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Diffusion & Interventions

• Diffusion of innovations

• Behavior change

• Behavior change is short term whereas diffusion looks at the long view of how new behaviors spread

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Diffusion of Innovations

New ideas and practices originate enter communities from some external source. These external sources can be mass media, labor exchanges, cosmopolitan contact, technical shifts and so on. Adoption of the new idea or practice then flows through interpersonal contact networks.

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Diffusion of Innovations

• Rogers wrote consecutive texts on this topic:– 1962 1st Edition– 1971 2nd Edition (with Shoemaker)– 1983 3rd Edition– 1995 4th Edition– 2003 5th Edition

• Synthesized, elaborated, codified, explained diffusion of innovations

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ELEMENTS OF THE DIFFUSION OF INNOVATIONS

1) The rate of diffusion is influenced by the perceived characteristics of the innovation such as relative advantage, compatibility, observability, trialability and complexity, radicalness, and cost.

2) Diffusion occurs over time such that the rate of adoption often yields a cumulative adoption S-shaped pattern.

3) Individuals can be classified as early or late adopters.4) Individuals pass through stages during the adoption

process typically classified as (1) knowledge, (2) persuasion, (3) decision, (4) implementation or trial, and (5) confirmation.

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Characteristics of an Innovation

• Relative advantage• Compatibility• Complexity• Trialability• Observability• Cost• Radicalness

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4 Elements according to Rogers

• Innovation: An idea or practice perceived as new– Perceived attributes: relative advantage,

compatibility, complexity, trialability, observability

• Communication channels– Homophily vs. heterophily

• Time: at the individual & macro levels• Social system

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Hypothetical Cumulative and Incidence Adoption Curves for Diffusion

Homogenous Mixing

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1 2 3 4 5 6 7 8 9 10

Time

Pe

rce

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Ad

op

ters

CumulativeAdopters

NewAdopters

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Diffusion ofInnovations

Hierarchy of Effects Steps to Behavior Change(SBC)

Stages of Change

Rogers (1995) McGuire (1989) Piotrow et al. (1997) Prochaska et al.(1992)

1. Awareness 1. Recall 2. Liking 3. ComprehendingMessage4. Knowledge ofBehavior

1. Recalls message2. Understands topic3. Can name source ofsupply

1. Pre-contemplation

2. Persuasion 5. Skill acquisition6. Yielding to it7. Memory storage ofcontent

4. Responds favorably5. Discusses withfriends/family6. Thinks others approve7. Approves him/herself8. Recognizes innovationmeets need

2. Contemplation

3. Decision 8. Information search andretrieval9. Deciding on basis ofretrieval

9. Intends to consult aprovider10. Intends to adopt11. Goes to provider

3. Preparation

4. Implem-entation

10. Behaving accord withdecision

12. Initiates use13. Continues use

4. Action

5. Confirmation 11. Reinforcement ofdesired acts12. Post-behaviorconsolidating

14. Experiences benefits15. Advocates otherspractice16. Support practice in thecommunity

5. Maintenance

Behavior Change Stages in Four Models

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Diffusion

• Takes time• Is difficult even when something is

seemingly worthwhile• Is guided and influenced by many factors,

some obvious, some not so obvious• Provides a macro – micro perspective on

behavior change

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Diffusion

• Process by which an innovation is communicated through certain channels over time among the members of a social system

• Communication is special in that it attempts to reduce uncertainty about the innovation

• Diffusion vs. Dissemination vs. Technology Transfer

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Hypothetical Diffusion When Adopters Persuade Non-adopters at a Rate of One

Percent(Homogenous Mixing)

Time Cum.

Ado.

Non

Ado.

New

Ado.

Cum.

