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Communication-Based Influence Components Model
Brian Cugelman, Mike Thelwall, Phil Dawes
University of Wolverhampton Statistical Cybermetrics Research Group and the Wolverhampton Business School
http://cybermetrics.wlv.ac.uk
Persuasive 2009The 4th International Conference on Persuasive Technology
Claremont, California, USA
Overview1. Real-world online interventions2. Behavioural influence frameworks3. Applying communication theory online4. Communication theory to frame online
influence5. Applying the model to 32 interventions 6. Closing
1. Real-world online
interventions
Examples of online interventions
• Quit smoking
• Exercise more
• Drive safer
• Drink less
• Eat healthier
• Eat more
• Eat less
Designing online interventions
1. Research behaviour and audiences2. Pick a suitable theory
– Health Belief Model, Stages of Change, Theory of Planned Behaviour, Social Cognitive Theory, Diffusion of Innovations, Communications Theory
3. Pick techniques to influence users’ psychology4. Build a mock-up5. Do some market testing, piloting, focus groups...6. Build a beta, test it, make various political, ad hoc and
last minute concessions7. Finish it8. Roll it out and keep revising it
Result
Complex real-world interventions
Difficulty describing real-world interventions
• Mixed theories and constructs are difficult to identify
• Interventions may be based on budget limits, technical capacity, politics, staffing and various ad hoc decisions
• Confound overt and covert factors
Difficulty applying systems to describe online interventions
• Too many theories to choose from, with numerous overlapping constructs
• Too many taxonomies of behavioural change techniques, with numerous conceptualizations
• Influence concepts blend many smaller techniques, causing over-fit and under-fit
2. Behavioural influence
frameworks
Behavioural Influence Frameworks
1. Evidence-based behavioural medicine
2. Cialdini
3. CAPTOLOGY
4. Stages of change
5. Community-based social marketing
Different arrangements between frameworks
• Psychological principles
• How people use/interact with technology
• Stages and processes of change
• Intervention planning processes
• Shopping list of what works
Result
There is no “one size fits all”, “off the shelf” taxonomy to describe online interventions
Influence components approaches
1. Evidence-based kernels
2. Behavioural Change Consortium
3. Evidence-based behavioural medicine
BehaviouralDeterminant
BehaviouralOutcomeInfluencers
Influence Components Model
BehaviouralOutcome
BehaviouralDeterminants
Influencers
Packages
Intent
Attitude Norm
Efficacy
Improve diet
3. Applying communication
theory online
One-Way: one-to-one, one-to-many(O
ne
-Wa
y)
On
e-t
o
One
Impersonal
Many
Mass Media
(Tw
o-W
ay
) O
ne
-wit
h Interpersonal Mass Interpersonal
one-with-one
one-to-one
One-Way
Shannon-Weaver (1949)
Two-Way: one-with-one(O
ne
-Wa
y)
On
e-t
o
One
Impersonal
Many
Mass Media
(Tw
o-W
ay
) O
ne
-wit
h Interpersonal Mass Interpersonal
one-with-one
one-to-one
Two-Way
Osgood and Schramm (1954)
Mass/Interpersonal Divide(O
ne
-Wa
y)
On
e-t
o
One
Impersonal
Many
Mass Media
(Tw
o-W
ay
) O
ne
-wit
h Interpersonal Mass Interpersonal
one-with-one
one-to-one
Mass-Interpersonal Communication(O
ne
-Wa
y)
On
e-t
o
One
Impersonal
Many
Mass Media
(Tw
o-W
ay
) O
ne
-wit
h Interpersonal Mass Interpersonal
one-with-one
one-to-one
Conclusion
• For online interventions targeting mass populations, the mass-interpersonal model captures the persuasiveness of interpersonal communication and reach of mass media approaches
4. Communication theory to frame online influence
Medium(Signal)
Source Encode Message Decode Audience
<-------------Feedback <-------------
Aristotle Speaker Speech Audience
Lasswell (1948) Who (Says What)In Which Channel
To Whom(With What
Effect?)
