This paper focuses on the attributed nature of the voice-based agents Alexa and Google Assistant in conversational contexts. Using Piaget’s equilibration theory, enhanced by Hubbard’s concept of personhood the paper considers how people categorize voice-based agents along a thing–person spectrum and whether this categorization reflects assimilation or accommodation of these technologies. The results of two studies (a hypothetical conversation with the agent via an online-survey, N = 1288, and a real conversation with the agent, N = 105) are indicating a modified classification towards personified things, which is reinforced by younger age and a higher quality of interaction. Implications, limitations, and further research regarding a more detailed classification of conversational agents are discussed.
We aim to investigate the nature of doubt regarding voice-based agents by referring to Piaget’s ontological object–subject classification “thing” and “person,” its associated equilibration processes, and influential factors of the situation, the user, and the agent. In two online surveys, we asked 853 and 435 participants, ranging from 17 to 65 years of age, to assess Alexa and the Google Assistant. We discovered that only some people viewed voice-based agents as mere things, whereas the majority classified them into personified things. However, their classification is fragile and depends basically on the imputation of subject-like attributes of agency and mind to the voice-based agents, increased by a dyadic using situation, previous regular interactions, a younger age, and an introverted personality of the user. We discuss these results in a broader context.
As commercial voice-based personal agents (VPAs) like Google Assistant increasingly penetrate people’s private social habitats, sometimes involving more than one user, these social situations are gaining importance for how people define the human-machine relationship (HMR). The paper contributes to the understanding of the situation’s impact on HMR on a theoretical and methodological level. First, Georg Simmel’s theory on the “Quantitative determination of the group” is applied to the HMR. A 2x1 between-subjects quasi-experiment (N = 100) contrasted the defined HMR in dyadic social situations (one human interacting with the Google Assistant) to the defined HMR in triadic social situations (two humans interacting with the Google Assistant). Second, the method of central tendency analysis was extended by the more robust and informative comparison of distributions and quantiles using the two-sample Kolmogorov–Smirnov test and the shift function. The results show that the triadic situation, compared to the dyadic one, led to a more confounded categorization of the VPA’s subjecthood in terms of self-similarity, while simultaneously strengthening a definition of the relationship that resembled those of a business relation through lowered intimacy and feedback, mainly grounded in a more realistic definition of the agent’s inability to understand affects. In contrast to Simmel’s inter-human theory the relationship’s dimension of reciprocity and commitment remained unaffected by the situation. The paper discusses how these effects and non-effects of the triad could be explained by drawing on Simmel as well as peculiarities of HMR and methodology. Finally, it offers preliminary hypotheses about the situation’s implications for the HMR and outlines avenues for future research. Free download until 09/30/2022 at: https://authors.elsevier.com/a/1fZVe3pfaRpm5N
Research on the social implications of technological developments is highly relevant. However, a broader comprehension of current innovations and their underlying theoretical frameworks is limited by their rapid evolution, as well as a plethora of different terms and definitions. The terminology used to describe current innovations varies significantly among disciplines, such as social sciences and computer sciences. This article contributes to systematic and cross-disciplinary research on current technological applications in everyday life by identifying the most relevant concepts (i.e., Ubiquitous Computing, Internet of Things, Smart Objects and Environments, Ambient Environments and Artificial Intelligence) and relating them to each other. Key questions, core aspects, similarities and differences are identified. Theoretically disentangling terminology results in four distinct analytical dimensions (connectivity, invisibility, awareness, and agency) that facilitate and address social implications. This article provides a basis for a deeper understanding, precise operationalisations, and an increased anticipation of impending developments.