250+ Papers Using Connect^TM Participants*

Abstract

This study explored the impact of celebrity status and victim framing on social judgments. People are inclined to favor others because appearance, fame, and familiarity (Nayak, 2015). Language can also influence people’s judgements; one study found that people increase their support for someone who is labeled a “victim,” even when they are the alleged perpetrator of a sexual assault (Flusberg et al., 2022). The primary goal of this study was to examine how language and fame interact to shape people’s social evaluations. Six hundred participants across sic conditions, read an article about a domestic abuse case revolving a celebrity couple or a stranger couple. The U.S subjects read an article that either framed the male alleged perpetrator as a victim, the female accuser as a victim or no victim-framed protagonist. Results indicated a main affect for support for each protagonist when framed as the victim compared to the baseline condition. Participants in the celebrity status condition were less likely to support the female accuser across all conditions compared to the stranger condition. The research suggest that people evaluations of others can be based off familiarity and language manipulation.

Abstract

We investigated if the double-edged sword model, which describes the role of growth mindsets in both promoting and reducing stigma, replicates in the context of mental illness. Do stronger growth mindsets relate to more prejudice via increased blame, but less prejudice via reduced social essentialist thinking? In two correlational studies (N = 328; N = 343), stronger growth, relative to fixed mindsets, of mental illness related negatively to prejudice, in large part because they related negatively to social essentialist thinking and, unexpectedly, also negatively to blame. We ran two additional studies, randomly assigning participants (N = 605; N = 397) to a mental illness or obesity condition, to examine links between mindsets and blame across the two contexts. In the obesity condition, growth mindsets were strongly associated with greater blame, perceived controllability, and prejudice but not in the mental illness condition—growth mindsets again were associated with less prejudice. Differences in narratives about controllability are one potential driving force of these disparate findings

Abstract

The emergence of transformer models that leverage deep learning and web-scale corpora has made it possible for artificial intelligence (AI) to tackle many higher-order cognitive tasks, with critical implications for industry, government, and labor markets in the US and globally. Here, we investigate whether the currently most powerful, openly-available AI model – GPT-3 – is capable of influencing the beliefs of humans, a social behavior recently seen as a unique purview of other humans. Across three preregistered experiments featuring diverse samples of Americans (total N=4,836), we find consistent evidence that messages generated by AI are persuasive across a number of policy issues, including an assault weapon ban, a carbon tax, and a paid parentalleave program. Further, AI-generated messages were as persuasive as messages crafted by lay humans. Compared to the human authors, participants rated the author of AI messages as being more factual and logical, but less angry, unique, and less likely to use story-telling. Our results show the current generation of large language models can persuade humans, even on polarized policy issues. This work raises important implications for regulating AI applications in political contexts, to counter its potential use in misinformation campaigns and other deceptive political activities.

Abstract

Anecdotal evidence suggests that Eastern consumers respond more favorably to anthropomorphic products than their Western counterparts. In the present work, we examine the validity of this common intuition and uncover the specific cultural dimension underlying this difference in consumer response. Specifically, across a cross-national field study and three controlled experiments, we demonstrate that collectivistic consumers favor anthropomorphic products more than non-anthropomorphic products, whereas non-collectivistic consumers do not display this relative preference. This interactive effect holds across various product categories, regardless of whether collectivistic thinking is measured, manipulated, or operationalized based on nationality or ethnicity. We offer managerial and theoretical implications that stem from our findings.

Abstract

Though on and off-the-field misconduct is common among U.S. college athletic programs, little is known regarding the ramifications that may result. Drawing on social learning theory, the current research suggests consumers intentions (e.g., likelihood of attending a game) differ depending on violator's team role. Across one qualitative and five experimental studies, we demonstrate that consumers' intentions are influenced by violator's team role, such that likelihood of attending a game is lower when a coach (vs. student athlete) misbehaves, an effect driven by evaluation of the academic institution. This effect is robust across both winning and losing records and moderated by perceived fairness of the university's actions toward the violator.

Abstract

The purpose of this study is threefold. First, this study improves practitioners’ and scholars’ understanding of the efficacy of incorporating ethically informed language into crisis responses, specifically the degree of ethics of care. Second, the present study applies the situational crisis communication theory (SCCT) to explore the most effective crisis responses (rebuild-apology x diminish-excuse) to backlash to a corporate social advocacy (CSA) initiative. The tertiary goal of this study is to explore how different ethics-of-care-informed crisis responses, specifically diminish-excuse or rebuild-apology, impact corporate reputation, subsequent supportive or non-supportive behavioral intentions toward the corporation, and activism intentions toward the issue. Methodologically, this study contributes to the growing body of research on CSA and provides an empirically tested example of incorporating ethics of care into experimental research. In addition, this study considers the potential of mitigating or elevating crisis as a result of backlash to CSA and teases out both supportive and non-supportive intentions toward a company, as well as increased activism intentions on behalf of an issue. Results indicate that stakeholders may respond to lower perceived care in diminish-excuse crisis response with greater activism intentions in an attempt to fill a perceived void in the corporations’ CSA efforts. In addition, findings indicate that providing no response to backlash to CSA as a crisis can potentially increase boycott intentions. The practical and theoretical implications of applying SCCT and ethics of care to a CSA context, as well as future research, are posited.

Abstract

Recommendation algorithms shape what users see on platforms ranging from search engines to social media to online marketplaces. It is typically assumed that users behave exogenously—that is, their response to a recommendation depends on that recommendation and not on the algorithm that generated it. In this work, we investigate whether users break this assumption; specifically, whether they strategize. For example, a user may not click on a video on YouTube not because they are uninterested, but because they believe YouTube’s algorithm will overfit to the click. To determine whether users strategize, we conduct a lab experiment and survey. We test two hypotheses consistent with user strategization and find strong support for both. Our findings suggest that users strategize; users are able to strategize their explicit feedback (e.g., clicks) moreso than implicit feedback (e.g., dwell time). Our survey further reveals that a large majority of users admit to strategizing. In response to an open-ended question, we also gain insights into why and how users strategizing, surfacing intriguing behaviors (e.g., creating multiple accounts, leaving Spotify playing for days, and searching for content in private browsing mode).

Abstract

The purpose of the current study was to explore how the interaction between race (Black vs. Asian) and sexual identity (homosexuality vs. heterosexuality) would impact a marginalized person’s experience with aggressive behavior. Through mechanisms involving gender norm violations and cumulative disadvantage (an intersectionality theory), I predicted that participants would express greater aggression against a counterpart who is gay and Black than a counterpart who is gay and Asian; greater aggression against a counterpart who is gay and Black than a counterpart who is straight and Black; and greater aggression against counterpart who is gay and Asian than a counterpart who is straight and Asian. To test this, I recruited 461 men who were White and heterosexual and had them to engage in a competitive task with a counterpart who was Black and homosexual, Black and heterosexual, Asian and homosexual, or Asian and heterosexual. The task competitive was the subtraction paradigm (PSAP). In the PSAP, participants try to earn as many as points as possible for money, while a “counterpart”(a line of computer code unbeknownst to the participants) purposely sabotages them by taking away their points. I did not find support for my original hypotheses. However, we did find interesting results in my exploratory analyses, which should open up avenues for future research.

Abstract

This study offered empirical insights on the potential of eXplainable Artificial Intelligence (XAI) in raising user awareness of algorithmic bias and driving their trust in AI systems. We focus on the context of controversial facial recognition algorithms to examine the popular “explanation by example” approach in XAI, where users receive explanatory examples closely resembling their input. From the perspective of imposed users of AI systems, the experiment results demonstrated that this XAI approach allows users to gauge the congruence between these examples and their circumstances. The congruence between the user and the explanatory example acts as a positive indicator of fairness and inclusiveness of the AI system. Conversely, when a disparity exists between the user and the explanatory examples, it evokes perceptions of unfairness and exclusion, thereby prompting users not to put blind trust in the system and raising users’ awareness of algorithmic bias stemming from non-inclusive datasets. The results further highlight the moderating role of users’ prior experience with discrimination. The study findings thus attest to the instrumental value of XAI in helping users understand the decision-making process of AI, and in the process, raising awareness for potential algorithmic bias.

