a study of performance and effort expectancy factors … · 4/1/2013 · a study of performance...
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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.
ii
Copyright2013
By
SunilS.Patel
iii
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.
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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
CHAPTER3:INTRODUCTION..........................................................................................................................25
Introduction..............................................................................................................................................25
ResearchQuestions................................................................................................................................25
ResearchDesign......................................................................................................................................26
Sampling.....................................................................................................................................................28
Instrumentation......................................................................................................................................29
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DataCollection.........................................................................................................................................32
DataAnalysis............................................................................................................................................33
Summary....................................................................................................................................................43
CHAPTER4:INTRODUCTION..........................................................................................................................44
Overview....................................................................................................................................................44
DataValidationandDescriptiveStatistics...................................................................................45
InstrumentAnalysis..............................................................................................................................48
HypothesesAnalysis..............................................................................................................................51
Summary....................................................................................................................................................66
CHAPTER5:INTRODUCTION..........................................................................................................................67
Overview....................................................................................................................................................67
SummaryofFindings............................................................................................................................67
DiscussionandConclusionsFromFindings................................................................................69
Implications...............................................................................................................................................75
RecommendationsforFutureResearch.......................................................................................79
Summary....................................................................................................................................................81
APPENDICES............................................................................................................................................................83
APPENDIXA:INSTRUMENTS............................................................................................................83
APPENDIXB:IRBAPPROVALANDINFORMEDCONSENTNOTICE.................................88
REFERENCES...........................................................................................................................................................91
vi
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
18. Ho2aAnalysisofVariance.....................................................................................................................55
19. Ho2aRegressionModelSummary.....................................................................................................55
20. Ho2aCoefficients......................................................................................................................................55
21. Ho2bGenerationalMultivariateAnalysis.......................................................................................56
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22. Ho2bTestsofBetween-SubjectsEffects...........................................................................................56
23. Ho2bPairwiseComparisons.................................................................................................................57
24. Ho3aAnalysisofVariance.....................................................................................................................58
25. Ho3aRegressionModelSummary.....................................................................................................58
26. Ho3aCoefficients......................................................................................................................................59
27. Ho3bGenderMultivariateAnalysis...................................................................................................59
28. Ho3bGenderTestsofBetween-SubjectsEffects...........................................................................60
29. Ho3bPairwiseComparisons.................................................................................................................61
30. Ho4aAnalysisofVariance.....................................................................................................................61
31. Ho4aRegressionModelSummary.....................................................................................................62
32. Ho4aCoefficients......................................................................................................................................62
33. Ho4bGenerationandGenderInteractionMultivariateAnalysis..........................................63
34. Ho4bGenerationandGenderTestsofBetween-SubjectsEffects...........................................64
35. Ho4bPairwiseComparisons.................................................................................................................65
36. Ho4bPairwiseComparisons.................................................................................................................65
viii
LISTOFFIGURES
Figure:
1. Technologyacceptancemodel.............................................................................................................8
2. Theoreticalframework...........................................................................................................................9
3. Mediationmodel.....................................................................................................................................19
4. Modifiedtechnologyacceptancemodel........................................................................................34
5. Mediationprocessmethodology......................................................................................................37
6. Correlationresults.................................................................................................................................68
1
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
2
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
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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).
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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
5
(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
6
(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
7
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).
8
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
(BI)
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.
9
TAMwasdesignedforthecontextofITtomeasureemployees'perceptionsofa
technology'susefulness,easeofuse,andbehavioralintentiontousethetechnologyas
determinantsofpredictingactualsystem/technologyadoption.Ithasbeenusedtogain
insightsintoemployees'effectiveness(usefulnessofthetechnology)resultingfromthe
introductionofITtoolingintheirjobs.Ithasalsoassistedbusinessleaderstobetter
determinewhetherornottheconsequencesofITacceptanceaddedvaluetothebusiness
(Igbaria&Tan,1997)throughenhancementsinemployeeeffectiveness(Yi&Hwang,
2003).
Perceived Usefulness (PU)
Perceived Ease of Use (PEOU)
Behavioral Intention to Use
(BI)
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.
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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
11
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.
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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.
13
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.
14
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).
15
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.
