Chiara Longoni

Biographic Data

ID203182
NAMEChiara Longoni
GIVEN NAMESChiara
FAMILY NAMELongoni
SIGNATURELONGONI C
AFFILIATIONSBocconi University
ORCID0000-0002-4945-4957
VERIFIEDYes
TOTAL WORKS10
TOTAL CITATIONS83
AUTHOR COUNT9
EDITOR COUNT1
FIRST PUBLICATION YEAR2013
LATEST PUBLICATION YEAR2026
H-INDEX3

  • Consumers with Weaker Applications Are Less Receptive to Algorithmic Evaluation

    Open AccessQiao Liu, Gerald Häubl et al.ARTICLEJournal of Consumer Research2026

    Many organizations are adopting algorithms for evaluating various consumer applications (e.g., loans, insurance). This research explores how consumers react to this practice, with the goal of understanding what leads some consumers to react more positively or negatively to being evaluated by an algorithm than others. In the context of consumers applying for access to valued services, opportunities, or benefits, applicant strength (i.e., how stron…

  • AI-induced indifference: Unfair AI reduces prosociality

    Open AccessRaina Zexuan Zhang, Ellie J Kyung et al.ARTICLECognition2025

    The growing prevalence of artificial intelligence (AI) in our lives has brought the impact of AI-based decisions on human judgments to the forefront of academic scholarship and public debate. Despite growth in research on people's receptivity towards AI, little is known about how interacting with AI shapes subsequent interactions among people. We explore this question in the context of unfair decisions determined by AI versus humans and focus on …

  • The impact of generative artificial intelligence on socioeconomic inequalities and policy making

    Open AccessValerio Capraro, Austin Lentsch et al.ARTICLEPNAS Nexus2024

    Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may hel…

  • Addressing climate change with behavioral science: A global intervention tournament in 63 countries

    Open AccessMadalina Vlasceanu, Kimberly C Doell et al.ARTICLEScience Advances2024

    Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interve…

  • Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect

    Open AccessChiara Longoni, Luca CianARTICLEJournal of Marketing2022

    Rapid 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…

  • National identity predicts public health support during a global pandemic

    Open AccessJay Joseph Van Bavel, Aleksandra Cichocka et al.ARTICLENature Communications2022

    Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during …

  • Predicting attitudinal and behavioral responses to Covid-19 pandemic using machine learning

    Open AccessTamara Pavlovic, Tomislav Pavlović et al.ARTICLEPNAS Nexus2022

    At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multi-national data collected by the International Collaboration on the Social…

  • Understanding, explaining, and utilizing medical artificial intelligence

    Open AccessRomain Cadario, Chiara Longoni et al.ARTICLENature Human Behaviour2021Cited by: 10References: 42

    Medical artificial intelligence is cost-effective and scalable and often outperforms human providers, yet people are reluctant to use it. We show that resistance to the utilization of medical artificial intelligence is driven by both the subjective difficulty of understanding algorithms (the perception that they are a 'black box') and by an illusory subjective understanding of human medical decision-making. In five pre-registered experiments (1-3…

  • Resistance to Medical Artificial Intelligence

    Open AccessChiara Longoni, Andrea Bonezzi et al.ARTICLEJournal of Consumer Research2019Cited by: 62References: 57

    Artificial 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 …

  • A green paradox: Validating green choices has ironic effects on behavior, cognition, and perception

    Open AccessChiara Longoni, Peter M Gollwitzer et al.ARTICLEJournal of Experimental Social…2013Cited by: 11References: 45