Abstract

Public opinion is often shaped by elite discourse, with most Americans receiving elite opinions via the media: newspapers, television, and now the Internet. All teach citizens about the world and the preferences and attitudes of political leaders and pundits. Since the mid 1990s, in an increasingly varied and crowded media landscape, these elite cues no longer produce the kind of cross-party political consensus that characterized much of the 20th century. Citizens choose information environments that provide news which is congenial to their political identities and more easily avoid ideas and news in conflict with them. Knowing that Americans opt into congenial news environments, I theorize that the current polarized and energized electorate is the result of differing emotional environments among partisan and nonpartisan news sources. I argue that the emotionality of partisan news media likely drives current upward trends in both political polarization and participation. Anger-driven political behavior, observed in greater voter turnout and out-partisan hostility, is the likely result of exposure to far more anger cues in partisan media than are typically expressed in nonpartisan news media. I find that partisan news is angrier, less anxious, and less enthusiastic than nonpartisan news, because partisan anchors construct an emotional context for politics by expressing anger, assigning blame, and motivating copartisan action. This dissertation explores how media institutions construct the emotional context of politics, focusing on how the partisan identities of news outlets are associated with variation in the emotional landscape of news. This variation is not unique to the contemporary period: the partisan press of the 19th century dominated the pages of political newspapers, where partisan editors and publishers expressed anger, assigned blame, and galvanized copartisans during elections. I theorize that the same emotionality of the 19th century partisan press has reemerged with the rise of cable television news programming over the first two decades of the 21st century. Elite cues from partisan news anchors are likely mobilizing and enraging the American public through increased expressions of anger to their television news audiences. I establish this by mapping emotional landscape of contemporary American news, focusing on two distinct time periods: 2000-2008, in which I analyze the early partisan cable news environment with the establishment of both Fox News and MSNBC alongside the more established CNN; and 2010-2020, in which I test the oft-repeated theory that during the 2016 presidential campaign Donald Trump changed the media environment. To do this, I construct and validate a methodological framework for measuring emotional cues delivered to audiences by xviii the hosts of news programs, enabling me to measure differences in the emotional cues that are delivered to partisan and nonpartisan news viewers. I find that nonpartisan news is more anxious and more enthusiastic than partisan news over both time periods, but that the key difference is that partisan news is more intensely and frequently angry. I also find that while expressions of anger increased in partisan news from 2000-2008 as Fox established itself as a ratings contender, there is no perceptible shift in the emotional environment from 2010-2020. Donald Trump did not cause changes to the media, but rather took advantage of a partisan press that was already conveying anger to the mass public.

Abstract

Waste from the fashion industry continues to be a major source of pollution. At the consumer use phase, this problem is compounded by inefficient purchase decision making that leads to underutilization. While advances in technology can lead to improved decision making, current decision support systems utilize methods that are not effective for the apparel industry as they rely on past purchases or user similarity metrics to make recommendations. Currently, human-assisted expert knowledge is state of the art in apparel purchase decision support. Such knowledge could prove invaluable in computer-supported decision making but has not yet been empirically assessed. This study sought to first explore predictive expert style advice to identify body and garment attributes as well as garment/body attribute-value pairs used to predict subjective aesthetic appeal of garments with regards to body shape using content analysis. Then, based on the attribute-value pairs identified, to develop a theory on dressing advice based on body shapes, and quantitatively assess the extent to which the body/garment attribute-value pairs predict aesthetic judgments of taste. Qualitative content analysis was conducted on n = 5 books containing body-shape based style advice to understand how authors use body and garment relationships to achieve desired appearance. An instrument was then developed and utilized to empirically assess the findings of iv the qualitative study with crowdsourced judgments of outfit aesthetics using workers from CloudResearch. The results of the study suggest that factors identified as predictive of aesthetic preference from authors could be hierarchical in nature with different levels of importance for different attributes. The findings of this research indicate that while authors’ knowledge may not be fully predictive of young adults’ preference, it could form the basis for a better understanding of which factors are most important in design of user-centered apparel recommendation. Further, as one of the first studies to empirically evaluate expert advice in clothing recommendation, it highlights the importance of a validation phase where expertise is assessed prior to integration into computational system design. Finally, the findings offer a novel opportunity for apparel experts to reflect on established (but unvalidated) “rules” or theories in aesthetics.

Abstract

Research has recognized age biases against young leaders, yet understanding of how gender, the most frequently studied demographic leader characteristic, influences this bias remains limited. In this study, we examine the gender-specific age bias toward young female and young male leaders through an intersectional lens. By integrating intersectionality theory with insights on status beliefs associated with age and gender, we test whether young female and male leaders face an interactive rather than an additive form of bias. We conducted two preregistered experimental studies (N1 = 918 and N2 = 985), where participants evaluated leaders based on age, gender, or a combination of both. Our analysis reveals a negative age bias in leader status ascriptions toward young leaders compared to middle-aged and older leaders. This bias persists when gender information is added, as demonstrated in both intersectional categories of young female and young male leaders. This bias pattern does not extend to middle-aged or older female and male leaders, thereby supporting the age bias against young leaders specifically. Interestingly, we also examined whether social dominance orientation strengthens the bias against young (male) leaders, but our results (reported in the SOM) are not as hypothesized. In sum, our results emphasize the importance of young age as a crucial demographic characteristic in leadership perceptions that can even overshadow the role of gender.

Abstract

When secrets come to mind, do people try to suppress them or do they engage with them? Whereas earlier research suggested that people try to suppress secrets, recent work suggests that people often engage with their secrets. Although thought suppression tends to be associated with greater distress, engagement may be ameliorative. In two longitudinal studies of 653 adults (55% women; Mage = 41.3, SD = 12.4) keeping a secret from their partner, we show that engagement with and suppression of secrets are highly positively related. Like suppression, the more people engage with secrets, the more negative affect and guilt they report feeling. Longitudinal analyses indicate that whereas changes over time in engagement and suppression both predicted reduced secret preoccupation, reductions in suppression (but not engagement) mediated reductions in guilt and negative affect. These results indicate that suppression and engagement are more intimately connected than previously thought. We found no evidence that engagement was ameliorative.

Abstract

Trust and situational awareness (SA) are crucial to the adoption and safety of automated vehicles (AVs). Appropriate design of AV explanations could promote drivers’ acceptance, trust, and SA, enabling drivers to get more benefits from the technology. This study investigated the effects of error type and information type of AV explanations on drivers’ trust and SA. We recruited 300 participants for an online video study with a 3 (information type) × 2 (error type) mixed design. Linear mixed model analyses showed that compared with false alarms, misses were associated with more trust decrease after the error and more trust decrease after the post-error recovery. Compared with why information, how information was associated with lower SA generally and risked potential over-trust in false alarms. Therefore, we recommend deploying AV decision systems that are less miss-prone and including why information in AV explanations.

Abstract

Previous research has established that brand narratives can strengthen the connection between consumers and brands. The present investigation raises the question of whether this finding holds when narratives are written in metaphoric terms. Three studies, including a pilot study of Amazon.com brand reviews (N = 1,000) and two experiments (N = 4,017), illustrate that metaphoric (vs. non-metaphoric) narratives can actually weaken the self–brand connection. The studies illuminate that, while metaphoric narratives are prevalent in consumer reviews, such reviews seem less likely to provide the narrative structure necessary to establish a strong connection with the brand. This work contributes to the novel insight that while consumers often use metaphors as a way to talk about brands, relying on metaphors may actually weaken the review writer’s connection with the brand. This effect remains robust even when considering high levels of brand familiarity and linguistic abilities of the brand review writer.

Abstract

According to the theory of IT-mediated state-tracking, the intended and unintended consequences of constantly checking digital devices can be judged by the resulting problem of attention and by whether the checking led to information that served the individual’s enduring goal. While this perspective offers numerous benefits over the common practice of labeling excessive information technology use as addiction, as of yet, the concepts of problem of attention (PoA) and service to enduring goal (SEG) lack empirical measures. Thus, this paper develops measurement scales for the constructs PoA and SEG following an established construct development methodology. We evaluate the measures’ validity and reliability and demonstrate that PoA and SEG differ from existing similar concepts. With the help of our newly developed constructs the quality of constant checking habits can be assessed which enables future studies to scrutinize the theorized preventive role of self-control in the context of smartphone habits.