Shannon-Weaver (1949) Source (message)
Transmits Signal(noise)
Receives Destination(message)
Osgood and Schramm (1954)
Interpreter A
(Feedback from Interpreter B through same
process)
Encoder Message Decoder Interpreter B
(Then loops round)
Berlo’s S-M-C-R (1960 ) Source Encodes Channel(Message)
Decodes Receiver
DeFleur (1970) Source (message)
Transmits Signal(noise)
Receives Destination(message)
(Then loops round)
McQuail and Windahl (1981)
Organization
(Feedback from audience and other sources)
Sends many identical messages
Each receiver decodes
Each receiver decodes in the
context of a social group
Audience
(Then loops round as this is a circular
model)
O'Keefe (2002) Source Medium(message)
Receiver(Context)
Azjen (1992) Source Channel(message)
Receiver(Situation)
Result
Osgood and Schramm (1954) model was adapted and placed within the context of persuasive effects described by Azjen
(1992) and O'Keefe (2002)
Framework to house influence components
SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
Communication-Based Influence Components Model
5. Applying the model to 32
interventions
Applying the model
• Used in a meta-analysis of online behavioural change interventions (being finalized)
• 32 interventions from 31 papers • Bersamin et al. (2007), Bewick et al. (2008), Bruning Brown et al. (2004) , Celio et al. (2000), Chiauzzi et al. (2005), Cullen et al. (2008),
Dunton et al. (2008), Gueguen et al. (2001), Hunter et al. (2008), Jacobi et al. (2007), Kim et al. (2006), Kosma et al. (2005), Kypri et al. (2004), Kypri et al. (2005), Lenert et al. (2004), Marshall et al. (2003), McConnon et al. (2007), McKay et al. (2001), Moore et al. (2005), Napolitano et al. (2003), Oenema et al. (2005), Patten et al. (2006), Petersen et al. (2008), Roberto et al. (2007), Severson et al. (2008), Strecher et al. (2004), Strom et al. (2000), Swartz et al. (2006), Tate et al. (2001), Verheijden et al. (2004) and Winett et al. (2007)
Context SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
0% 10% 20% 30% 40% 50%
Geographic
Individual & friend
Family
Institution
Individual
Media channel SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
0% 10% 20% 30% 40% 50% 60%
Website &Therapist
Website, Email& Therapist
Website
Website &Email
Source (pseudo-source) SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
0% 10% 20% 30% 40% 50% 60% 70%
Credibility
Similarity
Attractiveness
Not specified
Source encoding SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
Only time encoding was recorded. Usability, framing and other factors were not recorded due to infrequent reporting.
0% 10% 20% 30% 40% 50% 60% 70% 80%
SequentialRequests
SingleInteraction
Not specified
MultipleInteractions
Intervention message (source)
SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
Top 10 out of 40
0% 10% 20% 30% 40% 50% 60% 70% 80%
Barrier identification/Problem solving
Provide information on where and when toperform the behaviour
Fear Arousal
Provide normative information about others’behaviour
Action planning
Prompt self-monitoring of behaviour
Provide feedback on performance
Provide instruction on how to perform thebehaviour
Goal setting (behaviour)
Provide information on consequences ofbehaviour in general
Abraham and Michie (2008)
Audience interpreter SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Social-professional role and identity
Environmental context and resources
Nature of the behaviours
Self-efficacy
Emotion
Behavioural regulation
Memory, attention and decision
Skills
Beliefs about consequences
Social influences (Norms)
Motivation and goals (Intention)
Knowledge
Michie, Johnston, Abraham, Lawton, Parker and Walker (2005)
Audience encoding SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
Was not coded, as the vast majority used web forms
Feedback message (audience)
SourceInterpreter
InterventionMessage
AudienceInterpreter
FeedbackMessage
Media ChannelContext
Decode
EncodeDecode
Encode
0% 10% 20% 30% 40% 50% 60%
Adaptation / Content matching
Demographic Matching
Not specified
Personalization
Tailoring
Closing
• The model worked well for the meta-analysis and proved a useful framework
• It can serve as a tool for designing online interventions
• Full meta analysis with effect sizes is being completed now
Thank you
University of Wolverhampton Statistical Cybermetrics Research Group and the Wolverhampton Business School
http://cybermetrics.wlv.ac.uk
