Research News: Self-Set Goals Significantly Improve Gig-Worker Performance — New Field Experiment by Professor Xing Hu

Hong Kong, 2025 —
Professor Xing Hu of the HKU Business School has released new research providing rare field-experimental evidence on how self-set goals influence performance in the rapidly expanding gig-economy labor market. Conducted in collaboration with a major Asian food-delivery platform, this study offers one of the most rigorous examinations to date of how internal motivational mechanisms affect worker behavior when traditional monitoring is limited.

The research addresses a central question in behavioral operations and platform management:
Can voluntary, non-binding self-goals meaningfully shape worker effort in decentralized gig-platforms?

To answer this, Professor Hu and her team partnered with a food-delivery platform to run a large-scale, real-world field experiment. Thousands of riders were randomly encouraged to set their own performance goals, either in the form of order-quantity targets or revenue-based targets, while a control group received no prompts.

Key Findings

1. Self-goal setting significantly increases overall effort and performance.
   Workers assigned to the self-goal setting conditions completed substantially more orders and were more active on the platform.
2. Order-quantity goals outperform revenue-based goals.
   While both forms increased effort, workers were far more likely to attain quantity goals.
   By contrast, revenue-goals tended to be set too ambitiously, causing lower attainment despite higher motivation.
3. Worker psychology plays a decisive role.
   Many gig workers misjudge revenue variability, leading them to set unrealistic revenue targets.
   Order-based goals are more concrete, predictable, and cognitively easier to plan around.
4. Goal-setting can be cost-effective for platforms.
   Because goals are voluntary and non-binding, platforms do not need monetary incentives to activate the effect—goal prompts alone influence behavior.

Implications for Gig-Platform Management

Professor Hu’s findings provide actionable, evidence-based recommendations:

• Platforms should encourage order-based self-goals to improve both effort and goal attainment.
• Non-monetary psychological interventions can rival or exceed the effects of financial incentives.
• Behavioral nudges can help platforms optimize workforce engagement in high-variability environments.

Academic Impact

This study contributes to multiple literature streams:

• Behavioral Operations — revealing how internal motivation interacts with decentralized work design
• Platform Economy — offering insights for labor allocation and operational efficiency
• Dynamic Decision-Making — showing how self-goals alter temporal effort patterns
• Labor and Gig-Economy Research — addressing the challenge of effort monitoring without traditional managerial oversight

The paper has been highlighted by peers in Operations Management as one of the most promising applications of behavioral theory in large-scale field settings.

Next Steps

Building on these findings, Professor Hu and her collaborators are designing follow-up experiments to examine:

• Whether peer-goal transparency amplifies motivation
• How AI-recommendations or platform-personalized goals influence worker behavior
• Cross-platform comparisons across ride-hailing, logistics, and micro-freelancing markets
• Long-term retention, fatigue, and sustainability of self-goal mechanisms

The research reflects Professor Hu’s broader agenda: leveraging data-driven and behavioral insights to improve operations and create more efficient, equitable digital labor systems.