In June 2023, the US Supreme Court struck down race-based affirmative action in university admissions—a decision largely due to allegations that elite universities had engaged in racial discrimination against Asian American applicants. Central to both sides of the affirmative action debate was the recognition that stereotypes of Asian Americans as “model minorities” in the classroom played a role in college admissions. While much attention has focused on Asian Americans in education, far less research considers whether their later careers mirror their early success. Do Asian Americans’ educational achievements translate to success in the labor market? To answer this, we examine how Asian Americans navigate the transition from school to the workplace. Beginning with a review of the research on Asian Americans’ strategic adaptation in educational contexts, we then shift the discussion to how Asian Americans’ adaptive strategies extend into the labor market. Rather than being guided by personal interests and aptitudes, many Asian Americans pursue paths they believe will offer greater social mobility in the face of societal and organizational bias. In spite of their strategic adaptation, the advantage that Asian Americans experience in education often disappears and, in some cases, reverses when the model minority goes to work.

Economic inequality has widened considerably in the United States and globally, raising pressing questions about the mechanisms that reproduce social stratification. One prominent manifestation of this inequality is the "class ceiling" in organizations: individuals from lower social classes earn less and advance more slowly than their higher-class counterparts, even with comparable credentials. We identify a crucial behavioral pathway contributing to this class ceiling: a social-class gap in the propensity to negotiate. We propose that lower-class individuals are less likely to initiate negotiations than their higher-class counterparts, a pattern that can compound economic disadvantage over time. Across four studies (total N = 10,211)-including a nationally representative sample of employees (Study 1), a sample of business school students (Study 2), a field study of an online labor market (Study 3), and a survey study of workplace negotiations and career outcomes (Study 4)-we find consistent evidence for this gap. This gap is partially explained by lower-class individuals' reduced sense of power and heightened concerns about social backlash. Additionally, Study 5 (N = 1,133) finds that such concerns may be justified: an experiment with human resources (HR) professionals shows that identical negotiation requests elicit stronger social backlash when initiated by lower-class (vs. higher-class) individuals. Together, these findings reveal a pernicious double bind for lower-class individuals: remaining silent perpetuates economic disadvantage, yet initiating negotiation exposes them to greater social penalties. Addressing this double bind is critical for ensuring that negotiation serves as a vehicle for upward mobility rather than a gatekeeper of privilege.

Despite the growing use of generative artificial intelligence (GenAI) in entrepreneurship, research on its impact remains fragmented. To address this limitation, we provide an integrative review of how GenAI influences entrepreneurs at each stage of the entrepreneurial process: (1) opportunity recognition and ideation, (2) opportunity evaluation and commitment, (3) resource assembly and mobilization, and (4) venture launch and growth. Based on our review, we propose the Empowerment–Entrapment Framework to understand how GenAI can both empower and entrap entrepreneurs, highlighting GenAI’s role as a double-edged sword at each stage of the entrepreneurial process. For example, GenAI may improve venture idea quality but introduce hallucinations and training data biases; boost entrepreneurial self-efficacy but heighten entrepreneurial overconfidence; increase functional breadth but decrease relational embeddedness; and boost productivity but fuel “workslop” and erode critical thinking, learning, and memory. Moreover, we identify core features of GenAI that underlie these empowering and entrapping effects. We also explore boundary conditions (e.g., entrepreneurs’ metacognition, domain expertise, and entrepreneurial experience) that shape the magnitude of these effects. Beyond these theoretical contributions, our review and the Empowerment–Entrapment Framework offer practical implications for entrepreneurs seeking to use GenAI strategically throughout the entrepreneurial process while managing its risks.

Political leadership positions are dominated by men. Arguably the most visible of a society’s positions of power, these positions may be important symbols of women’s ability to lead. If so, levels of female political representation within a society should shape its stereotypes of women’s suitability for political leadership. This project will test this possibility by conducting a secondary analysis of a dataset that measured the stereotypes of women, men and political leaders in 44 countries covering six continents (Nsample = 23,256). Specifically, we will test whether, as a country’s representation of women in political leadership increases, there is a greater increase in the congruence of that country’s stereotypes of political leaders and women than in the congruence of that country’s stereotypes of political leaders and men. We will also conduct exploratory analyses to identity whether other socio-ecological and cultural variables correlate with cross-national heterogeneity in the content and congruence of these stereotypes. This project will shed light on the way in which macro-level variables can shape micro-level gendered leadership beliefs.

