1,720,986 research outputs found
Advertising a desired change: when process simulation fosters (vs. hinders) credibility and persuasion
No full textAds promising a desired change are ubiquitous in the marketplace. These ads typically include visuals of the starting and ending point of the promised change ("before/after" ads). "Progression" ads, which include intermediate steps in addition to starting and ending points, are much rarer in the marketplace. Across several consumer domains, the authors show an ad-type effect: progression ads foster spontaneous simulation of the process through which the change will happen, which makes these ads more credible and, in turn, more persuasive than before/after ads (Studies 1-3). The authors also show that impairing process simulation and high skepticism moderate the ad-type effect (Studies 4-5). Finally, they show effect reversals: if consumers focus on achieving the desired results quickly, and it is possible to do so, progression ads and the associated process simulation backfire in terms of credibility and persuasion (Studies 6-7). These findings contribute to existing research by identifying conditions under which progression ads have beneficial or disadvantageous effects. These findings have managerial implications because they run counter to current marketing practices, which favor before/after over progression ads
Lower artificial intelligence literacy predicts greater AI receptivity
No full textAs artificial intelligence (AI) transforms society, understanding factors that influence AI receptivity is increasingly important. The current research investigates which types of consumers have greater AI receptivity. Contrary to expectations revealed in four surveys, cross country data and six additional studies find that people with lower AI literacy are typically more receptive to AI. This lower literacy-greater receptivity link is not explained by differences in perceptions of AI’s capability, ethicality, or feared impact on humanity. Instead, this link occurs because people with lower AI literacy are more likely to perceive AI as magical and experience feelings of awe in the face of AI’s execution of tasks that seem to require uniquely human attributes. In line with this theorizing, the lower literacy-higher receptivity link is mediated by perceptions of AI as magical and is moderated among tasks not assumed to require distinctly human attributes. These findings suggest that companies may benefit from shifting their marketing efforts and product development towards consumers with lower AI literacy. Additionally, efforts to demystify AI may inadvertently reduce its appeal, indicating that maintaining an aura of magic around AI could be beneficial for adoption
Resistance to medical artificial intelligence is an attribute in a compensatory decision process: response to Pezzo and Beckstead (2020)
No full textIn Longoni et al. (2019), we examine how algorithm aversion influences utilization of healthcare delivered by human and
artificial intelligence providers. Pezzo and Beckstead’s (2020) commentary asks whether resistance to medical AI takes the
form of a noncompensatory decision strategy, in which a single attribute determines provider choice, or whether resistance to
medical AI is one of several attributes considered in a compensatory decision strategy. We clarify that our paper both claims
and finds that, all else equal, resistance to medical AI is one of several attributes (e.g., cost and performance) influencing
healthcare utilization decisions. In other words, resistance to medical AI is a consequential input to compensatory decisions
regarding healthcare utilization and provider choice decisions, not a noncompensatory decision strategy. People do not always
reject healthcare provided by AI, and our article makes no claim that they do
Algorithmic transference: people overgeneralize failures of AI in the government
No full textArtificial intelligence (AI) is pervading the government and transforming how public services are provided to consumers across policy areas spanning allocation of government benefits, law enforcement, risk monitoring, and the provision of services. Despite technological improvements, AI systems are fallible and may err. How do consumers respond when learning of AI failures? In 13 preregistered studies (N = 3,724) across a range of policy areas, the authors show that algorithmic failures are generalized more broadly than human failures. This effect is termed "algorithmic transference" as it is an inferential process that generalizes (i.e., transfers) information about one member of a group to another member of that same group. Rather than reflecting generalized algorithm aversion, algorithmic transference is rooted in social categorization: it stems from how people perceive a group of AI systems versus a group of humans. Because AI systems are perceived as more homogeneous than people, failure information about one AI algorithm is transferred to another algorithm to a greater extent than failure information about a person is transferred to another person. Capturing AI's impact on consumers and societies, these results show how the premature or mismanaged deployment of faulty AI technologies may undermine the very institutions that AI systems are meant to modernize
Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect
No full textRapid development and adoption of AI, machine learning, and natural language processing applications challenge managers and policy makers to harness these transformative technologies. In this context, the authors provide evidence of a novel "word-of-machine" effect, the phenomenon by which utilitarian/hedonic attribute trade-offs determine preference for, or resistance to, AI-based recommendations compared with traditional word of mouth, or human-based recommendations. The word-of-machine effect stems from a lay belief that AI recommenders are more competent than human recommenders in the utilitarian realm and less competent than human recommenders in the hedonic realm. As a consequence, importance or salience of utilitarian attributes determine preference for AI recommenders over human ones, and importance or salience of hedonic attributes determine resistance to AI recommenders over human ones (Studies 1-4). The word-of machine effect is robust to attribute complexity, number of options considered, and transaction costs. The word-of-machine effect reverses for utilitarian goals if a recommendation needs matching to a person's unique preferences (Study 5) and is eliminated in the case of human-AI hybrid decision making (i.e., augmented rather than artificial intelligence; Study 6). An intervention based on the consider-the-opposite protocol attenuates the word-of-machine effect (Studies 7a-b)
Resistance to Medical Artificial Intelligence
No full textArtificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A-3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for consumers' unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to consumers other than the self (study 8), or (c) that only supports, rather than replaces, a decision made by a human healthcare provider (study 9). These findings make contributions to the psychology of automation and medical decision making, and suggest interventions to increase consumer acceptance of AI in medicine
Proximity bias: Interactive effect of spatial distance and outcome valence on probability judgments
No full textAcross a range of decision contexts, we provide evidence of a novel proximity bias in probability judgments, whereby spatial distance and outcome valence systematically interact in determining probability judgments. Six hypothetical and incentive-compatible experiments (combined N = 4007) show that a positive outcome is estimated as more likely to occur when near than distant, whereas a negative outcome is estimated as less likely to occur when near than distant (studies 1-6). The proximity bias is explained by wishful thinking and thus perceptions of outcome desirability (study 3), and it does not manifest when an outcome is less relevant for the self, such as the case of outcomes with little consequence for the self (studies 4 and 5) or when estimating outcomes for others who are irrelevant to the self (study 6). Overall, the proximity bias we document deepens our understanding of the antecedents of probability judgments
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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