16
CHAPTER2
LITERATUREREVIEW
Introduction
ThisstudyexaminedITmanagers'perceptionsofESStechnologyacceptancefactors
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
17
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
1. IstherearelationshipbetweenvariablesofITmanagers'behavioralintentiontouse
ESStechnology,perceivedusefulness,andperceivedeaseofuse?
GenerationalGroups
2. IstherearelationshipordifferencebetweenITmanagers'ageandgenerationalgroups
andthevariablesofperceivedusefulness,perceivedeaseofuse,andbehavioral
intentiontouseESStechnology?
18
GenderGroups
3. IstherearelationshipordifferencebetweenITmanagers'genderandthevariablesof
perceivedusefulness,perceivedeaseofuse,andbehavioralintentiontouseESS
technology?
AllConstructsandMediators
4. IstherearelationshipordifferencebetweenITmanagers'behavioralintentiontouse
ESStechnologyandthevariablesofage,gender,perceivedusefulness,andperceived
easeofuse?
TechnologyAcceptanceFactors
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.
19
Perceived Usefulness (PU)
Perceived Ease of Use (PEOU)
Behavioral Intention to Use
(BI)a
b, b’
c
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
20
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.
21
• 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.
22
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
23
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
24
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.
25
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:
TechnologyAcceptanceFactors
1. IstherearelationshipbetweenvariablesofITmanagers'behavioralintentiontouse
ESStechnology,perceivedusefulness,andperceivedeaseofuse?
Ho1a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'perceivedbehavioralintentiontouseESStechnologyandvariablesofperceivedusefulness,andperceivedeaseofuse.Ho1b:ITmanagerperceivedeaseofuseisnotpositivelyrelatedtoperceivedusefulness.
GenerationalGroups
2. IstherearelationshipordifferencebetweenITmanagers'ageandgenerationalgroups
andthevariablesofperceivedusefulness,perceivedeaseofuse,andbehavioral
intentiontouseESStechnology?
Ho2a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,andage.
26
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.
ResearchDesign
Thisstudyusedacorrelationresearchdesignandgatheredinformationfromthe
targetpopulationoverasingleperiodoftime.Thesurveymethodologydescribedviewsof
employeesacrossgenerationalgroupsandgendertypesontheirperceptionsofESS
technologyusefulness,easeofuse,andbehavioralintentiontouseESStechnologyas
27
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
28
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
29
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).
30
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
31
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.
32
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
33
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.
34
Perceived Usefulness (PU)
Perceived Ease of Use (PEOU)
Behavioral Intention to Use
(BI)
Generational Groups
Gender
H1a, H2a, H3a, H4a
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.
35
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
Ho4a
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).
36
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
37
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').
Perceived Usefulness (PU)
Perceived Ease of Use (PEOU)
Behavioral Intention (BI) to
use ESS technology
c
a
b, b’
Figure5.Mediationprocessmethodology.Adaptedfrom“TheModerator-MediatorVariableDistinctioninSocialPsychologicalResearch:Conceptual,Strategic,andStatisticalConsiderations”byR.M.Baron,andD.A.Kenny,1986,JournalofPersonalityandSocialPsychology,51,p.1176.
38
Thenullhypothesiswouldberejectediftheregressionanalysisresultedinap-value
significantatthep<.05levelforPUonBI.Thiswouldindicatethatsomeformof
mediationexists.IfbothPUandPEOUaresignificantatp<.05level,partialmediation
exists.Furthermore,ifPEOUisnolongersignificantaftercontrollingforPU,fullmediation
exists,althoughthisscenariowasnotexpectedbasedonresearchliteraturefindings.
Anullhypothesesrejectionwouldindicatethatperceivedeaseofusedoesnot
significantlyinfluenceperceivedusefulness.However,iftherewereastatistically
significantnegativerelationshipofPEOUtoPU,itwouldhaveindicatedthatPEOUisthe
potentialmoderator.
ResearchHypothesisHo2a
Ho2a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,andage.
HypothesisHo2aexaminedwhetherastatisticallysignificantrelationshipexisted
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.
Thenullhypothesiswouldberejectediftheregressionanalysisresultedinap-value
significantatthep<.05levelforPU,PEOU,andageonBI.Nullhypothesisrejectionwould
indicatethatastatisticallysignificantrelationshipexistsbetweenITmanagers'perceived
intentionstouse/adoptESStechnologyandthevariablesofPEOU,PU,andage.Retaining
39
thenullhypothesiswouldindicatethatoneormoreoftheIVswasnotsignificanttoBI.
