Intergroup competitiveness

Mixed-Motive competitiveness

In-group competitiveness

Ø Popularity: % potential population touching meme

Ø Velocity: Rapidity of market diffusion

Ø Longevity: Duration of meme circulation

Ø Fecundity: Span & Popularity of meme derivations

Ø Speciation: Evolutionary development

Drawing on several theories (meme, narrative

rationality, frame, general systems, evolution,

information, social identity, communicative

competence, social network analysis, and diffusion

of innovations). M

D proposes memes compete at

multiple levels to occupy information-ecology niches.

D provides a heuristic framework for organizing

manifold investigations into the roles new media

play in diffusing ideas in cyberspace and their

representation or role in realspace events. M

seeks to integrate theories and stimulate new theory

development in the fields of big data and new media.

Topic-

Relevant

Outcomes

GEO TECHNICAL

CONTEXT(S)

SOCIETAL

CONTEXT(S)

SOCIAL

CONTEXT/NETWORK(S)

CMC

COMPETENCE

NETWORK LEVEL

‘ALTRUISM’ FACTORS:

OBJECTIVE/STRUCTURAL

N past memes (e.g., tweets)

N nodes (actors/followers)

Network Interdependence

N/Centrality of Influencers

Network Homophily

Network Edge Heterophily

NETWORK LEVEL

‘COMPETING’ FACTORS:

SUBJECTIVE/RECEPTIVENESS

Counter-Memes & Frames

Frame/Narrative (In)Fidelity

Subjective Homophily/Heterophily

Niche: Relative (Dis)Advantage

Cascade Threshold(s)/Norms

INDIVIDUAL LEVEL

COMPETENCE FACTORS:

Motivation/Knowledge/Skills

Source Credibility

Actor Centrality/Propinquity

Message/Media Adaptability

MEME LEVEL

ADAPTIVE FACTORS:

Distinctiveness/Entropy

Reproduction/Redundancy

Simplicity/Trialability

Media Convergence

Media Expressivity/Richness

Trope/Frame/Appeal Credibility

MEME(S)

GEO-TECHNICAL LEVEL

System Limitation/Trauma

Geospatial Scope/Span

Proximity/Density Facilitation

MEME

EFFICACY

Attention

Popularity

Velocity

Longevity

Fecundity/

Speciation

MULTILEVELMODELOFMEMEDIFFUSION(M

SOCIETAL LEVEL

Rival Social Networks

Counter-Memes & Frames

Diffusion Stage Exhaustion

Mitigating Publicity

Media Inaccessibility

Rhetorical Exigency

Rhetorical Exigency: “Any exigence is an

imperfection marked by urgency; it is a defect,

an obstacle, something waiting to be done, a

thing which is other than it should be” (Bitzer,

1992, p. 6)

MEMES

“Inanyrhetoricalsituationtherewillbeatleastonecontrollingexigence

whichfunctionsastheorganizingprinciple:itspecifiestheaudienceto

beaddressedandthechangetobeeffected.Theexigencemayormay

notbeperceivedclearlybytherhetororotherpersonsinthesituation;it

maybestrongorweakdependingupontheclarityoftheirperception

andthedegreeoftheirinterestinit;itmayberealorunrealdepending

onthefactsofthecase;itmaybeimportantortrivial”(Bitzer,1992,6)

Bitzer,L.F.(1968).TheRhetoricalSituation.Philosophy & Rhetoric,1(1),1-14.

Bitzer,L.F.(1992).TheRhetoricalSituation.Philosophy & Rhetoric,251-14.

Garret,M.,&Xiaosui,X.(1993).TheRhetoricalSituationRevisited.RSQ: Rhetoric Society Quarterly,23(2),30-40.

Gorrell,D.(1997).TheRhetoricalSituationAgain:LinkedComponentsinaVennDiagram.Philosophy & Rhetoric,30(4),395-412.

Grant-davie,K.(1997).RhetoricalSituationsandTheirConstituents.Rhetoric Review,15(2),264-279.

Larson,R.L.(1970).LloydBitzer's"RhetoricalSituation"andtheClassificationofDiscourse:ProblemsandImplications.Philosophy

& Rhetoric,3(3),165-168.

Miller,A.B.(1972).RhetoricalExigence.Philosophy & Rhetoric,5(2),111-118.

Vatz,R.E.(1973).TheMythoftheRhetoricalSituation.Philosophy & Rhetoric,6(3),154-161.

