Experimental Data on Self-Perception and Task Allocation in Corporate Governance

Performance Prediction and Performance-Based Task Allocation [Dataset] contains experimental data on self-assessment and observer perceptions in performance-based decisions. The dataset was authored by Stefan T. Trautmann and harvested by heiDATA from Dataverse, with a last recorded update on 2026-06-07. It likely contains results from two experiments studying how performance predictions develop over time and influence task allocation.

Use Cases

• Modeling the divergence between self-perception and observer perception based on experimental results.
• Analyzing the impact of performance predictions on task allocation decisions.
• Studying the development of performance estimates over time as described in the experiments.

Strengths

• Data is based on two controlled experiments, providing a structured foundation for analysis.
• Last updated date of 2026 06 07 is recorded, indicating recent platform activity.
• Author and harvesting organization (heiDATA) are explicitly named, providing provenance.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and sample data are unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Trautmann, Stefan T.; heiDATA Harvested Dataverse

Collection Method

Data was collected from two experiments studying performance predictions and allocation.

Time Range

Experimental time range is not specified.

Freshness

Last updated 2026-06-07 07:10:08; freshness should be verified.

Geography

Geographic coverage is not specified.