Marketing and branding efforts have shifted from broadcast media, as in magazines or television, toward bi-directional/conversational media. Firm representatives are increasingly digital, and thus dynamic, autonomous and personalizable. Rooted in the shift in marketing practice, this dissertation seeks to identify and quantify effective approaches to the design and implementation of the entities that represent firms and brands in customer interactions, e.g., AI-enabled conversational agents, digital brand personalities. This thesis consists of three essays relating to the mediums in which digital entities exist (Social Media Pages, Messaging Applications, and Voice Based applications). In my first essay on this topic, I evaluate how Politeness (Brown & Levinson 1987), a theory used to describe human request behavior, can be adapted to Social Media posts to further garner off platform sales conversions. This is important as it shows that the language used in Social Media posts are not uniformly perceived and can be tailored for customers depending on their relationship with the focal firm. The second essay moves from posts on social media, to messaging platforms. More specifically, in the context of customer service, I evaluate how the humanness of a conversational agent, (i.e. the number of social cues present), influences customer service conversion outcomes, and customer price sensitivity. Our findings suggest that making an agent more humanlike can increase the rate of conversion for customers, however, customers also become more price sensitive in this particular “ultimatum game” like scenario. This shows that efforts to humanize conversational agents need to be carefully thought through and implemented to best support the context. In my final chapter, I explore the interactions between two key design choices for voice-based AI agents: i) disclosure of an agent’s autonomous nature, and ii) aesthetic personalization (implemented via voice cloning). Through use of a Behavioral Economics game, we evaluate these features impact on trust. Overall, we find that people prefer a cloned version of an A.I. voice compared to a default male voice and no message control. Disclosure, on its own, does not significantly impact trust. When examining the interaction of message medium and agent disclosure, we find that dynamic voice cloning, in tandem with disclosure, achieves the highest user trust levels.
University of Minnesota Ph.D. dissertation. May 2021. Major: Business Administration. Advisors: Gautam Ray, Gordon Burtch. 1 computer file (PDF); xiv, 164 pages.
Humanizing Digital Experiences: Three Essays on the Design of Digital Entities.
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