboration,whichcomplexityhassteadilydecreased.Giventhis

study'sfindingsasrelatedtoeaseofuseasamplifyingperceivedusefulness,ITusability

researchispoisedtogaingreatersignificanceinthecontextoffollow-onstudiesrelatedto

humannetworks,communities,collaboration,andcommunication/interactionmedia

research.

Additionalresearchimplicationsincludetheexpandingmethodsinwhich

enterprisesocialsoftwareisdeliveredbasedonitseaseofuse,usefulness,andavailability.

Forexample,deviceagnosticcomputinghasexperienceddramaticgrowthwhichhas

acceleratedmobileapplicationsprovidingenterprisesocialsoftwarecapabilities.Asa

result,communicationflowandknowledgesharinghavethecapabilitytospanpersonal

andbusinesssocialnetworks.Aseaseofusegrowsinthecontextofdrivingbusiness

productivity,agreaterpotentialforcoalescenceofthepersonalandbusinessuseofsocial

softwaremightbepresented.

RecommendationsforFutureResearch

1. Asaresultofthecontinualadvancesintechnology(includingESStechnology

capabilities),furtherresearchisrecommendedinthecontextofbusinessuseofESS

ontopicsthatinclude:a)determiningwhetherESStechnologyfeaturesand

capabilitiesdifferfromoneanotherbasedontechnologyadoptionfactors,andb)

examiningwhetheranintroductorysetofESSfeatures/capabilities,thatifadopted

aheadofanotherfeaturewouldsupportadoptionofadditional,follow-onadvanced

features.

2. Industryspecialization,corporateculture,andothercorporatecharacteristicsmay

influencetheemployees’adoptionofESStechnology.Additionalresearchis

recommendedtodeterminewhetherthesefactorscontributetoemployeeadoption

ofESStechnology.

3. Managersareoftenimportantinfluencersinsubordinateemployees’on-the-job

behavior.AdditionalresearchisrecommendedtodeterminetheextenttowhichIT

managersinfluencetheiremployees’adoptionofESStechnology.Wattaletal.

(2009)performedastudyofamultinationalelectronicscorporationandfoundthat

“employees’usageofblogsispositivelyassociatedwithblogusebytheemployees’

managers”(p.7).Theircasestudyandtheresultsfromthisstudyprovideabasis

forfurtherresearchtogeneralizetheresultstoITmanagersacrossindustriesand

thecomponentsthatcompriseESStechnology,althoughagreatergeneralization

wouldbeITmanagersandnon-ITmanagers.

4. Thisstudywasbasedonacross-sectionalsurveyresearchdesigncapturingIT

managers'perceptionsofpre-andpost-adoptionofESStechnologyatasinglepoint

intime.Alimitationofthistypeofdesignisunderstandingandcapturingdataofan

individuals'decisionmakingprocessesontheirjourneyofacceptingorabandoning

theuseofatechnology.Therefore,additionalresearchisrecommendedviaa

longitudinalresearchdesigntocapturedatapre-use,duringuse,andpost-adoption

datatomorethoroughlyexaminechangesinanindividuals'behavioralintentionto

adoptESStechnologyandthefactorsinvolvedinadoptiondecisions.

5. Additionalresearchisrecommendedtomoreeffectivelydetermineiftimeand

experiencearefactorsthatimpactgendersaliencetoPU,PEOU,andBI.Venkatesh

andMorris(2000)andMintonandSchneider(1980)foundthateaseofusewas

moresalienttowomenthantomen.Theyalsofoundthatmen’seaseofuseofthe

systemwentupsomewhatwithtimeandexperience,althoughwomen'seaseofuse

wentdownwithmoretimeandexperience.Thisstudydidnotexaminethese

factors,whichmayhaveuncovereddynamicsthatcouldprovideinsightto

applicationdevelopersandusabilityexpertswhendesigningESSapplications.

6. ThisstudydidnotdistinguishbetweenmandatoryuseversusvoluntaryuseofESS

technology.Additionalresearchisrecommendedforstudyingwhetherthereisa

differencebetweenthetwoadoptionmodels.Brownetal.(2002)arguedthatusers'

beliefsaboutatechnology’seaseofuseandusefulnessaremorelikelytobe

minimizedinmandatoryuseenvironments,whilethebehavioralintentiontouse

thesystemisinflated,andindicatedthatusersmaynotwanttoperformthe

mandatedbehaviorbutwilldoitanyway.Additionally,thereispotentialfora

reversemediationrelationshipbetweenPEOUandPUwhenindividualsmust

performspecificbehaviorsinmandatoryusesituations.

7. ThisstudyfocusedonITmanagers.Additionalresearchisrecommendedtostudy

otherrolesintheorganizationasrelatedtoexperienceandskill.Forexample,itis

conceivablethatITmanagerswouldgenerallyhaveagreaterlevelofexperienceand

skillwithITthantheirnon-ITmanagercounterparts(e.g.marketingmanagers,sales

managers,non-managers).Thereforenon-ITusersmightdifferintheirbehavioral

intentiontouseESStechnologythantheirITsavvycounterparts.