Women are greatly underrepresented in positions of political leadership around the world. In seeking to explain this underrepresentation, some researchers have pointed to people’s tendencies to stereotype leaders as more similar to men than women as these tendencies can support the belief that women are unsuited to leadership. This project aimed to test whether a subtle linguistic intervention was able to ameliorate this gender bias in political leadership stereotypes across different languages and national contexts (country N = 42, sample N = 22,995). Specifically, this project examined whether gender fair language (e.g., the use of paired pronouns ‘he or she’) can reduce the tendency for people to stereotype political leaders as more similar to men than women. To increase the rigor with which these stereotypes were measured, this project complemented the dominant ‘cheap talk’ measure of personal stereotype content with a novel incentivised community measure of this content. As expected, participants stereotyped political leaders as more similar to men than women; this pattern was stronger on the incentivised measure of community stereotypes. Unexpectedly, there was no evidence that paired pronouns reduced this bias (there was weak exploratory evidence that paired nouns increased participants’ tendencies to stereotype political leaders as more similar to women). This project suggests that the ability of gender fair language interventions to ameliorate gender bias in the political leadership domain may be limited.

The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science1-3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts' choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, in which, for one claim per study, at least five reanalysts independently reanalysed the original data. The statistical appropriateness of the reanalyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent reanalyses yielded the same result (within a tolerance region of ±0.05 Cohen's d) as the original report; with a four times broader tolerance region, this indicator increased to 57%. Of the reanalyses conducted, 74% reached the same conclusion as the original investigation, 24% yielded no effects or inconclusive results and 2% reported the opposite effect. This exploratory study indicates that the common single-path analyses in social and behavioural research should not be simply assumed to be robust to alternative analyses4. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.

We develop a theoretical perspective on how and for whom large language model (LLM) assistance influences creativity in the workplace. We propose that LLM assistance increases employees' creativity by providing cognitive job resources. Furthermore, we hypothesize that employees with high levels of metacognitive strategies-who actively monitor and regulate their thinking to achieve goals and solve problems-are more likely to leverage LLM assistance effectively to acquire cognitive job resources, thereby increasing creativity. Our hypotheses were supported by a field experiment, in which we randomly assigned employees in a technology consulting firm to either receive LLM assistance or not. The results are robust across both supervisor and external evaluator ratings of employee creativity. Our findings indicate that LLM assistance enhances employees' creativity by providing cognitive job resources, especially for employees with high (vs. low) levels of metacognitive strategies. Overall, our field experiment offers novel insights into the mediating and moderating mechanisms linking LLM assistance and employee creativity in the workplace. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Artificial intelligence (AI) is transforming human life. While some studies find that people prefer humans over AI (AI aversion), others find the opposite (AI appreciation). To reconcile these conflicting findings, we introduce the Capability-Personalization Framework. This theoretical framework posits that when deciding between AI and humans in a context, individuals focus on two dimensions: (a) perceived capability of AI and (b) perceived necessity for personalization. We propose that AI appreciation occurs when (a) AI is perceived as more capable than humans and (b) personalization is perceived as unnecessary in a given decision context, whereas AI aversion occurs when these conditions are not met. Our Capability-Personalization Framework is substantiated by a meta-analysis of 442 effect sizes from 163 studies (N = 82,078): AI appreciation occurs (d = 0.27, 95% CI [0.17, 0.37]) when AI is perceived as more capable than humans and personalization is perceived as unnecessary in a given decision context; otherwise, AI aversion occurs (d = -0.50, 95% CI [-0.63, -0.37]). Moderation analyses suggest that AI appreciation is more pronounced for tangible robots (vs. intangible algorithms), for attitudinal (vs. behavioral) outcomes, in between-subjects (vs. within-subjects) study designs, and in low unemployment countries, while AI aversion is more pronounced in countries with high levels of education and internet use. Overall, our integrative framework and meta-analysis advance knowledge about AI-human preferences and offer valuable implications for AI developers and users. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

In the United States, in nearly all cases, one must register in order to vote—yet, a substantial portion of the eligible electorate remains unregistered. Despite this, relatively little is known about how to increase the likelihood that a voter registers. Here, we tested the impact of 10 expert-crowdsourced, theoretically-based psychological interventions on a sample of eligible, yet unregistered, U.S. voters ahead of the 2024 presidential election (N = 12,896). Eight of the interventions increased intentions to vote, and five led individuals to click on the voter registration website. Escalating Commitment, which sequentially employed several social pressure strategies, was the strongest intervention across these outcomes. However, none of the interventions had a significant effect on actual voter registration or voter turnout. The results highlight a substantial disconnect between voters’ intentions and their ultimate behaviors. We discuss potential structural and psychological barriers that undermine the translation of intent into action.