Ado.1

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0

5

9.75

18.55

33.66

55.99

80.63

96.25

99.86

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100

95

90.25

81.45

66.34

44.01

19.37

3.75

0.14

0

4.75

8.8

15.11

22.33

24.64

15.62

3.61

0.14

0

9.75

18.55

33.66

55.99

80.63

96.25

99.86

100

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Hypothetical Cumulative and Incidence Adoption Curves for Diffusion

Homogenous Mixing

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

Time

Pe

rce

nt

Ad

op

ters

CumulativeAdopters

NewAdopters

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The Diffusion of Knowledge, Attitudes and Practices (KAP)

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1 6 11 16

Time

Pe

rce

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Know ledge

Attitude

Practice

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Example of Diffusion

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3233

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The Two-Step Flow Hypothesis of Mass Media

Influence

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MassMedia

OpinionLeaders

Friends

Family

Coworkers

Others

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Mathematical Models Used to Derive Diffusion Rate Parameters

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History• Early pre-paradigmatic research by

Anthropologists, Economists, & Sociologists interested in cultural change (1903-1940)

• In 1943, Ryan & Gross published a study farmers’ adoption of hybrid seed creating the paradigm

• By 1962 Rogers published “Diffusion of Innovations” which solidified the paradigm

• Coleman, Katz & Menzel’s (1966) study of Medical Innovation solidified the theory on diffusion networks

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Ryan & Gross

• Studied the diffusion of hybrid seed corn, retrospectively 1928-1941

• 2 communities in Iowa, 255 of 257 farmers adopted

• Contrasted economic and social variables

• Established diffusion paradigm

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Number of Diffusion Publications Over Time

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10152025303540

41 43 46 49 51 53 55 57 59 61 63 65 67 69 72 75 80

Year

U.S. & EuropeanDeveloping NationsNations

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Diffusion Publications and Research Innovations:Ratio of Innovations to Publications Remained Constant

020406080

100120140160180200

1941-1945 1946-1950 1951-1955 1956-1960 1961-1966

Number of Publications

Number of Research Innovations

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Reasons for Decline

• It was perceived as fallow intellectually (15 of 18 variables used by Ryan & Gross)

• Political climate was against cultural imperialism. It was politically incorrect – associated with technological hegemony

• Environment suffered from the spread of technological innovations (pesticides, herbicides)

• Social scientists not trained in matrix methods to investigate network reasons for diffusion

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Research on Innovation Diffusion in Many Fields

• In Demography and fertility transition studies• In Sociology by re-newed attention on

diffusion networks• In Communication as a tool to evaluate

communication campaigns• In Organizations as a means to understand

and plan change

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Diffusion Networks

• A specific branch and approach to diffusion study

• Some might argue that diffusion is only diffusion when one looks at networks and that other “diffusion” studies are behavior or social change

• Diffusion networks has been historically the branch of networks focused on behavior change

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Lineage of Diffusion Network ModelsFrom Valente (2006)

Type (1) Social integration

• Social Factors are important - Ryan & Gross 1943

• Social Integration - Coleman Katz & Menzel 1966

• Opinion Leaders - Rogers 1964

• Norms - Becker 1970

• Rogers & Kincaid 1981

Type (2) Bridges & Structure

• Weak Ties - Granovetter 1973

• Burt 1987 1992

• Watts (2002)

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Lineage (cont.)

Type (3) Critical levels• Schelling 1972• Thresholds - Granovetter 1978• Critical Mass - Marwell, Oliver et al. 1988; Markus 1988• Network Thresholds - Valente 1995/1996Type (4) Dynamics• Marsden & Polodny 1990• Spatial & Temporal Heterogeneity – Strang & Tuma,

1995• Valente 1995 2005

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(1) Social Integration/ Opinion Leaders

• Integration can be measured many ways

• Behavior is a function of being embedded within a/the community

• Usually operationalized as receiving ties

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Coleman Katz & Menzel 1966

• Actually 1957 was first paper

• Data collected 1955-1956

• Interviewed all MDs in 4 Illinois cities: Peoria, Bloomington, Galesburg, & Quincy

• Sampled prescription records first 3 days of each month to measure Time of Tetracycline Adoption

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Diffusion of Tetracycline for Marginal versus Integrated Doctors

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0.00

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1.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

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Diffusion Network Simulation w/ 3 Initial Adopter Conditions (Valente & Davis, 1999)

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Opinion Leaders

Random

Marginals

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Diffusion Network Game

• Distribute red, white & blue chips

• Give:– Red to OLs– Blue to Randoms and – White

• Allow them to give chips to those people who nominated them

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Diffusion Network Game:Opinion Leader Model

