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Minnesota State University, MankatoCornerstone: A Collection of
Scholarly and Creative Works forMinnesota State University,
MankatoAll Theses, Dissertations, and Other CapstoneProjects Theses, Dissertations, and Other Capstone Projects
2016
IT Centralization and the Innovation Value Chainin Higher Education: A Study for Promoting KeyInnovations Through Innovation Management andOrganizational DesignEdmund Udaya ClarkMinnesota State University Mankato
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Recommended CitationClark, Edmund Udaya, "IT Centralization and the Innovation Value Chain in Higher Education: A Study for Promoting KeyInnovations Through Innovation Management and Organizational Design" (2016). All Theses, Dissertations, and Other CapstoneProjects. Paper 652.
ITCentralizationandtheInnovationValueChaininHigherEducation:AStudy
forPromotingKeyInnovationsThroughInnovationManagementand
OrganizationalDesign
EdmundUdayaClark
ThisDissertationisSubmittedinPartialFulfillment
oftheRequirementsfor
theEducationalDoctorateDegree
inEducationalLeadership
MinnesotaStateUniversity,Mankato
Mankato,MN
(ApprovedMay,2016)
ii
Date:May2,2016
Thisdissertationhasbeenexaminedandapproved.
ExaminingCommittee:
__________________________________________________Dr.CandaceRaskin,Advisor
__________________________________________________Dr.JasonKaufman,CommitteeMember
__________________________________________________Dr.ScottWurdinger,CommitteeMember
iii
Abstract
Thepurposeofthepresentstudywastoinvestigatetheimpactof
organizationalcentralizationinhighereducationtechnologysupportunitson
institutionalinnovativeness.Thecentralizationtoolsusedforthepresentstudy
includedmeasuresdevelopedbyHage&Aiken(1971),Kaluzny,etal.(1974),and
Ferrell&Skinner(1988).TheinnovativenessmeasureswereestablishedbyHansen
&Birkinshaw’s(2007)toolforevaluatinginnovationvaluechainactivitiesin
organizations.Dataweregatheredfromanation-widesample(n=303)ofIT
workersat38researchoneinstitutionsintheUnitedStates.Theresultsindicated
thatinnovationvaluechainactivities(ideageneration,conversion,anddiffusion)
werenegativelyimpactedascentralizationincreased.However,thesefindings
variedsignificantlybythetypeofinstitutionbeingmeasured,thephaseofthe
innovationvaluechainbeingstudied,andthetypeofreportinglineforeach
participant.
iv
Acknowledgements
“Darknesscannotdriveoutdarkness:onlylightcandothat”(MartinLuther
King).Thankstomycommittee,CandaceRaskin,JasonKaufman,andScott
Wurdinger,andtheentirefacultyoftheEducationalLeadershipprogramfor
teachingmetoshinethelightofknowledgeonthemostimportantissuesin
education.
“Comingtogetherisabeginning;keepingtogetherisprogress;working
togetherissuccess”(HenryFord).ThankstomyveryspecialEducational
Leadershipdoctoralcohort,RobbieBurnett,KellyKillorn,JayMeiners,Kenneth
Turner,BeVang,MeganWeerts,KaylaWestra,andWayneWhitmoreforcoming
together,keepingtogether,andworkingtogether;teachingmesomuchandfor
inspiringmealongtheway.
“Doyoureallywanttolookbackonyourlifeandseehowwonderfulitcould
havebeenhadyounotbeenafraidtoliveit?”(CarolineMyss).Thankstomy
parents,Edmund&ChouwaneeClark,andmychildren,Adam,Audrey,Jackson,and
John,foralwaysbelievingthatIcandoanything.
“Ourchiefwantissomeonewhowillinspireustobewhatweknowwecould
be”(RalphWaldoEmerson).Thanksfinallytomywife,DonaClark,foralways
inspiringmetofindandbecomethebestversionofme.
v
TableofContents
Abstract………………………………………………………………………………….iii Acknowledgements……………………………………………………………………...iv List of Figures…………………………………………………………………………...vi Chapter I - Introduction…………………………………………………………….…..1
Background of the Problem……………………………………………….…….1
Purpose Statement….……………………………...…………………………….6
Delimitations and Limitations…………………………………………………..7
Definition of Key Terms…………………………………………………………7 Chapter II - Review of the Literature…………………………………………………..9 Chapter III - Method…………………………………………………………………...35 Chapter IV - Results……………………………………………………….…………...41 Chapter V - Discussion…………………………………………………….…………...51 References……………………………………………………………………………….61 Appendix A - Centralization Assessment Combination Instrument………………...79 Appendix B - InnovationValueChainSurvey………………………...……………81
vi
List of Figures Figure 1 - A Model of Five Stages of the Innovation-Decision Process………..…….22 Figure 2 – Innovation Value Chain Models…………………………………………...25 Figure 3 – R1 University Locations…………………...……………………………….37 Figure 4 – Respondent Demographics…………………...………………………...….42 Figure 5 – Distribution of Participation in Decision-Making scores …………….….43 Figure 6 – Distribution of Hierarchy of Authority scores ………………..………….45 Figure 7 – Distribution of Centralization scores ………………….………………….45 Figure 8 – Innovation generation, conversion, and diffusion as a function of centralization……………………………………………………………………………46 Figure 9 – Correlation plots for centralization to generation, conversion, and diffusion …………………...………………………………………………………….……………52
1
ChapterI
Introduction
BackgroundoftheProblem
Theimportanceofinnovationmanagementforhighereducation
organizationshasneverbeenmorecritical.Avarietyoffactorshavecombinedto
presentnewchallengesforAmericanuniversities,particularlypublicuniversities.
Thesechallengesconsistofaseaofdemographicchanges,combinedwitha
correspondingchangeinstudentneedsanddesires(Snyder&Dillow,2015;
Desrochers,etal.,2010).Thereisatrendofdecreasingfundingfromstatesfor
publiceducationthathaspersistedsince1980(Mortenson,2012).Thesefunding
changeshavebeenaccompaniedwithaccountabilitydemandsforincreased
efficiencyandgreaterstudentoutcomes,fromgraduationratestoemploymentat
graduation(Huisman&Currie2004;King,2007;McLendon,Hearn,&Deaton,
2006).Theentriesofnewprivateandfor-profitinstitutionshavecreatedanintense
levelofcompetitionforstudentsaroundtheglobe(Hemsley-Brown&Oplatka,
2006;Dill,2003).Finally,rapidchangesintechnologyhaveaffordednew
opportunities,butmanyinstitutionshavestruggledtokeeppace(Boezerooij2006;
Kassens-Noor,2012;Al-Qahtani&Higgins,2013;Merchant,etal.2014).
Manyscholarshavestudiedthesefactors—fromaccountabilityto
demographicsandtechnologicalprogress—andmeasuredtheirimpactsonhigher
education.However,asresearchersprobethesetrends,newgapsintheliterature
haveemerged.Forexample,haveaccountabilitydemandsandfundingshortfalls
madeuniversitiesmoreorlessinnovative?Someuniversitieshavedecidedto
2
decentralizeacademicunitsintoself-sustainingspin-offs,whileothershavedecided
tocentralizecontrolasmuchaspossible.Havecutsinstudentandfacultysupport
impactedtheabilityofschoolstoadaptswiftlytoemergingchallenges?Which
modelsworkbesttopreservetheabilitytogenerate,adopt,anddiffusenewideas?
Theissuespresentedbythesequestionsarecompoundedwhennational
demographicchangesarealsotakenintoaccount.Theblendofwhiteandnon-
whitestudentshaschangeddrasticallyoverthepast20years,withanincreasingly
highpercentageofincomingstudentsrepresenting“at-risk”populations(Gavigan,
2010;Klemencic&Fried,2015).Thesestudentssufferfromagreaterrateof
attritionthantraditionalstudents,andrequireincreasedattentionand
interventionsinorderforthemtosucceed(Jones&Watson,1990).Whiletheterm
“at-risk”broadlyencompassesalargepopulationthatincludesfirst-generation
students,studentsfromlowersocio-economicbackgrounds,immigrantstudents,
andstudentsofcolor,thereisevidencethatagreatmanystrategiesmustbe
employedandtailoredtopromoteretentionineachofthesesegments(Dumbrigue,
Moxley,&Durack,2013).
Furthermore,incomingstudentshavedifferentneedsandwantsinregardto
technologythantheyhaveinpastgenerations.Thesestudentshavebeen
collaboratingelectronicallysinceaveryyoungage,usingcloud-basedservices
providedbyGoogleandMicrosoft.Theyhaveturnedinassignmentsonlinesince
the3rdgrade,andaccessedtheirgradesandassignmentsviatheweb.Theaverage
childhashadacellphonesinceage10andmorethanhalfofthesechildrenusea
smartphonetoaccesssocialmediaandinteractwiththeirpeers(Boerma,2014).
3
Accompanyingthischangeinstudentsisadynamicrateofchangeinthe
technologiesavailabletoconsumers.Bandwidthavailabilityandusagehas
quadrupledin10years.ThelastdecadehasseentherapidconversionofDVDsto
Blu-Raydiscstopurelystreamingmedia.Cableandsatelliteprovidersconfronta
newgenerationofcustomersthathavenointerestintheirofferings,otherthanthe
bandwidththattheycanprovidetoaccessonlinesitesandservices(Steel&Marsh,
2015).Highdefinitionvideohasmorethanquadrupledinresolutionandvirtual
realityispoisedtobecomeanewstandardforinteractingwithdigitalmedia(Sydell,
2016;Kuusisto,2015).
Asnewtechnologieshaveemerged,sohavenewplayersinthehigher
educationspace.For-profit,fullyonlineuniversitiesliketheUniversityofPhoenix
andCapellamadehugesplashesinitially,butcurrentlyfacestiffchallengesinthe
formofpoorstudentoutcomes,highdebt,andacriticalfederalgovernmentstance
(Lam,2015).However,otherplayerslikeEdXandCourserahavepartneredwith
large,prestigiousuniversitieslikeStanfordandMITtodeliverinstructioninnew
formatslikeMOOCs(massivelyopenonlinecourses),self-pacedmodules,and
adaptivelearningapproaches.Whiletheseofferingsaddprestigeandserveas
valuablemarketingtoolsforthemosteliteinstitutions,thevastmajorityofschools
havenotbeenparticipantsinthesenewpartnerships(Hampson,2012).
Institutionalresponsestothesechallengeshavebeenvariedandconsistofa
widerangeofapproaches,successes,andfailures.Responsestobudgetreductions
haveincludedcentralizationeffortsandspin-offsofprofessionalschools.Data
analyticsandlearneranalyticshavebeenmadepossiblebynewsoftwareplatforms
4
andhavebeenadoptedbymostinstitutionstomeettheneedsofchangingstudent
demographics.Thesetoolshavebeenusedtoimproverecruitmenteffortsaswellas
theretentionofexistingstudentsateachcollege(Creasey,2008).Manyschools
havelaunchedonlinecoursesandprogramstomatchtheconvenienceofferedby
for-profitonlineinstitutions.Mosthaveadoptedsomeamountofblendedor
technology-enhancedlearningapproaches,whichutilizetechnologicalaffordances
toprovidebetterlearningexperiences.Finally,agrowingnumberofinstitutions
haveestablishedinstructionalinnovationcenterstosupportfacultyinadoptingnew
approachestoteachingandlearning,fromonlinecoursestoflippedclassrooms,
wherelecturesarepre-recordedandpresentedonlinewhileclasstimeisusedfor
in-depthdiscussions(Graham,Woodfield,&Harrison,2012).However,despite
theseattemptstoinnovate,manycollegeshavefailedtosurvivethiscombinationof
criticalstresses.AMoody’spredictionforecaststhattheclosureofsmallcolleges
anduniversitieswilltripleto15peryearby2017(Woodhouse,2015).
ProblemStatement
Alargebodyofevidencehasdemonstratedthecriticalimportanceof
innovationtoorganizations(Borins,1998,2001;Andrewsetal.,2006;Christensen
etal.,2004;Damanpouretal.,2009;Tiddetal.,2001).Furthermore,scholarshave
alsoidentifiedmanyproblemsinvolvedinsuccessfullyimplementingthese
innovations(Ensminger,2005;Griffith,Zammuto,&Aiman-Smith,1999;Meyer&
Goes,1988;Surry&Ely,2002;Polley,etal.,1999),includingtheculturaland
resourcebarrierstoimplementationwithinorganizations(Aubert&Hamel2001;
Denis,etal.,2002;Heide,etal.,2002;Fennell&Warnecke1988;Rogers2003).
5
However,thereisverylittleresearchthatprovidesguidanceonhoworganizational
structurescaninfluencethelikelihoodofimplementationofkeytechnological
innovations(Damanpour&Wischnevsky2006;Tidd,2001).WhileDamanpour’s
(1996)researchmappedthebroadimpactofcentralizationoninnovativenessin
largeandsmallfirms,DamanpourandWischnevsky’s(2006)papersuggeststhat
unitswithinanorganizationcanalsohavelargeimpactsontheinnovationcycle:
“Futureresearchonthegenerationofinnovationshouldcompareand
contrastindependententrepreneurialorganizationswiththeautonomous
unitsofestablished,largeorganizationsengagedinthegenerationof
innovation—notwiththoseorganizationsintheirentirety”(p.279).
