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Experiencing Voice-Activated Artificial Intelligence Assistants in the Home: A Phenomenological Approach
Abstract
Voice-controlled artificial intelligence (AI) assistants, such as Amazon’s Alexa or Google’s Assistant, serve as the gateway to the Internet of Things and connected home, executing the commands of its users, providing information, entertainment, utility, and convenience while enabling consumers to bypass the advertising they would typically see on a screen. This “screen-less” communication presents significant challenges for brands used to “pushing” messages to audiences in exchange for the content they seek. It also raises questions about data collection, usage, and privacy. Brands need to understand how and why audiences engage with AI assistants, as well as the risks with these devices, in order to determine how to be relevant in a voice-powered world. Because there’s little published research, a phenomenological approach was used to explore the lived meaning and shared experience of having an AI assistant in the home. Three overarching types of experiences with Alexa were revealed: removing friction, enabling personalization, and extending self and enriching life. These experiences encapsulated two types of explicit and implicit goals satisfied through interaction with Alexa, those that related to “Helping do,” focused on functional elements or tasks that Alexa performed, and those related to “Helping become,” encapsulating the transformative results of experiences with Alexa enabling users to become better versions of themselves. This is the first qualitative study to explore the meaning of interacting with AI assistants, and establishes a much-needed foundation of consumer understanding, rooted in the words and perspectives of the audience themselves, on which to build future research.
Subject Area
Marketing|Communication|Artificial intelligence
Recommended Citation
Jones, Valerie K, "Experiencing Voice-Activated Artificial Intelligence Assistants in the Home: A Phenomenological Approach" (2019). ETD collection for University of Nebraska-Lincoln. AAI27667440.
https://digitalcommons.unl.edu/dissertations/AAI27667440