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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