Defense Date

Summer 6-7-2022

Degree Type

Dissertation

Specialization

Marketing

Department

Business Administration

Chair or Co-Chair

Dr. Mona Sinha

Committee Member or Co-Chair

Dr. Jennifer Hutchins

Committee Member

Dr. Saurabh Gupta

Reader

Dr. Patrick van Esch

Abstract

As the digital era continues to have a strong influence on how consumers effectively leverage technology, the prospect of introducing artificial intelligence, including smart speakers, into our homes and routines has become largely unavoidable (Bressgott, 2019; Davenport et al., 2020). Consumer use of smart speakers can provide both a competitive advantage for firms (though large amounts of valuable consumer data), as well convenience benefits for users. However, the availability of this data requires continued engagement with these devices in a deep, meaningful manner. This paper employs a mixed methods strategy to investigate the underlying reasons for how individual user, task, and technology characteristics influence deep customer engagement with smart speakers. While much research has been conducted concerning technology adoption and self-service technology adoption, in particular, this research seeks to add to current marketing and IS literature by examining the drivers of actual, continued, and deep engagement with smart speakers in the post-adoption phase. Currently, we see mixed findings between a willingness and resistance to engage with AI technology, many of which seem to be rooted in a) user characteristics such as personality, b) technology characteristics such as perceived anthropomorphism, and/or c) task characteristics such as willingness to delegate tasks to AI (Serenko, 2007; Swartz, 2003; Waytz et al., 2010a). Therefore, depth interviews in study one of this paper seek to examine how user, task, and technology characteristics that interact to influence or deter engagement with smart speakers. It also employs a metaphor analysis technique to identify moderating variables that may strengthen or weaken relationships between user, task, and technology characteristics and engagement. Findings from study one brought forth several user, task, and technology characteristics that were used in the development of a new empirical model. Study 2 tests this model through partial least squares structural equation modeling (PLS-SEM), subsequently contributing empirical evidence on drivers of engagement with smart speakers to the current body of literature (Wagner & Schramm-Klein, 2019).

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