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1

0 1 2 3

Time

Perc

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Leaders

Randoms

Marginals

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Diffusion Network Game

• Distribute Red, White & Blue Chips to different initial starts– Red = awareness– White = attitude– Blue = behavior

• Can only receive a white chip if have red one; only receive a blue one if have red & white

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Diffusion Network Game: KAP-gap Demo

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Perc

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Attitude

Practice

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(2) Structural

• Structural models require data from the entire network

• Can use sociometric data to identify bridges

• Can also use to measure structural equivalence and constraint

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Granovetter, Strength of Weak Ties (1973), AJS

• Seminal article

• Cited thousands of times

• Granovetter was White’s student

• First faculty appointment at JHU

• Left JHU for Stonybrook, now at Stanford

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Granovetter, Strength of Weak Ties (cont.)

Cognitive balance inclines friends of friends to know friends - transitivity. Granovetter shows Figure 1 which is the forbidden triad, i.e., this type of network configuration rarely occurs.

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A B

C

The Forbidden Triad

A B

C If A & B are linked and A & C are linked then it implies that C & B are linked

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SWT: Bridges created shorter paths

Bridges - individuals who link otherwise disconnected sub-groups. Individuals who act as bridges have weak ties. So a bridge is composed of weak ties, but not all weak ties are bridges.

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A BC

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E

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F

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Weak Tie

K

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(3) Critical Levels

• Tipping points

• Macro vs. micro tipping points, critical mass vs. thresholds

• Most CM/threshold models were not explicitly social network explanations

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(4) Dynamics

• Can model how ideas/behaviors spread through a network

• Simplest model assumes static (fixed) network and the idea spreads on that network

• Start with initial adopters and let the behavior percolate through the network

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Network Exposure

Exposure=33% Exposure=66% Exposure=100%

= Non User = User

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Exposure Equation

i

itijNt S

ASE

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where E is the exposure matrix, S is the social network, A is the adoption matrix, n is the number of respondents, n+ indicates the sum of each row, and t is the time period. The exposure equation is a very general model in which the social network can be direct relations, positional relations, narrowly focused, or broadly focused.

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Computing Network Weighted Scores Such as Network Exposure

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1 2 3 4 …………...……….N

1 2 3 4 ………….….N

N x N AdjacencyMatrix (or weightmatrix)

Nx1Vectorof Scores

X =

Nx1 Vector ofNetwork Weighted Scores

Nx1Vectorof Row Totals

–

:

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Computing Network Weighted Scores Such as Network Exposure

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1 2 3 4 …………...……….N

1 2 3 4 ………….….N

Nx1Vectorof Scores

X =

Nx1 Vector ofNetwork Weighted Scores

Nx1Vectorof Row Totals

–

:

0 1 0 1 0 ….1 0 1 0 0 ….0 1 0 1 1 ….1 0 0 0 1 ….1 0 0 1 0 …...

10110..

22322..

.51.0.33.51.0..

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NxT Matrix of Exposure Scores

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1 2 3 4 …………...……T

1 2 3 4 ………….….N

0.00 0.25 0.50 0.50 ...0.00 0.00 0.00 0.00 ….0.00 0.00 0.00 0.00 ….0.25 0.25 0.25 0.25 ….0.33 0.33 0.66 1.00 …...

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4. Personal network exposure

Personal network exposure is the degree an individual is exposed to an innovation through his/her personal network.

Network exposure provides:

1. awareness information

2. influence/persuasion

3. detailed information on how to get the innovation, possible problems, updates, refills, enhancements, novel uses

4. something to talk about

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Network Exposure (cont.)

5. social support needed to face opposition6. reinforcement and a sense of belonging7. relay experiencesExposure computed on direct ties; and on ties of ties by

using the geodesic and weighing the ties by its inverse.Every network has a different maximum geodesic measure

so we need to approximate the influence of any one point on any other point. Luckily the flow matrix has been created which does precisely that.

