Context-Aware Recommendation Via Interactive Conversational Agents: A Case in Business Analytics; User Experiences with Voice Assistance: How Voice Assistance Effects User Interactions with Applications and Information Delivery Systems

Kimche, Livia, School of Engineering and Applied Science, University of Virginia
Seabrook, Bryn, EN-Engineering and Society, University of Virginia
Doryab, Afsaneh, EN-Eng Sys and Environment, University of Virginia

In an era of information overload and excessive screen time, it seems that there is an inability to quickly retrieve helpful analytics in order to make informed decisions. While technological advances such as keyword search, dashboards, customizable data reports, and notifications have made information access more flexible, the underlying assumption is that the user knows what to look for. However, this assumption may not hold in many situations. For example, identifying needed information and key metrics affecting a business in Human Resource Management Systems (HRMS) can prove to be difficult. Voice assistance and recommendation systems can help improve these issues by allowing users to efficiently reach key insights which are relevant to their needs and their context. The technical paper of this thesis will explore these concepts of applicating context aware recommendations to a conversational voice assistant. The STS research paper of this thesis will explore the question, "How does the sociotechnical relationship of voice assistance technology optimize user experience?" The research paper will use frameworks including technological determinism, the social construction of technology, and risk analysis to analyze how users experience voice assistance technology in society. Additionally, influential user adoption factors and user experience optimization will be addressed through out this research.

BS (Bachelor of Science)
Voice Assistance, User Experience, User Interaction, Adoption, Voice Assistance Technology, Smart Speakers
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