Giventhatinstitutionsofhighereducationareorganizeddifferently,andthat
technologysupportorganizationsinsideeachinstitutionarealsoorganized
differently,thereisaneedtofurtherunderstandhowthesestructuraldifferences
mayimpactinnovationgenerationanddiffusionrates.Currentliteratureon
innovationhasfocusedonmanyfactorsthatmaypromoteadoptionanddiffusionin
organizations.However,verylittleresearchhasbeendoneinunderstandingthe
impactofthesenewchallengesinpublichighereducationonorganizational
innovativeness,specifically:accountability,fundingchanges,demographicchanges,
increasingcompetition,andrapidtechnologychanges.Furthermore,scarce
researchhasbeenconductedinmeasuringthevalueofcommoninstitutional
responsesandtheirpositive(ornegative)impactsonorganizationalinnovativeness.
Moreover,literatureoncentralizationhasgenerallyunderestimated(ornot
considered)theroleofsubunitswithinagivenorganization,andhasnottypically
6
includedhighereducationasanareaofstudy.Itisimportanttoisolateaspectsof
centralizationthatcanbecontrolledbyhighereducationinstitutionsandthatalso
havelargerimpactsontheinnovationprocess.Finally,theinnovationvaluechain,
whichdescribesinnovationasaprocessprogressingfromideagenerationto
conversiontodiffusion,islikelytobegreatlyimpactedbythelevelofcentralization
oftechnologysubunitswithinhighereducationinstitutions.Thisconnectionhas
notbeenexploredinpreviousresearch.
PurposeoftheResearch
Thepurposeofthisresearchistomeasuretheimpactofcentralizationin
publichighereducationtechnologysupportunitsontheirrespectiveinstitutions’
abilitiestogenerate,convert,anddiffusenewinnovations.Understandingwhat
organizationalapproacheswouldenhanceeachphaseoftheinnovationprocess
couldincreasethecapabilitiesofdeeplystressedinstitutionstosurviveinarapidly
changingenvironment.
Hypotheses
Hypothesis1.Thisstudyhypothesizesthatmoredecentralizedtechnology
structureswillshowgreatereffectivenessduringtheideagenerationphasethan
morecentralizedstructures.
Hypothesis2.Thisstudyhypothesizesthatmoredecentralizedtechnology
unitswillshowgreatereffectivenessduringtheideaconversionphasethanmore
centralizedstructures.
7
Hypothesis3.Thisstudyhypothesizesthatmorecentralizedtechnology
structureswillshowgreatereffectivenessduringthediffusionphasethanmore
decentralizedstructures.
Hypothesis4.Thisstudyhypothesizesthatmorecentralizedtechnology
structureswillshowlowereffectivenessforallthreephasesoftheinnovationvalue
chainthanmoredecentralizedstructures.
Hypothesis5.Thisstudyhypothesizesthatparticipationindecisionmaking
measureswillshowmoresignificantcorrelationswithinnovationvaluechain
phasesthanhierarchyofauthoritymeasures.
SignificanceoftheResearch
Thisstudywillilluminatethestrengthsandweaknessesofspecificand
controllableaspectsofcentralizationforfaculty,staff,andadministratorshopingto
utilizenewtechnologiestoachievetheirrespectiveinstitutionalgoals.Theresults
fromthisstudywillhelpcollegesanduniversitiestochooseanddesign
organizationalstructuresandprocessestohelppromotekeyinnovations,while
providingthemwithabroaderunderstandingofthestrengthsandweaknessesof
theircurrentorganizationalmodels.Specificaudiencesthatwillbenefitmostfrom
thisresearchincludepresidentsandadministratorsatR1institutions.
Delimitations
Thisstudywillonlyincludedatafromresearchone(R1)institutionsinthe
UnitedStates,whichmaynotbegeneralizabletoschoolsthatfallintoother
categories(e.g.,private,Master’scomprehensive,etc.).
DefinitionofKeyTerms
8
Centralization.Forthepurposesofthisstudy,thistermisdefinedasameasureof
both1)participationindecisionmakingand2)hierarchyofauthorityinagiven
organization.
Innovation.Forthepurposesofthisstudy,thistermisdefinedas“anidea,practice,
orobjectthatisperceivedasnewbyanindividualorunit,”which“presentsanew
alternativeoralternatives,aswellasanewmeansofsolvingproblems”(Rogers,
2003).
InnovationValueChain.Forthepurposesofthisstudy,thistermisdefinedasa
viewofinnovationas“asequential,three-phaseprocessthatinvolvesidea
generation,ideadevelopment,andthediffusionofdevelopedconcepts”(Hansen&
Birkinshaw,2007).
9
ChapterII
LiteratureReview
Publichighereducationinstitutionsfaceavarietyofintensifyingpressures,
fromstateaccountabilityregimesandchangingstudentdemographicstonew
competitorsandpotentiallydisruptivetechnology-basedapproaches.These
pressureshaverequireduniversitiestoconsidernewwaysofdoingbusinessin
ordertoremaincompetitive.Asthesepressuresmount,differentinstitutionshave
optedtocentralizeordecentralizeoperationswhileattemptingtoincreasetheir
organizationalinnovativeness.Undertheseconditions,itisreasonabletoassume
thatpublichighereducationinstitutionswoulddesiretounderstandthepotential
impactsoftheircentralizationanddecentralizationdecisionsontheirabilitiesto
managetheinnovationprocess.
Thisstudywillfocusspecificallyontechnologyserviceunitcentralizationin
publichighereducation(asmeasuredbycentralizationsurveytoolscreatedby
Hage&Aiken(1971),Kaluzny,etal.(1974),andFerrell&Skinner(1988))andthe
relationshipofthisrelativecentralizationtoinnovationmanagementpractices
withintheseinstitutionsasdeterminedbyHansen&Birkinshaw’s(2007)
InnovationValueChain(IVC)tool.Consequently,thisreviewoftheliteraturewill
focusonthreeprimaryareasofinterest:1)accountability,funding,demographic,
andcompetitivepressuresinpublichighereducationthathaveledtocallsforrapid
changes,2)centralizationandtoolsforunderstandinglevelsofcentralization,the
importanceofsubunitsinorganizations,andthelinkageofsubunitswithimpactsin
technologyadoptionmodels,and3)studiesthathighlightthecurrent
10
understandingoffactorsthatleadtoinnovationgeneration,conversion,and
diffusion;alongwithananalysisoftheinnovationvaluechainframework.This
chapterwillthenconcludewitharationaleforthepresentstudy.
PressuresonPublicHigherEducationandInstitutionalResponses
Accountability.U.S.publichighereducationinstitutionsareunder
increasingpressurefromtheirrespectivestategovernmentstojustifytheirfunding
(Spellings,2006).Infact,approximately50%oftheUnitedStateshadimplemented
accountabilityprogramsforpublichighereducationinstitutionsby2013
(Dougherty,etal.,2013).Stateaccountabilityregimesgenerallyarecomposedof
twocategoriesofdemands:1)callsforincreasedefficiency,and2)callsforbetter
studentoutcomes.Inthefirstcategory,legislativeaccountabilitymeasuresoften
includedemandsfordecreasedadministrativecostsandincreasedoperational
efficiency(Leveille,2006).Oneresultofthesedemandsisatrendtoward
centralizationofoperationsandsupportunitsatmanyinstitutions(Geiger,2015).
Someinstitutions,liketheUniversityofMinnesota,havebeenpubliclyshamedfor
theiradministrativebloat(Belkin&Thurm,2012).Inresponse,theUniversityof
Minnesotapromisedtocentralizeoperationsandcutmillionsinadministrative
costs.Similarly,otherlargeinstitutions,fromArizonaStateUniversitytothe
UniversityofTexasatAustin,havefocusedonreducingadministrativecostsand
relatedpositions(Lindsay,2015).
Thesecondcategoryofaccountabilitydemandscallforbetterstudent
outcomes,includingretention,graduation,andemploymentrates(Huisman&
Currie2004;King,2007;McLendon,Hearn,&Deaton,2006).The2016report,
11
“AmplifyingHumanPotential:EducationandSkillsfortheFourthIndustrial
Revolution”,commissionedbyInfosyscorporation,foundthatformercollege
studentsaroundtheglobearequestioningwhethertheireducationsadequately
preparedthemfortheircareers(Infosys,2016).Thepressurecomesnotonlyfrom
studentconsumersandstatelegislators,butalsofromnationalbodies,includingthe
U.S.DepartmentofEducation(DOE).In2006,theDOE’sReport“ATestof
Leadership:ChartingtheFutureofU.S.HigherEducation”calledforincreased
accountability,access,andaffordabilityinhighereducation(Spellings,2006).
Creasey(2008)writesaboutthetrend:
Recentlyhighereducationadministratorshavebeenheldaccountableto
provide(a)measurements,(b)process,and(c)policy.Moreover,
administratorsareexpectedtorespondtochancellors,provosts,boards,and
committeesto(a)justifyexpenditures,(b)engageinstrategicplanning,(c)
managetheirorganizations,and(d)understandthevalueofITinvestments.
(p.1)
Thisheightenedoversightofuniversitiesandcollegesextendsfromensuring
thealignmentofbusinessprocesseswithITinvestments,to“demonstratingthe
impactoftechnologyonstudentlearningoutcomes”(Creasey,2008,p.1).These
pressures,bothfromnewgenerationsofstudentsandfaculty,aswellasfromstate
andnationalorganizations,callforincreasedintegrationoftechnological
innovationsatinstitutionsofhighereducation.
DemographicChangesandIncreasedCompetition.National
demographicshavealsobeenafactorinthecallsforchange.TheUSDepartmentof
12
EducationrecentlyfoundthatthepercentageofstudentsofcolorinAmerican
collegeshasrisendramaticallyfrom1976to2011,whilethepercentageofwhite
studentshasdeclinedfrom84%to61%(Snyder&Dillow,2015;Desrochers,etal.,
2010).
Manyofthesegrowingpopulationsfallunderthecategoryofat-risk
students,whichrequirenewandimprovedapproachestotheireducation(Gavigan,
2010).KlemencicandFried(2015)describeadecliningdomesticpopulationof18-
24yearoldsintheUnitedStatesalongwitharapidlyincreasingagingpopulation.
SiemensandMatheos(2012)writeoftheimplicationsofapopulationexplosionin
Asiaandafocusonskills-basededucationinaneverexpandingtechnological
environment.KlemencicandFriedconclude:
Competitionfor…studentsamonghighereducationinstitutionswillbecome
stronger,creatingincentivesfortherecruitmentofforeignstudentsandfor
supplementingthetraditionalstudentswithlifelonglearners.Thus,higher
educationinstitutionswillhavetoadjusttheiracademicprogramsand
organizationalstructuresandbecomemorepermeable,de-emphasizingtheir
socialselectivityandaccommodatingtheneedsofanincreasinglydiverse
studentpopulation.(p.13)
Intensecompetitionisanotherfactorthatisforcinguniversitiestoinnovate
andadoptnewbusinessmodels.Hemsley-BrownandOplatka(2006)describethe
intensifyingawarenessofuniversitiesfortheirneedtocompetethusly:“Inthe
contextofincreasingcompetitionforhome-basedandoverseasstudentshigher
educationalinstitutionsnowrecognizethattheyneedtomarketthemselvesina
13
climateofinternationalcompetition”(p.316).Similarly,Dill(2003)positsthat“US
highereducationisthemostmarket-orientedsystemintheworld”(p.137).This
market-orientedcompetitivenessisdrivenbylargeandgrowingnumbersofprivate
andfor-profitinstitutionscompetingforthesamepoolofstudents,alongwith
federalprogramsthatfundindividualstudentsandresearchers.Furthermore,
Hoxby(1997)concludesthatincreasingcompetitivenessispartiallyduetothe
erosionofgeographicmonopoliesinpublichighereducationovertime(i.e.,that
studentsusedtoattendin-statepublicschoolsatamuchhigherratethantheydo
today).
Itisthereforecriticalthatinstitutionsconsciouslyengageinthepracticeof
innovationmanagement,sothatnewopportunitiestoadvancestudentandfaculty
successareleveragedappropriatelyandmorequickly.
FundingPressures.Addingtothispressurefromaccountabilitydemands
anddemographicchangesisapersistentdeclineinallbuttwostates(Wyomingand
NorthDakota)instatefundingforpublicuniversitiessincetheearly1980s
(Mortenson,2012).AMoody’s(Moody’s,2013)financialoutlookreportconcluded:
For2013,Moody’srevisesitsoutlookfortheentireUShighereducation
sectortonegative,markingashifttonegativefromstableforeventhe
sector’smarketleadingdiversifiedcollegesanduniversities.Theoutlookfor
theremainingmajorityofthesectorremainsnegative,asithasbeensince
2009.Thenewsector-widenegativeoutlookreflectsmountingpressureon
allkeyuniversityrevenuesources,requiringbolderactionsbyuniversity
leaderstoreducecostsandincreaseoperatingefficiency.(p.1)
14
TechnologicalAdvances.Finally,manyscholars(Boezerooij2006;
Kassens-Noor,2012;Al-Qahtani&Higgins,2013;Merchant,etal.2014)have
pointedouttheglobalanddistance-amelioratingeffectsofe-learning,socialmedia,
andvirtualrealityofferings,whichhavefundamentallychangedthenatureand
potentialofeducationaldeliveryviatechnologicalmeans.Arecentreporton
technologyandeducation(Infosys,2016)foundthatrapidtechnologicalchangewas
promotinganemergingemphasisonskills-basedlearning,particularlyin
technology-relatedskills.Thereport,consistingofsurveysofthousandsof16-25
yearoldsfromcountrieswiththeninelargestglobaleconomies,discoveredthat
“youngpeopleagreeoverwhelminglythattechnologyhaspositivelyinfluencedtheir
development”(p.28).Alargemajorityofthesesurveyparticipantsagreedthat
technologyhadenablednewwaystoaccesseducationalresourcesandofferings.
