(Georgia Institute of Technology, 2023-08-01)
Hyun, Na Kyong
Rapid and ongoing technological innovation is transforming the lives of ordinary
consumers. My dissertation examines how consumers’ perception of and relationships with
modern technologies (smart agents, AI, voice-based interfaces) influence persuasion, behavior,
and well-being. As firms become “smarter” than ever, utilizing vast customer information and
advanced technologies to improve their marketing efforts, my research aims to inform managerial decisions that generate economic and social value, while ensuring consumer welfare.
Chapter 1: Personality Perceptions of Consumer Smart Agents
The ongoing evolution of consumer smart agents into daily interaction partners is raising
important new questions about “social” perception and cognition in the context of consumer
technology. Building on research in social perception of both human and nonhuman entities, I investigate how consumers assign humanlike personality traits to smart agents. The goals of
Essay 1 are to construct a parsimonious, psychometrically valid instrument that captures
perceptions of smart agent personality and to demonstrate this instrument’s utility for addressing important, managerially-relevant questions regarding consumer-device interactions. Across a
series of studies, I develop a hierarchical model of smart agent personality that contains two
high-level factors (“friendly” and “reliable”) with seven underlying facets. I demonstrate the
reliability and validity of the measurement instrument with multiple methods, and I use follow up experiments to document unique and theory-compatible antecedents to each dimension. In a final study, I document how different agent “voices” impact downstream interaction variables through perceptions of agent friendliness and reliability. My findings suggest that consumers
perceive smart agent personalities in a stable and coherent manner, and that careful construction of these personalities is a means of differentiation, diversification, and targeting to specific
segments.
Chapter 2: Vocal Similarity, Trust, and Persuasion in Consumer-Recommender Interactions
I extend the principle of similarity-based attraction to the domain of the human voice, by
examining how similarity in voice (timbre) can influence consumer choice. Using machine
learning, I generate an objective measure of vocal similarity between an individual consumer and a recommender using mel-frequency cepstral coefficients (MFCCs) which capture vocal timbre. First, using data from 2,791 Kickstarter campaigns, I show that a spokesperson’s voice that is
closer to an average-voice (i.e., the average MFCC scores from a large sample of sampled
voices) results in higher persuasion, as measured by fundraised amount and campaign success –
a result driven by vocal similarity. These effects are attenuated when external signals of
campaign validity (staff endorsements) are present. Then, in five laboratory studies, I show that vocal similarity with a recommender (both human and simulated-human through AI) leads to
greater trust, and consequently a higher likelihood of accepting the recommendation. I also show
that objective and perceived voice similarity have similar results, with objective similarity
mapping on to perceived similarity. The methods and findings provide a deeper understanding of consumer and recommender interactions, including new tools for voice analytics.
Together, my essays inform understanding regarding consumer perception of modern
technologies, factors that drive persuasion and customer relationship formation, and
opportunities for future research in this emerging area.