Wilkerson,K.E.(1970).OnEvaluatingTheoriesofRhetoric.Philosophy & Rhetoric,3(2),82-96.

Meme: A meme is an act or meaning structure that

is capable of replication, which means imitation

(Dawkins, 1976), requiring:

• Variation

• Selection

• Retention

MEMES

“Memes may best be understood as

cultural information that passes along

from person to person, yet gradually

scales into a shared social phenomenon”

(Shifman, 2013, pp. 364-5)

Dawkins,R.(1976).The selfish gene.NewYork,NY:OxfordUniversityPress.

Shifman,L.(2013).Memesinadigitalworld:Reconcilingwithaconceptualtroublemaker.Journal of Computer-Mediated

Communication, 18,362–377.doi:10.1111/jcc4.12013

CMC Competence: The appropriate and effective

use of electronic media or information

communication technologies (ICTs) in

interacting with others (Spitzberg, 2006)

MEMES

Spitzberg,B.H.(2006).Towardatheoryofcomputer-mediatedcommunicationcompetence.Journal of Computer-

Mediated Communication,11,629-666.DOI:10.1111/j.1083-6101.2006.00030.x

H: The more motivated, knowledgeable, and skilled a

communicator is with adapting ICTs in interacting with

others, the more appropriate, effective, credible and

influential that person will be in messaging others.

Social Network: The graph comprised of the nodes

(persons) and their communications (links) with

others in that graph.

MEMES

Sources:

Key Concepts:

In-degree:

Out-degree:

Centrality:

Affinity path:

Societal Context: The sociocultural factors, such as

formal and informal social movements,

campaigns, and social norms that constitute

and construct public sentiments and beliefs.

MEMES

Sources:

Key Concepts:

RivalSocialNetworks

Counter-Memes&Frames

DiffusionStageExhaustion

MitigatingPublicity

MediaInaccessibility

Geotechnical Context: The geographic, spatial, and

technological factors, such as the digital divide,

proximity, and population dynamics that enable

and constrain the communication of public

sentiments and beliefs.

MEMES

Sources:

Key Concepts:

SystemLimitation/Trauma

GeospatialScope/Span

Proximity/DensityFacilitation

MEMES AND EVOLUTION

Diffusion of Innovations Model: A diffusion is “a special type

of communication concerned with the spread of

messages that are perceived as new ideas” (Rogers,

2003, p. 35).

Rogers,E.M.(2003).Diffusion of innovations(5thed.).NewYork:FreePress.

Rogers,E.M.,&Kincaid,D.L.(1981).Communication networks: Toward a new paradigm for research.NewYork:Free

Press.

Asymmetric fitness: “selfishness [i.e., adaptiveness,

competitiveness] beats altruism within groups. Altruistic

groups beat selfish groups. Everything else is

commentary” (Wilson & Wilson, 2007).

Implication: Within groups or social networks, memes (and

their authors) compete for status (to be heard), but when

a given homogenous group or network is competing

against another group for status, cooperative groups

compete better than groups experiencing entropy, chaos

or intragroup competition.

MEMES AND EVOLUTION

Information ecologies: M

D proposes that memes,

as forms of information, occupy a broader

information environment in which fitness is

influenced by adaptation to the availability of

attention as a scarce resource (Simmons et al., 2014)

Echo chambers: Information niches evolve their

own information ecologies, forming what is

commonly referred to as echo chambers,

corresponding to “communities,” in which

certain memes are preferentially advantaged

by the ecology.

MEMES AND EVOLUTION

Meme competition:Socialnetworksare,“incertain

limits,…automaticallypoisedatcriticality—inthesense

thatmemepopularitiesaredescribedbyacritical

branchingprocess—and…thecriticalitycanbeascribed

tothecompetitionbetweenmemesforthelimited

resourceofuserattention.Wedubthismechanism

“competitioninducedcriticality”(CIC)”(Gleeson,Ward,O’Sullivan&Lee,

2014,p.048701;seealso:Feng,etal.,2015)

Echo chambers:”whenthemediafollowanaudience

orientedstrategyofinformationdelivery(i.e.everybody

triestomimicthemostsuccessfulmedium),thereisa

smootheningofconsensustransition….Sucheffecttends

todisappearwithincreasingthenumberofmedia.On

theotherhand,competition(polarization)amongmedia

producesafragmentationoftheopinions’spacethus

preventingasystem-wideconsensus”(Quattrociocchi,Caldarelli&Scala,

(2014,p.5)

Meaningful

Event(s)

e.g., memes

responding to:

• Disaster

• Election

• Movie

• Disease

• Etc.