Summary

Adrivingpremiseforthisstudywastheresultofthesteepriseinconsumeruseof

socialnetworkingsoftwaretechnologyforpersonaluse(e.g.FacebookandTwitter)and

thecorrespondingincreaseininterestfrombusinessleaderstoadoptsocialsoftwarefor

theiremployeestoimprovebusinessproductivity.Thepurposeofthisstudywasto

examineITmanagers’perceptionsofenterprisesocialsoftware(ESS)acceptancefactorsto

predictwhetherornotITmanagerswouldadoptanduseESSintheirownjobs.An

additionalfocusofthisstudywastheexaminationofgenerationalandgendergroupsto

determinewhetherdifferencesexistedbetweenthegroupsandtechnologyadoption

factors.Theresultsofthestudywereintendedtoprovideinsightstobusinessleadersand

executivesastheyshapepotentialESSdeliveryplansfortheirownorganizations.The

populationselectedforuseinthisstudyincludedITmanagersintheUnitedStateswhere

ESStechnologywasavailabletouseorwouldbecomeavailableforuseintheirjobs.

Theresultsdemonstratedtheexistenceofstrongrelationshipsbetweenthe

technologyacceptancefactorsandanITmanager’sbehavioralintentiontouseESS

technology.Thatis,theeasierthetechnologywastouse,andthemoreusefulitwas,the

greatertheamountofbehavioralintentiontoadoptandusethetechnology.Additionally,

resultsindicatedthatperceivedeaseofusehadapositiverelationshiptoperceived

usefulness,suggestingthattheeasierthetechnologywastouse,themoreusefulitbecame.

Resultsalsofounddifferencesbetweengenerationandgendergroups.Generationalgroup

comparisonssuggestedthatGenerationXandYweresimilaranddifferedfromBaby

BoomersonlyintheirbehavioralintentiontoadoptESStechnology.Thisfurthersuggests

thattheoutcomewasduetothefactthatGenerationsX'sandY’sexposuretotechnology

involvedalargerpercentageoftheirlifespanwhencomparedtoBabyBoomers,giventhe

factorsincludedinthisstudy.Furthermore,maleandfemalegenderswerealsofoundto

differ.TheresultsinthiscomparisonsuggestedthatfemaleITmanagerswereslightlyless

acceptingthantheirmaleITmanagercounterparts.

Overallresultsindicatedthateaseofuseandusefulnessareimportantfactorsin

determiningone’sbehavioralintentiontouseESStechnologyandthatunderstanding

differencesingenerationalandgendergroupsmightaltertheuseofESSorhowitis

deliveredintheworkplace.Additionalresearchisrecommendedtoextendtheresults

providedbythisstudytonon-ITmanagers.However,theresultspresentedinthisstudy

areanticipatedtobegeneralizabletoITmanagersincompaniesthroughouttheUnited

States.

APPENDIXA

INSTRUMENTS

Davis&Venkatesh(1996)InstrumentReuseRequest

SunilPatel<sunil.patel1120@gmail.com> Mon,Jan23,2012at8:32PM

To:fdavis@walton.uark.edu,vvenkatesh@walton.uark.edu

DearDr.DavisandDr.Venkatesh,IamwritingtorequestyourpermissiontousetheTAMPerceivedUsefulnessandEaseofUseinstrumentpublishedinthe1996articlepublishedintheInternationalJournalofHuman-ComputerStudies(volume45,page45).IamadoctoralcandidateattheUniversityofNorthTexasandIamdoingmydissertationresearchtoexamineInformationTechnology(IT)managerperceptionsoftechnologyacceptanceofEnterpriseSocialSoftware(ESS).Iamplanningtoadministeryour1996revisedTAMinstrumenttoITmanagersacrosstheU.S.Iwouldbedelightedtosendyoutheresultsofthestudy.Additionally,Iwillbeincludingitemstothefinalinstrumentgatheringinformationaroundageandgender.Wordingmaybealteredslightlytosuittheneedofthetechnologybeingaskedabout.Doyouapproveofthisrequest?Yourreplytothisrequestisgreatlyappreciated.Pleasedonothesitatetocontactmeifyourequirefurtherinformation.Respectfullyyours,SunilPatelSunil.Patel1120@gmail.com214-802-3541DoctoralStudentUniversityofNorthTexasAppliedTechnologyandPerformanceImprovement

FredDavis<FDavis@walton.uark.edu> Mon,Jan23,2012at9:03PM

To:SunilPatel<sunil.patel1120@gmail.com>

Youhavemypersmissiontousethe1996IJHCSinstrumentforyourdoctoralresearch.

