Why do some collaborative online organizations succeed?

Examples

Most COO do not succeed

• COO size is highly skewed, with top organizations getting most of the contributions

There have been three main approaches

Approach 1: Why are Wikipedia and Linux so successful?

• Why people contribute (Nov, von Krogh, Lakhani, Lampe)
• Who contributes (Antin, Shaw and Hargittai)
• How work is organized (Arazy, Butler, Crowston, Keegan, Matei, Zhu)

The weakness of Approach 1

• Selecting on the dependent variable

Approach 2: Predicting COO outcomes based on membership, structure, and design

The weakness of approach 2

• Groups are not independent

Approach 3: Online Organizational Ecology predicts outcomes based on community-level relationships

The weakness of approach 3

• Organizational ecology treats organizations as agents and people as resources

Studying organizational outcomes using individual decisions

• How do people allocate their efforts in a complex environment with lots of choices?

People are influenced by individual attributes, technology, and the state of the system

What is the “system”?

• An earlier generation of communication scholars suggested “open systems” (Katz and Kahn, 1966; Rogers and Argawala, 1976; Farace, Monge, and Russell, 1977)
  • A system takes in inputs, processes them, and produces outputs
  • Systems are composed of subsystems and compose suprasystems (e.g., a firm composed of departments composed of work groups)

Digital trace data lets us study the relationship between people and systems

• Earlier researchers had difficulty gathering the type of data needed for open systems approaches
• COO data is:
  • Fine-grained data about behavior and interactions
  • Within and between groups
  • Unobtrusive

Four projects on individual decisions in new collaborative online organizations

Early-stage communication networks and community outcomes

Integrative work groups are more productive and successful

• Many theories suggest that integrative groups with low hierarchy should be successful:
  • Coordination
    • Information flow (Katz, 2005)
    • Transactive memory / Shared mental models (Wegner, 1985; Mathiew, 2000)
  • Social Integration
    • Legitimate peripheral participation (Lave and Wenger, 1991)
    • Group identity (Scott, 2007)

Integrative structures identified with social network analysis correlate with success

• Low hierarchy, few people on the periphery (Cummings and Cross, 2003)
• High density (Balkundi and Harrison, 2006)

• Early-stage COO should benefit even more from integrative networks

Edits taken from wiki talk pages on Wikia

Relationships between communication structures and productivity

There is basically no relationship between communication structures and survival

Why do people start new communities?

Previous research typically treats small communities as failures

A puzzle

Why do people keep starting communities if they are so likely to fail?

Learning from founders

• 300+ founders responded about their:
  • Motivations
  • Goals
  • Experience

Top goals

• High-quality information
• Long-lasting community
• High-growth community

Most projects are on niche topics for small communities

Projected contributors after 30 days

Who starts new communities?

Starting new organizations

• Entrepreneurs:
  • Have more diverse experience than others (Backes-Gellner & Moog, 2013)
  • Are more likely to have worked with entrepreneurs (Nanda & Sørensen, 2010)

• Successful entrepreneurs:
  • Have more experience (Cassar, 2014)
  • Have large, diverse social networks (Stam et al., 2014)

We examined the behavior and network position of ~61,000 wiki editors

Many founders are learning the system

• Nearly 90% of wikis were founded by new users
• ~1% of existing users founded a wiki

Non-newbie founders are more active with more diverse experience, but at the periphery of social networks

Overall, past behavior and networks have little relationship with community growth

Social exposure and participation processes in online communities

• How do people decide which groups to participate in?
  • Exposure processes + decisions processes

Social computing research theorizes that people decide to participate in a group based on expected utility

• People estimate expected utility of joining based on future activity levels (Resnick et al.)
  • Join if expected benefits exceed expected costs

People are exposed to groups via social ties

• Two categories of exposure to COO (Kraut et al.)
  • Impersonal exposure
  • Interpersonal exposure

These theories focus on individual level outcomes

• Decision rules should predict
  • Group level outcomes
  • Population level outcomes
• These are rarely tested

Online group sizes have heavy-tailed distributions

The number of groups each user belongs to is also heavy-tailed

A simple model that produces heavy-tailed distributions

• Cumulative advantage (Merton, 1968; Barabási, 1999)
  • Future activity levels are based probabilistically on current activity

Do social computing theories of exposure and participation decisions explain heavy-tailed participation?

• Possible cumulative advantage mechanisms
  • Expected utility is based on current size
  • Large COO have larger set of neighbors to share with
  • People in many communities have access to more neighbors

Agent-based modeling

• Simplified, simulated system of interacting agents
• Allows us to:
  • Explicitly model micro processes
  • Test the macro-level implications of theories

Our agent-based model

• Start with \(N\) potential contributors and \(X\) potential communities
  • Every month:
    • Each user is presented with an “exposure set” of \(x\) communities (exposure)
      • The user decides which communities to participate in (decision)
• Naive versions of each theory
• Combined version

Simulation Results

Null model as baseline

Expected utility models are skewed but not heavy-tailed

Naive versions of social exposure are not skewed

A combined version is robust with community sizes roughly similar to reddit

Conclusion

• Word of mouth exposure plus expected utility participation is a partial explanation for heavy-tailed community size distributions
• Two main weaknesses
  • No model was as skewed at both the head and tail
  • No model explained the heavy tail of participation rates

Conclusion

• Framework for theories to be informed by higher-level behavior
  • Could test whether people actually share larger groups
  • Are people more likely to join when they already belong to many COO?
• Simulation can enrich social computing theories

Systems of COO

• Communities are interdependent
  • Founders and joiners are influenced by state of other COO
  • Past experience and luck can influence future behavior
• COO data can help us understand these recursive processes

Small, temporary organizations

• Most COO are intentionally small
  • In aggregate, these are valuable
  • Create narrow public goods without requiring oligarchy

Social motivations aren’t all that important

• Founders cared more about the artifact than the community
• New COO didn’t require integrative networks to be productive or survive
• Explanations
  • Strong selection effects mean only the motivated join
  • Ease of leaving means dissenters leave

Affordances matter

• Low costs to join and leave and create COO
  • Porous boundaries of COO
  • Also influences individuals and populations

Productivity Model

Survival Model

Robustness tests

Cutoff @ 500

Robustness tests

Cutoff @ 900

Robustness tests

Dichotomize edges @ 3

Coreness description

Degenerate graph example

Density with size quartiles

ABM Project

Word of mouth results are fragile

Combined models are much less fragile