Meme

…

Cross-sectional

inferences/echoes

(maps) about

event(s)

TYPESOFMEMETICDIFFUSIONPATTERNS:

Evememic diffusion:event-generateddiffusionofmemeslinkedtotheeventor

experience(fromevenire: Latinex-“out”andvenire “tocomeout,happen,result”),in

whicheventsstimulatesimilartextualexpressionsabouttheexperienceofaneventor

setofevents(e.g.,flutweets;Nageletal.,2013).

Theamountofrainpositivelypredictssocial

networkpostsabouttherain(Coviello,Fowler,&

Franceschetti,2014)

Note:“ThewordmemederivesfromtheGreekmimema,signifying‘somethingwhichisimitated’…In1870the

AustriansociologistEwaldHeringcoinedthephraseDie Mneme(fromtheGreekMneme,meaningmemory”

(Shifman,2013,p.363)

EVEMEMIC

TYPESOFMEMETICDIFFUSIONPATTERNS:

Mimetic

Event(s)

e.g., memes replicating/

responding to:

• Leave Brittny alone

• Charlie bit my finger

• http://www.memes.com/

• http://knowyourmeme.com/

• http://www.memecenter.com

• Etc.

Meme

…

Meme

…

Meme

…

Longitudinal/Sequential inferences

(maps) about event(s)

Meme

…

Meme

…

Meme

…

Meme

…

Etymemic diffusion:meme-generateddiffusionof

directlylinkedmemes(from:Greeketymon—“truesense”

+logia “studyof,aspeakingof”),inwhichanoriginal

memegeneratesfurtherdirectlylinkedmemesresultingin

asortofgeneticspeciationofagiventextualformover

time(e.g.,theriotkiss,Hahner,2013).

ENTYMEMIC

Meaningful

Event(s)

e.g., memes

responding to:

• Disease

• Disaster

• Election

• Movie

• Etc.

Cross-sectional

inferences

(maps) about

event(s)

Meme

…

Meme

…

Meme

…

Meme

…

Meme

…

Longitudinal/Sequential inferences

(maps) about event(s)

Mimetic

Event(s)

e.g., memes replicating/

responding to:

• Leave Brittny alone

• Charlie bit my finger

• http://www.memes.com/

• http://knowyourmeme.com/

• http://www.memecenter.com

• Etc.

TYPESOFMEMETICDIFFUSIONPATTERNS

Meme

…

Meme

…

Meme

…

EVEMEMIC ENTYMEMIC

POLYMEMIC

e.g..,surveillanceofTweetsre:50adverseFDAdrugevents

showedthatwhile2spikesappeared“precipitatedby

informationuniquetoTwitter,”3subsequentspikesappeared

“promptedbyeventsthatarealsopresentinsomeother

source(suchasnewsarticles,healthportals,andresearch

abstracts)”(Abbasi&Adjeroh,2014,p.62)

“Socialmediawaslong-believedtobealag

indicatorforfinancialandpoliticalevents.

However,morerecently,ithasbeenfoundtobe

aneffectiveleadindicator”(Abbasi&Adjeroh,

2014,p.61)

Theremaybereciprocaleffects—Tweeters

duringpresidentialdebatesfeelthatdebatesare

moreimportant,paymoreattention,andfeel

morevalencedtowardcandidates.Whichcauses

which?(Houstonetal.,2013)

LINGUISTIC AGENCY:

• Linguistic agency of disease  severity of

disease;

• Linguistic agency of vax  belief in

mandatory vaccination policies (Bell et al.,

2014)

NARRATIVE FORM:

• Narratives > statistics (Betsch et al. 2011;

see also: Shelby & Ernst, 2013))

• Narratives + statistics + disclaimer (Betsch et

al., 2013)

NARRATIVE FORM:

“we observed a stable narrative bias…

normatively irrelevant information conveyed

by narratives from online discussion boards

systematically increased perceptions of

vaccination risks” (Bell et al., 2014)

GIST-STATISTIC-STORY:

Both providing a clear gist and providing

statistics increased Facebook sharing of

Disneyland vax articles on Facebook, but not

stories (Broniatowski et al., 2016)

APPEAL TYPE:

