265 research outputs found
sj-docx-1-mrj-10.1177_00222437221110139 - Supplemental material for Algorithmic Transference: People Overgeneralize Failures of AI in the Government
No full textSupplemental material, sj-docx-1-mrj-10.1177_00222437221110139 for Algorithmic Transference: People Overgeneralize Failures of AI in the Government by Chiara Longoni, Luca Cian and Ellie J. Kyung in Journal of Marketing Research</p
Supplemental Material, jmr.18.0269-File003 - Advertising a Desired Change: When Process Simulation Fosters (vs. Hinders) Credibility and Persuasion
No full textSupplemental Material, jmr.18.0269-File003 for Advertising a Desired Change: When Process Simulation Fosters (vs. Hinders) Credibility and Persuasion by Luca Cian, Chiara Longoni and Aradhna Krishna in Journal of Marketing Research</p
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)
Art, industry, solidarity : the adventures of italian hatters from the 15th to the 20th century
No full textvalutazione degli scambi idrici tra i cordi d'acqua superficiale e falda per la gestione integrata delle risorse idriche: il caso del Fiume Olona
No full textAdvertising 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
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
"The SIDDHARTA chip: a CMOS multi-channel circuit for Silicon Drift Detectors Readout in Exotic Atoms Research"
No full text- …