Abstract

The purpose of these studies was to examine persuasive methods of climate change (CC) advocacy among Americans who feel strongly about this issue. Specifically, using the Global Warming’s Six Americas (Maibach, et al., 2011) framework, Alarmed and Concerned Americans were recruited to examine different appeals encouraging pro-environmental behaviors that varied by the number of proposed behaviors (seven versus one) and by the proposed actor of these behaviors (the participant themself versus legislators in the U.S. government; Study 1) and to establish the perceived efficacy of tools created to help individuals overcome behavioral barriers to CC opinion leadership (i.e., “Strategies” for initiating conversations about CC, “Counterarguments” to common misinformation and denial claims, and “Posts” that can be easily shared across social media sites; Study 2). In Study 1, participants were randomized to view one of four messages (Single Quantity–Self Actor, Single–Legislator, Multiple–Self, Multiple–Legislator) and completed a battery of attitudinal and behavioral measures related to CC. Against expectation, participants who read either of the two messages highlighting Multiple behaviors and either of the two messages highlighting Self-initiation were the more likely to engage, generally, in future pro-environmental behaviors; furthermore, participants who read the Multiple–Self message were more likely to engage in clean electricity practices and products. In Study 2, participants read four messages encouraging engagement in CC opinion leadership, with three of the messages including additional information to help overcome barriers to initiating difficult conversations. They also completed a personality assessment, which helped determine if participants’ effectiveness ratings of the messages uniquely corresponded with specific aspects of their personality. As projected, Agreeableness was positively related to the effectiveness ratings of the Posts message; additionally, Agreeableness was positively related to the effectiveness ratings of the Strategies message. However, lower Extraversion and higher Neuroticism were not uniquely related to effectiveness ratings for either of the three interventions as hypothesized. Clarification of these results, limitations of the study's methodology, and future research possibilities are also discussed.

Abstract

Financial mindfulness has become a popular term used by practitioners to promote various financial products and services. Despite its industry-wide prevalence, there is still limited conceptual clarity of what financial mindfulness is and how to measure it effectively. In this article, we define financial mindfulness (FM) as “the tendency to be highly aware of one’s objective financial state while possessing a non-judgmental acceptance of that state,” and we develop and validate an 8-item scale to measure individual differences in financial mindfulness. Through 10 studies, we demonstrate that the FM-Scale has strong psychometric properties, is distinct from conceptually related scales, and predicts actual investment preferences and credit scores. Importantly, the FM-Scale is associated with a broad range of known financially detrimental behaviors (i.e., financial withdrawal, impulse buying, and sunk cost bias) above and beyond related scales (i.e., current money management stress and self-control). Our work is the first to introduce and define mindfulness in the context of consumer finance, and to provide a reliable and succinct way to measure it.

Abstract

The current research aims to define perceived productivity, understand how it is measured, and examine the predictors of perceived productivity. I used topic modelling to create a conceptual definition (Study 1a), which was later validated against people’s own views of productivity (Study 2). I also conducted a rapid review (Study 1b) and evaluated the construct validity of the Subjective Productivity Scale (Study 2). Finally, I investigated predictors of perceived productivity using data from Connect (Studies 3a, 3b) and UpBeing app users (Study 4). Key findings relate perceived goal progress to perceived productivity. While the research provides a comprehensive view of perceived productivity, more research is needed to understand how it relates to objective productivity.

Abstract

Language understanding plays a crucial role in psychological measurement and so it is important that semantic cues should be studied for more effective and accurate measurement practices. With advancements in computer science, natural language processing (NLP) techniques have emerged as efficient methods for analyzing textual data and have been used to improve psychological measurement. This dissertation investigates the application of NLP embeddings to address fundamental methodological challenges in psychological measurement, specifically scale development and validation. In Study 1, a word embedding-based approach was used to develop a corporate personality measure, which resulted in a three-factor solution closely mirroring three dimensions out of the Big Five framework (i.e., Extraversion, Agreeableness, and Conscientiousness). This research furthers our conceptual understanding of corporate personality by identifying similarities and differences between human and organizational personality traits. In Study 2, the sentence-based embedding model was applied to predict empirical pairwise item response relationships, comparing its performance with human ratings. This study also demonstrated the effectiveness of fine-tuned NLP models for classifying item pair relationships into trivial/low or moderate/high empirical relationships, which provides preliminary validity evidence without collecting human responses. The research seeks to enhance psychological measurement practices by leveraging NLP techniques, fostering innovation and improved understanding in the field of social sciences.

Abstract

Despite prior research documenting that firms led by narcissistic executives experience numerous detrimental effects, narcissists are more promotable, enjoy longer tenures, and earn higher compensation than their peers. These outcomes suggest firms accrue some benefits from executive narcissism. We provide evidence of one such benefit: the ability to positively influence external stakeholder perceptions of the firm. In a laboratory setting, we find that narcissistic individuals are more likely to engage in persuasion to elicit higher firm valuations from financial analysts, especially when the analyst is more influential with investors. We triangulate this finding with archival evidence from analyst valuations and conference calls. CFO narcissism is associated with overly optimistic analyst valuations, and their valuations are less affected by recent earnings news, particularly when that news is negative. Consistent with attempts at persuasion, conference calls reveal that narcissistic CFOs exhibit greater levels of engagement with analysts, speak more optimistically, and are more likely to use argumentative prose and corporate euphemisms. Collectively, the evidence suggests that narcissistic CFOs use their persuasive skills to positively influence analyst perceptions of the firm.

Abstract

ObjectiveGuilt proneness is associated with both high motivation to succeed and enhanced concern for others. However, in competition, achieving success requires harming others' interests, which demotivates guilt-prone individuals. Given the prevalence of competition in social and professional life, we examine the relation between guilt proneness, general motivation, and competitive motivation.MethodTwo experiments and two laboratory studies (N = 1735) measured guilt proneness, general motivation, and competitive motivation, and their effects on competitive preferences and choices. Study settings included students' choice of playing a game individually vs. competitively (Study 1), physicians' likelihood to seek residency in medical fields characterized by high competitiveness (Study 2), amateur athletes' preferences between inclusive and win-oriented team strategies (Study 3), and online workers' evaluations of a hypothetical scenario (Study 4).ResultsGuilt proneness was related positively to general motivation, but negatively to competitive motivation. Guilt proneness, indirectly through lower competitive motivation, predicted a lower likelihood of pursuing competitive paths and preference for non-competitive strategies. Emphasizing prosocial aspects of competitiveness attenuated these effects.ConclusionsGuilt proneness is related to high general motivation but to a lower desire to win. Guilt-prone individuals strive for excellence, but through non-competitive paths, whereas people with lower guilt proneness prefer competing.

Abstract

Political polarization has been on the rise in recent years, and understanding how partisans view each other is an important step toward bridging divides. A common narrative in political discourse emphasizes the “sheepleness” of the other side—people view outgroup members as being influenced externally by their upbringing and the media they consume. The present studies examine the way partisans use the sheepleness lens to explain how and why their opponents came to identify with the outgroup. Using a combination of qualitative and quantitative methods across six studies, I investigate the attributions people make to explain their opponents’ political beliefs and behaviors, and the impact of these attributions on their perceptions of outgroup members. I found correlational and causal evidence for the relationship between perceived sheepleness and outgroup favorability, mediated by perceived unintelligence. I also found consistent party differences in perceived sheepleness, perceived unintelligence, and outgroup favorability, with Republicans displaying more positivity toward the outgroup. The findings offer insight into how partisans view each other.

Abstract

Social media users often push back against harmful rhetoric with satirical and aggressive counterspeech. How do the interconnected race and gender identities of the person posting counterspeech and the person viewing it impact evaluations of the comment? Across two online experiments, we manipulate the race (Black or White) and gender (man or woman) of an individual whose tweet opposes ignorance about White privilege to examine if identity influences perceptions of incivility and intentions to flag the tweet for removal among Black and White men and women participants. Results demonstrate White men were most likely to find the tweet uncivil and report it, and this was especially the case when the tweet came from a Black woman, regardless of the tone. These studies highlight the importance of recognizing power and intersectionality in social media content moderation and creating policies that counteract the uniquely severe treatment of Black women by White men.

Abstract

Constituents in the United States have used local public meetings in recent years to shape policy on some of the most high-profile race-related issues. However, public meeting participation remains less studied relative to other modes of participation. This study investigates the extent to which race shapes the way a message is heard and evaluated by the public audience and the degree to which its impact depends on the issue of the message. To carry out the study, I set up a 2 × 3 experiment containing six short treatment videos in which I manipulated the race of the actor/speaker (Black or White) and the message issue (innocuous service request, race-related policy, and race-neutral policy). Subjects were randomly assigned to watch one of the six videos and then asked to evaluate the message in the video. The early results of this study show that unlike previous studies revealing racial bias in a range of political contexts in the United States, race exerts neither independent nor conditional effects on the evaluation of the message as reasonable. I discuss the limitations and implications of these findings and useful considerations to think about in expanding this study.