Allofthesefactorsaddtothepressureonpublichighereducation
institutionstobecomemoreinnovative.
InstitutionalResponses.Institutionsofhighereducation,bothpublicand
private,haveraisedtuitionsignificantlyoverthepast25years,evenwhenadjusted
forinflation(Ehrenberg,2012;Baum&Ma,2012;Archibald&Feldman,2012).
Desrochers,etal,(2012)attributethesetuitionincreasesatpublicinstitutionsto
declinesinstatefunding,particularlyafterthe2001recession.Tuitionhasbeenthe
moststablesourceofrevenuefortheseinstitutions,consideringallsourcesof
funding,fromfederalgrantstodonorgifts.Desrochers,etal.(2012)foundthatas
increasingcostshavebecomelesssubsidizedbystates,tuitionhasrisen
accordingly.
15
Institutionshavealsoturnedtonewwaystodelivertheircoursestowider
audiences.Theserangefromcommonlocally-deliveredonlinecoursestoMassive
OpenOnlineCourses(MOOCs)deliveredthroughpartnershipswithlargenon-profit
organizations,likeEdXandCoursera.Typically,theselatterofferingsinvolvehigher
profileinstitutionslikeMIT,Harvard,andBerkeley,andareconsideredameasureof
institutionalprestige.Voss(2013)states:“Theparticipatingcollegesand
universitieshavestatedthattheybelievetheirinvolvementwiththeseinitialefforts
willextend,enhance,andpreservetheirinstitutionalreach,brand,andreputation”
(p.2).
Meanwhile,lesserprofileinstitutionshavebeenexpandingtheironline
offeringsinordertoattractnewstudentsandoffermoreconveniencefortheirlocal
students(Rosenberg,2001;Bates,2005).Alargenumberofscholars(Giannoni,et
al.,2003;Covington,etal.,2005;Maguire,2005)havehighlightedtheproblemof
facultyresistancetoonlineteachingmodalities.YetAllen&Seaman(2007)found
thatthedemandfromstudentsforonlinecourseshasgrownveryrapidlyandthat
mostuniversityleadersexpectthistrendtocontinue.Furthermore,thistrendhas
alsoemergedincorporateenvironments,whereemployeesareexpectedtoengage
inonlinelearningaspartoftheirrequiredtrainingsandprofessionaldevelopment
activities.(Yoon,2003;Smart&Cappel,2006).Thistensionbetweenstudent,
workforce,andfacultyexpectationshighlightstheimportanceofinnovationand
changemanagementinhighereducation.
Oneofthemostcommondemandsofaccountabilityregimesforpublic
highereducationisforincreasedefficiencyanddecreasedadministrativecosts
16
(Desrochers,2010).However,thecombinationoffundingcutsandaccountability
measuresthatcallforincreasedadministrativeefficiencyandcentralizationmaybe
workingagainstthedemandsforinstitutionstoinnovateinordertopromote
studentsuccessforarapidlychangingcollegestudentprofile(CurrieHuisman2004,
p.1).Paradoxically,thesepressureshaveresultedinconflictinginstitutional
responses.Zusman(2005)foundthatschoolscouldeitherrespondbycentralizing
ordecentralizingoperationsundertheseconditions.Forexample,someinstitutions
haveaskedtheirprofessionalschoolstobecomeself-supportingautonomousunits,
whichwouldequatetodecentralizationoftheseprograms.Ontheotherhand,
whenfacultypositionsareretrenchedtheremainingfacultyhavelessinputin
governanceandcurriculumdecisions(Slaughter&Rhoades2004).Some
researchershaveproposedthearrivalofa“centralizeddecentralization”modelin
highereducation(Watkins,1996;Boezerooij2006),wherestaffandservicesare
developedandpaidforbylocaldepartmentsandcollegeswhileunderthebroad
controlofinstitutionalpoliciesandobjectives.Inthemidstofsomanyconflicting
priorities,someinstitutionshavedecidedtotrytobuckagainsttheirstate
accountabilityregimesbyacceptinglessinappropriationsinreturnformorelocal
autonomyandcontrol.
Manyoftheattainableimprovementsavailabletothesecollegesand
universitieswillrequirenewandjudiciousimplementationsoftechnological
innovations,whethertheserelatetoadvancementsindataanalytics,improved
communicationsystems,orenhancedclassroomtechnologies(Creasey,2008).
Undertheseconditions,itisreasonabletoassumethatpublichighereducation
17
institutionswoulddesiretounderstandthepotentialimpactsoftheircentralization
anddecentralizationdecisionsontheirabilitiestomanagetheinnovationprocess.
InnovationConcepts
Researchininnovationisveryrichandconstantlyexpanding.Thisisinlarge
partduetotheassertionofmanyscholarsthatinnovationisthemostimportant
factorinthesuccessoforganizations(Crossan&Apaydin,2010;Kemp,etal.,2003;
Rosenbusch,etal.,2011).Crossan&Apaydin(2010)foundthenumberofresearch
articlesconcerninginnovationhasrisenexponentiallyinrecentyears,while
lamentingtheabsenceofunifyingdefinitions,variables,andframeworksforthe
field.
Evendefiningtheword“innovation”hasbecomeacontentiousissueinits
ownright.FromShumpeter’s(citation)definitionin(1942)(“industrialmutation
thatincessantlyrevolutionizestheeconomicstructurefromwithin,incessantly
destroyingtheoldone,incessantlycreatinganewone”(p.73),totheUnitedStates
SecretaryofCommerce’slengthydefinitionin2008(“thedesign,invention,
developmentand/orimplementationofneworalteredproducts,services,
processes,systems,organizationalstructures,orbusinessmodelsforthepurposeof
creatingnewvalueforcustomersandfinancialreturnsforthefirm”(p.v.),thereis
aplethoraofproposedmeaningsinbetween.
However,themostcitedpublicationoninnovationisRogers’(2003)work,
DiffusionofInnovations,astudyofhownewinnovationsaredisseminatedthrough
societies.Rogersdefinesinnovationas“anidea,practice,orobjectthatisperceived
18
asnewbyanindividualorunit,”onewhich“presentsanewalternativeor
alternatives,aswellasanewmeansofsolvingproblems”(p.12).Diffusionis
definedas“theprocessinwhichaninnovationiscommunicatedthroughcertain
channelsovertimeamongmembersofasocialsystem”(p.5).Thisdefinition
establishesaprocess,orsequence,inpromotingtheutilityandusageofnew
innovations,bothforindividualsandfororganizations.Indeed,Rogerssaysthat
thisinnovationdecisionisa“processthatoccursovertimeandconsistsofaseries
ofdifferentactions”(p.169).
Inhiswork,Rogersdefines5stagesforthisinnovationcommunication
processforindividuals,alongwithenablers(andinhibitors)ofeachstage.These
stagesbeginwithknowledge,progressingtopersuasion,andsubsequentlydecision,
implementation,andconfirmation.Rogersalsospecifiesaseriesof“prior
conditions”whichhelppredictsuccessfortheinnovationprocess.Theseinclude
previouspractices,thefeltneedsandproblemsoftheindividual,theinnovativeness
oftheindividual,andthenormsofher/hissocialsystems.Thisseminalworkledto
alargenumberofsubsequentstudieswhichanalyzedeachphase,fromknowledge
toconfirmation.Thefivestagesarenextdiscussedinturn:
Theknowledgestageisprecededbyapriorconditionofneed.Untilan
individualhasidentifiedaneedforimprovement,s/hemaypractice“selective
awareness”(Rogers,p.171)andnotrecognizethevalueofavailableinnovations
andtheirapplicationstotheirsituation.Theknowledgestageconsistsofthree
typesofawareness.First,anindividualmustbeawareoftheexistenceofa
particularinnovation;second,theindividualmustpossesstheknowledgetousethe
19
innovation;andthird,theindividualmustunderstandthebasicprinciplesforusing
theinnovationsothatitcanbeusedmosteffectively.Avarietyofscholarshave
focusedontheindividualandorganizationalcharacteristicsthatmighthelp
promotesuccessintheknowledgephase.WhileBaldrigeandBurnham(1975)
foundnosignificantcorrelationswithindividualcharacteristics,laterresearchers
(Glynn,1996;Stoker,etal.,2001)foundthatattributeslikefamiliaritywiththe
worksetting,creativity,self-efficacy,andlesserneedforworkdirectionresultedin
greaterinitialinnovationbehaviors.Others,likeGeorge,etal.(2006),haveused
HallandHord’s(1987)ConcernsBasedAdoptionModeltoidentifyindividual
feelingsaboutparticularinnovations,whichledtogreateradoptionandgreater
implementationasindividualsbecamemoreinterestedinusingtheinnovations.
Someotherscholarshavealsoinvestigatedtheorganizationalfactorsthat
mayenhancesuccessintheknowledgestage.Abrahamson&Rosenkopf(1997)and
FritschandMonz(2010)foundthatthequalityofthesocialnetworkiskeyto
advancingknowledgesharingwithinanorganization.Fitzgerald,etal.,(2002)
concludedthatstrongorganizationalsiloswereadetrimenttoRogers’innovation-
decisionprocess.
Thesecondstage,persuasion,concernstheattributesofinnovationsthat
makethemmoreorlessattractivethanthecurrentlyemployedalternatives.These
characteristicsincluderelativeadvantage,whichencompassesbothpriceand
effectiveness;compatibility,bothwithindividualandorganizationalneedsand
norms;relativecomplexity,whereeaseofusehelpsacceleratetheinnovation-
decisionprocess;andreinvention,whereuserscanreusetheinnovationinnew
20
waysthanitwasoriginallyintended.Allofthesecharacteristicsleadtheindividual
toforma“favorableorunfavorableattitudetowardtheinnovation”(Rogers,p.
174).KleinschmidtandCooper(1995)establishedthestrongsignificanceofthese
attributesintheinnovation-adoptiondecision,andreviewsoftheliterature
(Tornatzky&Klein,1982;VanderPanne,etal.,2003,Jeyaraj,etal.,2006)have
confirmedthispositivecorrelation.Themorethatthesepositivetraitsare
exhibited,themorelikelythattheindividualwillchoosetoadopttheinnovation.
Thethirdstage,adoption(orrejection),isthedecisionpointforthe
individual,whochooseswhethertouseorabandontheinnovation.Rogers(2003)
foundthatthedecisiontoadoptwasmadeeasieriftheinnovationcouldbeusedon
atrialbasis.Thisattributeoftrialability,wheretheinnovationistriedoutinlow
stakesenvironments,hasbeeninvestigatedbyLin&Chen(2012),Jeyeraj,etal.,
(2006),andDucharme,etal.,(2007),whofoundmediumstrengthcorrelations
amidstsomemixedresults.Intheadoptionstage,theTechnologyAcceptance
Model(TAM),developedbyDavis(1989)andexpandedbyVenkatesh(2000),has
becomethemostoftenusedtooltopredictindividualadoption(Jeyeraj,etal.,
2006).
Venkatesh,etal.(2003)subsequentlycreatedtheUnifiedTheoryof
AcceptanceandUseofTechnology(UTAUT),whichattemptstobringtogetherall
researchfindingsofindividualadoptionintoaunifiedtheory.Thevariables
identifiedinUTAUTinclude:performanceexpectancy,effortexpectancy,attitude
towardusingtechnology,socialinfluence,facilitatingconditions,self-efficacy,and
individualanxiety.Thesevariablesreflectthelatestthinkingonthemostimportant
21
variablesinindividualadoption,andintroducesomeorganization-relatedconcepts
whichwillbediscussedlaterinthissection.
Thefourthstage,implementation,isdescribedasthepoint“whenan
individualorotherdecision-makingunitputsaninnovationtouse”(Rogers,p.
179).Importantly,thisisthestagewhereinnovationsare“re-invented”tomatch
theuniqueneedsofindividualswithintheirparticularenvironments(Rogers,p.
180).KleinandKnight(2005)andHolahan,etal.(2004)areamongawiderangeof
scholarsthathavestudiedimplementationintopicsrangingfromcomputersin
manufacturingfirmstosciencecoursesinpublicschools.Klein&Knight(2005)
foundthatimplementationisaffectedby6mainfactors:1)theexistenceofpolicies
anorganizationhascreatedtoaddressimplementation,2)organizationalclimate,3)
managementsupportfortheinnovation,4)availablefinancialresourcestosupport
theimplementation,5)thelearningorientationoftheorganization,and6)long-
termorientation(or“managementpatience”(p.245)).Manyofthesevariables
againpointtoenvironmentalvariableswithinanorganization,andwillbediscussed
shortly.
