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Niklas Deierlein

dblp:239/8021 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Human-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
Usability and user experience research · 44% Human-robot interaction · 44% Ubiquitous computing and smart environments · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Usability and user experience research › user perception
social acceptability
0.412019
Understanding the Social Acceptability of Mobile Devices using the Stereotype Content Model · CHI 2019
Human-robot interaction › human behavior modeling › social perception
stereotype content model
0.412019
Understanding the Social Acceptability of Mobile Devices using the Stereotype Content Model · CHI 2019
Ubiquitous computing and smart environments › mobile computing
mobile devices
0.112019
Understanding the Social Acceptability of Mobile Devices using the Stereotype Content Model · CHI 2019

Methods — techniques the papers use, named apart from their topics

user study · 0.4stereotype content model · 0.4
YearPublicationVenuePosition
2019 Understanding the Social Acceptability of Mobile Devices using the Stereotype Content Model
abstract
Understanding social perception is important for designing mobile devices that are socially acceptable. Previous work not only investigated the social acceptability of mobile devices and interaction techniques but also provided tools to measure social acceptance. However, we lack a robust model that explains the underlying factors that make devices socially acceptable. In this paper, we consider mobile devices as social objects and investigate if the stereotype content model (SCM) can be applied to those devices. Through a study that assesses combinations of mobile devices and group stereotypes, we show that mobile devices have a systematic effect on the stereotypes' warmth and competence. Supported by a second study, which combined mobile devices without a specific stereotypical user, our result suggests that mobile devices are perceived stereotypically by themselves. Our combined results highlight mobile devices as social objects and the importance of considering stereotypes when assessing social acceptance of mobile devices.
Valentin Schwind, Niklas Deierlein, Romina Poguntke, Niels Henze
CHI2