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Three Studies with Data on Time-of-adoption & Social Networks

Medical Innovation Brazilian Farmers

Korean

Family Planning

Country USA Brazil Korean

# Respondents 125 Doctors 692 Farmers 1,047 Women

# Communities 4 11 25

Innovation Tetracycline Hybrid Corn Seed Family Planning

Time for Diffusion 18 Months 20 Years 11 Years

Year Data Collected 1955 1966 1973

Ave. Time to 50% 6 16 7

Highest Saturation 89 % 98 % 83 %

Lowest Saturation 81 % 29 % 44 %

Citation Coleman et al (1966)

Rogers et al (1970) Rogers & Kincaid (1981) 49

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Datasets

• Provide static view of network

• 1 based on observational data on adoption (but it is sampled)

• 2 based on recall- though recall is probably pretty good

• They are varied and the network data are pretty good

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Two of these Datasets Have Received the Most Attention

• Medical innovation by Coleman, Katz & Menzel (1966):

Burt, 1987; Marsden & Podolny 1990; Strang and Tuma, 1993; Valente, 1995; 1996; Van den Bulte & Lilien, 2001

• Korean family planning by Rogers & Kincaid (1981):

Dozier, 1977; Montgomery, 1994; Valente, 1995; 1996.

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Regression on Time to Adoption by Network Exposure & External Contacts

Medical Innovation

N=125

Brazilian Farmers

N=792

Korean Fam. Plan.

N=1,025

Exposure Direct Contacts

0.54 1.31* 1.09

Exposure via SE 0.88 2.85** 1.02

Attitude toward Science

0.61*

Journals 1.16*

Income 1.01*

Visits to City 1.00

# of children 1.10**

Campaign Exposure 1.04*

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Maximum Likelihood Logistic Regression on Adoption by Time, Ties Sent/Received & Network Exposure.

Medical Innovation

N=947

Brazilian Farmers

N=10,092

Korean Fam. Plan.

N=7,103

Time (as %) 0.21 0.72 0.31

Time – Log 0.68 1.94 0.67

Sent 0.91 0.90 0.96

Received 1.06 1.02 1.06**

Exposure via Direct Contacts

0.64 1.07 1.19

SE Exposure 0.93 2.47* 1.12

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Exposure Adoption?

• Represents a challenge to the diffusion and other behavior change models

• Could be a function of location on the diffusion curve – more likely after critical mass

• Very disappointing from a replication perspective

• What model can explain this?

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Network Threshold

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PN Threshold=33% PN Threshold=66% PN Threshold=100%

= Non User = User

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Graph of KFP Communication NetworkRogers & Kincaid, 1981

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Graph of Time of Adoption by Network Threshold for One Korean Family Planning Community

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575859 60

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Time

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100%

0%

1963 1973

5858

Cross-Sectional Data

(N=611)

Panel Data

(N=141)

Low

Threshold

High

Threshold

Low

Threshold

High

Threshold

Campaign Exposure

2.36** 1.92 1.71* 1.26

*p<.05; **p<.01

Controls for education, age, income, and number of children

Table: Adjusted Odds Ratios for the Likelihood of Low and High-threshold Adoption.

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Network Structure

• Network structure is partly defined by centrality.

• Central members, popular students for example, both influence and are influenced by group norms

• Central members can also contribute disproportionately to peer influence at micro level

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Agent Based Models

• Advent of computing has enabled scientists to generate hypothetical scenarios and model how people interact

• Fundamental issue is:– Do assumptions match reality– Are the processes reasonable

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First Contact Diffusion (Rumor)/Random Seeds

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0.2

.4.6

.81

Perc

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0 5 10 15 20time

Real RandomCentralized Clustered

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Rate of Diffusion

Network Structure

Real Rnd Cent Clustered

Seeds Leaders 0.16 0.42 0.41 0.27

Random 0.18 0.43 0.41 0.27

Between 0.20 0.45 0.47 0.27

Marginals 0.20 0.44 0.45 0.27

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Simulated Network Structural Properties

Size Density Centralization

Clustering Recip. %

Real 150 1.46% 3.28 15.4 43.0

Random 150 1.46% 3.25 1.85 1.05

Centralized 150 1.46% 7.67 1.63 1.01

Clustered 150 1.46% 2.81 15.5 10.3