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Woodland Owner Networks and Peer-to-Peer Learning

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  • Master volunteer programs are very common in Extension natural resources. These programs are built on the two-step flow model.

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  • 1. Woodland Owner Networks and Peer-to-Peer Learning Eli Sagor University of Minnesota Extension Shorna Broussard Allred Cornell University Maureen McDonough Michigan State University Small-scale Forestry 2009 Morgantown, WV
  • 2. Project Origins
  • 3. Outline
    • Networks and peer-to-peer learning
    • Current projects
    • Building a network
    • Next steps
  • 4. What is peer-to-peer learning?
      • Everyone a teacher, everyone a learner
      • One-on-one or group
      • Learner driven
      • Peer-validated
      • Different ways of knowing
  • 5. Rationale: peer networks
    • Preferred source of info & advice
    • Accessible
    • Efficient
  • 6. Structural network theory
    • Social memory
    • Heterogeneity
    • Resilience
    • Learning
    Crona, Bodin, and Ernstson 2006 Network density Reachability Centrality Betweenness / modularity
  • 7. Structural network theory
    • Social memory
    • Heterogeneity
    • Resilience
    • Learning
    Crona, Bodin, and Ernstson 2006 Network density Reachability Centrality Betweenness / modularity
  • 8. Structural network theory
    • Social memory
    • Heterogeneity
    • Resilience
    • Learning
    Crona, Bodin, and Ernstson 2006 Network density Reachability Centrality Betweenness / modularity
  • 9. Structural network theory
    • Social memory
    • Heterogeneity
    • Resilience
    • Learning
    Crona, Bodin, and Ernstson 2006 Network density Reachability Centrality Betweenness / modularity
  • 10. Diffusion models: two-step flow Source: Watts & Dodds 2007, J. Consumer Research
  • 11. Diffusion models: network Source: Watts & Dodds 2007, J. Consumer Research
  • 12. Reachability / network distance
    • Networks in which more actors (info sources) are reachable across short “distances” can more easily access and distribute information
    • Bodin, Crona, and Ernstson 2006
    • Oh et al. 2004
  • 13. Social learning
    • One actor proactively seeking new information from another. Optimized in large, low-density networks.
  • 14. Social influence
    • One actor persuading another to adopt a viewpoint, belief, or behavior. Optimized in small, dense, “tight-knit” networks.
  • 15. Other social network concepts
    • Weak ties: Distant, infrequent contacts. Most efficient for codified knowledge
    • Strong ties: Close, frequent, trusted contacts. Most efficient for tacit knowledge
    Reagans & McEvily 2003 Granovetter 1973
  • 16. Some roles to foster peer-to-peer learning
    • Organize learning space
    • Moderate information
    • Support volunteers
    • Support organizations
  • 17. Davis Role: Organize learning space
  • 18. Role: Moderator
  • 19. Role: Support volunteers
  • 20. Role: Strategic network support
    • Creating efficient connections where they’re missing.
  • 21. Current projects
    • Egocentric network analysis: MA, MN, Finland
    • Master volunteer program evaluation
    • Social marketing
    • Qualitative case studies: US, Australia, Finland
    • Strategic networks
    • Network optimization: Positions and roles
  • 22. Egocentric networks
    • Who do you turn to for advice? -Family, peer, professional -Location -Opinions -Network density
    • Type and nature of peer influence
  • 23. Research questions
    • What are the outcomes of different models of peer-to-peer outreach?
    • What kind of information flows through woodland owner networks, and how?
    • How and why do outcomes differ from alternative programs?
    • How does participation affect network size and access to trusted information?
  • 24. Forest Landowner Networks in New York Shorna Broussard, Cornell
  • 25. What did you do, in part, as a result of your contact and communication with a NYMFO?
  • 26. Take home messages: NY
    • Positive experiences with MFO peers
    • Common topics: land characteristics goals stewardship using a professional forester
    • Common behavior outcomes: seeking information setting goals hiring a private consulting forester
  • 27. A qualitative case study approach
  • 28. Cases
    • Western US Extension master volunteer (e.g. Master Woodland Stewards)
    • Midwestern US woodland owner co-operative
    • Eastern US community-based organization
    • Landcare, Australia
  • 29. Homophily, heterophily, and structural equivalence
    • Homophily: “She’s just like me.”
    • Structural equivalence: “We’re in similar situations”
    • Relationships between homophily, structural equivalence, influence, trust, information flow, and behavior?
  • 30. Roles: Two-step flow model
  • 31. Roles: network model
  • 32. Building a network
  • 33. Symposium photo
  • 34. Symposium photos
  • 35. Symposium photo
  • 36. Symposium: Priority issues
    • How to start a new network.
    • What would it take to grow existing networks?
    • Gap analysis: Where (or for what audiences) is this not happening?
    • How to evaluate success, including return on investment?
    • Other peer-to-peer research: Outcomes, roles
    • How to integrate into existing programs?
    • How to build and support our network?
    • Public policy changes needed to support this?
    • How to leverage other Federal (not necessarily forestry-related) programs?
  • 37.  
  • 38. Ning site screenshot
  • 39. Future directions
    • Mapping woodland owner social networks
    • Measuring social network effects on woodland owner behavior
    • Developing a practical toolkit for program organizers
    • Building the network
  • 40.
    • Project steering committee
    • Brett Butler , US Forest Service FIA / NWOS
    • Mark Buccowich , US Forest Service, NA
    • Shorna Broussard Allred , Cornell University
    • Karl Dalla Rosa , US Forest Service, Co-op Forestry
    • Dylan Jenkins , TNC Pennsylvania
    • David Kittredge and Paul Catanzaro , UMass Amherst
    • Amanda Kueper , University of Minnesota
    • Jim Johnson , Oregon State University
    • Maureen McDonough , Michigan State University
    • James Malone , AL Treasure Forest Assoc.
    • Don Mansius and Kevin Doran , Maine Forest Service
    • Eric Norland , CSREES
    • David Robertson , Virginia Tech
  • 41. Join us! http://WoodlandOwnerNetworks.ning.com or http://bit.ly/10G9ky Eli Sagor, [email_address]