ThefinalstageofRogerstheoryofdiffusionofinnovationsisconfirmation,
inwhichindividualslookforexternalfeedbackontheinnovationandseek
“reinforcementfortheinnovation-decisionalreadymade,andmayreversethis
decisionifexposedtoconflictingmessagesabouttheinnovation”(Rogers,p.189).
Here,thegoalistoavoiddissonance,astheindividualhasalreadybuiltupabodyof
knowledgeaboutaparticularinnovation,analyzeditsrelativeadvantages,andhas
implementedit.Negativefeedbackhasapowerfulaffectatthispointonthe
22
individual’sdesiretocontinueusingtheinnovation,whichmayleadto
discontinuationofimplementation.
Figure 1. A Model of Five Stages of the Innovation-Decision Process. Rogers (2003), page 170. Reprinted under Fair Use guidelines.
Importantly,Rogersdifferentiatesthestagesofinnovationwithin
organizationsfromthatofindividuals.Theseorganizationalstagesstartwithan
initiationphase,comprisingagenda-settingandmatching;andanimplementation
phase,composedofredefining/restructuring,clarifying,androutinizing.
Agenda-settingistheprocessofclarifyinganorganizationalvisionanda
problemthatmustbeaddressedforthefirmtobesuccessful.Thiscloselymatches
thepre-existingneedconditionintheindividualinnovation-decisionmodel.
Matchingistheprocessofselectinginnovationsthatwillbestaddresstheidentified
problem(s)intheagenda-settingstage.Oncethesetwostageshavecompleted,the
adoptiondecisionhasbeenmade,andtheorganizationmovestothe
implementationphase.
23
Duringtheimplementationphase,anorganizationgoesthroughtheprocess
ofredefining/restructuring,whichconsistsofre-inventingtheinnovationsothat
itcanworkwiththeorganization,whilealsoadaptingtheorganizationto
accommodatethenewinvention.AccordingtoVanDeVen(1986),“innovations
transformthestructureandpracticesof[theirorganizational]environments”(p.
605).Organizationaladaptationcouldincludethecreationofneworganizational
units,likeaskunkworks,orarestructuringofthecurrentorganization.
Next,theorganizationwillentertheclarifyingstage,inwhichthe
organizationgrowstounderstandthemeaningandpotentialscopeandscaleof
changethatwillbeassociatedwiththeinnovation.Rogerswarnsthatimplementing
aninnovationtoorapidlyatthisstagecan“leadtodisastrousresults”(p.427).If
theinnovationfeelsforcedquicklybytheorganizationonindividuals,rejectionis
morelikelytooccur.Thisconceptrelatestotheimportanceofaparticipative
environment(Stoker2001;Fitzgerald,etal.,2002),whichallowsindividualstofeel
lessdirectedandmoreself-efficacious.
Thefinalstepintheorganizationalimplementationphaseisroutinization,
wheretheinnovation“hasbecomeincorporatedintotheregularactivitiesofthe
organizationandhaslostitsseparateidentity”(Rogers,p.428-429).Atthispoint,
thesubjectinquestionisnolongerviewedasinnovativeandtheorganizationwill
eitherstartwithnewagenda-settingordiscovernewmatchesforitsidentified
problems.
Rogerspositsthatinterestininnovationinorganizationshasincreased
greatlybecauseoftheintroductionofnewcomputer-relatedtechnologies.Henotes
24
that“theimplementationofmanyofthesenewtechnological…innovationshas
failed”(p.418)andconcludesthatorganizationalinnovationhasbecomeacritical
areaoffocusformanagers.
Ferlie&Shortell(2001)focusonorganizationalpre-existingfactorsthathelp
advanceinnovationsinorganizations,includingorganizationalcultureandclimate.
Theyconcludethatsuccess“liesintheorganization'sabilitytoprovideanoverall
climateandcultureforchangethroughitsvariousdecision-makingsystems,
operatingsystems,andhumanresourcepractices"(p.287).Theseassertionsfound
validationinVenkateshetal.’s(2000,2003)TechnologyAcceptanceModel(TAM)
andsubsequentUnifiedTheoryofAdoptionandUseofTechnology(UTAUT),which
highlightstheimportanceofadequateorganizationalresourcesfortheinnovation,
compatibilitywithexistingsystemsinuse,andadequatetechnologysupportforthe
innovation.ThesefactorsfromTAMandUTAUTillustratetheimportanceofthe
informationtechnologysubunitsofagiveninstitution,andwillbediscussedmore
thoroughlyinthecentralizationsectionbelow.
InnovationValueChain
Theinnovationvaluechain(IVC),proposedinseparateformatsbyHansen&
Birkinshaw(2007)andRoper,Du,&Love(2008),isawaytomeasurethe
innovationmanagementactivitiesoforganizationsandlargeentities,including
entirecountries.VariousstudieshaveusedtheIVCtomeasuretheinnovation
supportingactivitiesofIrelandandSwitzerland(Roper&Avanitis,2012),Japan
(Kodama,2009),andChina(Guan&Chen,2010),amongothers.Initssimplest
format,theIVCdescribesasequentialprocessofideageneration,ideaconversion,
25
andideadiffusionunderHansen&Birksinshaw’s(2007)model,oralternately,as
knowledgesourcing,knowledgetransformation,andknowledgeexploitationunder
Roper,Du&Love’s(2008)model.
Figure 2. Innovation Value Chain Models. Adapted from Sheu & Lee (2009). AccordingtoHansenandBirkinshaw’s(2007)model,thesubcomponentsof
thisthreestageprocessincludesiximportantmanagementtasks:internalsourcing,
cross-unitsourcing,externalsourcing,selection,development,anddiffusionofthe
innovationwithinthefirm.Internalsourcingissimplythegenerationofinnovative
ideasfromwithinthefirm.Cross-unitsourcingtakestheinternalideageneration
processastepfurtherbyinvolvingthecollaborationofdifferentunitswithinthe
sameorganizationtogenerateideas.Externalsourcingistheprocessofintegrating
innovativeideasfromoutsidetheorganization.Thesesourcingprocessesgenerate
ideasthatmustthenbescreenedthroughaselectionprocessandfurtherdeveloped
sothattheresultinginnovationcanworkwithinthefirm.Finally,theinnovations
shouldbediffusedthroughouttheorganization.Thesetaskstakentogether
constitutetheorganizationalactivitiesoftheinnovationvaluechain.
26
Hansen&Birkinshaw’s(2007)paperproposesaquestionnairetoquickly
determineagivencompany’sIVCstrengthsandweaknessesinordertofocus
managementeffortsontherevealedweaknesses.Giventhemanypressures
previouslydescribedinthischapter,fromstateaccountabilitystandardsto
increasingcompetition,institutionshavemuchtogainthroughunderstandingthese
factorsintheirperformance.
Thequestionnairebeginswithseriesofquestionsthatprobethecreationof
newideaswithinaunit,viacollaborationwithotherunits,orviasourcesoutsideof
theorganization.Keyperformanceindicatorsareidentifiedtoshowmeasuresthat
canbeusedtovalidatethequalityofresponses.Thenextsectioncoversthe
conversionphaseoftheIVC,whereideasarescreenedandpotentiallyfunded,and
innovationsareturnedintoviableproductsandbusinesspracticesforthe
organization.Finally,thesurveyendswithaquestionabouttheeffectivenessof
innovationdiffusionwithinthefirm,wheretheinnovationisdisseminated
throughouttheorganization(seeAppendixBforthefullinstrument).
Roper,etal.(2008)useamoremathematicalapproachtoexploreIVC
activitieswithincorporations,particularlymanufacturingfirms.Forexample,the
formulaforknowledgesourcingwithinafirmis:KS∗ =ˇʹKSkit + ʹRIjit + ʹKUCjit + ʹGOVTjit + ʹMKTjit +εjit,
jit∗
0123(1) KSjit = 1 if KSjit > 0; (p.963).ThekeydifferenceinRoper’sapproachascomparedto
Hansen&Birkinshaw’sapproachisthattheendresultinRoper’smodelisan
innovationthatcanbe“exploited”(VanHorne,etal.,2006,p.757);inotherwords,
aproductthatcanbesoldtoacustomer.Forthisreason,Roper’sapproachhas
beenusedtosurveyinnovationdevelopmentactivitiesonanationalscale;the
27
concernsofnationaleconomiesusuallyfocusonGrossDomesticProduct(GDP)and
othermeasureswhichinvolvesalesofparticulargoodstoconsumers.
Incontrast,theHansen&Birkinshawmodelcanbeusedtoanalyzeactivities
withintheorganizationthathavenodirectcustomers.Therefore,theHansen&
Birkinshawtoolismoreapplicableforthepurposesofthisdissertation.
Lane(2007)declaresthat“inacademia,especiallyinscientificandmedical
fields,individualsappeartobestronglyindependentandconservativeinnature,
andgenerallyskepticalofeducationalchange”(p.86).PutinthelensesofHansen
andBirkinshaw’sIVC,whatpartofthechainisweakest?Isitthegenerationofnew
ideas,theirconversionintoactionableapproaches,ortheirdiffusion(orallthree)?
CentralizationConcepts
Whilemanystudiesprovideaframeworkforunderstandinginnovation
adoptionfromaculturalandsocialcontext,relativelyfewstudieshavefocusedon
theimportanceoforganizationalstructuresasanelementininnovationadoption
andimplementation(Gupta,Smith,&Shalley,2006,Siggelkow&Levinthal,2003;
Westerman,McFarlan,&Iansiti,2006),andevenlesssoinhighereducation
settings.Twoadditionalfactorsthatcanbeinvestigatedunderthislensincludethe
structuresofsubunitswithinaninstitutionandtheircorrespondinglevelsof
centralization.
Thereareawidevarietyofmethodstomeasurethecentralizationof
organizations.Centralizationcanbedescribedmathematically,asameasureof
centralityandtherelationshipofanodetoothernodes.Thisapproachwasfirst
proposedbyAlexBavelasin1948tostudycommunicationwithinagroup,
28
researchedandrefinedbymanyscholars,andrefinedstillfurtherbyFreemanto
providecomparativedataaboutnodesin1978.Thismeasureiswidelyusedinthe
socialsciencestodescribecentralityinsocialnetworks.
Usingadifferentapproach,Treisman(2002)proposedapoliticalmodelof
centralizationwhichcouldbeusedfordescribingtherelativecentralizationofstates
andnations.Hismodelcomprisessixcomponents:1)verticaldecentralization,2)
decisionmakingdecentralization,3)appointmentdecentralization,4)electoral
decentralization,5)fiscaldecentralization,and6)personneldecentralization.
InTreisman’smodel,verticaldecentralizationissimplyameasureofthe
numberoftiersinanorganization,fromitshighestleveltothelowestlevel.
Decision-makingdecentralizationreferstotheauthoritytomakedecisionsateach
ofthetiers,andappointmentdecentralizationconcernswherehiringdecisionscan
bemade.Electoraldecentralizationdescribesthenumberoftiersthathavedirect
elections.Forexample,intheU.S.government,thePresidentandCongressare
elected,butsomepositionsareappointedratherthanelected.Finally,fiscal
decentralizationconcernsthepercentageofrevenuesthataredistributedto
subunitswithinthegovernment,andpersonneldecentralizationisameasureofthe
percentageofadministrationthatcanbefoundatthesubunitlevel.
HageandAiken(1967)createdatooltomeasurethelevelofcentralizationin
non-profitorganizationsthathassincebeenutilized,validated,andmodifiedmany
times(Dewar,Whetton,Boje,1980).TheHage&Aikentoolhasbeenusedtostudy
avarietyoforganizationalfactorsandperformanceindicators,fromcomplexityand
formalization(Hage&Aiken1967)topatientoutcomes(Aiken,etal.,2000)and
29
organizationaldesign(Agranoff1976).Thesurveyconsistsoftwoparts:1)a
measureofparticipationindecisionmaking,and2)ameasureofthehierarchyof
authorityintheorganization.Laterscholars,likeKaluzny,etal.(1974),alsochoose
tomeasurecentralizationasadegreeofparticipationindecisionmaking,asdoes
Ferrell&Skinner(1988).
Notably,HageandAiken(1971)usedtheirmeasureofcentralizationtofind
itsimpactoninnovationinastudyofhealthcarefirms.Thefindingsfromthisstudy
wereinconclusiveonthistopicbecausefulldatawasnotavailableatthetimeof
publication.Otherscholars,likeDamanpour(1996)havesoughttomeasurethe
impactofcentralizationandformalizationoninnovationwithinfor-profitandnon-
profitfirms.Damanpourfoundthatcentralizationwasmorenegativelyrelatedto
innovationinfor-profitorganizationsthaninnot-for-profitorganizations.In
Damanpour’s(1996)model,centralizationleadstolowerrisk-takingwithnew
innovations.Alowerrateofrisk-takingismoreharmfulinfor-profitfirms,dueto
thefactthatnon-profitfirmshavegreateraccountabilitytoexternalcontrolsthan
for-profitfirms(e.g.,legislativefunding,stateandfederalrequirements,etc.).This
addedaccountabilityfornon-profitsmakesthemmorestaticingeneralthanfor-
profitfirms,andsolesswillingtotakeriskswithnewinnovations.
