Understanding and Designing User Trust in Smart Personal Assistants

In the project "Understanding and Designing User Trust in Smart Personal Assistants" funded by the Swiss National Science Foundation (SNF), the team led by Prof. Dr. Leimeister investigates the impact of intelligent, conversational systems and their individual design features on users and trust. The project aims to integrate theories and methods from design research, behavioral science, and other disciplines to understand the use and effects of conversational AI.

Current AI technologies not only surpass humans in many tasks, such as statistical classification but also increasingly exhibit human-like forms and behaviors. The adaptive and learning nature of intelligent agents creates a contextualized yet unpredictable dialogue with the user. These developments are reflected in the growing prominence of human-machine interactions in our daily lives and their implementation in organizational use cases. While user trust is a well-explored topic in IS research, interactions with AI-based conversational agents pose new and complex questions.

The project seeks to amalgamate theories and methods from design research, behavioral science, and other disciplines to comprehend the deployment and impacts of conversational AI. Through experimental studies on text- and speech-based information systems, the research group examines technological and social factors related to user perception and behavior, as well as long-term changes in values and usage triggered by Conversational AI. Different use cases (customer service, healthcare) and design mechanisms (speech and text elements) are considered in this research.

Category

Swiss National Science Foundation

Project start

01.04.2020

Project end

01.04.2024

Status

ongoing

Area

Team Leimeister

In the project "Understanding and Designing User Trust in Smart Personal Assistants" funded by the Swiss National Science Foundation (SNF), the team led by Prof. Dr. Leimeister investigates the impact of intelligent, conversational systems and their individual design features on users and trust. The project aims to integrate theories and methods from design research, behavioral science, and other disciplines to understand the use and effects of conversational AI.

Current AI technologies not only surpass humans in many tasks, such as statistical classification but also increasingly exhibit human-like forms and behaviors. The adaptive and learning nature of intelligent agents creates a contextualized yet unpredictable dialogue with the user. These developments are reflected in the growing prominence of human-machine interactions in our daily lives and their implementation in organizational use cases. While user trust is a well-explored topic in IS research, interactions with AI-based conversational agents pose new and complex questions.

The project seeks to amalgamate theories and methods from design research, behavioral science, and other disciplines to comprehend the deployment and impacts of conversational AI. Through experimental studies on text- and speech-based information systems, the research group examines technological and social factors related to user perception and behavior, as well as long-term changes in values and usage triggered by Conversational AI. Different use cases (customer service, healthcare) and design mechanisms (speech and text elements) are considered in this research.

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