An experiment comparing 4 appeal types: (a)

lack of evidence of harm; (b) dangers of the

diseases; (c) images of saved children; & (d)

narrative of saved child, showed none

worked, and some backfired (Nyhan et al.,

2014; cf. MacDonald et al., 2013)

FEAR APPEALS:

Parental vax resistance is based more on fear

of actively doing harm (vaccinating) than not

doing something (not vaccinating) that results

in harm (Wroe et al., 2004)

SENTIMENT:

Mere exposure to negative vax sentiments in

tweets increases the likelihood of subsequent

posting of negative opinions on vaccines,

supporting the value of denialism as a

rhetorical strategy (Dunn et al., 2015)

REMINDERS:

Patient reminder messages demonstrated a

significant predictive role in increasing adult

patient immunization (OR = 2.52-3.80; Stone

et al., 2002; see also: Lau et al., 2012)

EFFECTS OF MEDIUM:

First ‘hit’ exposure to anti-vax websites 

knowledge,  fear of adverse effects,

whereas first hit pro-vax sites  vax

knowledge (Allam et al., 2014)

EFFECTS OF MEDIUM:

97% of health info. seekers accessing web

stick with initial 10 hits (Eyesenbach, 2002);

anti-vax web info for > 10 min. =  vax

exemption intention (Betsch et al., 2010)

Axiom 1: Meme fitness is positively related to meme diffusion.

: Within a given meme-stream or corpus or social network, the more distinctive

(informationally unique) a meme, the more successfully it will diffuse

: Within a given meme-stream or corpus or social network, the more a meme

decreases entropy (uncertainty), the more successfully it will diffuse.

: Within a given meme-stream or corpus or social network, the more a meme is

repeated and/or morphed, the more successfully it will diffuse.

: Within a given meme-stream or corpus or social network, the more user-friendly

(convenient, efficient) a meme is to recall, reproduce, and send, the more

successfully it will diffuse.

: Within a given meme-stream or corpus or social network, the more media

convergent a meme, the more successfully it will diffuse.

: Within a given meme-stream or corpus or social network, the more informationally

rich and/or present the meme, the more successfully it will diffuse.

: Within a given meme-stream or corpus or social network, the more it reflects

empirically-verified persuasive message design strategies, the more successfully it

will diffuse.,

Meme Level

: The more motivated, knowledgeable and skilled a source is with information

communication technologies, the more successfully the source’s memes will

diffuse

: The higher the credibility of a source within a given audience or social network, the

more successfully the source’s memes will diffuse

: The more centrally located a source is within a social network, the more

successfully the source’s memes will diffuse within that network.

: The greater the propinquity (closer the link) between a source and other nodes, the

more successfully the source’s meme will diffuse to those nodes.

: The greater the competence of a source at adapting memes to their most

appropriate technological medium, the more successfully the source’s memes will

diffuse.

Source Level

: Axiom: The past is present: ceteris paribus, meme-types that have successfully

diffused in the past will be the best (default) predictor of the types of memes likely

to diffuse in the future.

: The larger the social network, the greater the potential for successful meme

diffusion (given that the average meme’s length and number of meme replications

decrease with increasing social network size)

: The greater the interdependence of social network nodes, the more successfully

memes generated by those nodes will diffuse to other network nodes.

: The greater the network centrality of influentials within a social network, the more

successfully those influentials’ memes will diffuse.

: The greater the structural homophily among social network nodes, the more

successfully memes generated by those nodes will diffuse to other homophilous

nodes.

: The greater the structural heterophily of a social network’s boundaries and liaisons

(bridges) to other social networks, the more successfully memes will diffuse.

: Axiom: Meme diffusion success is a function of both within-network homophily and

the heterophily of that network’s connections with other social networks, both

homophilous and heterophilous.

Objective Social Network Level

: The greater the counter-memes and/or counter-frames competing for attention

within a social network, the less successfully a given meme will diffuse.

: The lower the narrative fidelity a meme has within the dominant status quo frames

or master narratives, the less successfully that meme will diffuse.

: The greater the interdependence of social network nodes, the more successfully

memes generated by those nodes will diffuse to other network nodes.

: The greater the network centrality of influentials within a social network, the more

successfully those influentials’ memes will diffuse.

: The greater the structural homophily among social network nodes, the more

successfully memes generated by those nodes will diffuse to other homophilous

nodes.