AndersonandKing(1993)describearelated“innovationdilemma”(p.11)
thatwasidentifiedinpreviousstudiesofcentralizationandinnovation:namelythat
earlystagesofinnovationarefacilitatedbydecentralization,whilelaterstagesare
facilitatedbycentralization.Additionally,Kim(1980)foundthatcentralization’s
impactoninnovationinorganizationsdependedonthetypeoforganization.
30
Resourceallocationdecision-making(definedbyKimasparticipationindecision-
making)was“significantlyrelatedtoprogramchangesinserviceorganizations,
whiledecision-makingaboutwork(definedbyKimashierarchyofauthority)
was…significantlyrelatedtomorefrequentproductchangesinmanufacturing
organizations”(p.241).
Importantly,theHage&Aiken(1971),Damanpour(1996),Andersonand
King(1993),andKim(1980)studiesdidnotestablishtherelativecentralizationof
subunitswithinthefirmstheystudied,andthepotentialimpactofsubunit
structuresonorganizationalinnovativeness.
Subunits.Somescholarshaveassertedthatsubunitswithinagivenfirmcan
haveanequallylargeroleontheadoptionandimplementationprocess(Tushman,
etal.,2010;Pennings,etal.,2014;Abrunhosa,A.,&Sá,P.M.,2008).Kim(1980)
statesthata“plausiblewaytoinvestigatethecontingencyrelationshipbetween
organizationalstructureandthedifferentphasesoftheinnovationprocessmaybe
toassumethattheorganizationhasdifferentsubunitstodealwithdifferentstages”
(p.228).However,Kim(1980)didnotpursuethislineofresearchinhispaper.In
highereducation,subunitsmayconsistofdivisions,colleges,departments,and
administrativeserviceunits.Thesesubunitscanbehighlycentralizedor
decentralized,dependingonbudgetaryconstraintsandinstitutionalculturalnorms.
Tushman,etal.(2010)identifyfourdistincttypesoforganizational
structuresbasedonareviewoftheliterature.Thefirstmodel,thefunctional
structure,describesanorganizationbasedonoperationalunitsthatoperateas
discretebusinessfunctions.Innovationscholars(Audia,Locke,andSmith,2000;
31
CampbellandPark,2005;CarrollandTeo,1996;ChristensenandBower,1996;Hill
andRothaermel,2003)havefocusedontheinertialaspectofthisorganizational
model,claimingthatpartnershipsandacquisitionsareessentialpathwaysto
innovationbecausetheyarenecessarytoovercometheinternalinertiaofcultural
normsandseniorleadershipbiases.
Thesecondmodel,thecross-functionalteamstructure,highlightsthe
importanceofstrategicdirectivesandtechnologicalchanges,whichrequire
coordinatedcollaborationsbetweenunitswithinafunctionalstructureinorderto
accomplishadesiredgoal(Donaldson&Preston,1995;Gresov,1989;Miles,etal.,
1978;NadlerandTushman,1997).Thesecross-functionalteamsfocusoncreating
newideasby“innovatingviastructuraloverlays”withinanorganization(Tushman,
etal.,2010,p.6).
Thethirdandfourthmodels,asdescribedbyChristensen(1997),
WheelwrightandClark(1992),andO’ReillyandTushman(1997),arethespin-out
structureandtheambidextrousstructure,respectively.Spin-outsaredefinedas
“distinctinnovationunit[s]withoutgeneralmanagercontroland/orseniorteam
support,”whileambidextrousunitsaredescribedas“distinctinnovationunit[s]
withgeneralmanagercontrolandseniorteamsupport”(Tushman,etal.,2010,p.
11).Spin-outsareemployedtoencouragenew-to-the-firminnovations,while
ambidextrousstructuresseektopromotebothnewideasandexpandonexisting
innovations.
Whilehighereducationhasbeenorganizedverytraditionallyintofunctional
units,alloftheseorganizationalmodelscanbefoundinthesubunitsofpublic
32
highereducationinstitutions.DespitethefactthattheHage&Aikenmeasureof
centralizationhasbeenusedtomeasurefirmsattheorganizationallevel,the
importanceofsubunitsandtheirownvaryingstructuresandlevelsofcentralization
hasbeenneglected.Furthermore,asinnovationstudieshaveadvancedtoinclude
theprocessaspectsofinnovationmanagement,broadmeasuresofinnovationas
describedintheDamanpour(1996)studyappeartobeinsufficientfortheanalysis
oftheimpactofsubunitcentralizationaspartofinnovationmanagementpractices.
Damanpour’sresearchfocusesontherelativeinnovativenessoffirms,ratherthan
thestrengthsandweaknessesoftheirinnovationmanagementpractices(as
determinedvis-à-vistheIVC).
Inanextensivereviewofinnovationscholarship,Crossan&Apaydin(2010)
delineatebetweeninnovationasoutcomeandinnovationasprocess.Theformeris
ameasureofnewproducts,services,andactivitiesatagivenfirm,whilethelatteris
areflectionofparticularmanagementpracticesthatpromote(ordemote)
innovativeness.Thisdissertationfocusesoninnovationasprocessinaneffortto
helpinstitutionsunderstandtheirrespectivestrengthsandweaknessesinthat
process.Giventhefinancialandlegislativepressuresonpublichighereducationto
becomemoreefficient(andthereforemorecentralized)whilesimultaneously
becomingmoreinnovative,itisimportanttoknowtherelationshipofcentralization
andefficiencystrengthsandweaknessesininnovationmanagement.
Rationale
Inconclusion,theroleofhighereducationserviceunitsintheinnovation
processhasnotbeenadequatelyresearched,andthequestionofwhichserviceunits
33
tostudyhasnotbeenfullyansweredintheseenvironments.Technologyunitsin
publichighereducationseemparticularlyripeforstudy,giventheirpotential
impactinawidevarietyoffactorsthatpromoteinnovationgeneration,conversion,
anddiffusion.WhilescholarslikeFullerandSwanson(1992)investigatedtheroles
ofinformationtechnologysubunitsonorganizationalinnovativeness,theydidnot
factorintherelativecentralizationofthesesubunitsorincludeeducational
institutions.Perhapsmostimportantly,theyfocusedonorganizational
innovativenessasanoutcome,ratherthaninnovationasaprocess.Recently
developedandvalidatedmeasures,suchasVenkatesh’s(2000,2003)TAMand
UTAUTmodels,includeanumberoffactorsthatcouldbeimpactedbythe
functionalityandagilityoflocaltechnologysupportunits.Specificfactorsthataffect
innovationadoption,fromindividualapprehensiontofacilitatingconditionslikethe
availabilityoftrainingandsupport,givefurtherevidencethattechnologyservice
unitsareverylikelyanimportantconsiderationintechnologyadoptionand
diffusion.
Althoughstudieshavedemonstrated1)thecriticalimportanceofinnovation
(Borins,1998;Andrewsetal.,2006;Christensenetal.,2004;Damanpouretal.,
2009;Tiddetal.,2001),2)themanyproblemsinvolvedinsuccessfullymanaging
innovation(Ensminger,2005;Griffith,Zammuto,&Aiman-Smith,1999;Meyer&
Goes,1988;Surry&Ely,2002;VandeVen,1999),and3)theculturalandresource
barrierstochangewithinorganizations(Aubert&Hamel2001;Denisetal.2002;
Fennell&Warnecke1988;Ferlieetal.2001;andRogers2003),thereisinadequate
researchonhowsubunitorganizationalstructurecaninfluencethesuccessof
34
innovationmanagementforkeytechnologicalinnovations.Giventhatinstitutionsof
highereducationareorganizeddifferently,andthattechnologysupport
organizationsinsideeachinstitutionarealsoorganizeddifferently,thereisaneed
tofurtherunderstandhowthesestructuraldifferencesmayimpacttheinnovation
managementprocess.ThisstudywillapplyHansen&Birkinshaw’s(2007)
innovationvaluechaintodeterminehowrelativecentralizationoftechnologyunits
promotesordemoteseachphaseoftheinnovationvaluechainacrosstheinstitution
anduseHage&Aiken’s(1967),Kaluzny,etal.’s(1974)andFerrellandSkinner’s
(1988)surveyinstrumentstoidentifytherespectivecentralizationoftechnology
organizationsinpublicResearchOneLeveluniversities.
Manyinternalandexternaldrivers—rangingfromstudentsuccessand
institutionalrankingstofinancialandoutcome-basedaccountabilitymeasures—
incentivizeinstitutionsofhighereducation.Yeteachinstitutionemploysoneof
manyorganizationalmodelsduetoavarietyoffactorsspecifictotheinstitution.
Studieshaveshowntheimportanceofimplementingnewinnovationsinorderto
addressthesedrivers.Docertainorganizationalmodelsyieldbetterresultsto
promotevariousphasesofthegeneration,conversionanddiffusionofthese
innovations?Thisstudywillilluminatethestrengthsandweaknessesofspecific
organizationalmodelsforfaculty,staff,andadministratorshopingtoutilizenew
technologiestoachievetheirrespectiveandinstitutionalgoals.Thelarger
communitywillbenefitbybeingabletoidentifypotentialactionstoaddress
shortcomingsintheirinnovationmanagementprocesses,andtheinnovation
researchcommunitywillbenefitfromnewdatainanunder-researchedtopicarea.
35
ChapterIII
Method
ThepurposeofthisquantitativestudywastoapplyHageandAiken’s(1971),
Kaluzny,etal.’s(1974),andFerrellandSkinner’s(1988)centralizationtoolsto
HansenandBirkinshaw’s(2007)innovationvaluechaintoolinordertoexplore
whetherthereexistedarelationshipbetweencentralizationandtheinnovation
process.Morespecifically,thepresentstudyhopedtofindadifferenceinthe
relativestrengthsandweaknessesinideageneration,conversion,anddiffusionof
technologicalinnovationsamongResearchOne(R1)DoctoralUniversitiesbased
upontherelativecentralizationlevelsoftechnologyunits.TheResearchOne
classificationisasubcategoryofclassificationsofhighereducationinstitutions
developedbytheCarnegieFoundationfortheAdvancementofTeaching(“The
CarnegieClassificationofInstitutionsofHigherEducation,”n.d.).Itwasexpected
thatthenumberofinstitutionsinthiscategorywouldprovidegreatdepthand
varietytotheoverallstudy,andthatstronginferencesaboutITorganizational
structuresandtheirrelationshiptoinnovationmanagementactivitieswouldbe
derivedfromtheresultingdata.Towardthispurpose,fivehypothesesweretested:
Hypothesis1.Thisstudyhypothesizedthatmoredecentralizedtechnology
structureswouldshowgreatereffectivenessduringtheideagenerationphasethan
morecentralizedstructures.
Hypothesis2.Thisstudyhypothesizedthatmoredecentralizedtechnology
unitswouldshowgreatereffectivenessduringtheideaconversionphasethanmore
centralizedstructures.
36
Hypothesis3.Thisstudyhypothesizedthatmorecentralizedtechnology
structureswouldshowgreatereffectivenessduringthediffusionphasethanmore
decentralizedstructures.
Hypothesis4.Thisstudyhypothesizedthatmorecentralizedtechnology
structureswouldshowlowereffectivenessforallthreephasesoftheinnovation
valuechainthanmoredecentralizedstructures.
Hypothesis5.Thisstudyhypothesizedthatparticipationindecisionmaking
measureswouldshowmoresignificantcorrelationswithinnovationvaluechain
phasesthanhierarchyofauthoritymeasures.
Subjects
Surveyparticipantsconsistedofsubunit(college,school,subdivision)ITstaff
andmanagersworkingatR1universitiesacrosstheUnitedStates,aCarnegie
classificationthatconsistsof115institutions.TheR1universitiescategorywas
chosenbecauseitencompassesaconsiderablenumberofinstitutionsdistributedin
roughproportiontopopulationcentersthroughouttheUnitedStates(Figure1),and
alsocomprisesinstitutionswithavarietyororganizationalmodels,andsizes.
Importantly,theseinstitutionshaveresourcelevelsthatallowthemtomakechoices
regardingthecentralizationanddecentralizationoftheirsubunits.Also,therange
ofpotentialdecentralizationismuchhigherattheseinstitutions,giventhesizeand
scopeoftheirmissionsandcampuspopulations.TheCarnegieFoundation
identifiesR1schoolsasinstitutionswiththehighestresearchactivitythatawarded
atleast20research/scholarshipdoctoratedegreesin2015.
37
Figure 3. R1 University Locations. By Edmund Clark (2016). Source data retrieved from http://classifications.carnegiefoundation.org/.
SubunitITworkerswerechosenbecausetheywerewell-positionedto
respondtothetaskindependenceandhierarchyofauthorityquestionsposedinthe
threecentralizationmeasuresandbecausetheycouldalsoprovidemore
representativeanswerstotheinnovationvaluechainquestions.Thecentralization
questionsweregearedtowardworkersunderneathtoporganizationalpositions,
andtheinnovationvaluechaincouldprovideamorecompletepictureofagiven
institutionwhenmultiplesubunitmeasuresofideageneration,conversion,and
diffusionweretakenintoaccount.