Abstract

Obsessive-compulsive disorder (OCD) is a psychiatric disorder characterized by a series of symptoms relating to unwanted and distressing thoughts (IE: obsessions) that lead into the development of repetitive and disruptive behaviors (IE: compulsions) in order to quell the anxiety that stems from the obsessive thought(s). Problematic smartphone use (PSU) is the use of one’s smartphone that is viewed as being excessive or interferes with their daily activities. Prior research has identified a relationship between OCD and PSU. However, more research is needed to understand factors that mediate this relationship. In our study, we recruited 438 people and had them complete multiple measures assessing various levels of obsessive-compulsive behavior, Fear of Missing Out (FoMO), inhibitory anxiety (IA), boredom proneness, and PSU. With use of Structural Equation Modeling, several key findings were identified, such as OCD predicted boredom proneness, FOMO, and IA. Moreover, while FoMO and boredom proneness predicted PSU, IA did not. It was also discovered that FoMO and boredom proneness mediated the relationship of OCD and PSU, but IA did not. FoMO and boredom proneness appear to play an important role in the relationship between OCD and PSU. Though, experimental work is needed to evaluate the causal effects of this relationship

Abstract

CEOs increasingly engage in activism on issues such as gun control, voting rights, and abortion. Although such activism may benefit their firms, the stated goal is often to mobilize the public and precipitate change. In an experiment with 4,578 respondents, we study the effect of CEO activism on people’s willingness to contact their U.S. senators about abortion. On average, showing a CEO message supporting abortion rights is not more effective at mobilizing pro-choice citizens than showing no message or showing a message from other speakers. Additionally, CEOs do not provoke countermobilization by people who oppose abortion. We explore heterogeneous treatment effects and find that CEO activism is better at motivating pro-choice citizens to engage in politics when their senators are Democrats and thus likely receptive to pro-choice activism, consistent with stakeholder alignment theory. Our findings contribute to research on social movements and mobilization in markets by examining the ability of CEOs and organizations to act as catalysts for social change.

Abstract

This paper examines how Fox News influences social and moral preferences: two crucial inputs in people’s decision-making process. I conduct a survey among Americans aged 45 or older and use the variation in the channel positions of Fox News and MSNBC across different towns and cable providers as instruments. After confirming that these channel positions do not predict voting patterns before Fox started braodcasting, I find evidence that Fox shifted moral values to be more communal, some suggestive evidence that it decreased altruism and trust, and that Fox does not appear to affect negative reciprocity. In addition, these treatment effects are concentrated among those who did not vote for Bill Clinton or Bob Dole in the 1996 election.

Abstract

This dissertation contributes to retailing and marketing research by exploring how retailer channel decisions (i.e., physical store closures) due to the changes in the retail environment impact consumers’ shopping behaviors. The pandemic caused many retailers to remove physical stores and move online rapidly (e.g., Brooks Brothers and Neiman Marcus) (Pacheco, 2020). As the retail environment changes, understanding consumers’ shopping behaviors is critical for retailers to stay competitive, maintain, and attract new customers. Thus, Essay 1 studies how consumers’ emotions toward the changing retail environment affect their desire to return to their pre-pandemic shopping routine. Using scenario-based experiments, I explored the effects of consumers' emotions (hope and fear) on their likelihood of shopping in-store, the moderating role of subjective norms, and the mediating roles of internal locus of control and coping (problem- and emotion-focused). Essay 2 studies how multichannel consumer segments have changed their channel shopping behaviors across two store elimination phases. Using Latent Profile Analysis, I identified up-to-date multichannel consumer segments based on the segments' in-store and online channel usage pre- and during-COVID-19, the factors that described segment membership, and channel switching intentions by segment membership.

Abstract

The development of large language models (LLMs) has caused concern about their potential risks, including how their ability to generate texts plausibly written by a person could affect our shared perception of the social world. Yet, it remains unclear how best to assess and understand the models’ influence on our understanding of social reality. Building on insights into how social worlds are represented within texts, we take initial steps towards developing a framework for analyzing natural language generation’s (NLG) content, as well as its consequences for perceptions of social reality. We demonstrate our “synthetic duality” framework in two parts. First, we show that advanced LLMs can create, with minimal guidance, reasonable portrayals of actors and ascribe relational meaning to those actors – virtual social worlds within texts, or “Mondo-Breigers”. Second, we examine how these synthetic documents with interior social worlds affect readers’ view of social reality. We find that they change individuals’ perceptions of the ideas and rhetoric of actors depicted in the documents, likely by updating individuals’ expectations about the actors and their meanings. However, additional exploratory analyses suggest it is models’ style, not their construction of “Mondo-Breigers”, that might be affecting people’s perceptions. We end by discussing how our study illustrates a methodological approach for using generative AI to conduct sociological research, as well as how NLG may unsettle structural notions of individuality. Namely, reimagining the duality of individuals and groups could help theorize growing homogeneity in an increasingly NLG-informed world.

Abstract

Loneliness is an enduring global health issue. Effective evidence-based loneliness interventions exist but lack sufficient scale to reach many who might benefit from them. Single-session interventions (SSIs), which aim to condense the core elements of evidence-based treatments into brief and broadly acceptable self-guided experiences, offer an opportunity to make robust guidance for overcoming loneliness much more accessible. This dissertation’s objective was to inform the development of effective and broadly-appealing SSIs for loneliness. Study 1 (n = 908) showed that neither a 20-minute SSI version (p = 0.22) nor a 1-hour 3-session version of a self-guided online loneliness intervention (p = 0.23) changed loneliness over four weeks more than an active control SSI did. However, participants reported finding both loneliness interventions more appealing and valuable than the control (ps < 0.02). While some participants reported that the loneliness interventions had a lasting positive impact, the interventions did not appear to be more useful on average than an active control among lonely people aged 16 and older with internet access. Study 2 explored if popular online content was more effective at reducing distress than typical researcher-created SSI content among a sample of mostly crowdworkers struggling with psychological distress. The study (n = 916) showed that a popular online content-centered mental health SSI did not affect participants’ distress over four weeks differently than a mental health SSI with researcher-created content (p = 0.09) or finding content independently on the web (p = 0.42). On the contrary, participants assigned to the researcher-created SSI reported greater improvement than those assigned to the popular online content-based SSI in depressive symptoms (b = -0.44, p = 0.03) and loneliness (b = 0.29, p = 0.04), on average. Future work should aim to improve SSIs’ effectiveness, identify populations for which SSIs are most effective, and implement SSIs in accessible and appealing ways

Abstract

People are more likely to think statements are valid when they agree with them than when they do not. We conducted four studies analyzing the interference of self-reported ideologies with performance in a syllogistic reasoning task. Study 1 established the task paradigm and demonstrated that participants’ political ideology affects syllogistic reasoning for syllogisms with political content but not politically irrelevant syllogisms. The preregistered Study 2 replicated the effect and showed that incentivizing accuracy did not alleviate these differences. Study 3 revealed that syllogistic reasoning is affected by ideology in the presence and absence of such bonus payments for correctly judging the conclusions’ logical validity. In Study 4, we observed similar effects regarding a different ideological orientation: environmentalism. Again, monetary bonuses did not attenuate these effects. Taken together, the results of four studies highlight the harm of ideology regarding people’s logical reasoning.

Abstract

Metaphors are frequently utilized to frame discussions of complicated societal issues, which has been shown to influence decision-making. However, no studies have examined the effects of using multiple metaphors. Specifically, it is unclear how people will react when they read a report framed with two different metaphors that have different entailments. Will the metaphor introduced at the beginning of the report or the one added at the end make more of an impact? Or would the effects of the two metaphors cancel each other out? We hypothesized that participants would be more affected by the metaphor that was presented first. In the present study, participants read a series of four metaphorically framed vignettes. They were randomly assigned to one of two conditions: (1) One Metaphor Condition: where each vignette was framed using two instantiations of a single metaphor, or (2) Two Metaphor Condition: where each vignette was framed using a single instantiation of each of two different metaphors. After reading each vignette, participants answered a question about the vignette with two possible response options, one of which was conceptually consistent with the first metaphor frame. Results supported our hypothesis: overall, participants responded in a manner congruent with the first metaphor they read significantly above chance levels. While metaphor-congruent responding was higher in the One Metaphor Condition, there was not a significant difference between the two conditions on this measure. The implications of these results and the limitations of the current study are discussed.

