Agent-Based Modeling to Inform Online Community Theory and ...

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From reputation (IntrBrep) Incentive mechanisms (Wasko & Faraj, 2005) When an individual member is among the topic 10% contributors self contribution f ( ) max contribution / 10 40
Under Review at Information Systems Research Please do not cite without author permission Table 3. Pseudo-code for calculating benefits from sharing information Initialize information sharing benefit to zero /* only contribute when task valence or group valence is high */ IF any of identity benefit, bonds benefit, or recreation benefit > 3 THEN /* more likely to contribute when group is at stake*/ IF total messages < 100 THEN Increase information sharing benefit by 5 times (100 – total messages) / 100 /*more likely to contribute when perceiving others as under-contribution*/ IF average other contribution < 10% of self contribution THEN Increase information sharing benefit by 3 times self / other contribution ENDIF /* less likely to contribute in groups larger than 15*/ IF group size > 15 THEN Multiply information sharing benefit by (1 – (group size – 15) / (group size + 15)) ENDIF Table 4. Summary of the Effects of Community-Level and Personalization Moderation Member commitment (logins) Community activity (posts) Benefit from information access Benefit from interpersonal bonds Community-level moderation Personalized moderation • positive, significant • a greater effect in narrowfocus or high-traffic groups • positive, significant • a greater effect in broad-focus or high-traffic groups • negative, not significant • positive, significant • a greater effect in broad-focus or high-traffic groups • positive, significant • positive, significant • a greater effect in broad-focus or high-traffic groups • negative, significant • positive, significant • a greater effect in broad-focus or high-traffic groups
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Page 3 and 4: group outcomes. While theories from
Page 5 and 6: onds to examine the development of
Page 7 and 8: social science theories. The collec
Page 9 and 10: approving or rejecting messages bef
Page 11 and 12: with its members; and (3) other ben
Page 13 and 14: less likely to bring up off-topic s
Page 15 and 16: from sharing information to help ot
Page 17 and 18: erate level of interest in reading
Page 19 and 20: We took three steps to insure the e
Page 21 and 22: Here is an example to illustrate th
Page 23 and 24: higher message volume (p < .001). P
Page 25 and 26: to 17% lower relational benefit tha
Page 27 and 28: etween exchanging information and e
Page 29 and 30: driven by both informational and re
Page 31 and 32: Personalization can be implemented
Page 33 and 34: singly value friendship with other
Page 35 and 36: Figallo, C. 1998. Hosting web commu
Page 37 and 38: Raghu, T. S., Jayaraman, B., & Rao,
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Page 45 and 46: Informational Benefit Figure 8. Eff

Agent-Based Modeling to Inform Online Community Theory and ...

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