Demo Data from SIGIR 2026 / reviewers in the wild / expert
Franziska Doris Wolf
dblp:278/3599
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Human-robot interaction · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction
robot acceptance |
0.6 | 1 | 2022 | You Got the Job! Understanding Hiring Decisions for Robots as Organizational Members · HRI 2022 |
Methods — techniques the papers use, named apart from their topics
technology acceptance model · 0.6social identity theory · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | You Got the Job! Understanding Hiring Decisions for Robots as Organizational MembersabstractAs social robots will likely be central to future human-robot interactions at work, we assess hiring decisions for social robots as a natural first step prior to their integration into organizations. With a basis in the technology acceptance model and social identity theory, this study focuses on differences between humanoid robotic, android robotic and human candidates. We first examine performance-based evaluations of the applicants by focusing on expectation disconfirmation. While for the human candidate, the interplay between expectations and experiences is decisive for the judgement, for social robots, the actual experience of the hiring situation dominates the decision. Besides the rational decision criteria, we further look into social-cue-based evaluations as social biases in hiring situations. Categorization as social ingroup leads to an absolute preference for the human candidate (i.e., ingroup favoritism) with no differences in preference for the robotic social outgroup (i.e., outgroup homogeneity effect). Lea Heitlinger, Ruth Stock-Homburg, Franziska Doris Wolf |
HRI | 3 |