Carey K. Morewedge
Boston University
Public economicsExperimental psychologyFeelingSocial perceptionConsumption (economics)Developmental psychologyAttributionBusinessEconometricsArtificial intelligencePsychologyHealth careActuarial scienceEconomicsCognitionSelfMicroeconomicsImpact biasCognitive psychologyCognitive biasPerceptionPsychological interventionAffective forecastingHappinessDebiasingEvent (probability theory)Game designSocial psychologySocial cognition
100Publications
27H-index
3,073Citations
Publications 89
Newest
#1Carey K. Morewedge (BU: Boston University)H-Index: 1
#1Carey K. Morewedge (BU: Boston University)H-Index: 27
Object ownership changes how people perceive objects and self through psychological ownership—the feeling that a thing is MINE. Psychological ownership usually tracks legal ownership, but the two can and do diverge. In this integrative review, I propose a dual-process model of psychological ownership. Antecedents of psychological ownership form self-object associations prompting an implicit inference of psychological ownership, which can then be accepted, corrected, or rejected by explicit judgm...
1 CitationsSource
#1Sarah Whitley (UGA: University of Georgia)H-Index: 1
#2Ximena Garcia-Rada (Harvard University)H-Index: 5
Last. Carey K. Morewedge (BU: Boston University)H-Index: 27
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Funeral rituals perform important social functions for families and communities, but little is known about the motives of people planning funerals. Using mixed methods, we examine funeral planning as end-of-life relational spending. We identify how relational motives drive and manifest in funeral planning, even when the primary recipient of goods and services is dead. Qualitative interviews with consumers who had planned pre-COVID funerals (N=15) reveal a caring orientation drives funeral decisi...
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#1Romain Cadario (EUR: Erasmus University Rotterdam)H-Index: 6
#2Chiara Longoni (BU: Boston University)H-Index: 3
Last. Carey K. Morewedge (BU: Boston University)H-Index: 27
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Medical artificial intelligence is cost-effective, scalable, and often outperforms human providers. One important barrier to its adoption is the perception that algorithms are a “black box”—people do not subjectively understand how algorithms make medical decisions, and we find this impairs their utilization. We argue a second barrier is that people also overestimate their objective understanding of medical decisions made by human healthcare providers. In five pre-registered experiments with con...
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#1Haewon Yoon (IU: Indiana University)H-Index: 5
#2Irene Scopelliti (City University London)H-Index: 8
Last. Carey K. Morewedge (BU: Boston University)H-Index: 27
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Abstract Observational learning can debias judgment and decision making. One-shot observational learning-based training interventions (akin to “hot seating”) can produce reductions in cognitive biases in the laboratory (i.e., anchoring, representativeness, and social projection), and successfully teach a decision rule that increases advice taking in a weight on advice paradigm (i.e., the averaging principle). These interventions improve judgment, rule learning, and advice taking more than practi...
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#1Carey K. MorewedgeH-Index: 27
#2Ashwani MongaH-Index: 10
Last. Deborah A. SmallH-Index: 30
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Technological innovations are creating new products, services, and markets that satisfy enduring consumer needs. These technological innovations create value for consumers and firms in many ways, b...
6 CitationsSource
#1Eleanor Putnam-Farr (Rice University)
#2Carey K. Morewedge (BU: Boston University)H-Index: 27
We examine which social comparisons most affect happiness with pay that is unequally distributed (e.g., salaries and bonuses). We find that ensemble representation –attention to statistical properties of distributions such as their range and mean––makes the proximal extreme (i.e., the maximum or minimum) and distribution mean salient social comparison standards. Happiness with a salary or bonus is more affected by how it compares to the distribution mean and proximal extreme than by exemplar-bas...
#1Chiara LongoniH-Index: 3
#2Andrea BonezziH-Index: 8
Last. Carey K. MorewedgeH-Index: 27
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#1Chiara Longoni (BU: Boston University)H-Index: 3
#2Andrea Bonezzi (NYU: New York University)H-Index: 8
Last. Carey K. Morewedge (BU: Boston University)H-Index: 27
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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 derive negative utility if a provider is automated...
70 CitationsSource
#1Carey K. Morewedge (BU: Boston University)H-Index: 27
#2Meng Zhu (Johns Hopkins University)H-Index: 9
Last. Eva C. Buechel (SC: University of Southern California)H-Index: 8
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5 CitationsSource