Abstract

We propose a 3D generation pipeline that uses diffusion models to generate realistic human digital avatars. Due to the wide variety of human identities, poses, and stochastic details, the generation of 3D human meshes has been a challenging problem. To address this, we decompose the problem into 2D normal map generation and normal map-based 3D reconstruction. Specifically, we first simultaneously generate realistic normal maps for the front and backside of a clothed human, dubbed dual normal maps, using a pose-conditional diffusion model. For 3D reconstruction, we "carve" the prior SMPL-X mesh to a detailed 3D mesh according to the normal maps through mesh optimization. To further enhance the high-frequency details, we present a diffusion resampling scheme on both body and facial regions, thus encouraging the generation of realistic digital avatars. We also seamlessly incorporate a recent text-to-image diffusion model to support text-based human identity control. Our method, namely, Chupa, is capable of generating realistic 3D clothed humans with better perceptual quality and identity variety.

Abstract

Deep generative models have been recently extended to synthesizing 3D digital humans. However, previous approaches treat clothed humans as a single chunk of geometry without considering the compositionality of clothing and accessories. As a result, individual items cannot be naturally composed into novel identities, leading to limited expressiveness and controllability of generative 3D avatars. While several methods attempt to address this by leveraging synthetic data, the interaction between humans and objects is not authentic due to the domain gap, and manual asset creation is difficult to scale for a wide variety of objects. In this work, we present a novel framework for learning a compositional generative model of humans and objects (backpacks, coats, scarves, and more) from real-world 3D scans. Our compositional model is interaction-aware, meaning the spatial relationship between humans and objects, and the mutual shape change by physical contact is fully incorporated. The key challenge is that, since humans and objects are in contact, their 3D scans are merged into a single piece. To decompose them without manual annotations, we propose to leverage two sets of 3D scans of a single person with and without objects. Our approach learns to decompose objects and naturally compose them back into a generative human model in an unsupervised manner. Despite our simple setup requiring only the capture of a single subject with objects, our experiments demonstrate the strong generalization of our model by enabling the natural composition of objects to diverse identities in various poses and the composition of multiple objects, which is unseen in training data.

Abstract

Recent, high-profile acts of partisan violence have stimulated interest among academics and the general public in the etiology of support for such violence. Here, we report results of an exploratory study that (1) measures support for partisan violence with both abstract items (e.g., general support for partisan violence) and support for more specific acts (e.g., support for a partisan motivated shooting), (2) follows recently-established best practices by including attention checks to attenuate response bias, and (3) incorporates measures of a wide range of potential confounders as control variables. Across three data collections (total N = 2,003), including two with nationally representative samples, and tracking seven unique operationalizations of support for the use of violence against out-partisans, we find the most consistent and typically largest relationships with an individuals’ reported “need for chaos” (e.g., agreement with statements like: “Sometimes I just feel like destroying beautiful things”) and the extent to which they dehumanize supporters of the opposing party. We speculate this reflects a motivation to use extreme methods (need for chaos) toward one’s political rivals, liberated from the moral restraints that inhibit harming fellow human beings (dehumanization). System justification and social dominance orientation were also both positively related to support for partisan violence, which may reflect partisans’ desire to protect their preferred social order from out-partisans deemed to threaten it. Collectively, these results offer a framework for future research on support for partisan violence, highlighting the role of extreme orientations toward society and rival partisans.

Abstract

Biased responses in survey studies could seriously harm and mislead our economic decision-making. To mitigate survey response bias, we suggest an alternative way of combining two existing strategies, cheap talk and the Bayesian Truth Serum (BTS). In our three proof-of-concept experiments, we found that our alternative approach, named the C-BTS, helps elicit more truthful survey responses even in situations where neither the BTS nor cheap talk alone works well enough, especially in the context of economic valuation of goods. By applying the C-BTS, we have also confirmed that AI-powered services are already significantly enhancing the well-being of our citizens.

Abstract

Artificial intelligence (AI) is reshaping consumer interaction with brands, but little is known about how brands can implement AI tools effectively. Drawing on consumer uniqueness and self-construal theories, the authors examine the implementation of branded AI tools and their influence on consumers' experience, sense of uniqueness, and spending behavior. Across five studies, this research examines consumers' narratives about interacting with a branded AI tool (Study 1); tests the relationships between self-construal, AI-enabled consumer experiences, and avoidance of similarity (Studies 2A and 2B); evaluates in situ experience with a branded AI tool and its implications for spending behavior (Study 3); and delineates consumer preferences about the attributes of branded AI tools (Study 4). The findings reveal that individuals characterized by independent self-construal are prone toward perceiving higher recognition and hedonic values during their experience with branded AI tools, partially enhancing consumer avoidance of similarity and influencing their willingness to pay for products that the AI tool recommends. For practitioners, the findings suggest developing a two-fold value proposition strategy for consumers by generating personal and psychological value together with product and service recommendations.

Abstract

Marketers often use messages such as “Stock up and save” to encourage consumers to buy more units of a product. Governments use messages such as “Store at least a two-week supply of water and food” to encourage consumers to stock up on essentials for emergencies. This research finds that these messages may not work as effectively as hoped and introduces a method that can increase consumers' purchase quantity in these situations. Dubbed as SALE (“Short-Asking with Long-Encouraging”), this method couples a “long-encouraging” statement (e.g., “Stock up for two weeks”) with a “short-asking” statement (e.g., “Think about how many you will consume in one day”) in an advertisement. Two field studies, four lab experiments and a survey with salespeople demonstrated the effectiveness and novelty of SALE and identified the mechanism, moderators and boundary conditions of the effect.

Abstract

We examined how and why commission and omission transgressions differentially affect victims’ forgiveness, and whether apologies are differentially effective at eliciting forgiveness for these transgressions. In Study 1 (N = 376), using online vignettes, victims perceived commission (vs. omission) transgressions as higher in responsibility and intent, and these offense perceptions in turn predicted reduced forgiveness, both on their own and serially through perceptions of immoral behavior and immoral character. However, apologizing (vs. not apologizing) predicted greater forgiveness for both types of offenses. In Study 2 (N = 593), using recalled offenses, forgiveness was lower for commission (vs. omission) transgressions. Study 2 also replicated the mediating roles of offense perceptions and moral judgments, as well as the benefits of apologies for forgiveness across both types of offenses. These studies demonstrate how and why forgiveness might be less forthcoming for commissions, but suggest that apologies are effective for both commissions and omissions.

Abstract

Risk assessments are key legal tools that can inform a number of legal decisions regarding parole sentencing and predict recidivism rates. Due to assessments being historically performed by humans, they can be prone to bias and have come under various amounts of scrutiny. The increased capability and application of machine learning technology has lead the justice system to incorporate algorithms and codes to increase accuracy and reliability. This study researched laypersons’ attitudes towards these algorithms and how they would change when exposed to an algorithm that made errors in the risk assessment process. Participants were tasked with reading two vignettes and answering a series of questions to assess the differences in their perceptions towards machine learning and clinician-based risk assessments. The research findings showed that individuals lent more trust to clinicians and had more confidence in their assessments when compared to machines, but were not significantly more punitive when it came to attributing blame and judgement for the consequences of an incorrect risk assessment. Participants had a significantly more positive attitude towards clinician-based risk assessments, noting their assessments as being more reliable, informed, and trustworthy. Participants were also asked to come to a parole decision using the assessment of either a clinician or machine learning algorithm at the end of the study and rate their own confidence in their decision. Results found that participants were only significantly less confident in their decision when exposed to previous instances of risk assessments with error, but that there was no significant difference in their confidence based solely on who conducted the assessment.

Abstract

Warning labels from professional fact-checkers are one of the most widely used interventions against online misinformation. Prior work suggests that such warning labels are effective at reducing the belief and spread of false content on average. However, there is substantial distrust of fact-checkers, particularly among those on the political right. Does this distrust undermine the effectiveness of fact-checks? In the current work, we investigate this question empirically. In a first correlational study (N=1,000), we establish and validate a measure of trust in fact-checkers. We confirm that more Republican-favoring participants are less trusting of fact-checkers, and also find that skill-based traits such as procedural news knowledge and analytic thinking exacerbate this partisan asymmetry. Next, we conduct meta-analyses across 21 experiments (total N=14,133) in which participants evaluated actual true and false news posts and were randomized to either see no warning labels (control) or to see warning labels on a high proportion of the false posts. We find that warning labels were on average effective at reducing belief in (27.6% reduction), and sharing of (24.7% reduction), false headlines. While warning effects were somewhat smaller for participants with less trust in fact-checkers, warning labels nonetheless significantly reduced belief in, and sharing of, false news even for those most distrusting of fact-checkers. Together, these results suggest that fact-checker warning labels are a broadly effective tool for combatting misinformation.