Measures
Surveyparticipantsfromeachinstitutionwereaskedtoidentifytheir
position,subunit,andrelationoftheirsubunitstothecentralITunitateach
institution.Eachparticipantwasthenaskedtocompleteasinglesurveythat
combinedmultipleinstrumentscreatedbyHage&Aiken(1971),Kaluzny,etal.
(1974),Ferrell&Skinner(1988),andHansen&Birkinshaw(2007).Thefirstthree
instrumentsmeasureparticipationindecision-makingandhierarchyofauthority
38
(seeAppendixA).TheHageandAikentoolhasbeenpreviouslyvalidatedbyDewar,
Whetton&Boje(1980)inareviewoffourpreviousstudies,witharangefrom.81to
.95fortheparticipationindecisionmakingmeasuresand.70to.96inthehierarchy
ofauthoritymeasures.(SeeFigureXDewarWhettonBoje1980below).
Additionally,Kim(1980)validatedtheHageandAikenvariablesat.90for
participationindecisionmakingand.68forhierarchyofauthorityitems.The
presentstudyadaptedtheHageandAikentooltospecificallymeasurethese
variablesinITsubunits.
TheKaluzny(1974)toolhasbeencitedinover100studiesandbuildson
Hage&Aiken’sparticipationindecisionmakingtoolbyincludingadditional
organizationalfactorslikefundingandaffiliation.ThisstudyadaptedtheKaluzny
tooltofocusontechnologysubunitorganizationsandtheirparticipationin
decision-makinginrelationshiptocentraltechnologyorganizations.Similarly,the
FerrellandSkinner(1988)toolhasbeencitedinover400studiesandwasadapted
todemonstratetheparticipationindecisionmakingfordecentralizedtechnology
subunitsinrelationshiptocentralizedtechnologysubunitsinhighereducation.One
goalofthisdissertationwastoestablishafirmcorrelationbetweenallthreeofthese
centralizationtools,therebyprovidingfurthervalidityforanysubsequentlinkages
withtheinnovationvaluechainmeasures.
TheHansen&Birkinshaw(2007)innovationvaluechaintool(seeAppendix
B)madeupthelastportionofthissinglecombinedinstrument.Thistoolhasbeen
citedinover600researchpapersandreflectedthefocusoninnovation-as-process
utilizedinthepresentstudy.AsdescribedinChapter2,theHansenandBirkinshaw
39
(2007)tooldividestheinnovationprocessintothreemainphasesandsixsub-
phasesandthisdissertationattemptedtofindlinkagesbetweentheaforementioned
centralizationmeasuresandtheirimpactsineachinnovationvaluechainphaseand
sub-phase.
ProcedureforDataCollection
SubjectswereidentifiedviaparticipationintheMORLeadershipprogram,a
nationalleadershipdevelopmentprogramwithcohortsfromover30R1
institutions.Datawascollectedfromasingle-stageQualtrixsurvey(seeAppendixA
&Bforthecompletequestionlist)thatwasdistributedtosubunitITstaffateach
institutionwithincurrentandpreviousMORcohortgroups.Thesesubjectswere
thenrecruitedviae-mailthroughMORleadershiprepresentatives.Theeconomyof
designandrapidturnaroundfordatacollectionviaInternetsurveyjustifiedtheuse
ofthisinstrumentforthepresentstudy.
ProcedureforDataAnalysis
DatafromthesurveywasaggregatedandthenevaluatedthroughJASP,an
open-sourcesoftwareprogramforadvancedstatisticalanalysis.Theidentifying
informationofparticipantswasredacted.Thethreecentralizationmeasureswere
normalizedandcombinedandthencorrelatedwiththethreeinnovationvaluechain
sub-measuresandevaluatedforcross-dimensionalcorrelationsbyspecific
participantcharacteristics.First,thecentralizationmeasuresweretestedto
determinewhethertheywereinagreement.Then,thecentralizationmeasures
weretestedforthestrengthsoftheirrelationshipsineachphaseoftheinnovation
valuechain.Datawasscrubbedandcheckedforoutliers,usingstatisticalmethods
40
anddistributioncharts.Finally,correlationsweremeasuredbykeyparticipant
characteristics,includinginstitutiontype,organizationaltype,andreportingtype.
41
ChapterIV
Results
DemographicCharacteristics Thesurveyforthepresentstudyreceived525responses.Participant
responseswereexcludedifanyofthemeasurementitems(participation,hierarchy,
generation,conversion,anddiffusion)wereleftblank,iftherespondentdidnot
workatanR1institution,oriftherespondentwasnotpartofanITunit.These
exclusionseliminated222responses.
303centralizedanddecentralizedITstaffat38R1institutions(100%ofthe
R1institutionsservedbytheMORleadershipprogram)fullycompletedthesurvey
forthestudy.Ofthe38institutions,22werepublic,and16wereprivate.The
institutionsweregeographicallydistributedthroughouttheUnitedStateswith
locationsincludingpartsoftheNorthern,Southern,EasternandWesternregionsof
thecountry.Takentogether,these38institutionsconstituteapproximatelyone
thirdofthe115R1institutionsintheUnitedStates.
Thesubjectsweremadeupof168centralized(55.4%ofthetotal)and111
decentralized(36.6%)technologystaff,alongwith24additionaltechnologystaff
(7.9%)thatheldjointreportstobothcentralandnon-centralunits.Positions
rangedfromsystemadministratorstoCIOs,withgreatvarietyinbetween,including
academictechnologystaffanddesktopsupportspecialists.
Privateuniversityrespondentsincluded91surveyresponses,or30%ofthe
totalresponses,whilepublicuniversityrespondentsmadeuptheremaining212
responses(70%).Oftheprivateinstitutionresponses,54(59.3%)camefrom
42
centralizedITpositions,32(35.1%)fromdecentralizedpositions,and5(5.5%)
fromjoint-reportingpositions.Ofthepublicinstitutionrespondents,114(51.6%)
werecentralizedITpositions,79(37.3)weredecentralized,and19(9%)werejoint
reports.
Figure 4. Respondent Demographics. Edmund Clark (2016). Source data from survey results.
ParticipationinDecision-Making
Participantswereaskedtocompleteasinglesurveythatincludedtwo
separateinstrumentstomeasureparticipationindecisionmaking:1)fourquestions
withafive-pointscale(lowof0tohighof4,withamaximumscoreof16)adapted
fromHage&Aiken’s(1971)tool,and2)sixquestionswitha3-pointscale(0-2,with
amaximumscoreof12)adaptedfromKaluzny,etal.,(1974).Higherscoresfrom
theseinstrumentsindicatedlowerparticipation,andthereforemorecentralization.
Thesequestionswereintendedtoprobetowhatextenttherespondentwas
involvedwithorganization-widedecisionsinvolvingnewinitiatives,newhires,and
fundingfortechnology(seeAppendixA).
43
Thesemeasureswerethenscored;normalizedbyconvertingeachscoreinto
apercentageofthepossibletotal("#$%&'&(#%&)*++,
;"#$%&'&(#%&)*-+-
);andthenconverted
intoasingleparticipationscore("#$%&'&(#%&)*+."#$%&'&(#%&)*--
).Theresultingscores
rangedfromasinglescoreof0(or0%)—meaningthatthispersonparticipatedin
everyinstitutionalITdecision-makingprocess—tothreescoresof1(or100%),
meaningthattheseindividualsneverparticipatedininstitutionalITdecision-
making.Thecorrelationbetweenthetwoparticipationmeasureswaslarge(r=
.65),withameanscoreof.6159(outofamaximumscoreof1.0)forthefirsttest
andameanscoreof.5674forthesecondtest.Thecorrelationsreliablypredicted
eachother,bututilizingasimilarpointscalewouldhavelikelyresultedinacloser
correlationbetweenthetwomeasures.Thetotalparticipationscoredistribution
skewedhigh,whichindicateslowerparticipationinthedecision-makingprocess
(seeFigure5).
Figure 5. Distribution of Participation in Decision-Making scores. Edmund Clark (2016). Source
data from survey results.
44
HierarchyofAuthority
Thesurveyforthepresentstudyalsocontainedtwoinstrumentstomeasure
thehierarchyofauthorityexperiencedbyeachindividualattheirinstitutionwhenit
cametotechnologyactivities;orinotherwords,towhatextenttheindividualcould
actwithoutaskingforpermissionfromasuperiororcentralauthority.These
measuresweresimilarlyconstructed,andconsistedoffivequestionsadaptedfrom
Hage&Aiken(1971)ona4-pointscale(lowof0tohighof3foreachitem,
maximumscore15)alongwithfivequestionsadaptedfromFerrell&Skinner
(1988)onasimilar4-pointscale(0-3foreachitem,maximumscore15).Aswith
thepreviouslydiscussedparticipationmeasures,higherscoresindicateless
freedomtoact,andthereforemorecentralization.Thehierarchyscoresshoweda
largeandveryreliablecorrelation(r=.82)andrangedfromfourscoresof“0”
(meaningthatnopermissionwaseverneededtoact)to“1”(meaningthatnoaction
couldbetakenwithoutpermission).Notsurprisingly,threeofthefour“0”scores
camefromhighly-rankedstaffwithtitlesofCIOorDeputyCIO.
Theresultinghierarchymeasureswerethenscoredandnormalizedusinga
similarapproach(0&1$#$'23++4
; 0&1$#$'23-+4
),andsubsequentlyaveragedtocreatea
totalhierarchyscore(0&1$#$'23+.0&1$#$'23--
).Themeanscoreforthefirsthierarchy
measurewas.3328,whilethemeanscoreforsecondhierarchymeasurewas.3302.
Thehierarchyscoresskewedlowandwereabnormallydistributedtowards
independence,asseeninFigure6.Thisresultseemsconsistentwiththesizeand
relativedecentralizationoftheseresearchinstitutions.
45
Figure 6. Distribution of Hierarchy of Authority scores. Edmund Clark (2016). Source data from
survey results.
CentralizationScore
Thefinalderivedmeasurewasacentralizationscoreforeachresponse,
whichwascomposedoftheparticipationscore("#$%&'&(#%&)*+."#$%&'&(#%&)*--
)added
tothehierarchyscore(0&1$#$'23+.0&1$#$'23--
)tocreateatotal.Thesecentralization
scoresrangedfrom16.66to170.41andweredistributednormally,asseenin
Figure7.Thecentralizationmeasurewasusedastheindependentvariableto
Figure 7. Distribution of Centralization scores. Edmund Clark (2016). Source data from survey
results.
46
determinecorrelationswiththeinnovationgeneration,conversion,anddiffusion
measures.
InnovationValueChain
Thesurveyusedforthepresentstudyalsocontainedonefinalelement.The
innovationvaluechainquestionsdevelopedbyHansen&Birkinshaw(2007)
attempttomeasurethestrengthandweaknessofinnovationgeneration,
conversion,anddiffusionactivitiesinanorganization.Thesequestionsconsistedof
sixquestionsrelatingtoideagenerationona3-pointscale(lowof1tohighof3,
maximumscore18);fourquestionsrelatingtoideaconversionona3-pointscale
(1-3,maximumscore12);andthreequestionsrelatingtodiffusiononthesame3-
pointscale(1-3,maximumscore9).Higherscoresineachofthesecategories
indicatedpoorerperformance.
Findings
47
Figure 8. Innovation generation, conversion, and diffusion as a function of centralization.
Edmund Clark (2016). Source data from survey results.
Thecentralizationscoreforallinstitutionshadamoderateandpositive
correlationtothegenerationofideasscore(r=.33,DF=302,p<.0001).Thisresult
meansthatincreasedcentralizationresultedinmorenegativeeffectsforidea
generation.Therefore,thefirsthypothesiswassupported:thatmoredecentralized
technologystructureswouldshowgreatereffectivenessduringtheideageneration
phasethanmorecentralizedstructures.
Itwashypothesizedthatmoredecentralizedtechnologyunitswouldshow
greatereffectivenessduringtheideaconversionphasethanmorecentralized
structures.Thecentralizationscoreforallinstitutionshadaweakbutpositive
correlationtotheconversionscore(r=.29,DF=302,p<.0001).Thisresultmeans
thatmorecentralizationledtomorenegativeperformanceinideaconversion.
Therefore,thesecondhypothesiswassupportedbythestudy.
Thethirdhypothesis,thatmorecentralizedtechnologystructureswould
showgreatereffectivenessduringthediffusionphasethanmoredecentralized
structures,wasnotsupportedbythedata.Thecorrelationbetweenthe
centralizationscoreandthediffusionscorewasnonexistent(r=.08).Strangely,the
negativeimpactofcentralizationonthediffusionscoreinprivateschoolswas
significantandnegative(r=.33,DF=90,p=.0013).Thisfindingissurprising,in
thatitsuggeststhatcentralizationatpublicinstitutionshasalmostnoimpactonthe
diffusionprocess,whileithasamoderatelynegativeimpactatprivateinstitutions.
