Suchergebnisse

Recently emerging synthetic acoustically gender-ambiguous voices could contribute to dissolving the still prevailing genderism. Yet, are we indeed perceiving these voices as "unassignable"? Or are we trying to assimilate them into existing genders? To investigate the perceived ambiguity, we conducted an explorative 3 (male, female, ambiguous voice) × 3 (male, female, ambiguous topic) experiment. We found that, although participants perceived the gender-ambiguous voice as ambiguous, they used a profoundly wide range of the scale, indicating tendencies toward a gender. We uncovered a mild dissolve of gender roles. Neither the listener's gender nor the personal gender stereotypes impacted the perception. However, the perceived topic gender indicated the perceived voice gender, and younger people tended to perceive a more male-like gender.

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.

AbstractWhen intelligent voice-based assistants (VBAs) present news, they simultaneously act as interlocutors and intermediaries, enabling direct and mediated communication. Hence, this study discusses and investigates empirically how interlocutor and intermediary predictors affect an assessment that is relevant for both: trustworthiness. We conducted a secondary analysis using data from two online surveys in which participants (N = 1288) had seven quasi-interactions with either Alexa or Google Assistant and calculated hierarchical regression analyses. Results show that (1) interlocutor and intermediary predictors influence people's trustworthiness assessments when VBAs act as news presenters, and (2) that different trustworthiness dimensions are affected differently: The intermediary predictors (information credibility; company reputation) were more important for the cognition-based trustworthiness dimensions integrity and competence. In contrast, intermediary and interlocutor predictors (ontological classification; source attribution) were almost equally important for the affect-based trustworthiness dimension benevolence.