Computers in Human Behavior最新文献

Perceptions of algorithms as opaque, commonly referred to as the black box problem, can make people reluctant to accept a recommendation from an algorithm rather than a human. Interventions that enhance people's subjective understanding of algorithms have been shown to reduce this aversion. However, across four preregistered studies (N = 960), we found that in the online shopping context, after explaining the algorithm recommendation process (versus human recommendation), users felt dehumanized and thus averse to algorithms (Study 1). This effect persisted, regardless of the type of algorithm (i.e., conventional algorithms or large language models; Study 2) or recommended product (i.e., search or experience products; Study 3). Notably, considering large language models (versus conventional algorithms) as the recommendation agent (Study 2) and framing algorithm recommendation as consumer-serving (versus website-serving; Study 4) mitigated algorithm aversion caused by meta-dehumanization. Our findings contribute to ongoing discussions on algorithm transparency, enrich the literature on human–algorithm interaction, and provide practical insights for encouraging algorithm adoption.

This article addresses the effects of biased search engine autosuggestions on hiring discrimination against females and immigrants. In two pre-registered experiments (N₁ = 266, N₂ = 263), we exposed the participants to biased autosuggestions against these two groups in certain occupations (female lapidaries in Study 1 and immigrant rideshare drivers in Study 2) and measured the hiring preference. We found the biased autosuggestions affected the hiring preference, contingent on stereotypical beliefs of the respective groups: When the group was perceived less warm (e.g., females less warm than males), the biased autosuggestions increased users’ hiring discrimination against the group. In contrast, when the group was perceived warmer (e.g., immigrants warmer than non-immigrant citizens), the biased autosuggestions triggered a reactance response, reducing their hiring discrimination.

Prior research has shown that many individuals exhibit an aversion to algorithms and are resistant to the use of artificial intelligence (AI) in healthcare. In the present research, we show that an intervention that increases the salience of bias in decision making—either in general or specifically with respect to gender or age—makes individuals relatively more receptive to medical AI. This increased receptiveness to AI occurs because bias is perceived to be a fundamentally human shortcoming. As such, when the prospect of bias is made salient, perceptions of AI integrity—defined as the perceived fairness and trustworthiness of an AI agent relative to a human counterpart—are enhanced.

Feeling that a non-human object has emotions (hereinafter referred to as emotional state attribution) is generally known as animacy perception. Previous studies have considered appearance and motion separately as factors that evoke emotional state attribution. Then, if both the degree of human likeness in shape and the presence or absence of motion are considered simultaneously, is there a possibility of strong emotional state attribution even for objects that are not human-like in terms of shape? In this study, we experimentally investigated the influence of human likeness in shape and movements evoking social relations on emotional state attribution, including their interaction, using three types of objects (human figure, shimeji mushroom, and match) that were assumed to differ from humans in terms of shape. We conducted three experiments. In Experiment 1, although the human figure was rated as more human-like than the shimeji mushroom in terms of shape, emotions were attributed more strongly to the shimeji mushroom than to the human figure when accompanied by movements that evoked social relationships. In Experiment 2, a follow-up to Experiment 1 was conducted by eliminating the influence of three-dimensionality, and similar results to Experiment 1 were obtained. In Experiment 3, the influence of face parts was examined, and emotions were attributed more strongly when motion was given to an object without face parts. The results suggest that people may attribute emotions more strongly to faceless objects that resemble humans only to a certain extent in shape when they show movements that evoke social relations.

Mothers play an important role in setting the overall media culture within the home. Research suggests that children model parental behavior in many ways, including in media use habits. The current study explores profiles or patterns of maternal media use and associations between these patterns and parenting behaviors surrounding media use and parent and child problematic media use two years later. In our sample of 268 mothers of young children in a large Mountain West city, latent profile analysis was utilized, and four distinct profiles of media users were identified. Results suggest that mothers in a low media use profile set limits around their child's media use and report low levels of both technoference and parent problematic media use. Conversely, mothers in a higher social use profile exhibit high levels of parent and child problematic media use compared to other profiles. We encourage mothers to be mindful users of media, remembering that their media use behaviors can impact the overall media use climate in the home.

High levels of digital screen time are associated with heightened impulsivity, though the assumption that this relationship is causal has seldom been rigorously examined. Moreover, the nature of this association may vary across life stages. Adults in established adulthood (ages 30–45) often face increased career and family commitments, which may impact leisure activities and the relationship between screen time and impulsivity. This study examined the association between digital screen time, video game play, and facets of impulsivity (i.e., perseverance, premeditation, and urgency) in adults in established adulthood using a multi-method approach, including a genetically-informed co-twin design. By comparing within-twin pair differences in screen time and impulsivity, the co-twin approach controls for genetic and environmental confounding, providing a quasi-experimental design. The sample included 1289 participants (M_age = 33.46, %Female = 52.75%, N_twins = 704) from the Colorado Adoption/Twin Study of Lifespan behavioral development and cognitive aging (CATSLife). After controlling for adolescent impulsivity, phenotypic analysis found that digital screen time was positively associated with higher levels of urgency and lack of perseverance. Co-twin control analysis showed that the association between digital screen time and urgency persisted within twin pairs, suggesting that shared familial and genetic factors may not fully explain the relationship. Video game play did not show consistent associations with impulsivity. These findings suggest that the immediacy and rapid succession of stimuli in digital screen engagement could be associated with impulsive tendencies in established adulthood, independent of video game play. The study underscores the need for nuanced examination to understand the interplay between digital engagement and impulsivity in this unique life stage.