Additionally,toavoidthepossibilityofaTypeIorTypeIIerror,theresultsofthistest
requiredacomparisonwithHo1avalidatingthatbothPUandPEOUweresignificanttoBI
regardlessofageinvolvedasaninteractingvariable.
ResearchHypothesisHo2b
Ho2b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'generationalgroupsandthevariablesofperceivedeaseofuse,perceivedusefulness,andbehavioralintentiontouseESStechnology.
HypothesisHo2bexaminedwhetherastatisticallysignificantrelationshipexisted
betweenITmanagers'perceivedbehavioralintention(BIitems9,10)touseESStechnology
andvariablesofperceivedusefulness(PUitems1,2,3,4)andperceivedeaseofuse(PEOU
items5,6,7,8)forITmanagergenerationalgroups.AMANOVAanalysiswasusedtotest
therelationshipstodeterminewhethertherewereanydifferencesbetweengenerational
groupsonvariablesofBI,PU,andPEOU.Variablestotestincluded3DVs(PU,PEOU,BI)
and1IV(generationalgroups).BecausePU,PEOU,andBIvariables'constructscontain
multipleitems,compositemeanswerecomputedforeachofthevariables'constructs.
Thenullhypothesiswouldberejectediftheregressionanalysisresultedinap-value
significantatthep<.05levelforgenerationalgroupsusingWilks'sLambdateststatistic.
NullhypothesisrejectionwouldindicatethatITmanagers'perceivedintentionsto
use/adoptESStechnologydifferbetweengenerationalgroups.Ifthenullhypothesiswas
retained,itwouldindicatethattherewasnodifferencebetweengenerationalgroupson
variablesofPU,PEOU,andBI.Additionally,toavoidthepossibilityofaTypeIorTypeII
error,theresultsofthistestrequiredacomparisonwithHo1atovalidatethatbothPUand
PEOUweresignificanttoBI.
40
ResearchHypothesisHo3a
Ho3a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,andgender.
HypothesisHo3aexaminedwhetherastatisticallysignificantrelationshipexisted
betweenITmanagers'perceivedbehavioralintention(BIitems9,10)touseESStechnology
andvariablesofperceivedusefulness(PUitems1,2,3,4)andperceivedeaseofuse(PEOU
items5,6,7,8)wheninteractedwithgender.Multipleregressionanalysiswasusedtotest
therelationships.Variablestotestinclude1DV(BI)and3IVs(PU,PEOU,andgender).
SincethePU,PEOU,andBIvariables'constructscontainedmultipleitems,composite
meanswerecomputedforeachofthevariables'constructs.
Thenullhypothesiswouldberejectediftheregressionanalysisresultedinap-value
significantatthep<.05levelforPU,PEOU,andgenderonBI.Nullhypothesisrejection
wouldindicatethatastatisticallysignificantrelationshipexistedbetweenITmanagers'
perceivedintentionstouse/adoptESStechnologyandthevariablesofPEOU,PU,and
gender.Ifthenullhypothesiswasretained,oneormoreoftheIVswasnotsignificanttoBI.
Additionally,toavoidthepossibilityofaTypeIorTypeIIerror,theresultsofthistest
requiredacomparisonwithHo1avalidatingthatbothPUandPEOUweresignificanttoBI
regardlessofgenderinvolvedasaninteractingvariable.
ResearchHypothesisHo3b
Ho3b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'genderandthevariablesofperceivedeaseofuse,perceivedusefulness,andbehavioralintentiontouseESStechnology.
HypothesisHo3bexaminedwhetherastatisticallysignificantrelationshipexisted
betweenITmanagers'perceivedbehavioralintention(BIitems9,10)touseESStechnology
41
andvariablesofperceivedusefulness(PUitems1,2,3,4)andperceivedeaseofuse(PEOU
items5,6,7,8)forITmanagergenerationalgroups.AMANOVAanalysiswasusedtotest
therelationshipstodeterminewhethertherewereanydifferencesbetweengenderson
variablesofBI,PU,andPEOU.Variablestotestincluded3DVs(PU,PEOU,BI)and1IV
(gender).SincePU,PEOUandBIvariables'constructscontainedmultipleitems,composite
meanswerecomputedforeachofthevariables'constructs.
