A Taxonomy of Social Cues for Conversational Agents

By Jasper Feine, Ulrich Gnewuch, Stefan Morana, Alexander Maedche. Int. J. Hum. Comput. Stud., 2019

530 citations recorded by the source corpus.

Abstract Conversational agents (CAs) are software-based systems designed to interact with humans using natural language and have attracted considerable research interest in recent years. Following the Computers Are Social Actors paradigm, many studies have shown that humans react socially to CAs when they display social cues such as small talk, gender, age, gestures, or facial expressions. However, research on social cues for CAs is scattered across different fields, often using their specific terminology, which makes it challenging to identify, classify, and accumulate existing knowledge. To address this problem, we conducted a systematic literature review to identify an initial set of social cues of CAs from existing research. Building on classifications from interpersonal communication theory, we developed a taxonomy that classifies the identified social cues into four major categories (i.e., verbal, visual, auditory, invisible) and ten subcategories. Subsequently, we evaluated the mapping between the identified social cues and the categories using a card sorting approach in order to verify that the taxonomy is natural, simple, and parsimonious. Finally, we demonstrate the usefulness of the taxonomy by classifying a broader and more generic set of social cues of CAs from existing research and practice. Our main contribution is a comprehensive taxonomy of social cues for CAs. For researchers, the taxonomy helps to systematically classify research about social cues into one of the taxonomy's categories and corresponding subcategories. Therefore, it builds a bridge between different research fields and provides a starting point for interdisciplinary research and knowledge accumulation. For practitioners, the taxonomy provides a systematic overview of relevant categories of social cues in order to identify, implement, and test their effects in the design of a CA.

Subfields: Artificial Intelligence, Social Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/bae607617a09b099d48984289d73cd0f13b332f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gustavo Marcos-Matás
2. Martin Adam
3. Michael Wessel
4. Raquel Chocarro
5. Alexander Benlian
6. Mónica Cortiñas

In Social Psychology

1. Martin Adam
2. Michael Wessel
3. Alexander Benlian

In Human-Computer Interaction

1. Gustavo Marcos-Matás
2. Martin Adam
3. Michael Wessel
4. Raquel Chocarro
5. Alexander Benlian
6. Mónica Cortiñas

Citations in the corpus

• Teachers’ attitudes towards chatbots in education: a technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics (2021, methodology): “For that purpose, we use the computer-mediated communication (CMC) framework and the taxonomy of social attributes established by Feine et al. (2019) for conversational agents.”
• AI-based chatbots in customer service and their effects on user compliance (2020, background): “While some prior work exists that investigates verbal anthropomorphic design cues, such as self-disclosure, excuse, and thanking (Feine et al. 2019), due to limited capabilities of previous generations of CAs, these cues have often been rather static and insensitive to the user ’ s input.”