48
Thefourthhypothesis,thatmorecentralizedtechnologystructureswould
showlowereffectivenessforallthreephasesoftheinnovationvaluechainthan
moredecentralizedstructures,wassupportedbythedata.Intheall-institution
results,highercentralizationresultedinlowerinnovationprocessscoresin
generation,conversion,anddiffusion(r=.33,p<.0001;r=.29,p<.0001;andr=
.08,respectively).Whenreviewingcentralstaffresponsesontheirown,thesame
patternemerged(r=.35,p<.0001;r=.27,p<.0004;andr=.08,respectively).
Decentralizedstaffalsosawthispattern,withslightlymoresignificantnegative
impactsondiffusion(r=.31,r=.29,andr=.15,respectively).Interestingly,joint
reports(r=.29,.50,and.04,respectively)sawthelargestcorrelationonthe
conversionphase,butthesamplesizeforthisgroupincludedonly24responses.
Alsoofinterestwasthatprivateinstitutionssawmuchlargercorrelations(r=.40,
.33,and.33,respectively),thanpublicuniversities,whichyieldedtheonlyresults
thatwerepositive(butatanonsignificantlevel)fordiffusion(r=.30,.28,and-.05,
respectively).
Finally,itwashypothesizedthatparticipationindecision-makingmeasures
wouldshowmoresignificantcorrelationswithinnovationvaluechainphasesthan
hierarchyofauthoritymeasures.Suchafindingwouldechothevalidationstudies
conductedbyDewar,Whetton&Boje(1980),inwhichparticipationindecision
makingwasfoundtobeslightlymoreimpactfulinthefourstudiesreviewedthan
thehierarchyofauthoritymeasures(.95,.92,.93,and.81fortheparticipation
measurevs..79,.96,.93,and.70forthehierarchymeasure).Surprisingly,thestudy
didnotsupportthehypothesis.Participationscoresimpactedgeneration,
49
conversion,anddiffusionwithPearsoncoefficientsof.22,.18,and.18;while
hierarchyscoresshowedimpactsof.24,.23,and-.07respectively.Thismay
indicatethatthetypeoforganizationsstudied(e.g.,manufacturing,government,
education,etc.)haveimportanteffectsonthepowerofthesemeasures.
Institutiontypeandjointreportingstructuresasemergentfactors
Thetwostrongestresultsthatemergedthatwerenotanticipatedbythis
studywere:1)thattheinnovationvaluechainwasmuchmoreadverselyimpacted
bycentralizationinprivateinstitutionsthaninpublicinstitutions,and2)thatjoint
reportssawastrong(r=.50)adverseimpactofcentralizationonconversion.These
findingsmeritfurtherinvestigation.
Inordertovalidatethisfinding,aTietjen-Moorestatisticaltestwas
conductedtodeterminewhetherthereweresignificantoutliersinthe
centralization,generation,conversion,ordiffusionscoresforprivateinstitutions.
Onecentralizationscorewaseliminatedfromthedataandthreevalueswere
eliminatedfromthegenerationscores;however,correlationsdidnotchange
significantly(r=.39.,.34,and.35vs.r=.40,.33,and.33,respectively).
Privateuniversitiesdidnothavesignificantlyhighercentralizationscores
thanpublicuniversities(meanscoresof93and92,respectively).However,
decentralizedstaffreportedmuchhigherandmorenegativeimpactsfrom
centralization(r=.51,.39,.30)thancentralizedstaff(.30,.29.,.40).Bothsetsof
numbersclearlyindicatethatprivateinstitutionsexperiencenegativeinnovation
processissuesascentralizationincreases,andthisproblemisseenbothby
centralizedanddecentralizedstaff.Thatthispatternemergedinprivateinstitutions
50
ratherthaninpublicinstitutionsmayreflectanumberofmoderatingfactors,
includingbudget(15ofthe20highest-endowedR1universitiesareprivate
institutions,perhapslargebudgetsproducemorestaticenvironments),
organizationalstructures(centralizationmaybemorereflectiveofhierarchicaland
bureaucraticstructuresutilizedinpublicschools)andculture(perhapsprivate
schoolsaremoreculturallyentwinedwithtradition,potentiallyslowingthe
generation,conversion,anddiffusionofnewideas).
Jointreportsoccupiedasmallbutinterestingportionofthedataforthe
presentstudy.Staffwithjointreportsstronglyindicatedanegativeeffectof
centralizationontheconversionprocess.Itispossiblethatthenatureofthese
positions—bridginglocalandcentralunits—couldplacethematthefrontlines
wherenewideasareconvertedintoservicestobediffusedthroughouttherestof
theinstitution.Suchstrikingresultsareworthyofalargerinvestigation.
51
ChapterV
Discussion
HighereducationintheUnitedStatesfacesanunprecedentedcollectionof
simultaneouschallengesaftermanydecadesofrelativestasis.Thesechallenges
comeintheformofnewbusinessmodels,newstudentpopulations,new
competition,andnewtechnologies.Asfinancialpressuresmount,manyinstitutions
haveturnedtoorganizationalcentralizationasawaytoincreaseefficiencyand
maximizeoperationalinvestments.However,thecaseforcentralizationhasarisen
concurrentlywithanequallystrongcaseforinnovation.Callsforreformin
Americanhighereducationhavereachedanewcrescendoastuitionhasspiraled
upward,studentdebthasincreased,andsmallerschoolshavebeguntofailatever-
growingrates.
Inrecognizingthesechallenges,thisresearchstudysoughttoisolate
commonlyknownattributesofcentralizationandfocusonvariablesthatcouldbe
moreeasilycontrolledbyinstitutionsregardlessoforganizationalstructure.Two
factorswithahighlevelofvalidationinpreviousstudies,participationindecision-
makingandhierarchyofauthority,wereselectedasmeasuresofcentralizationthat
couldbemoreeasilychangedataninstitutionallevelthanothermeasures(e.g.,
politicalstructures,budgetmodels,etc.).
Thesevariableswerethencombinedtocreateacentralizationscoreinorder
todeterminehowimpactfulthistypeofcentralizationwasontheinnovation
process.Giventheincreasingimportanceoftechnologyforinnovationinallfields,
includingeducation,thisstudyfocusedonITsupportunitsandtheirstaffatR1
52
universities,whichrepresenttheflagshipinstitutionsforAmericanhigher
education.
Datawasgatheredfrom303ITworkersat38R1institutionsrepresenting
statesincludingCalifornia,Connecticut,Georgia,Illinois,Indiana,Iowa,Kentucky,
Maryland,Massachusetts,Michigan,Minnesota,Missouri,Nebraska,NewJersey,
NewYork,NorthCarolina,Ohio,Oregon,Pennsylvania,SouthCarolina,Texas,
Washington,andWisconsin.Theresultswerecompiledandanalyzedforpopulation
segments,institutionaltype,scoredistributions,andscorecorrelationsviaJASPand
MicrosoftExcel.
SummaryofFindings
Participantsinthepresentstudyfoundthatcentralizationhadnegative
impactsonideagenerationandconversion(seeFigure8).Thescaleofthese
impactsrangedfromweaktostrong,dependingontheinstitutiontype,theposition
type,andthepositionreportingmodel.Surprisingly,theimpactofcentralizationon
thediffusionprocessseemedtoshowextremelymixedresultsandvariedgreatly
dependingonwhethertheinstitutionwaspublicorprivate.
Figure 9. Correlation plots for centralization to generation, conversion, and diffusion. Edmund
Clark (2016). Source data from survey results.
53
Thehypothesisthatcentralizationwouldadverselyimpacttheidea
generationprocesswassupported,aswasthehypothesisthatcentralizationwould
alsohavenegativeimpactsforconversion.Thesefindingsaresignificantinthat
presentstudyhascontrolledforthetypesofinstitutionsstudied(R1institutions)as
wellasthesubtypeoforganization(ITsupportunits),andsohastherefore
establishedsomemeasureofcontrolforpotentiallymoderatingvariablessuchas
size,slackresources,specialization,andfunctionaldifferentiation(Greenhalgh,et
al.,2004).Thesecorrelationswerefoundregardlessofinstitutiontype(publicor
private),organizationtype(centralizedordecentralized)orstaffreportingtype
(centralized,decentralized,orjoint).However,thevariableofinstitutiontypewas
showntobeimportant,asthereweresignificantdifferencesinresultsfrompublic
vs.privateinstitutions.
Perhapsthemoststrikingexampleofthisdifferenceisthefindingthat
centralizationhadwidelydifferentimpactsoninnovationdiffusionatthesetwo
typesofinstitutions.Theoverallimpactforpublicswasnonsignificant(r=-.05,
range=-.07-.04),whiletheimpactforprivateswasnegativeandmuchmore
powerful(r=.33).Thefactthatbothcentralizedanddecentralizedstaffatprivate
schoolsagreedonthisfinding(rangeofr=.30-.40)callsforafocusedinvestigation
onthisissue.
Onepossibleexplanationisthatpublicschoolsexhibitmorebureaucratic
structuresthanprivateschools,andthereforearelessimpactedbyincreased
centralization.Thishypothesiswouldrequireacomparisonandevaluationof
organizationalstructuresattheseinstitutions.However,onewouldalsohaveto
54
explainwhythisincreasedcentralizationonlyhadasignificantlydifferentimpacton
innovationdiffusion.Itispossiblethatbureaucraticcontrolsonthediffusionofa
newinnovationaremoreacceptableatpublicuniversitiesthanatprivate
universities.
Budgetsizeisanotherpossibleexplanation.Itispossiblethatextremely
wealthyinstitutionsarelesspronetodiffusenewinnovationsbecausetheyare
underfewerpressurestodoso.Therefore,intentionaldiffusionthrough
centralizationwouldhaveanegativeeffectontheprocess,andwouldbeculturally
incompatible.
Anotherpotentiallysignificantfindingwasthatjointreportssawmuch
differentpatternsintherelationshipbetweencentralizationandtheinnovation
process.Fortheseindividuals,ideaconversionwasthemostnegativelyimpacted
fromcentralization.Theseresultsarenotasreliableastheotherfindings,giventhe
smallsamplesizeofthispopulation(n=24).However,themeanoftheconversion
scoreswasnotsignificantlydifferentfromthatofthetotalpopulation(µ=7.79vs.
7.77,respectively).Onepotentialexplanationisthatthesestaffoccupyaspace
whereideaconversionismorelikelytooccur.Inordertoinvestigatethis,onecould
conductastudytoexaminewhattypesofinnovationsaremorelikelytobeseenby
thispopulationaswellashowtheyfunctionwithintheinnovationprocess
continuum.
Implications
Theimplicationsofthesefindingsaresignificant,inthatthecentralization
factorsusedforthepresentstudyaretheoreticallyunderofthecontrolofmanagers
55
andsupervisors,regardlessoforganizationalstructureorinstitutiontype.
Irrespectiveofbudgetfactorsorreportingmodels,alargenumberofpotential
interventionscouldbeemployedtoincreaseparticipationindecision-makingwhile
allowingindividualstomakemorelocaldecisionstotrynewtechnologiesand
processes.Fortheformer,greateremploymentofcommunicationandfeedback
mechanismscouldincreaseparticipationwhilesimultaneouslyimprovingworking
relationshipswithdecentralizedunits.Inordertocontrolforthepotentialnegative
effectsofthelatter,anorganizationcouldspecifylengthsoftimefornewpilots
whileensuringthatpilotsareactivelyencouragedandthatresultsareshared
widely(whetherpositiveornegative).
Thepresentstudysoughttoestablishaconnectionbetweenmore
controllableaspectsofcentralizationandtheirimpactsontheinnovationprocess.
Giventhemanychallengesfacinghighereducation,anintensifiedfocuson
innovationwillbenecessarytoensurethatinstitutionssurviveandthriveasthey
moveintonewandunchartedwaters.Whileincreasingcentralizationandefficiency
maybemandatedbyexecutiveboardsandstatelegislatures,thereishopethatitis
possibletoaccomplishsuchgoalswhileprotectingandfortifyingtheinnovation
process.Theresultsofthisstudysuggestthatthereareindeedwaystoprevent
certainaspectsofcentralizationfromdisruptingthegenerationandconversionof
newideas.
StrengthsandLimitations
Thestrengthsofthepresentstudyincludefourprimaryitems:1)anew
operationalizationofpreviouslyvalidatedcentralizationinstrumentsonthe
56
innovationvaluechain,2)adiscoveryofimportantfactorsthatimpactthe
innovationprocessatR1institutions,3)adiscoverythatthetypeofinstitutionhas
animportanteffectoninnovationdiffusion,and4)acontributiontonewknowledge
inthestudyofhighereducationintheUnitedStates.
ThepresentstudywasthefirsttoemployHage&Aiken’s(1971)measuresof
participationindecision-makingandhierarchyofauthoritytodetermineimpactson
theinnovationprocessasmeasuredbyHansen&Birkinshaw’s(2007)innovation
valuechaintool.Thisoperationalizationhelpedtofurtherisolatehow
centralizationadverselyimpactstheinnovationprocess,therebycontributingtothe
ever-expandingbodyofinnovationresearch.
Byutilizingthetooldevelopedforthepresentstudy,intriguingcorrelations
werediscoveredbetweenthecentralizationmeasureandeachphaseofthe
innovationvaluechain.Thesecorrelationssuggestthatinstitutionsshouldcarefully
evaluatehowwelltheyenableparticipationandlocaldecision-makingastheywork
throughtheinnovationprocess.
