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SELF-ORGANIZATION
IN ONLINE
COLLABORATIVE
WORK SETTINGS
Ioanna Lykourentzou, Federica Lucia Vinella, Faez
Ahmed, Costas Papastathis, Konstantinos Papangelis,
Vassilis-Javed Khan, Judith Masthoff
SAGE/ACM Collective Intelligence journal, 2022
HOW TO FORM SUCCESSFUL
TEAMS?
CRUCIAL
ACROSS
FIELDS
Innovation
Education
New forms of labor, such as
crowd work and macrotasking
THE WAY WE
WORK IS
CHANGING
More and more people work in
ad-hoc, project-based teams
Crowd labor attracts complex
tasks, which depend on
collaboration
TEAM
FORMATION:
THEN AND NOW
Then: Face-to-face settings,
manager decides group
members, based on knowledge
of skills or collaboration history
Now: Algorithms play central
role due to crowd work’s scale
2022 Self-organization in online collaborative work settings 2
ALGORITHMIC
TEAM
FORMATION
TYPICALLY TOP-DOWN
Using pre-existing information (e.g., skills,
demographics) to optimize certain business
objectives (e.g., deadline, cost)
No worker agency (e.g., feedback)
Fit for well-defined tasks (known-requirements, little
dependency on collaboration dynamics)
LACK OF AGENCY IN WHO
ONE WORKS WITH CAN
Stifle creativity and initiative-taking
Increase psychological discomfort
Result in less-than-optimal collaboration results
Especially when the task concerned is open-ended,
creative, and complex
2022 Self-organization in online collaborative work settings 3
HOW TO DESIGN
ALGORITHMS THAT GIVE
WORKERS CONTROL
WHILE MAINTAINING
QUALITY?
Self-organization in online collaborative work settings 4
2022
SELF-ORGANIZING
PAIRS (SOPs)
2022 Self-organization in online collaborative work settings 5
SOPs: CORE PRINCIPLES
SELF-ORGANIZATION
Crowd workers try out and evaluate
different collaborators
Gradually converge to preferred pairs
Algorithm facilitates rather than directing
EXPLORATION-
EXPLOITATION
Cycles of intra-pair collaboration and inter-pair
competition motivate workers to find the best
matches while progressing the task
Exploration of new collaborations, exploitation of
existing ones
Self-organization in online collaborative work settings 6
2022
SOPs METHOD: STEPS
COLLABORATION
Participants collaborate in dyads to progress a creative task, which for this study is
continuing a short fictional story
Self-organization in online collaborative work settings 7
2022
PEER EVALUATION
Then they evaluate their in-between collaboration
Self-organization in online collaborative work settings 8
2022
SOPs METHOD: STEPS
BEST STORY VOTING
Next, the pairs compete for the best story continuation through peer review. The result is
appended to the main story.
Self-organization in online collaborative work settings 9
2022
SOPs METHOD: STEPS
COLLABORATOR SELECTION
Finally, participants indicate which collaborators they want to work with in the next round
(process repeats for three rounds).
Self-organization in online collaborative work settings 10
2022
SOPs METHOD: STEPS
BEHIND THE SCENES: SOPs
ALGORITHM
Self-organization in online collaborative work settings 11
2022
EVALUATION: EXPERIMENTAL
CONDITIONS
SOPS
People indicate preferred
collaborators
Algorithm respects user
choices
PLACEBO
Illusion of agency
Same interface as SOPs
Algorithm does not take choices
into account, pairs users randomly
NO-AGENCY
Lack of agency, similar to
existing top-down team
formation methods
Randomly initialized pairs, fixed
throughout the process
2022 Self-organization in online collaborative work settings 12
EXPERIMENTAL SETUP
PARTICIPANT
S AND
OUTPUTS
140 people
18 experiments
18 final stories, 196 unique
story continuations
Random allocation to condition
Total time per experiment: 30
minutes
STORY
EVALUATION
External story evaluation,
10 different judges per
story (AMT)
STORY QUALITY
CRITERIA
Grammar: How grammatically and
syntactically correct is the story?
Interest: How interesting is the
story?
Originality: How original is the story?
Plot: How good is the story plot?
Overall impression: Overall, how
much did you like the story?
2022 Self-organization in online collaborative work settings 13
QUALITY OF THE
CREATIVE OUTCOME
SOPs wrote stories of
higher quality across all examined
axes
Self-organization in online collaborative work settings 14
2022
EVALUATION: RESULTS
QUALITY OF THE
COLLABORATION
People in the SOPs condition
evaluated one other higher in
three axes:
 Collaboration
 Helpfulness
 Skillfulness
Self-organization in online collaborative work settings 15
2022
EVALUATION: RESULTS
COMPETENCIES IN
COMMON
SOP members were more
aligned in personal values,
skill similarity, and work
strategy
Self-organization in online collaborative work settings 16
2022
EVALUATION: RESULTS
IMPACT
2022 Self-organization in online collaborative work settings 17
SCIENTIFIC
Lay the ground for human-
centered, non-intrusive
collaborative crowd work
systems
Workers integral part of
algorithmic decision-making
INDUSTRIAL
Help platforms integrate
collaboration more
systematically into their
processes
SOCIETAL
Balanced coexistence between
human workers and collaboration
management algorithms
More respectful Future of Work
Better uses of human and
algorithmic capabilities
THANK YOU
Questions?