With the proliferation of social media, researchers have begun exploring novel forms of literacy. One emerging concept is life aesthetic literacy (LAL), or the capacity to interpret and assign meaning to artistic and creative content perceptively. This study explores the impact of LAL on social media platforms. To do so, it draws on probability-proportional-to-size sampling in the Taiwan Communication Survey to investigate 1,776 social media–savvy adults and adopts structural equation modeling, path analysis, latent class analysis, and multi-group analysis to validate the proposed models. The findings show that increased exposure to and involvement with artistic material on social media platforms positively influence LAL. Furthermore, enhanced LAL extends to improved perceived self-expansion and self-concept clarity. These findings provide empirical evidence that social media use has a multifaceted impact on contemporary literacy and self-perception. The findings provide practical guidelines for platforms to cultivate LAL through tailored content and engagement strategies.

Museum exhibitions, both temporary and permanent, form an essential link between a society and its cultural, historical, and artistic heritage sites. Curating artifacts and thematic displays in museum exhibitions can promote dialogue, foster cultural appreciation, and contribute to heritage preservation. The traditional way of holding museum exhibitions, heavily reliant on the expertise of designers and curatorial staff, makes them a labor-intensive process, from conceptualization to visitor engagement analysis. This review systematically compiles and examines how the application of digital transformation technologies (DTTs) has revolutionized museum exhibitions and augmented their future potential. DTTs such as artificial intelligence, immersive technologies, additive manufacturing, the Internet of Things, and cloud computing can help create engaging designs, improve accessibility and inclusivity, enhance educational potential, and allow for sophisticated visitor experience data collection and analyses, improving exhibit management. However, despite multiple specialized studies on DTTs and their roles in museum exhibitions, the connections between technology and application scenarios remain underexplored. By addressing this gap, this study is expected to inform and inspire practitioners in the museum and heritage sectors and present new research avenues for scholars.

In this AI-focused era, researchers are delving into AI applications in healthcare, with ChatGPT being a primary focus. This Greek study involved 182 doctors from various regions, utilizing a custom web application connected to ChatGPT 4.0. Doctors from diverse departments and experience levels engaged with ChatGPT, which provided tailored responses. Over a month, data was collected using a form with a 1-to-5 rating scale. The results showed varying satisfaction levels across four criteria: clarity, response time, accuracy, and overall satisfaction. ChatGPT's response speed received high ratings (3.85/5.0), whereas clarity of information was moderately rated (3.43/5.0). A significant observation was the correlation between a doctor's experience and their satisfaction with ChatGPT. More experienced doctors (over 21 years) reported lower satisfaction (2.80–3.74/5.0) compared to their less experienced counterparts (3.43–4.20/5.0). At the medical field level, Internal Medicine showed higher satisfaction in evaluation criteria (ranging from 3.56 to 3.88), compared to other fields, while Psychiatry scored higher overall, with ratings from 3.63 to 5.00. The study also compared two departments: Urology and Internal Medicine, with the latter being more satisfied with the accuracy, and clarity of provided information, response time, and overall compared to Urology. These findings illuminate the specific needs of the health sector and highlight both the potential and areas for improvement in ChatGPT's provision of specialized medical information. Despite current limitations, ChatGPT, in its present version, offers a valuable resource to the medical community, signaling further advancements and potential integration into healthcare practices.

Gig platforms increasingly harness AI algorithms to manage workers, offering notable efficiency and scalability benefits. However, the rise of worker resistance, such as manipulating algorithms with fake data, poses challenges to these benefits. These algorithms are often perceived as ''black boxes'', leading to issues around transparency. This research thus explores the impact of algorithmic transparency on worker resistance. Using a longitudinal design, we uncovered a paradox: Initially, greater transparency correlates with enhanced fairness perception and reduced resistance. However, beyond a certain threshold, further transparency starts to backfire, leading to decreased fairness perception and amplified resistance. This paradox challenges the prevailing notion that more transparency always leads to positive outcomes. Moreover, we examined the role of human managers, showing that their empathetic support and caring can mitigate worker resistance when transparency fails to foster fairness. This highlights the power of human touch in the algorithm-driven workplace. Overall, these insights suggest a hybrid management model, wherein the cold efficiency of algorithmic managers is complemented by the warm empathy of human managers, offering a blueprint for more productive and harmonious human-machine interactions in the gig economy.