Performing on-demand work via effective strategies: Setting goals contingent upon regulatory foci
Bibliographic record
Abstract
Integrating two theoretically distinct approaches to motivation that focus on situational and dispositional goals, we investigated the effects of goal setting (single vs. double goals) on performance of on-demand workers over time, contingent upon regulatory foci (a promotion vs. a prevention focus) and through task-effective strategies. We adopted a mixed-method, multi-study, and multi-wave research design. In Study 1, the results from a focus group involving high-performing drivers from a Chinese ride-hailing platform headquartered in Beijing, China showed that they typically set vague goals (do-your-best) on a regular basis to earn their living. Individuals differ fundamentally in the way they regulate their behaviors when pursuing multiple work-related goals. Complementing Study 1, two concurrent field experiments, Study 2a (Nanjing, n = 571) and Study 2b (Chengdu, n = 1,535) were conducted involving drivers registered with the same ride-hailing platform. The results from these experiments consistently showed that setting a single goal (i.e., working hour or revenue) led to significantly higher performance than did setting double goals (i.e., both working hour and revenue). Structural equation modeling (SEM) results showed that regulatory foci moderated the goal setting effect on task-effective strategies, which in turn, affected performance. Specifically, drivers with a low promotion focus produced less effective strategies in the double goal-setting condition than did those in the single-goal condition, which in turn, led to performance decrements. Overall, we showed the differential effect of setting single vs. double goals on performance, its boundary condition (regulatory foci) as well as mediating mechanism (task-effective strategies). Our findings offer novel insights into the theoretical integration of goal setting and regulatory focus as two separate yet complementary approaches to motivation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".