Self-organization in online collaborative work settings 18
2022
CORRESPONDING
AUTHOR
Ioanna Lykourentzou
i.lykourentzou@uu.nl

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Self-organization in online collaborative work settings

  • 1. SELF-ORGANIZATION IN ONLINE COLLABORATIVE WORK SETTINGS Ioanna Lykourentzou, Federica Lucia Vinella, Faez Ahmed, Costas Papastathis, Konstantinos Papangelis, Vassilis-Javed Khan, Judith Masthoff SAGE/ACM Collective Intelligence journal, 2022
  • 2. HOW TO FORM SUCCESSFUL TEAMS? CRUCIAL ACROSS FIELDS Innovation Education New forms of labor, such as crowd work and macrotasking THE WAY WE WORK IS CHANGING More and more people work in ad-hoc, project-based teams Crowd labor attracts complex tasks, which depend on collaboration TEAM FORMATION: THEN AND NOW Then: Face-to-face settings, manager decides group members, based on knowledge of skills or collaboration history Now: Algorithms play central role due to crowd work’s scale 2022 Self-organization in online collaborative work settings 2
  • 3. ALGORITHMIC TEAM FORMATION TYPICALLY TOP-DOWN Using pre-existing information (e.g., skills, demographics) to optimize certain business objectives (e.g., deadline, cost) No worker agency (e.g., feedback) Fit for well-defined tasks (known-requirements, little dependency on collaboration dynamics) LACK OF AGENCY IN WHO ONE WORKS WITH CAN Stifle creativity and initiative-taking Increase psychological discomfort Result in less-than-optimal collaboration results Especially when the task concerned is open-ended, creative, and complex 2022 Self-organization in online collaborative work settings 3
  • 4. HOW TO DESIGN ALGORITHMS THAT GIVE WORKERS CONTROL WHILE MAINTAINING QUALITY? Self-organization in online collaborative work settings 4 2022
  • 5. SELF-ORGANIZING PAIRS (SOPs) 2022 Self-organization in online collaborative work settings 5
  • 6. SOPs: CORE PRINCIPLES SELF-ORGANIZATION Crowd workers try out and evaluate different collaborators Gradually converge to preferred pairs Algorithm facilitates rather than directing EXPLORATION- EXPLOITATION Cycles of intra-pair collaboration and inter-pair competition motivate workers to find the best matches while progressing the task Exploration of new collaborations, exploitation of existing ones Self-organization in online collaborative work settings 6 2022
  • 7. SOPs METHOD: STEPS COLLABORATION Participants collaborate in dyads to progress a creative task, which for this study is continuing a short fictional story Self-organization in online collaborative work settings 7 2022
  • 8. PEER EVALUATION Then they evaluate their in-between collaboration Self-organization in online collaborative work settings 8 2022 SOPs METHOD: STEPS
  • 9. BEST STORY VOTING Next, the pairs compete for the best story continuation through peer review. The result is appended to the main story. Self-organization in online collaborative work settings 9 2022 SOPs METHOD: STEPS
  • 10. COLLABORATOR SELECTION Finally, participants indicate which collaborators they want to work with in the next round (process repeats for three rounds). Self-organization in online collaborative work settings 10 2022 SOPs METHOD: STEPS
  • 11. BEHIND THE SCENES: SOPs ALGORITHM Self-organization in online collaborative work settings 11 2022
  • 12. EVALUATION: EXPERIMENTAL CONDITIONS SOPS People indicate preferred collaborators Algorithm respects user choices PLACEBO Illusion of agency Same interface as SOPs Algorithm does not take choices into account, pairs users randomly NO-AGENCY Lack of agency, similar to existing top-down team formation methods Randomly initialized pairs, fixed throughout the process 2022 Self-organization in online collaborative work settings 12
  • 13. EXPERIMENTAL SETUP PARTICIPANT S AND OUTPUTS 140 people 18 experiments 18 final stories, 196 unique story continuations Random allocation to condition Total time per experiment: 30 minutes STORY EVALUATION External story evaluation, 10 different judges per story (AMT) STORY QUALITY CRITERIA Grammar: How grammatically and syntactically correct is the story? Interest: How interesting is the story? Originality: How original is the story? Plot: How good is the story plot? Overall impression: Overall, how much did you like the story? 2022 Self-organization in online collaborative work settings 13
  • 14. QUALITY OF THE CREATIVE OUTCOME SOPs wrote stories of higher quality across all examined axes Self-organization in online collaborative work settings 14 2022 EVALUATION: RESULTS
  • 15. QUALITY OF THE COLLABORATION People in the SOPs condition evaluated one other higher in three axes:  Collaboration  Helpfulness  Skillfulness Self-organization in online collaborative work settings 15 2022 EVALUATION: RESULTS
  • 16. COMPETENCIES IN COMMON SOP members were more aligned in personal values, skill similarity, and work strategy Self-organization in online collaborative work settings 16 2022 EVALUATION: RESULTS
  • 17. IMPACT 2022 Self-organization in online collaborative work settings 17 SCIENTIFIC Lay the ground for human- centered, non-intrusive collaborative crowd work systems Workers integral part of algorithmic decision-making INDUSTRIAL Help platforms integrate collaboration more systematically into their processes SOCIETAL Balanced coexistence between human workers and collaboration management algorithms More respectful Future of Work Better uses of human and algorithmic capabilities
  • 18. THANK YOU Questions? Self-organization in online collaborative work settings 18 2022 CORRESPONDING AUTHOR Ioanna Lykourentzou i.lykourentzou@uu.nl