Human-Robot Interaction Interview Analysis from 41 University Campus Participants

41 participants were interviewed at the University of Texas at Austin in 2025. The dataset contains analyzed results from short interviews about imagined encounters with robots in high-traffic pedestrian spaces, conducted by researcher Emily Norman. The research employed a constructivist Grounded Theory approach to analyze discursive accounts of human expectations and reactions.

Use Cases

• Modeling user personas for public-space robots based on thematic analysis results.
• Analyzing agency dynamics in human-robot interaction from discursive interview accounts.
• Informing design considerations for robotic systems based on participant expectations, concerns, and desires.
• Studying spontaneous reactions and deeper reflections on robot functionality and behavior in simulated real-world environments.

Strengths

• Includes analysis-ready thematic results from 41 participants, providing a diverse cross-section.
• Contains the original semi-structured interview protocol and questionnaire, detailing the research methodology.
• Data was carefully curated and anonymized, with all personally identifiable information removed to protect privacy.

Limitations

• Row count and file formats are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset's temporal and geographic scope is limited to a single university campus setting.

Provenance

Source

Texas Data Repository Harvested Dataverse

Collection Method

Semi-structured interviews lasting 8-16 minutes, conducted in a densely populated pedestrian area on campus.

Time Range

2025

Freshness

Last updated 2025-10-15 06:46:51.

Geography

University of Texas at Austin campus, USA