Abstract

This study investigated the impact of survivor and participant racial identity on the behavioral and affective responses to sexual assault disclosures. I implemented a novel approach to measuring disclosure recipient social reactions, measuring participants’ facial expressions of emotion while watching a sexual assault disclosure video. The present study used a 2 (participant racial identity: Black vs white) x 3 (survivor racial identity: Black, white, Asian American) design to examine the impact of intergroup and interracial biases on facial expressions, empathy, and positive and negative social reactions, such as offering aid or support versus blaming or distancing. Contrary to hypotheses, I found little evidence for affiliative facial mimicry of survivors, which may have been associated with participants’ motivation to socially distance themselves from the survivor. However, preliminary evidence for intergroup bias was observed for overall facial expressions of disgust and sadness. Notably, most differences in reactions were observed among Black-white dyads, providing some initial evidence for the specificity of this effect. These findings emphasize the importance of considering intergroup dynamics in the context of sexual assault disclosures and the impact of these dynamics on implicit reactions during the disclosure.

Abstract

While acting officials in federal agencies have become more common in recent years, presidents still utilize the traditional nomination process, which constrains presidents’ choices, for most executive branch appointments. Recent work emphasizes presidents’ incentives for using acting officials, but few scholars have considered what keeps presidents from using them even more often. We argue presidents’ use of acting officials, like other forms of unilateral action, is constrained by public opinion; while actings may be expeditious policy tools for presidents,the public perceives them to undermine the executive branch’s legitimacy and competence and punishes presidents accordingly. Through three survey experiments leveraging real-world instances of President Joe Biden’s usage of acting officials, we find little evidence the public reacts negatively to acting officials in agency leadership. While some institutional forces must encourage presidents to seek senatorial advise and consent for their nominees, our evidence does not indicate public opinion provides that constraint.

Abstract

In a pre-registered randomized experiment, we found that, relative to a reverse-chronological baseline, Twitter's engagement-based ranking algorithm may amplify emotionally charged, out-group hostile content and contribute to affective polarization. Furthermore, we critically examine the claim that the algorithm shows users what they want to see, discovering that users do *not* prefer the political tweets selected by the algorithm. Finally, we explore the implications of an alternative approach to ranking content based on users' stated preferences and find a reduction in angry, partisan, and out-group hostile content but also a potential reinforcement of echo chambers. The evidence underscores the necessity for a more nuanced approach to content ranking that balances engagement, users' stated preferences, and sociopolitical outcomes.

Abstract

Emotions are conspicuous components of radicalization, violent extremism, and conspiracy ideation. Of the emotions studied for their contribution to those social pathologies, envy has been relatively unexplored. We investigate the relationship between envy, radicalization, and conspiracy ideation. Envy appears to affect core aspects of radicalization, particularly the endorsement of extremism and the acceptance of violent means to achieve one’s ends, while radicalization facilitates the adoption of conspiracy ideation, rather than the latter being a cause of radicalization. Implications for future research on radicalization and violent extremism are discussed.

Abstract

Although research on self-concept is extensive, few studies have examined the relationship between specific aspects of self-concept structure (e.g., self-concept clarity) and self-regulation constructs (e.g., goal commitment). In addition, research has linked aspects of self-concept structure with well-being outcomes, but less is known about the mechanisms involved in these relationships. This research addresses these two issues by examining self-concept structure (self-concept clarity and self-concept differentiation) as antecedents of key self-regulatory mechanisms (goal commitment and goal conflict) and how these linkages may help explain connections between self-concept structure and well-being and performance. More specifically, two studies examined these relationships, with Study 1 investigating these variables in individuals’ lives in general and Study 2 focusing on these variables in the work context. Regarding self-regulatory mechanisms, SCC was related to goal commitment and SCD was related to both goal commitment and goal conflict. For psychological well-being, SCC was related to eudaimonic, hedonic, and work engagement, whereas SCD was related to eudaimonic and hedonic well-being. In addition, goal commitment was related to work engagement and goal conflict was related to eudaimonic well-being and positive affect. Finally, for performance-related outcomes, SCC was positively related to in-role job performance. In addition, goal commitment was related to goal progress and in-role job performance. However, results for the indirect effects were mixed. Taken together, these results suggest that aspects of self-concept structure may have notable implications for self-regulatory mechanisms, psychological well-being, and performance outcomes.

Abstract

Manipulative content and propaganda are an information quality concern on social media. Although attention has been turned toward mis- and disinformation, government-controlled social media pages have been able to quietly share information to encourage users toward beliefs without sharing content that may be clearly labeled as false. To combat the effect of quiet foreign government persuasion attempts, Facebook debuted a “state-controlled media” label in June 2020 to alert users that a post originates from a page associated with selected governments, including Russia and China. We conduct two online randomized experiments to better understand the causal impact of these labels on intentions to engage with content on Facebook. We augment our experiments by analyzing field data from Facebook before and after these labels were implemented and studying actual engagement. We find that labels are effective in reducing engagement on social media if users notice the labels and if the label is associated with a country that is perceived negatively. More users notice the label if trained. The combination of these studies suggests that these labels can successfully reduce engagement with posts by Russian and Chinese state-controlled pages but may even increase engagement for other countries perceived positively like Canada.

Abstract

What do people think of those who respond to confrontation by acknowledging personally prejudiced behavior? In six experiments (N = 3344), people judged a man who made a prejudiced comment and responded to confrontation by acknowledging, denying, or, in some cases, saying nothing about his prejudice. Participants consistently evaluated someone who acknowledged prejudice as warmer, more moral, and ironically, less prejudiced than someone who denied. People also perceived acknowledging as more appropriate and less typical than denying regardless of whether the prejudice was racism or sexism. Moreover, men and women, White, Black, and Asian people alike evaluated acknowledgements more positively than denials. Evidence from multiple experiments suggests that people form more positive impressions of those who acknowledge than deny because acknowledgment signals more of a learning orientation to prejudice and intergroup relations. Although people frequently respond to confrontation by denying prejudiced behavior, there appears to be an upside to acknowledging.

Abstract

This dissertation contributes to retailing and marketing research by exploring how retailer channel decisions (i.e., physical store closures) due to the changes in the retail environment impact consumers’ shopping behaviors. The pandemic caused many retailers to remove physical stores and move online rapidly (e.g., Brooks Brothers and Neiman Marcus) (Pacheco, 2020). As the retail environment changes, understanding consumers’ shopping behaviors is critical for retailers to stay competitive, maintain, and attract new customers. Thus, Essay 1 studies how consumers’ emotions toward the changing retail environment affect their desire to return to their pre-pandemic shopping routine. Using scenario-based experiments, I explored the effects of consumers' emotions (hope and fear) on their likelihood of shopping in-store, the moderating role of subjective norms, and the mediating roles of internal locus of control and coping (problem- and emotion-focused). Essay 2 studies how multichannel consumer segments have changed their channel shopping behaviors across two store elimination phases. Using Latent Profile Analysis, I identified up-to-date multichannel consumer segments based on the segments' in-store and online channel usage pre- and during-COVID-19, the factors that described segment membership, and channel switching intentions by segment membership.

Abstract

Objectives: There are racial and ethnic disparities in sleep duration, with members of historically marginalized groups typically reporting shorter sleep than White Americans. This study examines subjective social status (SSS) as a moderator, and variation in ideal sleep norms as a mediator, of differences in sleep duration between racial/ ethnic groups. Methods: Asian, Black, Hispanic, and non-Hispanic White respondents in an online survey reported their typical weeknight and weekend-night sleep duration, along with estimates of ideal sleep duration norms. Objective and subjective indicators of socioeconomic status were also assessed. A conditional process analysis was used to examine whether racial or ethnic differences in sleep duration were a) moderated by SSS and b) mediated by ideal sleep duration norms. Results: Racial/ethnic disparities in sleep duration varied by group. Hispanic participants reported shorter weeknight sleep than White participants. In Asian and Black participants, shorter weeknight sleep relative to White participants was only observed at medium (Black) or high (Black and Asian) levels of SSS. Shorter norms for ideal sleep duration partially mediated differences in sleep duration between Black and White adults, but not the other racial/ethnic groups. There was no evidence of moderated mediation. Neither income nor education moderated racial/ethnic disparities in sleep duration. Conclusions: Racial and ethnic disparities in sleep duration may partially depend on SSS. Continued research into moderators and mediators of racial/ethnic differences in sleep duration is warranted.

