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Communication-Based Influence Components Model

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Communication-Based Influence Components Model

  1. 1. 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 2009 The 4th International Conference on Persuasive Technology Claremont, California, USA
  2. 2. Overview <ul><li>Real-world online interventions </li></ul><ul><li>Behavioural influence frameworks </li></ul><ul><li>Applying communication theory online </li></ul><ul><li>Communication theory to frame online influence </li></ul><ul><li>Applying the model to 32 interventions </li></ul><ul><li>Closing </li></ul>
  3. 3. 1. Real-world online interventions
  4. 4. Examples of online interventions <ul><li>Quit smoking </li></ul><ul><li>Exercise more </li></ul><ul><li>Drive safer </li></ul><ul><li>Drink less </li></ul><ul><li>Eat healthier </li></ul><ul><li>Eat more </li></ul><ul><li>Eat less </li></ul>
  5. 5. Designing online interventions <ul><li>Research behaviour and audiences </li></ul><ul><li>Pick a suitable theory </li></ul><ul><ul><li>Health Belief Model, Stages of Change, Theory of Planned Behaviour, Social Cognitive Theory, Diffusion of Innovations, Communications Theory </li></ul></ul><ul><li>Pick techniques to influence users’ psychology </li></ul><ul><li>Build a mock-up </li></ul><ul><li>Do some market testing, piloting, focus groups... </li></ul><ul><li>Build a beta, test it, make various political, ad hoc and last minute concessions </li></ul><ul><li>Finish it </li></ul><ul><li>Roll it out and keep revising it </li></ul>
  6. 6. Result <ul><li>Complex real-world interventions </li></ul>
  7. 7. Difficulty describing real-world interventions <ul><li>Mixed theories and constructs are difficult to identify </li></ul><ul><li>Interventions may be based on budget limits, technical capacity, politics, staffing and various ad hoc decisions </li></ul><ul><li>Confound overt and covert factors </li></ul>
  8. 8. Difficulty applying systems to describe online interventions <ul><li>Too many theories to choose from, with numerous overlapping constructs </li></ul><ul><li>Too many taxonomies of behavioural change techniques, with numerous conceptualizations </li></ul><ul><li>Influence concepts blend many smaller techniques, causing over-fit and under-fit </li></ul>
  9. 9. 2. Behavioural influence frameworks
  10. 10. Behavioural Influence Frameworks <ul><li>Evidence-based behavioural medicine </li></ul><ul><li>Cialdini </li></ul><ul><li>CAPTOLOGY </li></ul><ul><li>Stages of change </li></ul><ul><li>Community-based social marketing </li></ul>
  11. 11. Different arrangements between frameworks <ul><li>Psychological principles </li></ul><ul><li>How people use/interact with technology </li></ul><ul><li>Stages and processes of change </li></ul><ul><li>Intervention planning processes </li></ul><ul><li>Shopping list of what works </li></ul>
  12. 12. Result <ul><li>There is no “one size fits all”, “off the shelf” taxonomy to describe online interventions </li></ul>
  13. 13. Influence components approaches <ul><li>Evidence-based kernels </li></ul><ul><li>Behavioural Change Consortium </li></ul><ul><li>Evidence-based behavioural medicine </li></ul>
  14. 14. Influence Components Model Intent Attitude Norm Efficacy Improve diet
  15. 15. 3. Applying communication theory online
  16. 16. One-Way: one-to-one, one-to-many
  17. 17. One-Way Shannon-Weaver (1949)
  18. 18. Two-Way: one-with-one
  19. 19. Two-Way Osgood and Schramm (1954)
  20. 20. Mass/Interpersonal Divide
  21. 21. Mass-Interpersonal Communication
  22. 22. Conclusion <ul><li>For online interventions targeting mass populations, the mass-interpersonal model captures the persuasiveness of interpersonal communication and reach of mass media approaches </li></ul>
  23. 23. 4. Communication theory to frame online influence
  24. 24. Receiver (Situation) Channel (message) Source Azjen (1992) Receiver (Context) Medium (message) Source O'Keefe (2002) Audience (Then loops round as this is a circular model) Each receiver decodes Each receiver decodes in the context of a social group Sends many identical messages Organization (Feedback from audience and other sources) McQuail and Windahl (1981) Destination (message) (Then loops round) Receives Signal (noise) Transmits Source (message) DeFleur (1970) Receiver Decodes Channel (Message) Encodes Source Berlo’s S-M-C-R (1960 ) Interpreter B (Then loops round) Decoder Message Encoder Interpreter A (Feedback from Interpreter B through same process) Osgood and Schramm (1954) Destination (message) Receives Signal (noise) Transmits Source (message) Shannon-Weaver (1949) To Whom (With What Effect?) (Says What) In Which Channel Who Lasswell (1948) Audience Speech Speaker Aristotle <-------------Feedback <------------- Audience Decode Message Encode Source Medium (Signal)
  25. 25. Result <ul><li>Osgood and Schramm (1954) model was adapted and placed within the context of persuasive effects described by Azjen (1992) and O'Keefe (2002) </li></ul>
  26. 26. Framework to house influence components Communication-Based Influence Components Model
  27. 27. 5. Applying the model to 32 interventions
  28. 28. Applying the model <ul><li>Used in a meta-analysis of online behavioural change interventions (being finalized) </li></ul><ul><li>32 interventions from 31 papers </li></ul><ul><li>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) </li></ul>
  29. 29. Context
  30. 30. Media channel
  31. 31. Source (pseudo-source)
  32. 32. Source encoding Only time encoding was recorded. Usability, framing and other factors were not recorded due to infrequent reporting.
  33. 33. Intervention message (source) Top 10 out of 40 Abraham and Michie (2008)
  34. 34. Audience interpreter Michie, Johnston, Abraham, Lawton, Parker and Walker (2005)
  35. 35. Audience encoding Was not coded, as the vast majority used web forms
  36. 36. Feedback message (audience)
  37. 37. Closing <ul><li>The model worked well for the meta-analysis and proved a useful framework </li></ul><ul><li>It can serve as a tool for designing online interventions </li></ul><ul><li>Full meta analysis with effect sizes is being completed now </li></ul>
  38. 38. Thank you University of Wolverhampton Statistical Cybermetrics Research Group and the Wolverhampton Business School http://cybermetrics.wlv.ac.uk

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