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Social Robot Trust & Adoption Study

Mixed-methods research investigating trust factors and adoption barriers for social robots in university campus environments through user surveys and interviews

Year:2023-2024Type:publicationPublished in:OzCHI '25 (Accepted)

Project Overview

Challenge

Understanding what factors influence trust and adoption of social robots in diverse university campus environments with multiple stakeholder groups.

Solution

Designed iterative mixed-methods study combining public questionnaires, controlled interviews, and user observations to identify trust barriers and application opportunities for campus social robots.

Key Features

Pepper robot programming for campus information and navigation tasks
Three-stage iterative data collection with participants
17 comprehensive semi-structured interviews for deeper insights
Questionnaire design and implementation using Google Forms
Thematic analysis identifying transparency as critical trust factor
Framework extension of Hancock's Factors of Trust model
Campus-wide deployment and user observation studies
Identification of 61 unique social robot application concepts

Results & Impact

Successfully identified transparency (operational and operator) as key trust factor, developed extended theoretical framework, and generated practical design recommendations for campus social robot deployment. Research accepted to OzCHI '25.

99
Study Participants
17
Deep Interviews
2025
OzCHI Accepted

System Architecture

Data Processing

Natural language understanding and dialogue management

AI Engine

Robot control systems with speech recognition

User Interface

Physical robot interaction with voice commands

Innovation

Mixed-methods research identifying transparency as the key factor for building trust between users and social robots in university environments.

Performance

Three-stage study with 99 participants and 17 detailed interviews. Genarate 61 unique application concepts and novel trust framework

Impact & Results

Project Impact

Successfully identified transparency (operational and operator) as key trust factor, developed extended theoretical framework, and generated practical design recommendations for campus social robot deployment. Research accepted to OzCHI '25.

Key Achievements:

Large-scale field study with 99 participants
Identified key trust factors for Human–robot interaction (HRI)
Accepted for publication at OzCHI 2025
99
Participants
17
Interviews
OzCHI 2025
Publication

Future Impact

Insights from this study will guide the development of more trustworthy social robots in academic and public environments.

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