Abstract

This research shows that people hold two distinct lay theories, or common-sense explanations, about how wealth is created. Specifically, people may adopt an income lay theory (i.e., that earning more money is the major driver of wealth) or an expense lay theory (i.e., that spending less money is the major driver of wealth). When the expense lay theory is more accessible, either dispositionally or situationally, people are likely to allocate more money to retirement accounts. Ten studies (including four in the web appendix) collectively offer triangulating evidence for this effect and suggest that it arises because the greater accessibility of an expense lay theory is associated with a future financial orientation. Overall, this work contributes to research on lay theories, intertemporal choice, retirement planning, and financial decision-making.

Abstract

This study examined the impact of self-uncertainty and challenge or threat appraisals on entitative group appeal. Predictions were based on uncertainty-identity theory’s findings regarding uncertainty and entitativity (Hogg, 2021), and on studies of challenge and threat appraisals in intergroup relations (Scheepers, 2009). Participants (N = 167) from CloudResearch Connect were primed with a sense of either challenge or threat as a cognitive/affective demand and their level of self-uncertainty was measured. Participants then responded to a series of questions regarding their attraction to entitative group characteristics. Hypothesis 1 predicted that there would be a positive relationship between self-uncertainty and attraction to entitative group characteristics. Hypothesis 2 predicted that challenge/threat appraisal state would moderate the relationship between self-uncertainty and attraction to entitative characteristics. Hierarchical linear regression of the main and interaction effects did not find support for Hypotheses 1 and 2. However, a significant relationship was found between self-uncertainty and reported threat appraisal, indicating that there may be a relationship between self-uncertainty and individuals’ appraisals of performance-motivated situations.

Abstract

Numerous psychological findings have shown that mere exposure to ideas makes those ideas seem more true, a finding commonly referred to as the “illusory truth” effect (e.g. Hasher, Goldstein, & Toppino, 1977). In the presence of pervasive misinformation, this basic feature of cognition may undermine the functioning of a democratic society (Pennycook, Cannon, & Rand, 2018). However, genuine beliefs do not only affect judgments about individual statements, they also imply other beliefs and drive decision-making. Here, we sought to examine whether mere exposure to statements produces genuine beliefs by examining whether people draw inferences from statements after mere exposure. Surprisingly, and in contrast to fluency or familiarity-based accounts of the illusory truth effect (e.g. Dechêne, Stahl, Hansen, & Wänke, 2010), we found that exposure to “premise” statements affected participants’ truth ratings for novel “implied” statements. This “illusory implication” effect suggests that the consequences of exposure to false statements reach further than previously thought and calls for a new mechanistic account of these effects.

Abstract

Self-control is essential for managing our actions, yet its exertion is perceived as effortful. Performing a task may require effort not only because of its inherent difficulty but also due to its potential for inducing boredom, as boredom has been shown to be self-control demanding by itself. So far, the extent of self-control demands during boredom and its temporal dynamics remain elusive. We will employ a multimethod approach to address this knowledge gap. Ninety-five participants will take part in an easy and difficult version of the Stroop task. During both tasks, they will indicate several times their current sensation of task difficulty, boredom, boredom-related effort, and difficulty-related effort. We will test if pupil size, as a physiological indicator for cognitive effort, is predicted more accurately by overall cognitive effort (difficulty- and boredom-related) than by task-difficulty-related effort alone. This research will uncover the level of effort in experiencing boredom which is pertinent not only for self-control research but also to any research area dealing with boredom or the performance of repetitive tasks.

Abstract

In recent years, understanding the decision-making process of black-box models has become not only a legal requirement but also an additional way to assess their performance. However, the state of the art post-hoc interpretation approaches rely on synthetic data generation. This introduces uncertainty and can hurt the reliability of the interpretations. Furthermore, they tend to produce explanations that apply to only very few data points. This makes the explanations brittle and limited in scope. Finally, they provide scores that have no direct verifiable meaning. In this paper, we present BELLA, a deterministic model-agnostic post-hoc approach for explaining the individual predictions of regression black-box models. BELLA provides explanations in the form of a linear model trained in the feature space. Thus, its coefficients can be used directly to compute the predicted value from the feature values. Furthermore, BELLA maximizes the size of the neighborhood to which the linear model applies, so that the explanations are accurate, simple, general, and robust. BELLA can produce both factual and counterfactual explanations. Our user study confirms the importance of the desiderata we optimize, and our experiments show that BELLA outperforms the state-of-the-art approaches on these desiderata.

Abstract

A growing literature in political science examines the tension between secrecy and accountability as democratic leaders conduct foreign affairs. However, this literature presents a puzzle: while observational work shows that leaders make efforts to avoid getting caught, experimental work demonstrates that the public does not have an aversion to leaders acting secretly. What, then, are the electoral costs of acting covertly? To demonstrate the electoral costs of covert operations, we study voters’ reactions to legislative oversight of covert operations, not their reactions to the operation itself. Using a preregistered and well-powered survey experiment with a 2× 2 factorial design, we show that the legislators’ audience (their in-party voters) overwhelmingly prefers public investigations into covert operations when the operation fails. When the operation succeeds, though, their preferences are much weaker. This dynamic has important implications for foreign policy—the president cannot take necessary risks in protecting the national interest and the investigations harm the country’s international reputation.

Abstract

Technological advancements simplify the application of dynamic pricing, i.e., the flexible and rapid adjustment of prices to changes in demand. Consequently, companies increasingly use dynamic pricing in their business models, although research reports negative consequences on customer fairness perceptions. This holds not only for one-sided businesses, but also for popular two-sided platforms. However, these platforms differ from one-sided businesses in that the total prices paid by customers consist of product prices and platform fees – and both price components can be dynamically adjusted. In an online experiment, we examine customers’ fairness perceptions and purchase intentions when product price and platform fee change dynamically. We find that dynamic price increases reduce fairness perceptions and purchase intentions, while the cause of the price increases is irrelevant to customers. These results indicate an imbalance in the risks and benefits of dynamic pricing between the pricing strategies of the platform and the provider.

Abstract

With the increasing prevalence of social networking sites (SNS), understanding how individuals perceive and judge each other in online contexts is vital. This dissertation investigates the accuracy of personality judgements made based on two popular platforms, Twitter and Instagram. This dissertation builds upon previous research which found Instagram profiles provided for more accurate personality judgements, explores the cues that contribute to accurate perceptions in these online spaces, and evaluates two methods of training individuals to improve personality judgement accuracy based on Twitter profiles. In Study 1, cues were coded on 102 social media profiles, with coders recording a variety of objective and subjective cues both common across platforms and unique to each platform. The Brunswick Lens Model was utilized to identify cues that were valid (actually pertaining to the targets’/profile owners’ personality) and/or utilized (used by judges to form impressions). The hypothesis that higher levels of anonymity on Twitter would explain differences in accuracy between platforms was also assessed. Anonymity was not found to differ significantly between platforms, but was found to influence normative perceptions, with less anonymous targets being perceived with higher normativity. Study 2 evaluated two methods for training and improving judgement accuracy based on Twitter profiles. Utilizing valid yet unutilized cues identified in Study 1, 100 judges received online training about the personality traits of open-mindedness and conscientiousness, and cues on Twitter profiles that are indicative of those traits. Half of these 100 judges also received personalized feedback about the accuracy of their judgements. Fifty judges served as a control group and received no training. It was predicted that judges that received training and feedback would be more accurate than judges that received only training, and that both training groups would be more accurate in their perceptions than the control group. Training was not found to significantly improve judgement accuracy, but valuable insights and avenues for future research were uncovered. This research contributes to the understanding of both the complexities of social relationships online and research on training and improving judgement accuracy.

