Effectiveness of Shared Leadership in Online Communities

1. Effectiveness of Shared Leadership in Online Communities • Haiyi Zhu • Robert E. Kraut • Aniket Kittur

2. Challenges for online communities

3. Challenges for online communities

4. Leadership in conventional organizations Good leaders are important to successful groups and organizations Burke et al 2006, Pearce et al 2002

5. Leadership in online communities Online communities are different from conventional organizations • Volunteer • Non-hierarchical • Large-scale

6. Leadership in online communities Followers?? Leaders?? Preece & Shneiderman 2009, Kittur et al 2007 etc.

7. Leadership in online communities Leadership behaviors Persuade and influence other people to pursue a common goal

8. Shared leadership model Pearce et al 2002, Pearce & Conger 2003 • Originally proposed by Craig Pearce and his colleagues. • Applied to online communities. Leadership behaviors can be demonstrated by any member, from peripheral members to central members

9. Research questions

10. Research questions

11. Types of leadership behaviors Burke et al 2006, Pearce et al 2002, Yukl 1998

12. Example messages sent by Wikipedians I am so impressed. This is a very fine article! Example of providing positive feedback

13. Example messages sent by Wikipedians “…there is a concern that the rationale you have provided for using this image under "fair use" may be invalid. ... If it is determined that the image does not qualify under fair use, it will be deleted within a couple of days… Example of providing negative feedback

14. Example messages sent by Wikipedians Hey, one of these days do you think you could take some pictures at Mission Mill? I’d like to spruce up the article but it really needs some photos. Thanks! Example of directing

15. Example messages sent by Wikipedians Drop me a line on my talk page sometime, we’ll get a coffee over at Hot Rize or the new King Kocoa… Example of social exchange

16. Automatic measurement using machine learning • Zhu et al (CHI’ 2011) Identifying Shared Leadership in Wikipedia • We successfully built machine learning models to classify four types of leadership behaviors from messages sent between Wikipedia editors. …. 500 hand-code messages as training set, 200 test set …. 21 features …. SVM machine learning model …. Ten-fold cross-validation …. The average accuracy is 89%; the agreement between the machine classifications and human judgments is quite high (Kappa = 0.7)

17. Distribution of leadership behaviors in Wikipedia Non-admin Admin

18. Research questions

19. Effectiveness of shared leadership

20. Theory and hypotheses Two key factors to motivate people (Path-goal theory) Perceived desirability of the task Perceived possibility of achieving the task

21. Theory and hypotheses Perceived desirability of the task Perceived possibility of achieving the task

22. Theory and hypotheses Perceived desirability of the task Perceived possibility of achieving the task

23. Theory and hypotheses Perceived desirability of the task Perceived possibility of achieving the task

24. Theory and hypotheses Perceived desirability of the task Perceived possibility of achieving the task

25. Theory and hypotheses Perceived desirability of the task Perceived possibility of achieving the task

26. Theory and hypotheses Perceived desirability of the task Perceived possibility of achieving the task

27. Analysis Used Wikipedia data from Wikipedia’s inception to January 2008 (approximately 182 million revisions). Restricted the analysis to registered Wikipedia editors who had edited any Wikiproject page at least once. The data were longitudinal. The analysis was on editor-week level. The data comprised of 31,676 unique editors, 2,053,405 editor-week observations and 1.6 million messages.

28. Analyze the effectiveness of shared leadership

29. Endogeneity

30. Propensity score matching • To ameliorate the endogeneity problem, we use propensity score matching (PSM) to approximate randomization. • PSM builds experimental and control groups by balancing the groups on potential confounding factors. • Effectively reduces the bias caused by these factors.

31. Propensity score matching 1 2 3

32. Propensity score matching Assign each editor a propensity score -- the possibility of receiving messages based on prior behaviors Edits t-1 , MsgReceivedt-1, MsgSentt-1, MsgReceived<t-1, MsgSent<t-1, Tenure 1 2 3

33. Propensity score matching Assign each editor a propensity score -- the possibility of receiving messages based on prior behaviors Edits t-1 , MsgReceivedt-1, MsgSentt-1, MsgReceived<t-1, MsgSent<t-1, Tenure 1 2 3 Match each editor who received leadership messages with another editor who did not receive a message but had the most similar propensity score 0.80 0.20 0.90 0.81 0.77 0.60 0.40 0.40