: The greater the heterophily of a social network’s boundaries and liaisons (bridges)

to other social networks, the more successfully memes will diffuse.

: Axiom: Meme diffusion success is a function of both within-network homophily and

the heterophily of that network’s connections with other social networks, both

homophilous and heterophilous.

Subjective Social Network Level

Counter-Memes & Frames

Frame/Narrative (In)Fidelity

Subjective Homophily/Heterophily

Niche: Relative (Dis)Advantage

Cascade Threshold(s)/Norms

TRUST-ETHOS:

Analysis of swine flu media coverage and

vaccination in Sweden found that “trust in

the authorities had greater significance for

the rate of vaccination than the perception of

concern” (Ghersetti & Odén, 2011, p. 111)

SOURCE BIAS:

Neutral sources enhance the credibility of

anti-vax messages over biased (web) sources

(Hause et al., 2015)

MEME HISTORY:

“the past expression of a sentiment by an

individual predicts an increased propensity

for that individual to express that same

sentiment again” (Salathé et al., 2013, p. 8)

NETWORK SIZE:

“Generally, larger opinionated neighborhood

sizes have an inhibitory effect on the

expression of opinionated sentiments”

(Salathé et al., 2013, p. 8)

MEME HISTORY:

“increasing negative reciprocal neighborhood

size has the expected effect of increasing the

likelihood of expressing a negative sentiment

[whereas positive reciprocity network size

decreases sentiment expression]” (Salathé et

al., 2013, p. 8)

MEME HISTORY:

There is a strong linear relationship between

N of followers and likelihood of retweets

(Suh, et al., 2010)

NETWORK INTERDEPENDENCE:

Parental neighborhood social capital

mediates the relationship between vax

knowledge and vax acceptance and vax rate

(Jung et al., 2013)

INFLUENTIAL CENTRALITY:

Schools with representatives who believe in

the efficacy of vaccinations have 

nonmedical exemption rates (Salmon et al.,

2004)

INFLUENTIAL CENTRALITY:

Interaction frequency is more predictive of tie

strength than individual characteristics (e.g.,

status) (Jones et al., 2013)

HOMOPHILY:

“Across all four models, those with more

provaccination discussion networks reported

higher beliefs in vaccine safety and greater

intent to vaccinate” (Nyhan et al., 2012, p.

304)

HOMOPHILY:

“support for vaccination among several types

of discussants [parents, spouses, or friends] is

significantly associated with vaccine

attitudes” (Nyhan et al., 2012, p. 304)

HETEROPHILY:

“Modularity” (the degree of fragmented sub-

communities) and Community N (number of

communities in a network) influence the

speed (Hi Mod = growth model; Hi cohesive

communities = spike model (D’Orazio, 2013)

HETEROPHILY:

An informatics study of vaccination (HPV)

tweets & blogs found a high concentration of

message sources directionally connected to

most other infrequent contributors (Huesch

et al., 2013)

COMPETITION:

Almost half of comments on vax policy online

news articles expressed negative sentiment,

along themes of freedom of choice, efficacy,

safety, and government distrust (Lei et al.,

2015)

COMPETITION:

Anti-vax web tactics (Kata, 2012):

skewing the science; Shifting hypotheses;

Censorship; Attacking the opposition

COMPETITION:

Anti-vax web frames (Bean, 2011; Kata,

2010): Safety & effectiveness; Alternative

medicine; Civil liberties; Conspiracy theory;

Morality/Religion/Ideology;

Misinformation/Falsehoods

COMPETITION:

Anti-vax “movements” as discourse frames

and “health movements” (Blume, 2006)

COMPETITION:

“it appears that reports of vaccination

experiences by ‘people like me’ are trusted

irrespective of the source that delivers them”

(Hause et al., 2015; see also Dubé et al., 2013)

PERCEIVED HOMOPHILY:

“it appears that reports of vaccination

experiences by ‘people like me’ are trusted

irrespective of the source that delivers them”

(Hause et al., 2015)

PERCEIVED HOMOPHILY:

Analysis of Zika social media memes indicates

that “the leading pseudo-scientific claims

build on existing narratives,” and … “these

pseudo-scientific claims are being advanced

by existing vaccine skeptic communities”

(Dredze et al., 2016)

PERCEIVED HOMOPHILY:

“there was significantly more information

flow between users who shared the same

sentiments than expected if the sentiments

were randomly distributed” (Salathé &

Khandelwal, 2011, p. 3)

