Examining the Impact of Robot Norm Violations on Participants’ Trust, Discomfort, Behaviour and Physiological Responses—A Mixed Method Approach
Bibliographic record
Abstract
As robots increasingly permeate diverse domains like healthcare, education, service industries and homes, accurately understanding humans’ responses to and behaviour towards robots is crucial. While many human-robot interaction (HRI) studies focus on either quantitative or qualitative approaches, we advocate a mixed-method approach. This study investigated robot norm violations by implementing a scenario where a mobile manipulator robot and a human, in-person, carry out a physical, competitive task. Sixty-two participants were recruited and randomly assigned to either an experimental or a control condition (balanced for age/gender). The scenario was a competitive scavenger hunt game where participants took turns with a robot. We investigated the robot behaviours’ effects on trust, discomfort, competence, enjoyment, participant behaviour and physiological changes. The mixed-method approach integrated physiological measurements, behavioural observations and qualitative responses, thus offering a comprehensive account of HRI dynamics in the context of norm violations. Questionnaire results reveal significant shifts in human perceptions and attitudes when social norms are violated by robots, compared to a norm-compliant control condition. Specifically, trust and enjoyment decrease, discomfort increases and the robot’s perceived competence is compromised. These findings are extended through additional analyses of participants’ physiological changes, behaviours and responses to open-ended questions. Behavioural observations indicated increased verbal engagement and emotional responses, while physiological data showed elevated stress levels in the experimental group. Our study highlights the advantage of a mixed-methods approach combining different qualitative and quantitative data, providing a more comprehensive picture of participants’ perceptions of a robot, and how they react and respond to robot norm violations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".