User Perceptions and Stereotypic Responses to Gender and Age of Voice Assistants

Technologies such as voiced automation can aid older adults aging in place by assisting with basic home and health tasks in daily routines. However, currently available voice assistants have a common design - they are vastly represented as young and female. Prior work has shown that humans apply stereotypes to human-computer interactions similarly to human-human interactions. When these stereotypes are activated, users may lose trust or confidence in the device or stop using it all together. The purpose of this study was to investigate if users can detect age and gender cues of voiced automation and to understand the extent to which gender, age, and reliability elicit stereotypic responses which were assessed using history-based trust. A series of health-related voice automation scenarios presented users with voice assistants varying in gender, age, and reliability. Results showed differences in age and gender perceptions across participant age groups but no differences for overall trust. A three-way interaction showed that when voiced automation reliability was low, participants rated the young female voice assistant as significantly more trustworthy than all other voice assistants. This work contributes to our understanding of how anthropomorphic characteristics like age and gender in emerging technologies can elicit varied trust responses from younger and older adults

The audio/visual mismatch and the uncanny valley: an investigation using a mismatch in the human realism of facial and vocal aspects of stimuli

Indiana University-Purdue University Indianapolis (IUPUI)Empirical research on the uncanny valley has primarily been concerned with visual elements. The current study is intended to show how manipulating auditory variables of the stimuli affect participant’s ratings. The focus of research is to investigate whether an uncanny valley effect occurs when humans are exposed to stimuli that have an incongruity between auditory and visual aspects. Participants were exposed to sets of stimuli which are both congruent and incongruent in their levels of audio/visual humanness. Explicit measures were used to explore if a mismatch in the human realism of facial and vocal aspects produces an uncanny valley effect and attempt to explain a possible cause of this effect. Results indicate that an uncanny valley effect occurs when humans are exposed to stimuli that have an incongruity between auditory and visual aspects

Buy Now

How Amazon combined branding and relationship marketing with massive distribution infrastructure to become the ultimate service brand in the digital economy. Amazon is ubiquitous in our daily lives—we stream movies and television on Amazon Prime Video, converse with Alexa, receive messages on our smartphone about the progress of our latest orders. In Buy Now, Emily West examines Amazon's consumer-facing services to investigate how Amazon as a brand grew so quickly and inserted itself into so many aspects of our lives even as it faded into the background, becoming a sort of infrastructure that can be taken for granted. Amazon promotes the comfort and care of its customers (but not its workers) to become the ultimate service brand in the digital economy. West shows how Amazon has cultivated personalized, intimate relationships with consumers that normalize its outsized influence on our selves and our communities. She describes the brand's focus on speedy and seamless ecommerce delivery, represented in the materiality of the branded brown box; the positioning of its book retailing, media streaming, and smart speakers as services rather than sales; and the brand's image control strategies. West considers why pushback against Amazon's ubiquity and market power has come mainly from among Amazon's workers rather than its customers or competitors, arguing that Amazon's brand logic fragments consumers as a political bloc. West's innovative account, the first to examine Amazon from a critical media studies perspective, offers a cautionary cultural study of bigness in today's economy

Human emotions toward stimuli in the uncanny valley: laddering and index construction

Indiana University-Purdue University Indianapolis (IUPUI)Human-looking computer interfaces, including humanoid robots and animated humans, may elicit in their users eerie feelings. This effect, often called the uncanny valley, emphasizes our heightened ability to distinguish between the human and merely humanlike using both perceptual and cognitive approaches. Although reactions to uncanny characters are captured more accurately with emotional descriptors (e.g., eerie and creepy) than with cognitive descriptors (e.g., strange), and although previous studies suggest the psychological processes underlying the uncanny valley are more perceptual and emotional than cognitive, the deep roots of the concept of humanness imply the application of category boundaries and cognitive dissonance in distinguishing among robots, androids, and humans. First, laddering interviews (N = 30) revealed firm boundaries among participants’ concepts of animated, robotic, and human. Participants associated human traits like soul, imperfect, or intended exclusively with humans, and they simultaneously devalued the autonomous accomplishments of robots (e.g., simple task, limited ability, or controlled). Jerky movement and humanlike appearance were associated with robots, even though the presented robotic stimuli were humanlike. The facial expressions perceived in robots as improper were perceived in animated characters as mismatched. Second, association model testing indicated that the independent evaluation based on the developed indices is a viable quantitative technique for the laddering interview. Third, from the interviews several candidate items for the eeriness index were validated in a large representative survey (N = 1,311). The improved eeriness index is nearly orthogonal to perceived humanness (r = .04). The improved indices facilitate plotting relations among rated characters of varying human likeness, enhancing perspectives on humanlike robot design and animation creation

A meta-analysis of the uncanny valley's independent and dependent variables

The uncanny valley (UV) effect is a negative affective reaction to human-looking artificial entities. It hinders comfortable, trust-based interactions with android robots and virtual characters. Despite extensive research, a consensus has not formed on its theoretical basis or methodologies. We conducted a meta-analysis to assess operationalizations of human likeness (independent variable) and the UV effect (dependent variable). Of 468 studies, 72 met the inclusion criteria. These studies employed 10 different stimulus creation techniques, 39 affect measures, and 14 indirect measures. Based on 247 effect sizes, a three-level meta-analysis model revealed the UV effect had a large effect size, Hedges’ g = 1.01 [0.80, 1.22]. A mixed-effects meta-regression model with creation technique as the moderator variable revealed face distortion produced the largest effect size, g = 1.46 [0.69, 2.24], followed by distinct entities, g = 1.20 [1.02, 1.38], realism render, g = 0.99 [0.62, 1.36], and morphing, g = 0.94 [0.64, 1.24]. Affective indices producing the largest effects were threatening, likable, aesthetics, familiarity, and eeriness, and indirect measures were dislike frequency, categorization reaction time, like frequency, avoidance, and viewing duration. This meta-analysis—the first on the UV effect—provides a methodological foundation and design principles for future research

