Demo Data from SIGIR 2026 / reviewers in the wild / expert
Benjamin Ryder
dblp:188/9863
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 2018
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1
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 |
Ubiquitous computing and smart environments · 50% Interaction techniques and input · 50% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Energy systems and smart grids · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Ubiquitous computing and smart environments
feedback design |
0.3 | 1 | 2018 | The Impact of Abstract vs. Concrete Feedback Design on Behavior Insights from a Large Eco-Driving Field Experiment · CHI 2018 |
Energy systems and smart grids
transportation energy |
0.1 | 1 | 2018 | The Impact of Abstract vs. Concrete Feedback Design on Behavior Insights from a Large Eco-Driving Field Experiment · CHI 2018 |
Methods — techniques the papers use, named apart from their topics
field experiment · 0.7construal level theory · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | The Impact of Abstract vs. Concrete Feedback Design on Behavior Insights from a Large Eco-Driving Field ExperimentabstractAbout 17% of the worldwide CO2-emissions can be ascribed to road transportation. Using information systems (IS)-enabled feedback has shown to be very efficient in promoting a less fuel-consuming driving style. Today, in-car IS that provide feedback on driving behavior are in the midst of a fundamental change. Increasing digitalization of in-car IS enables virtually any kind of feedback. Still, we see a gap in the empirical evidence on how to leverage this potential, raising questions on future HCI-based feedback design. To address this knowledge gap, we designed an eco-driving feedback IS and, building upon construal level theory, hypothesize that abstract feedback is more effective in reducing fuel consumption than concrete feedback. Deployed in a large field experiment with 56 participants covering over 297,000km, we provide first empirical evidence that supports this hypothesis. Despite its limitations, this research may have general implications for the design of real-time feedback. André Dahlinger, Felix Wortmann, Benjamin Ryder, Bernhard Gahr |
CHI | 3 |
| 2017 | Preventing traffic accidents with in-vehicle decision support systems - The impact of accident hotspot warnings on driver behaviour
Benjamin Ryder, Bernhard Gahr, Philipp Egolf, André Dahlinger, Felix Wortmann |
Decis. Support Syst. | 1 |