Thediscoverythatprivateuniversitiessufferfromcentralizationindifferent
andmorepowerfulwaysthanpublicuniversitiesisanimportantfinding.This
suggeststhattherearesignificantmoderatingcircumstancesthatmustbe
accountedforwhencomparingpublicandprivateR1institutions,andthattheyare
notashomogenousassometimesbelieved.
Finally,thepresentstudycontributedtothebodyofknowledgeofhigher
educationintheUnitedStatesbyexpandingunderstandingoftheimportanceofIT
57
subunitsintheinnovationprocessaswellasthehighlightingkeyvariablesthat
affecttheinnovationprocessattheseinstitutions.
Thepresentstudyalsohadsomenotablelimitations.First,theinstitutions
thatrespondedmaynotadequatelyrepresenttheentirebodyofAmericanR1
institutions.Certaingeographicalregionsofthecountry,includingtheSoutheast
andSouthwest,wereeitherinadequatelyrepresentedornotrepresentedatall.
Furthermore,itisunknownhowwellfindingsatthese38R1institutionswould
translateforthevastmajorityofinstitutionsthatoccupyothertiersintheCarnegie
classificationsystem.
Second,theuseofanationalITleadershipdevelopmentprogramto
determineparticipantshaslimitationsinthatthepopulationreflectsatop-heavy
population(i.e.,moreoften,higherrankingmanagers).Itispossiblethatincluding
morestaffattheoperationallevel(e.g.,programmers,systemadministrators,
desktoptechnicians,etc.)wouldchangetheseresultssignificantly.Moreover,entry
intotheMORLeadershipProgramrequiressponsorshipfromthecentralIToffice
(usuallyfromtheCIOoftheinstitution),sotherelationshipsbetweenthese
populationsofdecentralizedandcentralizedstaffmaynottypifystandard
relationshipsatthesetypesofinstitutions.
Finally,asinanystudyofthisscope,thepotentialimportanceofmoderating
variablesloomslarge.InGreenhalgh,etal.’s(2004)reviewofinnovationdiffusion
literature,organizationalfactorssuchassize,maturity,functionaldifferentiation,
specialization,slackresources,anddecentralizationtakentogetherhada.39
correlationwithinnovationadoptionanddiffusion.Inthisrespect,thefindingsof
58
thepresentstudyareextremelysignificant,inthatdecision-makingandhierarchyof
authoritymadeupanextremelylargeportion(.33,.29)ofthistotal.
However,onecouldtheorizethatmoderatingvariables,likeorganizational
size,shouldbeaccountedformorespecificallyinthesecorrelations.Because
Damanpour’s(1996)studydefinedlargeorganizationsasanyorganizationswith
morethan500employees,allinstitutionsinthisstudyqualifiedaslarge.Therefore,
sizewouldbeahiddenvariablewithaneffectthatmustbeaccountedfor.(Infact,
allR1institutionswouldqualifyas“large”underthisdefinition,sonewwaysof
discoveringtheimpactoforganizationalsizemustbedevelopedandinvestigatedin
thissector.)Alargemetastudyof53relatedstudiesconductedbyCamison-Zornoza
etal.,(2004)foundacorrelationbetweensizeandinnovationat(r=.15).However,
thisstudydidnotincludeeducationalinstitutions.Illustratingthemixedresults
foundinthisareaofresearch,Damanpour’s(1996)studyoncentralizationand
innovationfoundthat“theeffectofsizeoncentralization-innovationwas
nonsignificant”(p.11).
WhileitispossiblethatothervariablesdiscussedbyGreenhalgh,etal.
(2004)(namely,functionaldifferentiation,specialization,andslackresources)
beyondthosepreviouslydiscussedinthepresentstudy(participationindecision-
making,hierarchyofauthority,andorganizationalsize)haveextremelysmall
impactsoninnovativeness,itisunlikely.Therefore,eachofthesevariablesshould
alsobeisolated,measured,andcorrelatedtofindtowhatextenttheyserveas
moderatingvariablesforthisstudyandotherslikeit.Whilethefocusofthepresent
studyonITsubunitsandR1institutionshassubstantiallycontrolledforthese
59
variables,thereisnearlyalwaysroomforimprovement,especiallyincreatingan
operationalcontinuumforeachofthesevariables.Theselimitationshavethe
potentialtolimitthegeneralizabilityofthefindingsinthepresentstudy.
Recommendationsforfurtherresearch
Theresultsfromthepresentstudy,alongwithitsconcomitantstrengthsand
weaknesses,yieldseveralrecommendationsforfurtherinvestigation.First,the
studycouldbeexpandedtoincludeagreaterrepresentationofallITworkers,
includingworkersthatdonotparticipateintheMORprogram,aswellasincluding
moretypesofinstitutions.Suchastudycouldbeconductedwithamore
encompassingdistributionlist,likethatofEducause.Includingthisexpanded
populationcouldrevealnewpatternsandcorrelationsbetweencentralizationand
theinnovationprocess.
Second,thefindingofdifferencesinthecentralization-innovationprocess
betweenpublicandprivateR1shasthepotentialtobeafruitfulareaofresearch.
Thesemoderatingfactorsshouldbeidentifiedandmeasuredsothatfutureresearch
inhighereducationinnovationcanaccountforthesedifferences.Itispossiblethat
identifiedmoderatorscouldalsoapplytoothertypesofinstitutionsandsituations.
Finally,theimpactofthejointreportingstructureinITsubunitsshouldbe
investigatedmorethoroughly.Itispossiblethatincreasingcentralizationdemands
willresultinpopulationgrowthforthistypeofposition,andthereforeitis
importanttounderstandtheuniquechallengesandopportunitiesconfrontedbythis
groupofindividuals.Theideathatspecifictypesofreportingarrangementscan
60
haveimportantimpactsontheinnovationprocessmayuncoverafertileareaofnew
research.
61
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AppendixA
CentralizationAssessmentCombinationInstrument(Adaptedquestionswithoriginalversionsofthesequestionsinbracketsandboldtext)Instrument1:Hage&Aiken(1971).Centralization(measuredin2parts).(Part1)Participationindecision-making1.Howfrequentlydoyouusuallyparticipateinthedecisionontheadoptionofnewprograms?[Originalversion:same]2.Howfrequentlydoyouusuallyparticipateindecisionsontheadoptionofnewpolicies?[Originalversion:same]3.Howfrequentlydoyouusuallyparticipateinthedecisiontohirenewstaff?[Originalversion:same]4.Howfrequentlydoyouusuallyparticipateinthedecisionsonthepromotionsofanyoftheprofessionalstaff?[Originalversion:same](Responseset:1,alwaysthrough5,never)(Part2)Hierarchyofauthority1.AnytechnologydecisionImakehastohaveapprovalfromthecentralITunit.[Originalversion:AnydecisionImakehastohavemyboss’approval.]2.TherecanbelittleactiontakenhereuntilacentralITsupervisorapprovesadecision.[Originalversion:Therecanbelittleactiontakenhereuntilasupervisorapprovesadecision.]3.Apersonwhowantstomaketheirowntechnologydecisionswouldbequicklydiscouraged.[Originalversion:Apersonwhowantstomakehisowndecisionswouldbequicklydiscouraged.]4.Evensmallmattershavetobereferredtosomeonehigherupforafinalanswer.[Originalversion:Evensmalltechnologymattershavetobereferredtosomeonehigherupforafinalanswer.]5.IhavetoaskthecentralITunitbeforeIdoalmostanything.[Originalversion:IhavetoaskmybossbeforeIdoalmostanything.](Responseset:4,definitelytrue;3,moretruethanfalse;2,morefalsethantrue;1,definitelyfalse.)Instrument2:Kaluzny,etal.,(1974).Participationindecision-making.Theindexofparticipationindecisionmakingwasbasedontheextenttowhichindividualsindicatedparticipationindecisionsconcerningthefollowingitems:(1)allocationofoverallorganizationtechnologyfunds,[Originalversion:(a)allocationoftotalorganizationalincome]
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(2)adoptionandimplementationofneworganization-widetechnologyprogramsandservices,[Originalversion:(b)adoptionandimplementationofneworganization-wideprogramsandservices](3)developmentofformalaffiliationwithothertechnologyorganizations,[Originalversion:(c)developmentofformalaffiliationwithotherorganizations](4)appointmentandpromotionofadministrativetechnologypersonnel,[Originalversion:(d)appointmentandpromotionofadministrativepersonnel](5)appointmentoftechnologystaffmembers,and[Originalversion:(e)appointmentofmedicalstaffmembers,and](6)long-rangeplanningforneworganization-widetechnologyprogramsandservices.[Originalversion:(f)long-rangeplanningfornewhospital-wideprogramsandservices](Responseset:(1)considerableparticipation,(2)someparticipation,and(3)noparticipation.)Instrument3:Ferrell&Skinner(1988).Hierarchyofauthority.1.AnymajortechnologydecisionthatImakehastohaveapprovalfromthecentralITunit.[Originalversion:AnymajordecisionthatImakehastohavethiscompany’sapproval.]2.InmydealingswiththecentralITunit,evenquitesmallmattershavetobereferredtosomeonehigherupforafinalanswer[Originalversion:Inmydealingswiththiscompany,evenquitesmallmattershavetobereferredtosomeonehigherupforafinalanswer.]3.MydealingswiththecentralITunitaresubjecttoalotofrulesandproceduresstatinghowvariousaspectsofmyjobaretobedone.[Originalversion:Mydealingswiththiscompanyaresubjecttoalotofrulesandproceduresstatinghowvariousaspectsofmyjobaretobedone.]4.IhavetoaskcentralITunitrepresentativesbeforeIdoalmostanythinginmylocalITunit.[Originalversion:IhavetoaskmycompanyrepsbeforeIdoalmostanythinginmybusiness.]5.IcantakeverylittleactiononmyownuntilthecentralITunitoritsrepresentativesapproveit[Originalversion:Icantakeverylittleactiononmyownuntilthiscompanyoritsrepsapproveit.](Responseset:4,definitelytrue;3,moretruethanfalse;2,morefalsethantrue;1,definitelyfalse.)
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AppendixB
InnovationValueChainSurvey
(Hansen&Birkinshaw,2007)
Activity Phase
Ourculturemakesithardforpeopletoputforwardnovelideas Inhouseidea
generation
Highscoresindicatethatyourcompanymaybeanidea-poorcompany
PeopleinourunitcomeupwithveryfewgoodideasontheirownFewofourinnovationprojectsinvolveteammembersfromdifferentunitsorsubsidiaries Cross-pollination
amongbusinessesOurpeopletypicallydon'tcollaborateonprojectsacrossunits,businesses,orsubsidiariesFewgoodideasfornewproductsandbusinessescomefromoutsidethecompany
Externalsourcingofideas
Ourpeopleoftenexhibita"notinventedhere"attitude--ideasfromtheoutsidearen'tconsideredasvaluableasthoseinventedwithinWehavetoughrulesforinvestmentinnewprojects--it'softentoohardtogetideasfunded Selection Highscores
indicatethatyourcompanymaybeanconversion-poorcompany
Wehavearisk-averseattitudetowardinvestinginnovelideasNew-product-developmentprojectsoftendon'tfinishontime DevelopmentManagershaveahardtimegettingtractiondevelopingnewbusinessesWe'reslowtorolloutnewproductsandbusinesses
Diffusion
Highscoresindicatethatyourcompanymaybeandiffusion-poorcompany
Competitorsquicklycopyourproductintroductionsandoftenmakepre-emptivelaunchesinothercountriesWedon'tpenetrateallpossiblechannels,customergroups,andregionswithnewproductsandservices(ResponseSet:1,DoNotAgree;2,PartiallyAgree;3,Agree)
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AdaptedHansen&Birkinshawquestionsusedforthepresentstudy:(a)Ourinstitutionalculturemakesithardforpeopletoputforwardnovelideas.(b)Peopleinmyunit/division/collegecomeupwithveryfewgoodideasontheirown.(c)Ourinnovationprojectsrarelyinvolveteammembersfromunitsoutsideofmydivision/college.(d)Ourpeopletypicallydon'tcollaborateonprojectsacrossunits,divisions,andcolleges.(e)Goodideasfornewservicesandeducationalofferingsrarelycomefromoutsidetheinstitution.(f)Ourpeopleoftenexhibita"notinventedhere"attitude--ideasfromtheoutsidearen'tconsideredasvaluableasthoseinventedwithin.(g)Wehavetoughrulesforinvestmentinnewprojects--it'softentoohardtogetideasfunded.(h)Wehavearisk-averseattitudetowardinvestinginnovelideas.(i)Newinnovationprojectsoftendon'tfinishontime.(j)Academicleadershaveahardtimegettingtractiondevelopingneweducationalofferings.(k)Ourinstitutionisslowtorolloutnewservicesandeducationalofferings.(l)Ourservicesandeducationalofferingsarequicklycopiedatotherinstitutions.(m)Wedon'tpenetrateallpossiblechannels,customergroups,andregionswithnewservicesandeducationalofferings.(ResponseSet:1,DoNotAgree;2,PartiallyAgree;3,Agree)