DSH Perspective
This project interrogates systemic risks in the online survey supply chain by focusing on panel providers and intermediaries. We examine two misconduct vectors: (1) maximizing completions at the expense of user experience through opaque redirects and poor profiling; (2) manipulating screening to inflate acceptance rates, effectively distorting sample validity. These practices waste resources, corrupt datasets, and undermine trust. The project advances a more Honest, Resilient, and Efficient research ecosystem by producing hard evidence of how panel incentives degrade data quality.
Research Leads
DSH Team
Objectives
Map and categorize misconduct practices among panel/intermediary layers.
Quantify redirect intensity, screening manipulation, and payout friction.
Measure UX complexity and transparency vs. data reliability outcomes.
Benchmark risk profiles across providers, countries, and devices.
Context and Relevance
Online survey platforms are central to today’s research and decision-making. Yet their reliability is often compromised by opaque business models and distorted incentive structures. Providers frequently prioritize speed and completion rates over user experience or sample validity, creating systemic inefficiencies and undermining trust in survey data. By exposing these dynamics, the project contributes to building a more transparent and sustainable ResTech ecosystem aligned with digital sustainability principles.
Methodology
A comparative field study with N≈250 respondents across ≥8 countries. Each respondent completes 4 survey sessions with distinct providers (≈1,000 sessions total). For every session, respondents log the intermediary chain, UX frictions, screening dynamics, payout, and suspected manipulation using standardized instruments. Provider-level indices are computed to flag high-risk practices and identify patterns of misconduct.
Expected Results / Outputs
Typology of panel/intermediary misconduct practices in the online survey industry;
Quantitative evidence of how opaque incentive structures degrade data quality;
Risk benchmarking across providers, markets, and devices;
Recommendations for more transparent and sustainable incentive models.