Thenullhypothesiswouldberejectediftheregressionanalysisresultedinap-value
significantatthep<.05levelforgenerationalgroupsusingWilks'sLambdateststatistic.
NullhypothesisrejectionwouldindicatethatITmanagers'perceivedintentionsto
use/adoptESStechnologydifferedbetweengendertypes.Ifthenullhypothesiswas
retained,itwouldindicatethatthetherewasnodifferencebetweengendertypeson
variablesofPU,PEOU,andBI.Additionally,toavoidthepossibilityofaTypeIorTypeII
error,theresultsofthistestrequiredacomparisonwithHo1avalidatingthatbothPUand
PEOUweresignificanttoBI.
ResearchHypothesisHo4a
Ho4a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,age,andgender.
HypothesisHo4aexaminedwhetherastatisticallysignificantrelationshipexisted
betweenITmanagers'perceivedbehavioralintention(BIitems9,10)touseESStechnology
andvariablesofperceivedusefulness(PUitems1,2,3,4)andperceivedeaseofuse(PEOU
items5,6,7,8)wheninteractedwithageandgendertypes.Multipleregressionanalysiswas
usedtotesttherelationships.Variablestotestinclude1DV(BI)and4IVs(PU,PEOU,age,
42
andgender).BecausethePU,PEOU,andBIvariables'constructscontainedmultipleitems,
compositemeanswerecomputedforeachofthevariables'constructs.
Thenullhypothesiswouldberejectediftheregressionanalysisresultedinap-value
significantatthep<.05levelforPU,PEOU,age,andgenderonBI.Nullhypothesis
rejectionwouldindicatethatastatisticallysignificantrelationshipexistedbetweenIT
managers'perceivedintentionstouse/adoptESStechnologyandthevariablesofPEOU,PU,
age,andgender.Ifthenullhypothesiswasretained,oneormoreoftheIVswasnot
significanttoBI.Additionally,toavoidthepossibilityofaTypeIorTypeIIerror,the
resultsofthistestrequiredacomparisonwithHo2aandHo3avalidatingthatbothPUand
PEOUweresignificanttoBIregardlessofageandgenderinvolvedasinteractingvariables.
ResearchHypothesisHo4b
Ho4b:ThereisnostatisticallysignificantdifferencebetweenITmanagers'generationalgroupsandgendertypesandthevariablesofperceivedusefulness,perceivedeaseofuse,andbehavioralintentiontouseESStechnology.
HypothesisHo4bexaminedwhetherastatisticallysignificantrelationshipexisted
betweenITmanagers'perceivedbehavioralintention(BIitems9,10)touseESStechnology
andvariablesofperceivedusefulness(PUitems1,2,3,4)andperceivedeaseofuse(PEOU
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.
43
Thenullhypothesiswouldberejectediftheregressionanalysisresultedinap-value
significantatthep<.05levelforgenerationalgroupsandgendertypesusingtheWilks's
Lambdateststatistic.NullhypothesisrejectionwouldindicatethatITmanagers'perceived
intentionstouse/adoptESStechnologydiffersbetweengenerationalgroupsandgender
types.Ifthenullhypothesiswasretained,itwouldindicatethattherewasnodifference
betweengenerationalgroupsandgendertypesonvariablesofPU,PEOU,andBI.
Additionally,toavoidthepossibilityofaTypeIorTypeIIerror,theresultsofthistest
requiredacomparisonwithHo2bandHo3bvalidatingthatbothPUandPEOUwere
significanttoBI.
Summary
Thischapterdiscussedthestudy'sresearchdesign,sampling,instrumentation,data
collectionprocedures,andthedataanalysis.Theresearchcarriedoutwasbasedonthe
proceduresoutlinedinthischapter.Chapter4discussesthefindingsofthestudy.
44
CHAPTER4
FINDINGS
Overview
ThisstudyexaminedITmanagers'perceptionsofESStechnologyacceptancefactors
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;
45
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
46
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.
47
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
48
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
49
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
50
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
51
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.
52
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.
53
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
54
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
Ho2a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,andage.
