1,720,969 research outputs found
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
Sharing with Friends versus Strangers: How Interpersonal Closeness Influences Word-of-Mouth Valence
No full textWe examine how interpersonal closeness (IC) – the perceived psychological proximity between a sender and a recipient—influences word-of-mouth (WOM) valence. We propose that high levels of IC tend to increase the negativity of WOM shared, whereas low levels of IC tend to increase the positivity of WOM shared. We suggest this effect stems from low versus high levels of IC triggering distinct psychological motives. Low IC activates the motive to selfenhance, and communicating positive information is typically more instrumental to this motive than communicating negative information. In contrast, high IC activates the motive to protect others, and communicating negative information is typically more instrumental to this motive than communicating positive information. Four experiments provide evidence for the basic effect and the underlying role of consumers’ motives to self-enhance and protect others via mediation and moderation. Implications for understanding of how WOM spreads across strongly versus weakly tied social networks are discussed
Positive with Strangers, Negative with Friends: How Interpersonal Closeness Affects Word-of-Mouth Valence through Self-Construal
No full textThree experiments show that the closer consumers feel to a message recipient, the greater the likelihood that they will share negative relative to positive word-of-mouth. We attribute this effect to high vs. low interpersonal closeness activating interdependent vs. independent self-construal and subsequently affecting information sharing
Do Others Influence What We Say? The Impact of Interpersonal Closeness on Word-of-Mouth Valence
No full textThree experiments show that the closer consumers feel to a message recipient, the greater the likelihood that they will share negative relative to positive word-of-mouth. We attribute this effect to high vs. low interpersonal closeness activating low vs. high construal level and subsequently affecting information sharing
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
On the Persuasiveness of Opinions Versus Advice: An Information Diagnosticity Perspective
No full textOn braggarts and gossips: a self-enhancement account of word-of-mouth generation and transmission
No full textPrevious research on word of mouth (WOM) has presented inconsistent evidence on whether consumers are more inclined to share positive or negative information about products and services. Some findings suggest that consumers are more inclined to engage in positive WOM, whereas others suggest that consumers are more inclined to engage in negative WOM. The present research offers a theoretical perspective that provides a means to resolve these seemingly contradictory findings. Specifically, the authors compare the generation of WOM (i.e., consumers sharing information about their own experiences) with the transmission of WOM (i.e., consumers passing on information about experiences they heard occurred to others). They suggest that a basic human motive to self-enhance leads consumers to generate positive WOM (i.e., share information about their own positive consumption experiences) but transmit negative WOM (i.e., pass on information they heard about others' negative consumption experiences). The authors present evidence for self-enhancement motives playing out in opposite ways for WOM generation versus WOM transmission across four experiments
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
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