Taking Time into Account: Understanding Microworkers’ Continued Participation in Microtasks
Bibliographic record
Abstract
Microtask crowdsourcing platforms foster digital assembly lines where microtasks are performed by on-demand microworkers. The sustainability of microtask crowdsourcing platforms and the timely completion of microtask batches hinge on microworkers’ continued participation, but our understanding of microworkers’ continuance remains unclear. Drawing on the theory of the allocation of time and the research on work motivational orientations, we hypothesize that three motivational orientations (i.e., compensation, enjoyment, and microtime structure) are positively associated with microworkers’ perceived relative advantage of microworking, which in turn enhances microworkers’ intent to continue microworking. The relationships between the three motivational orientations and relative advantage are moderated by microworkers’ perceived situational boredom and microwork status (i.e., ad hoc, part-time, and full-time). An online survey on Amazon’s Mechanical Turk provides empirical evidence for the predictive powers, which are contingent on situational boredom, of the three motivational orientations on relative advantage, suggesting that fine-grained microtasks cater to the inherent desire to structure time at a microlevel. We further found that the relationships between motivational orientations and relative advantage differ across ad hoc, part-time, and full-time microworkers. Our study sheds light on theorizing users’ participation by incorporating the time lens and distinguishing heterogeneous effects across temporal contexts and user types. Our findings also provide important practical implications for the design and organization of microwork as well as the governance of the crowdsourcing platform.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".