Evidence map›Paper›PMID 42388213›Full record

ArticleFrontiers in robotics and AI2026

Politeness cannot make up for robots' errors.

Shikhar Kumar, Eliran Itzhak, Noam Tractinsky, Galit Nimrod, Vardit Sarne-Fleischmann, Yael Edan

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Shikhar KumarDepartment of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
Eliran ItzhakDepartment of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
Noam TractinskyDepartment of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
Galit NimrodDepartment of Communication Studies, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
Vardit Sarne-FleischmannDepartment of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be'er Sheva, Israel.
Yael EdanDepartment of Industrial Engineering and Management, Ben-Gurion University of the Negev, Be'er Sheva, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We investigated the impact of robot politeness and error-prone behavior on user perceptions through two user studies involving non-humanoid robots. Politeness was operationalized at two levels based on Lakoff's, (1973) politeness rules-one condition implemented all three of Lakoff's rules, demonstrating the highest level of politeness, while the other omitted them, resulting in a behavior that was strict but not impolite. The correctness was manipulated by comparing an error-free robot behavior to a behavior that included intentional errors. The studies were conducted using two different tasks with two robot types-a mobile robot and a manipulator robot-and involved 59 young adult participants (ages 24-28) with engineering backgrounds. Participants consistently rated the correct and polite robots most favorably. However, politeness did not offset the negative effects of erroneous behavior. In both studies, the robot that was correct but strict was rated more positively than the one that was polite yet made mistakes. These findings suggest that, at least in utilitarian task settings involving technically proficient users, politeness alone cannot compensate for performance failures. Moreover, a polite robot that makes errors may even frustrate users more than a straightforward but accurate one. The findings also emphasize the importance of evaluating HRI performance across different robot types and tasks, as these factors significantly shape user perceptions.

Indexed as

assistive robothuman-robot interaction (HRI)politenessrobot errorstypes of robots

Identifiers

PMID42388213
PMCPMC13318723

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.