PERCEIVED HOMOPHILY:

“most communities were dominated by

either positive or negative sentiments

towards the novel vaccine” (Salathé &

Khandelwal, 2011, p. 3)

PERCEIVED HOMOPHILY:

The more active a conspiracy theorist on one

topic, the more active across topics, and the

more they exhaust the corpus of a given topic

(Bessi et al., 2015)

COUNTER-FRAMES:

Vax denialists operate from a competing

epistemic frame, in which credibility is

more democratic, truth can be emotional

as well as rational, and reject traditional

conceptions of competence & expertise

(Navin, 2013)

COUNTER-FRAMES:

“Antivax political strategy has shifted… we

call it ‘vaccine choice’… as bad science and

conspiracies repeatedly lost in legislative

votes, anti-vaxxers updated their

marketing”(Diresta & Lotan, 2015; see

also: Sturm et al., 2005)

SOCIAL VS. MAINSTREAM MEDIA:

A vaccine sentimeter found that interest

in vax issues may be more reactive, but

shorter-termed in Twitter than in

traditional media (Bahk et al., 2016)

SPATIALPOLICYEFFECTS:

Stateswithmorerelaxedexemptionshave

highernon-medicalexemptions(Omeret

al.,2006;Wangetal.,2014)

SPATIALPOLICYEFFECTS:

Vaccination-relatedTwittertrafficishighestin

stateswiththehighestexemptionrates,and

mostliberalexemptionpolicies(Oregon,

Vermont),andamplified(retweeted)mostin

otherliberalexemptionstates(Wisconsin,

Mississippi,Iowa)(Radzikowskietal.,2016)

GEOSPATIALDISTANCE:

Pertussisincidencecorrelatedwith

geospatialclusteringofvaxexemptionsin

Michigan(Omeretal.,2008)

GEOSPATIAL DISTANCE:

Perceivingalongdistanceasabarrierto

vaxactualvaccinationstatus(Daniset

al.,2010)

OUTCOMES:Neigeretal.(2015)

identify16separate

operationalizationsofTwitter

metricsthatcanbeusedtoassess

low,medium,orhighengagement

(i.e.,reciprocalconnections)in

publichealth.

OUTCOMES:

• Size:Nofdevices/peopletouchedbymeme

• Velocity:sharingrate/hour

• Daystopeak:daystohitmaximum(asymptote)

• Variability:coefficientofvariation(i.e.,(SDshares/day)/

(Mshares/day)

• RetweetRate:sharingrate

• SocialCurrency:Twittershare/millionYouTubeviews

• Lifespan:power-law(steadydiminishment)vs.rippling

decay(asnewcommunitiesadopt(Owens,2013;see

also:Wu&Huberman,2007)

OUTCOMES:Memescanbestudiedas

exactreplicas,orevolvedvariant(mutation)

forms.Researchonover460million

Facebookpostsandtheirtracesfoundthat

notonlydotheyfitanevolutionaryfitness

(Yule)model,thedifferentialfitnessof

variantsdependedonpoliticalaffiliations

(i.e.,informationalniches)(Adamicetal.,

2014)

COMMUNICATION INFLUENCES

Time

KNOWLEDGE

PERSUASION

DECISION

CONFIRMATION

ADOPTION

REJECTION

RECEIVER

• Personality

• Need for innovation

• Perceived threat

• Etc.

Continued

adoption

Discon-

tinuance

Later

adoption

Continued

rejection

SOCIAL SYSTEM

• Trialability

• Voluntariness

• Norms/Others’ use

• Group identity

advantage

• Homophily

AFFORDANCES

• Ease of use

• Compatibility

• Communicability

• Image-enhancing

ability

• Relative advantage

Skills/

Knowledge

Motivation

Adapted from: Compeau, D. R., et al. (2007). From prediction to explanation: Reconceptualizing and extending the

perceived characteristics of innovating. Journal of The Association for Information Systems, 8, 209-439..

DIFFUSION OF INNOVATIONS

CUMULATIVE FREQUENCY

DISTRIBUTION OF ADOPTION

INNOVA-

TORS

EARLY

ADOPTERS

EARLY

MAJORITY

LATE

MAJORITY

LAGGARDS

100

TIME

MACRO: DIFFUSION OF INNOVATIONS

Each role will

have distinct

network

structure(s)

CROSS-SECTIONAL

FREQUENCY

DISTRIBUTION OF

ADOPTION