34. Propensity score matching Assign each editor a propensity score -- the possibility of receiving messages based on prior behaviors Edits t-1 , MsgReceivedt-1, MsgSentt-1, MsgReceived<t-1, MsgSent<t-1, Tenure 1 2 3 Match each editor who received leadership messages with another editor who did not receive a message but had the most similar propensity score 0.80 0.20 0.90 0.81 0.77 0.60 0.40 0.40 Conduct post-matching analysis Pre-matching: 503,209 receiving messages and 1,550,146 not receiving Post-matching: 503,209 receiving messages and 503,209 not receiving Analysis: fixed effects regression with each pair as a group

35. Effectiveness of shared leadership

36. Interpretations of the results Outcome: The difference in percent between the edits in the subsequent week and the edits in the prior week - 61% ** ** + 33% + 19% ** - 14% ** + 9% ** + 23% ** ** indicates that p value is less than 0.0001; * indicates that p value is less than 0.05

37. Interpretations of the results Outcome: The difference in percent between the edits in the subsequent week and the edits in the prior week - 61% ** ** + 33% + 19% ** - 14% ** + 9% ** + 23% ** ** indicates that p value is less than 0.0001; * indicates that p value is less than 0.05

38. Interpretations of the results Outcome: The difference in percent between the edits in the subsequent week and the edits in the prior week - 61% ** ** + 33% + 19% ** - 14% ** + 9% ** + 23% ** ** indicates that p value is less than 0.0001; * indicates that p value is less than 0.05

39. Interpretations of the results Outcome: The difference in percent between the edits in the subsequent week and the edits in the prior week - 61% ** ** + 33% + 19% ** - 14% ** + 9% ** + 23% ** ** indicates that p value is less than 0.0001; * indicates that p value is less than 0.05

40. Interpretations of the results Outcome: The difference in percent between the edits in the subsequent week and the edits in the prior week - 61% ** ** + 33% + 19% ** - 14% ** + 9% ** + 23% ** ** indicates that p value is less than 0.0001; * indicates that p value is less than 0.05

41. Interpretations of the results Outcome: The difference in percent between the edits in the subsequent week and the edits in the prior week - 61% ** ** + 33% + 19% ** - 14% ** + 9% ** + 23% ** ** indicates that p value is less than 0.0001; * indicates that p value is less than 0.05

42. Interpretations of the results Outcome: The difference in percent between the edits in the subsequent week and the edits in the prior week - 61% ** ** + 33% + 19% ** - 14% ** + 9% ** + 23% ** ** indicates that p value is less than 0.0001; * indicates that p value is less than 0.05

43. Interpretations of the results Outcome: The difference in percent between the edits in the subsequent week and the edits in the prior week - 61% ** From non-admin (baseline) From admin (additional) ** + 30% + 16% ** + 16% + 3% ** * - 10% - 10% ** ** + 6% + 2% ** + 17% + 6% ** ** ** indicates that p value is less than 0.0001; * indicates that p value is less than 0.05

44. Interpretations of the results Outcome: The difference in percent between the edits in the subsequent week and the edits in the prior week - 61% ** From non-admin (baseline) From admin (additional) ** + 30% + 16% ** + 16% + 3% ** * - 10% - 10% ** ** + 6% + 2% ** + 17% + 6% ** ** ** indicates that p value is less than 0.0001; * indicates that p value is less than 0.05

45. Discussions and implications Shared leadership model provides a broader way to understand leadership in online communities. • Everyone actually performs leadership behaviors. • Substantially influence others’ behaviors and thus significantly affect the continuous functioning of the communities.

46. Discussions and implications

47. Discussions and implications • Transactional and person-based leadership behaviors had the strongest positive effects. • Suggest interfaces and mechanisms to encourage these effective leadership behaviors. • Easy to connect with each other • Easy to reward each other • Easy to express appreciation for each other

48. Discussions and implications Aversive leadership seems to demotivate people to work. • Reducing the harmful activities might be a positive impact.

49. Discussions and implications Aversive leadership seems to demotivate people to work. • Reducing the harmful activities might be a positive impact. • Might also reduce the general contributions of good-intended contributors.

50. Limitations and future work True experiments Further examine what kinds of activities are affected by aversive leadership Omitted variable bias (Some unmeasured variables might confound the result) The demotivating effects of aversive leadership