Participant Reputation System Guide

How the Reputation System Works

The MobLab Research reputation system tracks participant quality across three core dimensions, automatically building reputation scores based on their study performance:

Core Reputation Badges

Badge	What It Tracks	How It's Lost
Verified Human	No use of AI tools like ChatGPT	Flagged for AI usage
High Focus	Attention to detail and passing attention checks	Flagged for failing attention checks
Punctuality Pro	Reliability for live study sessions	No-show for reserved live studies

Overall Account Reputation

Based on badges and approval rates, each participant receives an overall reputation status:

• High (Outstanding): Priority access to new study invitations
• Medium (Reliable): Standard access to studies
• Low (Restricted): Limited access until reputation improves

Our invitation system prioritizes participants with high reputation scores, ensuring that your studies reach the most reliable and quality-focused participants first.

Your Role: Closing the Feedback Loop

The reputation system only works when researchers actively flag problematic behaviors. Your actions directly impact which participants get priority access to future studies across the entire platform.

Three Ways to Contribute

1. Reject with Proper Feedback

When you reject submissions, include specific reasons in your feedback. Our system automatically scans for keywords and applies appropriate flags:

• Mentioning "AI use," "ChatGPT," or "detection of AI" flags for AI usage
• Mentioning "failed attention check" or "attention check" flags for low focus

Learn more: Providing Feedback for Rejected Submissions

2. Add Memos When Awarding Payments

Even when approving submissions with bonus payments, you can flag concerns through payment memos. The system detects the same keywords in memos to track problematic behaviors while still compensating participants for valid work completed.

Learn more: How to Award a Bonus Payment

3. Manually Flag Submissions

For precise control, you can manually flag or unflag submissions directly from your Submissions Dashboard. This feature gives you the flexibility to correct automatic flags or mark specific behaviors that might not have been caught automatically.

Note: Manual flagging is expected to roll out by the end of February 2026.

Learn more: Flagging Submissions with Abnormal Behavior

Building Initial Reputation Scores

We've worked with researchers across the platform to establish initial reputation scores for both new and existing participants. This collaborative effort helps the system identify quality participants from the start, giving you immediate access to a pre-vetted participant pool.

Benefits for Your Research

By participating in the reputation system, you gain access to:

• Higher quality data: Priority invitations go to participants with proven track records
• Fewer rejections: Well-performing participants mean less time reviewing and rejecting submissions
• Better response rates: Reliable participants are more likely to complete studies properly
• Community protection: Flagging problematic participants protects other researchers from the same issues

Participant Perspective

Participants can view their own reputation status, badges, and approval rates in their dashboard. This transparency helps them understand what behaviors to maintain or improve. They know that maintaining high reputation scores gives them priority access to earning opportunities.

Learn more about the participant view: Managing Your Reputation (Participant Help)

Best Practices

• Flag consistently: Apply the same standards across all submissions
• Be specific in feedback: Use clear terminology so the system can properly categorize issues
• Flag all instances: Even minor violations should be flagged to maintain accurate reputation tracking
• Trust the system: The automated invitation priority helps ensure quality participants reach your studies first