Semantic priming has been studied for nearly 50 years across various experimental manipulations and theoretical frameworks. Although previous studies provide insight into the cognitive underpinnings of semantic representations, they have suffered from small sample sizes and a lack of linguistic and cultural diversity. In this Registered Report, we measured the size and the variability of the semantic priming effect across 19 languages (N = 25,163 participants analyzed) by creating the largest available database of semantic priming values based on an adaptive sampling procedure. We found evidence for semantic priming in terms of differences in response latencies between related word-pair conditions and unrelated word-pair conditions. Model comparisons showed that inclusion of a random intercept for language improved model fit, providing support for variability in semantic priming across languages. This study highlights the robustness and variability of semantic priming across languages and provides a rich, linguistically diverse dataset for further analysis.

We show that generative artificial intelligence (AI) models-trained on textual data that are inherently cultural-exhibit cultural tendencies when used in different human languages. Here we focus on two foundational constructs in cultural psychology: social orientation and cognitive style. First, we analyse GPT's responses to a large set of measures in both Chinese and English. When used in Chinese (versus English), GPT exhibits a more interdependent (versus independent) social orientation and a more holistic (versus analytic) cognitive style. Second, we replicate these cultural tendencies in ERNIE, a popular generative AI model in China. Third, we demonstrate the real-world impact of these cultural tendencies. For example, when used in Chinese (versus English), GPT is more likely to recommend advertisements with an interdependent (versus independent) social orientation. Fourth, exploratory analyses suggest that cultural prompts (for example, prompting generative AI to assume the role of a Chinese person) can adjust these cultural tendencies.

Psychological studies on close relationships have often overlooked cultural diversity, dynamic processes, and potentially universal principles that shape intimate partnerships. To address the limited generalizability of previous research and advance our understanding of romantic love experiences, mate preferences, and physical attractiveness, we conducted a large-scale cross-cultural survey study on these topics. A total of 404 researchers collected data in 45 languages from April to August 2021, involving 117,293 participants from 175 countries. Aside from standard demographic questions, the survey included valuable information on variables relevant to romantic relationships: intimate, passionate, and committed love within romantic relationships, physical-attractiveness enhancing behaviors, gender equality endorsement, collectivistic attitudes, personal history of pathogenic diseases, relationship quality, jealousy, personal involvement in sexual and/or emotional infidelity, relational mobility, mate preferences, and acceptance of sugar relationships. The resulting dataset provides a rich resource for investigating patterns within, and associations across, a broad range of variables relevant to romantic relationships, with extensive opportunities to analyze individual experiences worldwide.

To date, little is known about what interventions can help individuals attain leadership roles in organizations. To address this knowledge gap, we integrate insights from the communication and leadership literatures to test debate training as a novel intervention for leadership emergence. We propose that debate training can increase individuals' leadership emergence by fostering assertiveness-"an adaptive style of communication in which individuals express their feelings and needs directly, while maintaining respect for others" (American Psychological Association, n.d.)-a valued leadership characteristic in U.S. organizations. Experiment 1 was a three-wave longitudinal field experiment at a Fortune 100 U.S. company. Individuals (N = 471) were randomly assigned to either receive a 9-week debate training or not. Eighteen months later, the treatment-group participants were more likely to have advanced in leadership level than the control-group participants, an effect mediated by assertiveness increase. In a sample twice as large (N = 975), Experiment 2 found that individuals who were randomly assigned to receive debate training (vs. nondebate training or no training) acted more assertively and had higher leadership emergence in a subsequent group activity. Results were consistent across self-rated, group-member-rated, and coder-rated assertiveness. Moderation analyses suggest that the effects of debate training were not significantly different for (a) U.S.- and foreign-born individuals, (b) men and women, or (c) different ethnic groups. Overall, our experiments suggest that debate training can help individuals attain leadership roles by developing their assertiveness. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Climate change is currently one of humanity’s greatest threats. To help scholars understand the psychology of climate change, we conducted an online quasi-experimental survey on 59,508 participants from 63 countries (collected between July 2022 and July 2023). In a between-subjects design, we tested 11 interventions designed to promote climate change mitigation across four outcomes: climate change belief, support for climate policies, willingness to share information on social media, and performance on an effortful pro-environmental behavioural task. Participants also reported their demographic information (e.g., age, gender) and several other independent variables (e.g., political orientation, perceptions about the scientific consensus). In the no-intervention control group, we also measured important additional variables, such as environmentalist identity and trust in climate science. We report the collaboration procedure, study design, raw and cleaned data, all survey materials, relevant analysis scripts, and data visualisations. This dataset can be used to further the understanding of psychological, demographic, and national-level factors related to individual-level climate action and how these differ across countries.