A Meta-analysis of the Uncanny Valley's Independent and Dependent Variables

The uncanny valley (UV) effect is a negative affective reaction to human-looking artificial entities. It hinders comfortable, trust-based interactions with android robots and virtual characters. Despite extensive research, a consensus has not formed on its theoretical basis or methodologies. We conducted a meta-analysis to assess operationalizations of human likeness (independent variable) and the UV effect (dependent variable). Of 468 studies, 72 met the inclusion criteria. These studies employed 10 different stimulus creation techniques, 39 affect measures, and 14 indirect measures. Based on 247 effect sizes, a three-level meta-analysis model revealed the UV effect had a large effect size, Hedges’ g = 1.01 [0.80, 1.22]. A mixed-effects meta-regression model with creation technique as the moderator variable revealed face distortion produced the largest effect size, g = 1.46 [0.69, 2.24], followed by distinct entities, g = 1.20 [1.02, 1.38], realism render, g = 0.99 [0.62, 1.36], and morphing, g = 0.94 [0.64, 1.24]. Affective indices producing the largest effects were threatening, likable, aesthetics, familiarity, and eeriness, and indirect measures were dislike frequency, categorization reaction time, like frequency, avoidance, and viewing duration. This meta-analysis—the first on the UV effect—provides a methodological foundation and design principles for future research

The persuasiveness of humanlike computer interfaces varies more through narrative characterization than through the uncanny valley

Indiana University-Purdue University Indianapolis (IUPUI)Just as physical appearance affects persuasion and compliance in human communication, it may also bias the processing of information from avatars, computer-animated characters, and other computer interfaces with faces. Although the most persuasive of these interfaces are often the most humanlike, they incur the greatest risk of falling into the uncanny valley, the loss of empathy associated with eerily human characters. The uncanny valley could delay the acceptance of humanlike interfaces in everyday roles. To determine the extent to which the uncanny valley affects persuasion, two experiments were conducted online with undergraduates from Indiana University. The first experiment (N = 426) presented an ethical dilemma followed by the advice of an authority figure. The authority was manipulated in three ways: depiction (recorded or animated), motion quality (smooth or jerky), and recommendation (disclose or refrain from disclosing sensitive information). Of these, only the recommendation changed opinion about the dilemma, even though the animated depiction was eerier than the human depiction. These results indicate that compliance with an authority persists even when using a realistic computer-animated double. The second experiment (N = 311) assigned one of two different dilemmas in professional ethics involving the fate of a humanlike character. In addition to the dilemma, there were three manipulations of the character’s human realism: depiction (animated human or humanoid robot), voice (recorded or synthesized), and motion quality (smooth or jerky). In one dilemma, decreasing depiction realism or increasing voice realism increased eeriness. In the other dilemma, increasing depiction realism decreased perceived competence. However, in both dilemmas realism had no significant effect on whether to punish the character. Instead, the willingness to punish was predicted in both dilemmas by narratively characterized trustworthiness. Together, the experiments demonstrate both direct and indirect effects of narratives on responses to humanlike interfaces. The effects of human realism are inconsistent across different interactions, and the effects of the uncanny valley may be suppressed through narrative characterization

Essays on the Design of Inclusive Learning in Massive Open Online Courses, and Implications for Educational Futures

This thesis examines the tensions and contradictions of Massive Open Online Courses (MOOCs) as a force for more inclusive tertiary education, particularly for adults without a college degree in the United States. Through a multimethodological research approach yielding three discrete papers, presented as chapters, this work seeks to augment and clarify the existing MOOCs literature across conceptual, quantitative, and qualitative domains. The first paper develops a conceptual framework, ‘hegemonic design bias,’ that describes the socio-technical development ecosystem in which MOOCs are embedded. This framework helps account for why MOOCs have yet to serve as a democratising force in education by highlighting the processes and constraints that bias MOOC production toward the already well-educated. The potential economic implications of these developments are also considered. The second paper provides insight into how underrepresented learners are engaging with entry-level MOOCs. The exploration of learning analytic data from an initial sample of more than 260,000 enrolees through cluster analysis and multinomial logistic regression indicates that students without a college degree are more likely to be high-performing learners compared to college-educated students in these courses. Additionally, students from approximated lower socioeconomic backgrounds are no less likely to be successful than students from approximated middle and higher socioeconomic backgrounds in these courses. The third paper provides insight into the opportunities and challenges producers face in building inclusive MOOCs through a qualitative analysis of six semi-structured interviews. The interviews unearthed diverse conceptions of inclusion among producers that reflect a sincere normative commitment to make inclusive MOOCs, though the conceptions were quite distinct and fragmented. Producers were intentional about utilising best-practice pedagogy, as well as innovative program design, to include diverse learners. Innovative technology partners helped create interactive, unique experiences, but this also led to challenges in harmonising the design process and required the considerable influence of intermediary actors. To conclude, I briefly consider the implications of these findings for research, practice, and policy, with particular attention to how the public and social sectors can incentivise improved design of MOOCs with the specific intent of helping adults without college degrees develop human capital in order to remain economically resilient amidst the disruptions of skills-biased technological change.Gates Cambridge Trus