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1 Diffusion & Interventions Diffusion of innovations Behavior change Behavior change is short term whereas diffusion looks at the long view of how new.

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Presentation on theme: "1 Diffusion & Interventions Diffusion of innovations Behavior change Behavior change is short term whereas diffusion looks at the long view of how new."— Presentation transcript:

1 1 Diffusion & Interventions Diffusion of innovations Behavior change Behavior change is short term whereas diffusion looks at the long view of how new behaviors spread 1

2 2 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. 2

3 3 Diffusion of Innovations Rogers wrote consecutive texts on this topic: – st Edition – nd Edition (with Shoemaker) – rd Edition – th Edition – th Edition Synthesized, elaborated, codified, explained diffusion of innovations 3

4 4 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. 4

5 5 Characteristics of an Innovation Relative advantage Compatibility Complexity Trialability Observability Cost Radicalness 5

6 6 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 6

7 7 Hypothetical Cumulative and Incidence Adoption Curves for Diffusion Homogenous Mixing 7

8 8 8

9 9 9 Behavior Change Stages in Four Models

10 10 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 10

11 11 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 11

12 12

13 13 Hypothetical Diffusion When Adopters Persuade Non-adopters at a Rate of One Percent (Homogenous Mixing) TimeCum. Ado. Non Ado. New Ado. Cum. Ado

14 14 Hypothetical Cumulative and Incidence Adoption Curves for Diffusion Homogenous Mixing 14

15 15 The Diffusion of Knowledge, Attitudes and Practices (KAP) 15

16 16 Example of Diffusion 16

17 17 The Two-Step Flow Hypothesis of Mass Media Influence 17 Mass Media Opinion Leaders Friends Family Coworkers Others

18 18 Mathematical Models Used to Derive Diffusion Rate Parameters

19 19 History Early pre-paradigmatic research by Anthropologists, Economists, & Sociologists interested in cultural change ( ) 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 19

20 20 Ryan & Gross Studied the diffusion of hybrid seed corn, retrospectively communities in Iowa, 255 of 257 farmers adopted Contrasted economic and social variables Established diffusion paradigm 20

21 21 Number of Diffusion Publications Over Time 21

22 22 Diffusion Publications and Research Innovations: Ratio of Innovations to Publications Remained Constant 22

23 23 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 23

24 24 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 24

25 25 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 25

26 26 Lineage of Diffusion Network Models From 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 Watts (2002)

27 27 Lineage (cont.) Type (3) Critical levels Schelling 1972 Thresholds - Granovetter 1978 Critical Mass - Marwell, Oliver et al. 1988; Markus 1988 Network Thresholds - Valente 1995/1996 Type (4) Dynamics Marsden & Polodny 1990 Spatial & Temporal Heterogeneity – Strang & Tuma, 1995 Valente

28 28 (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 28

29 29 Coleman Katz & Menzel 1966 Actually 1957 was first paper Data collected 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 29

30 30 Diffusion of Tetracycline for Marginal versus Integrated Doctors 30

31 31 Diffusion Network Simulation w/ 3 Initial Adopter Conditions (Valente & Davis, 1999)

32 32 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 32

33 33

34 34 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 34

35 35

36 36 (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 36

37 37 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 37

38 38 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. 38 AB C The Forbidden Triad AB C If A & B are linked and A & C are linked then it implies that C & B are linked

39 39 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. 39 A B C I E D H F G J Weak Tie K L

40 40 (3) Critical Levels Tipping points Macro vs. micro tipping points, critical mass vs. thresholds Most CM/threshold models were not explicitly social network explanations 40

41 41 (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 41

42 42 Network Exposure Exposure=33%Exposure=66%Exposure=100% = Non User= User

43 43 Exposure Equation 43 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.

44 44 Computing Network Weighted Scores Such as Network Exposure …………...……….N … … … …. ….N N x N Adjacency Matrix (or weight matrix) Nx1 Vector of Scores X= Nx1 Vector of Network Weighted Scores Nx1 Vector of Row Totals – :

45 45 Computing Network Weighted Scores Such as Network Exposure …………...……….N … … … …. ….N Nx1 Vector of Scores X= Nx1 Vector of Network Weighted Scores Nx1 Vector of Row Totals – : … … … … …

46 46 NxT Matrix of Exposure Scores …………...……T … … … …. ….N … … …..

47 47 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 47

48 48 Network Exposure (cont.) 5. social support needed to face opposition 6. reinforcement and a sense of belonging 7. relay experiences Exposure 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. 48

49 49 Three Studies with Data on Time-of-adoption & Social Networks Medical InnovationBrazilian Farmers Korean Family Planning CountryUSABrazilKorean # Respondents125 Doctors692 Farmers1,047 Women # Communities41125 InnovationTetracyclineHybrid Corn SeedFamily Planning Time for Diffusion18 Months20 Years11 Years Year Data Collected Ave. Time to 50%6167 Highest Saturation89 %98 %83 % Lowest Saturation81 %29 %44 % CitationColeman et al (1966) Rogers et al (1970)Rogers & Kincaid (1981) 49

50 50 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 50

51 51 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;

52 52 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 *1.09 Exposure via SE **1.02 Attitude toward Science 0.61* Journals1.16* Income1.01* Visits to City1.00 # of children1.10** Campaign Exposure1.04*

53 53 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 %) Time – Log Sent Received ** Exposure via Direct Contacts SE Exposure *

54 54 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? 54

55 55 Network Threshold 55 PN Threshold=33%PN Threshold=66%PN Threshold=100% = Non User= User

56 56 Graph of KFP Communication Network Rogers & Kincaid, 1981

57 57 Graph of Time of Adoption by Network Threshold for One Korean Family Planning Community 57 Time Threshold 100% 0%

58 58 Cross-Sectional Data (N=611) Panel Data (N=141) Low Threshold High Threshold Low Threshold High Threshold Campaign Exposure 2.36** *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.

59 59 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 59

60 60 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

61 61 First Contact Diffusion (Rumor)/Random Seeds 61

62 62 Rate of Diffusion Network Structure RealRndCentClustered SeedsLeaders Random Between Marginals

63 63 Simulated Network Structural Properties SizeDensityCentralizat ion ClusteringRecip. % Real % Random % Centralized % Clustered %


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