According to the justified true belief (JTB) account of knowledge, people can truly know something only if they have a belief that is both justified and true (i.e., knowledge is JTB). This account was challenged by Gettier, who argued that JTB does not explain knowledge attributions in certain situations, later called "Gettier-type cases," wherein protagonists are justified in believing something to be true, but their belief was correct only because of luck. Laypeople may not attribute knowledge to protagonists with justified but only luckily true beliefs. Although some research has found evidence for these so-called Gettier intuitions, Turri et al. found no evidence that participants attributed knowledge in a counterfeit-object Gettier-type case differently than in a matched case of JTB. In a large-scale, cross-cultural conceptual replication of Turri and colleagues' Experiment 1 (N = 4,724) using a within-participants design and three vignettes across 19 geopolitical regions, we did find evidence for Gettier intuitions; participants were 1.86 times more likely to attribute knowledge to protagonists in standard cases of JTB than to protagonists in Gettier-type cases. These results suggest that Gettier intuitions may be detectable across different scenarios and cultural contexts. However, the size of the Gettier intuition effect did vary by vignette, and the Turri et al. vignette produced the smallest effect, which was similar in size to that observed in the original study. Differences across vignettes suggest that epistemic intuitions may also depend on contextual factors unrelated to the criteria of knowledge, such as the characteristics of the protagonist being evaluated.

This article spotlights a widespread problem in research and practice: Asians are commonly categorized as a monolithic group in the United States. Regarding research, my 24-year archival analysis of Psychological Science shows that most U.S. studies did not specify which Asian subgroup(s) were examined. Regarding practice, my analysis of the diversity, equity, and inclusion (DEI) webpages and latest diversity reports of S&P 100 companies finds that none of them differentiated between Asian subgroups. Such use of the generic category “Asian” is problematic because it masks important differences among Asian subgroups: (a) Of all ethnic groups in the United States, socioeconomic inequality among Asian subgroups is the highest and fastest growing; (b) U.S. studies show that East Asians (e.g., ethnic Chinese)—but not South Asians (e.g., ethnic Indians)—experience a “bamboo ceiling” in consequential contexts, including leadership attainment, academic performance in law and business schools, and starting salaries. Thus, lumping Asians together can obscure the challenges faced by certain Asian subgroups and jeopardize the attention and resources they need. More broadly, this article demonstrates the importance of differentiating between ethnic subgroups in research (e.g., theorization, surveys, and data analysis) and practice (e.g., diversity reports) to foster DEI.

The Russian invasion of Ukraine on February 24, 2022, has had devastating effects on the Ukrainian population and the global economy, environment, and political order. However, little is known about the psychological states surrounding the outbreak of war, particularly the mental well-being of individuals outside Ukraine. Here, we present a longitudinal experience-sampling study of a convenience sample from 17 European countries (total participants = 1,341, total assessments = 44,894, countries with >100 participants = 5) that allows us to track well-being levels across countries during the weeks surrounding the outbreak of war. Our data show a significant decline in well-being on the day of the Russian invasion. Recovery over the following weeks was associated with an individual’s personality but was not statistically significantly associated with their age, gender, subjective social status, and political orientation. In general, well-being was lower on days when the war was more salient on social media. Our results demonstrate the need to consider the psychological implications of the Russo-Ukrainian war next to its humanitarian, economic, and ecological consequences.