Bestwishes

FredDavis

Davis&Venkatesh(1996)Instrument(providedhereforreference)

IntentiontouseAssumingIhadaccesstoWordPerfectinmyjob,Iintendtouseit. Strongly +3 +2 +1 0 -1 -2 -3 Strongly Agree DisagreeGiventhatIhadaccesstoWordPerfectinmyjob,IpredictthatIwoulduseit. Strongly +3 +2 +1 0 -1 -2 -3 Strongly Agree DisagreePerceivedusefulnessUsingWordPerfectwouldimprovemyperformanceinmyjob. Strongly +3 +2 +1 0 -1 -2 -3 Strongly Agree DisagreeUsingWordPerfectinmyjobwouldincreasemyproductivity. Strongly +3 +2 +1 0 -1 -2 -3 Strongly Agree DisagreeUsingWordPerfectwouldenhancemyeffectivenessinmyjob. Strongly +3 +2 +1 0 -1 -2 -3 Strongly Agree DisagreeIwouldfindWordPerfectusefulinmyjob. Strongly +3 +2 +1 0 -1 -2 -3 Strongly Agree DisagreePerceivedeaseofuseMyinteractionwithWordPerfectwouldbeclearandunderstandable. Strongly +3 +2 +1 0 -1 -2 -3 Strongly Agree DisagreeInteractingwithWordPerfectwouldnotrequirealotofmymentaleffort. Strongly +3 +2 +1 0 -1 -2 -3 Strongly Agree DisagreeIfindWordPerfectwouldbeeasytouse. Strongly +3 +2 +1 0 -1 -2 -3 Strongly Agree DisagreeIwouldfinditeasytogetWordPerfecttodowhatIwantittodo. Strongly +3 +2 +1 0 -1 -2 -3 Strongly Agree Disagree

Instrument

APPENDIXB

IRBAPPROVALANDINFORMEDCONSENTNOTICE

IRBAPPROVAL

INFORMEDCONSENTNOTICE

UniversityofNorthTexasInstitutionalReviewBoardInformedConsentNotice

Beforeagreeingtoparticipateinthisresearchstudy,itisimportantthatyoureadandunderstandthefollowingexplanationofthepurpose,benefitsandrisksofthestudyandhowitwillbeconducted.TitleofStudy:Astudyofperformanceandeffortexpectancyfactorsamonggenerationalandgendergroupstopredictenterprisesocialsoftwaretechnologyacceptance.StudentInvestigator:SunilPatel,UniversityofNorthTexas(UNT)DepartmentofLearningTechnologies.SupervisingInvestigator:JeffAllenPurposeoftheStudy:Youarebeingaskedtoparticipateinaresearchstudywhichinvolvesexaminingtechnologyacceptanceofsocialsoftwareinbusinesscontexts.StudyProcedures:Youwillbeaskedtorespondtoquestionsexaminingtheuseandadoptionofsocialsoftwaretechnologyinthecontextofbusiness.Thesurveythatwilltakeabout5-10minutesofyourtime.ForeseeableRisks:Noforeseeablerisksareinvolvedinthisstudy.BenefitstotheSubjectsorOthers:Weexpectthisstudywillcontributetoinformationtothefieldconcerningmanagers'perceptionsofsocialsoftwaretechnologyacceptancefactorsinpredictingitsuse/adoptioninbusinesscontexts.CompensationforParticipants:Theresearcherisnotofferingcompensationforyourparticipation.ProceduresforMaintainingConfidentialityofResearchRecords:Tohelpprotectyourconfidentiality,thesurveywillnotcollectinformationthatwillpersonallyidentifyyou.Alldatawillbestoredinapasswordprotectedelectronicformat.Theconfidentialityofyourindividualinformationwillbemaintainedinanypublicationsorpresentationsregardingthisstudy.QuestionsabouttheStudy:Ifyouhaveanyquestionsaboutthestudy,youmaycontactSunilPatelatsunil.patel1120@unt.eduorJeffAllenatjeff.allen@unt.edu.ReviewfortheProtectionofParticipants:ThisresearchstudyhasbeenreviewedandapprovedbytheUNTInstitutionalReviewBoard(IRB).TheUNTIRBcanbecontactedat(940)565-3940withanyquestionsregardingtherightsofresearchsubjects.ResearchParticipants’Rights:Yourparticipationinthesurveyconfirmsthatyouhavereadalloftheaboveandthatyouagreetoallofthefollowing:

• Youunderstandthatyoudonothavetotakepartinthisstudy,andyourrefusaltoparticipateoryourdecisiontowithdrawwillinvolvenopenaltyorlossofrightsorbenefits.Thestudypersonnelmaychoosetostopyourparticipationatanytime.

• Youunderstandwhythestudyisbeingconductedandhowitwillbeperformed.• Youunderstandyourrightsasaresearchparticipantandyouvoluntarilyconsentto

participateinthisstudy.• Youhavehadanopportunitytocontacttheresearcherwithanyquestionsabout

thestudy.Youhavebeeninformedofthepossiblebenefitsandthepotentialrisksofthestudy.

• Youunderstandyoumayprintacopyofthisformforyourrecords.

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