MultipleregressionanalysisresultedinanFstatisticof376.58,p<.01.Therefore,
nullhypothesisHo2awasrejected.Resultsindicatedstatisticallysignificantcorrelationsof
PUandBI(r=.85,p<.01)andPEOUandBI(r=.61,p<.01).AgeandBIwerenotfoundto
55
besignificantlycorrelated(r=.17,p>.05)althoughtheoverallregressionmodeldidfind
forrejectionofthenullhypothesis.SeeTables18,19,and20.
Table18
Ho2aAnalysisofVariance
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
Ho2aRegressionModelSummary
R Rsquare AdjustedRsquare Std.erroroftheestimate.86 .74 .74 .91
Note.Predictors:(Constant),Age,PEOU,PU;DependentVariable:BI.
Table20
Ho2aCoefficients
Unstandardizedcoefficients
Standardizedcoefficients
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
56
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).
57
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
58
Ho3a:ThereisnostatisticallysignificantrelationshipbetweenITmanagers'behavioralintentiontouseESStechnologyandthevariablesofperceivedusefulness,perceivedeaseofuse,andgender.
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
26.
Table24
Ho3aAnalysisofVariance
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
Ho3aRegressionModelSummary
R Rsquare AdjustedRsquare Std.erroroftheestimate.86 .74 .74 .91
Note.Predictors:(Constant),Gender,PU,PEOU;DependentVariable:BI.
59
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.
60
Table28
Ho3bGenderTestsofBetween-SubjectsEffects
SourceDependentvariable
TypeIIIsumofsquares df
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
Intercept PU 4134.16 1 4134.16 1463.65 p<.01 .79 1.00PEOU 2833.23 1 2833.23 1686.88 p<.01 .81 1.00BI 3245.71 1 3245.71 1048.07 p<.01 .72 1.00
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).
61
Table29
Ho3bPairwiseComparisons
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
Ho4aAnalysisofVariance
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
62
Table31
Ho4aRegressionModelSummary
R Rsquare AdjustedRsquare Std.erroroftheestimate.86 .74 .74 .91
Note.Predictors:(Constant),Gender,PU,Age,PEOU;DependentVariable:BI.Table32
Ho4aCoefficients
Unstandardizedcoefficients
Standardizedcoefficients
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).
63
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.
64
Table34
Ho4bGenerationandGenderTestsofBetween-SubjectsEffects
SourceDependentvariable
TypeIIIsumofsquares df
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
Intercept PU 2160.42 1 2160.42 778.43 p<.01 .66 1.00PEOU 1531.26 1 1531.26 914.31 p<.01 .70 1.00BI 1736.83 1 1736.83 569.91 p<.01 .59 1.00
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).
65
Table35
Ho4bPairwiseComparisons
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
Ho4bPairwiseComparisons
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
66
Summary
Thischapterprovidedtheresultsfromthedatacollectedandthestatisticaltests
performed.Theanalysesvalidatedtheinstrumentation,data,andmethodologyusedto
answerthestudy’sresearchquestionstoacceptorrejectthenullhypotheses.Methods
includedreliabilityandvalidityanalysis,correlationanalysis,multipleregression,and
MANOVA.Findingsresultedintherejectionofsevenofeighthypothesesoutlinedin
previouschapters.Chapter5providesasummaryofthestudy,discussionofitsfindings,
andrecommendationsforfutureresearch.
67
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
68
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
Perceived Ease of Use (PEOU)
Behavioral Intention to Use
(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.
69
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.
70
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
71
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
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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
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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
74
(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
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groupsandgendertypesbeingofdifferenttypesofvariables.Thenullhypotheses,if
consideredindependently,wouldhaveresultedinrejectionasperformedinbothHo2band
Ho3b.
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
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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
77
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
78
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
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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
80
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
81
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
82
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.
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APPENDIXA
INSTRUMENTS
84
Davis&Venkatesh(1996)InstrumentReuseRequest
SunilPatel<[email protected]> Mon,Jan23,2012at8:32PM
To:[email protected],[email protected]
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<[email protected]> Mon,Jan23,2012at9:03PM
To:SunilPatel<[email protected]>
Youhavemypersmissiontousethe1996IJHCSinstrumentforyourdoctoralresearch.
Bestwishes
FredDavis
85
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
86
Instrument
87
88
APPENDIXB
IRBAPPROVALANDINFORMEDCONSENTNOTICE
89
IRBAPPROVAL
90
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.
91
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