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Language bias, both positive and negative, is a well-documented phenomenon exhibited among human interlocutors. We examine whether this bias is exhibited toward virtual assistants, specifically, Apple's Siri and Google Assistant, with various accents. We conducted three studies with different stimuli and designs to investigate U.S. English speakers' attitudes toward Google's British, Indian, and American voices and Apple's Irish, Indian, South African, British, Australian, and American voices. Analysis reveals consistently lower fluency ratings for Irish, Indian, and South African voices (compared with American) but no consistent results of bias related to competence, warmth, or willingness to interact. Moreover, participants often misidentified voices' countries of origin but correctly identified them as artificial. We conclude that this overall lack of bias may be due to two possibilities: lack of humanlikeness of the voices and lack of availability of nonstandardized voices and voices from countries toward which those in the United States typically show bias. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) agents are increasingly being used as teammates, not just as tools, across many domains, and teammates’ moral behaviour can alter impressions of themselves and the team. How good, powerful, and active is an AI versus human team member engaging in an ethical or unethical behaviour? How good, powerful, and active is their team? To address these questions, we conduct four studies across three domains (chess, esports, and poetry composition) where participants rate their impressions of team members and teams presented in a scenario. In the scenario, a member of a hybrid team of 2 humans and 2 AIs is presented with an opportunity to cheat, and either does or does not. We manipulate which team member (AI vs human) is acting and the morality of that action (non-cheating vs cheating). Across the studies, results show that ethical behaviour significantly increases the goodness of the human more than the AI, and unethical behaviour significantly increases the power of the AI more than the human. However, there were no systematic human versus AI differences on team impressions. [ABSTRACT FROM AUTHOR]

Objective: This study manipulates the presence and reliability of AI recommendations for risky decisions to measure the effect on task performance, behavioral consequences of trust, and deviation from a probability matching collaborative decision-making model. Background: Although AI decision support improves performance, people tend to underutilize AI recommendations, particularly when outcomes are uncertain. As AI reliability increases, task performance improves, largely due to higher rates of compliance (following action recommendations) and reliance (following no-action recommendations). Methods: In a between-subject design, participants were assigned to a high reliability AI, low reliability AI, or a control condition. Participants decided whether to bet that their team would win in a series of basketball games tying compensation to performance. We evaluated task performance (in accuracy and signal detection terms) and the behavioral consequences of trust (via compliance and reliance). Results: AI recommendations improved task performance, had limited impact on risk-taking behavior, and were under-valued by participants. Accuracy, sensitivity (d'), and reliance increased in the high reliability AI condition, but there was no effect on response bias (c) or compliance. Participant behavior was only consistent with a probability matching model for compliance in the low reliability condition. Conclusion: In a pay-off structure that incentivized risk-taking, the primary value of the AI recommendations was in determining when to perform no action (i.e., pass on bets). Application: In risky contexts, designers need to consider whether action or no-action recommendations will be more influential to design appropriate interventions. [ABSTRACT FROM AUTHOR]

Affect control theory is a theory of interaction that takes into account cultural meanings. Affect control research has previously considered interaction with technology, but there remains a lack of theorizing about inclusion of technology within the theory. This paper lays a foundation for an affect control theory of technology by addressing key assumptions, presenting an empirical foundation using a survey of sentiments toward technology, and analyzing those sentiments. (Contains 2 tables.)

Interest in and ownership of smart home voice assistants like Amazon Alexa and Google Home devices have exponentially increased in recent years. Many people may purchase or be gifted such devices without knowing their potential for connecting with other home technology, listening to private conversations, sharing information with companies, and creating problems due to misunderstanding vocal commands or technological capabilities. Concerns and worries about these devices may be exacerbated over time or by a specific incident. To understand reactions to such situations, we conducted semi-structured in-depth interviews with 10 people who reported different types of worrying incidents with a range of smart home devices and their reactions to reduce that worry. Conducting a thematic coding analysis, we detail how each case study shows a person's worries about their smart home technology developed vis-à-vis the incident or over time, and their strategies to alleviate their worry. The two dominant reactions were restricted acceptance or discontinuance of the smart home technology, while three other interviews revealed nuanced reactions on the acceptance-rejection continuum. For each interviewee, we highlight their technology use, any major incidents, and their psychological processes leading up to their actions to reduce worry. This provides an in-depth look at worry around smart home technology products themselves, not their ability to perform, and how discontinuance, restricted acceptance, and other reactions reduce those worries. [ABSTRACT FROM AUTHOR]

Physical artifacts are not neutral but are increasingly recognized across the social sciences as important to structuring meaning and social interaction. Affect control theory shows promise as a framework for articulating and exploring the role of the material world in everyday life. In this study, we formalize, extend, and elaborate this line of research, instituting physical artifacts within affect control theory. We examine how physical artifacts function within affect control theory as modifiers of identities. We undertake a full-scale identity-modification study, collecting affective meaning data from 825 respondents on 58 identities, 52 physical artifacts, and 212 artifact-modified identities across a range of identities and artifact types. We empirically estimate how physical artifacts change perceptions of identities and illustrate the application of the new equations by deriving artifact-modified identities from a range of hypothetical scenarios. Using a transformation of the equations, we also simulate how people may use physical artifacts to create a desired impression when occupying different identities. Through establishing physical artifacts within affect control theory, this research raises new questions and opportunities for the theory and those interested in the design, use, and experience of physical artifacts. [ABSTRACT FROM AUTHOR]

What are the relationships between knowledge of, perceived benefits, adoption of, and use of smart home products? To explore this question, in our first two studies we focus on the general population's perceptions of benefits across many types of smart home products by creating a corpus of smart home product descriptions. Study 1 (n = 399) shows that previous product knowledge influences a range of perceived benefits. Study 2 (n = 242) demonstrates which benefits increase non-owners' likelihood of adopting these products. In study 3, we longitudinally survey eight residents in living laboratory houses equipped with 10 integrated smart home products. We find over a year access to the products increases perceptions of their benefits, but does not increase their actual use. Collectively, these studies contribute to an increased understanding of the relationship among benefits, use, and adoption of this emerging technology. [ABSTRACT FROM AUTHOR]

Objective: We survey Pokémon Go players regarding motivations/patterns of gameplay and sociability over time to understand human interaction in augmented game settings. We disentangle effects of solo versus social and past versus current gameplay through replication of the Pokémon Go Motive Scale. Methods: We use OLS regression to determine how motivation affects hours of gameplay, to measure changes in hours spent playing Pokémon Go, and to capture perceptions of engagement with the game over time. Results: We find seven motivations: exercise, fun, escapism, nostalgia, friendship maintenance, relationship initiation, and achievement. Initiating new relationships leads to increased social playing time and increased perceptions of Pokémon Go as both interesting and challenging. Conclusion: Intrinsic rewards hold the most staying power as a distinct motivation for gameplay. Implications go beyond Pokemon Go gaming to shed light on the differential impact of human motivation for social interaction in games that utilize augmented reality. [ABSTRACT FROM AUTHOR]