Abstract

The present research examines the effect of holistic-analytic thinking style on causal responsibility. Across seven studies (N = 4,103), participants’ thinking style was either measured or manipulated. Then, the valence or number of consequences varied in several scenarios involving a cause–consequence relationship. As a dependent measure, participants indicated the degree of responsibility attributed to the cause mentioned in each scenario. The results revealed that holistic (vs. analytic) participants assigned more responsibility to the cause when the consequences presented were a combination of positive and negative outcomes (vs. univalent), and when multiple (vs. single) consequences were triggered in the scenario. To explore the explanatory factor for these results, a final study manipulated the complexity of the consequences, along with the number. The results of this research suggested that holistic (vs. analytic) individuals consider the degree of complexity of consequences to establish causal attribution.

Abstract

The United States produces an exorbitant amount of waste. Recycle and reuse are two ways to manage the waste problem. Since even non-political environmental issues are often polarized in the United States, we attempt to affirm group identity by highlighting the moral values that appeal to conservatives and liberals to examine downstream effects on message derogation and behavioral intention. Specifically, we explore how message derogation is associated with risk perception, self-efficacy, and behavioral willingness to recycle and reuse, as moderated by political ideology. Our findings indicate that linking environmental behaviors to specific moral values may not be the best way to encourage citizens to recycle and reuse. In particular, conservatives tend to view a persuasive message about plastic waste as overblown and manipulative, thus highlighting the need to strategically design environmental communication targeting conservatives. Moreover, to combat message derogation, effective ways to elevate risk perceptions and enhance self-efficacy are needed.

Abstract

Recent years have brought increased accountability for personal misconduct, yet often, unequal consequences have resulted from similar offenses. Findings from a unique archival data set (N = 619; all university faculty) and three preregistered experiments (N = 2,594) show that the perceived artistic-versus-scientific nature of the offender’s professional contributions influences the professional punishment received. In Study 1, analysis of four decades of university sexual-misconduct cases reveals that faculty in artistic (vs. scientific) fields have on average received more severe professional consequences. Study 2 demonstrates this experimentally, offering mediational evidence that greater difficulty morally decoupling art (vs. science) contributes to the phenomenon. Study 3 provides further evidence for this mechanism through experimental moderation. Finally, Study 4 shows that merely framing an individual’s work as artistic versus scientific results in replication of these effects. Several potential alternative mechanisms to moral decoupling are tested but not supported. Practical and theoretical implications are discussed.

Abstract

Asynchronous Video Interviews (AVIs) have revolutionized the hiring process, offering flexibility, cost-effectiveness, and convenience to both organizations and job applicants. While recent studies have highlighted the potential for background cues in AVIs to inadvertently disclose non-job-related information about job applicants, researchers have yet to explore this with socioeconomic status (SES). This study investigates whether AVIs might reveal cues about an applicant’s SES, which might remain concealed during face-to-face interviews, thereby potentially introducing unique biases in the hiring process. We determined if evaluators could discern SES differences based on a job applicant’s background and whether these cues influenced the perceived hireability of the job applicant. To enhance the realism of our findings and understand when such biases may be exacerbated, we simulated the conditions a hiring manager might face by inducing cognitive load (CL). In a sample of N = 300 American Cloud Research Connect participants, we used a 2 (low; high SES) by 2 (low; high CL) betweensubjects experimental design. We found that although evaluators could identify differences in SES and did experience a difference in CL, these two factors did not directly influence the perceived hireability of the job applicant. However, contrary to our expectations, evaluators under significant CL took longer to decide on a job applicant’s suitability. Furthermore, we also investigated the role of evaluators’ characteristics, such as their own SES, attitude towards poverty (ATP), and social dominance orientation (SDO). Although these did not directly influence their ratings of the job applicant, we identified noteworthy correlations: participants’ perceptions of the SES from the background correlated with the job applicant’s a) perceived hireability, b) perceived SES, and c) perceived competence. These findings emphasize the need for further research into the subtle cues evaluators might use to gauge SES, which could impact a job applicant’s AVI evaluation.

Abstract

Previous research has demonstrated the harmful impacts of adverse childhood circumstances on adult romantic relationships. The current study examines the negative influence of childhood unpredictability on adult relationships and how this effect may be buffered and enhanced, respectively, by cognitive ability and Machiavellianism. A total of 256 participants completed measures of childhood unpredictability, Machiavellianism, cognitive ability, and relationship satisfaction. Individuals who experienced childhood unpredictability reported lower adult romantic relationship satisfaction. Machiavellianism was found to significantly enhance the negative effects of childhood unpredictability on adult relationships for men only. Cognitive ability was not a significant moderator.

Abstract

Within the social media community, influencers engage in a variety of collaborative practices such as tagging, reposting content from, or forming partnerships with other influencers and brands. While such collaborative efforts are a known practice, less is understood about how influencer collaborations affect consumers' perceptions of the partnering influencers, specifically when a status differential exists within the collaboration. We suggest that such collaborative practices, specifically those where the focal influencer has a higher status than the collaborating partner, may help to weaken consumers' perceptions that the influencer's actions are purely self-focused. A pilot study, analyzing both influencer–influencer collaborations and influencer–brand collaborations, provides evidence that influencers engage in collaborations with other influencers and brands of different status levels. Two studies then support our theorizing that influencers who collaborate with lower-status influencers are perceived as less self-serving and more altruistic, while influencers who collaborate with lower-status brands are only perceived as less self-serving. This suggests that, for influencers who desire to enhance how consumers perceive them, an effective strategy is to engage in collaborations with either a lower-status influencer or brand.

Abstract

The growing use of minority ownership labels (e.g., Black-owned or woman-owned) has effectively increased consumer demand for these businesses. The resulting growth occasionally leads to brand failuresas these businesses encounter growing pains. The current research examines when and why minority ownership awareness influences consumer behavior with special attention given to the context of brand failures. The authors suggest that minority ownership awareness minimizes the negative effect of brand failure because consumers want to compensate for the disadvantages in the marketplace that minorities face (i.e., compensatory assessment). This leads to increased brand evaluations and willingness to pay. Six experiments and analyses of over 40,000 Google reviews for Black-owned restaurants demonstrate the ownership awareness effect, its underlying process, and various boundary conditions. Consistent with a compensatory assessment account, the ownership awareness effect is limited to minority (vs. nonminority) owners, negative (vs. neutral) information, product (vs. moral) failures, upand-coming (vs. established) brands, and people with low (vs. high) social dominance orientation.

Abstract

The growing use of minority ownership labels (e.g., Black-owned or woman-owned) has effectively increased consumer demand for these businesses. The resulting growth occasionally leads to brand failuresas these businesses encounter growing pains. The current research examines when and why minority ownership awareness influences consumer behavior with special attention given to the context of brand failures. The authors suggest that minority ownership awareness minimizes the negative effect of brand failure because consumers want to compensate for the disadvantages in the marketplace that minorities face (i.e., compensatory assessment). This leads to increased brand evaluations and willingness to pay. Six experiments and analyses of over 40,000 Google reviews for Black-owned restaurants demonstrate the ownership awareness effect, its underlying process, and various boundary conditions. Consistent with a compensatory assessment account, the ownership awareness effect is limited to minority (vs. nonminority) owners, negative (vs. neutral) information, product (vs. moral) failures, upand-coming (vs. established) brands, and people with low (vs. high) social dominance orientation.

Abstract

Public opinion surveys are vital for informing democratic decision-making, but responding to rapidly changing information environments and measuring beliefs within niche communities can be challenging for traditional survey methods. This paper introduces a crowdsourced adaptive survey methodology that unites advances in natural language processing and adaptive algorithms to generate question banks that evolve with user input. The CSAS method converts open-ended text provided by participants into Likert items, and applies a multi-armed bandit algorithm to determine user-provided questions that should be prioritized. The method’s adaptive nature allows for the exploration of new survey questions, while imposing minimal costs in survey length. Applications in the domains of Latino information environments and issue importance showcase CSAS’s ability to identify claims or issues that might otherwise be difficult to track using standard approaches. I conclude by discussing the method’s potential for studying topics where participant-generated content might improve our understanding of public opinion.

Abstract

A long-standing debate in political psychology considers whether individuals update their beliefs and attitudes in the direction of evidence or grow more confident in their convictions when confronted with counter-attitudinal arguments. Though recent studies have shown that instances of the latter tendency, which scholars have termed attitude polarization and belief backfire, are rarely observed in settings involving hot-button issues or viral misinformation, we know surprisingly little about how participants respond to information targeting deeply held attitudes, a key condition for triggering attitude polarization. We develop a tailored experimental design that measures participants' positions regarding their most important issues and randomly assigns them to different mixtures of personalized pro-attitudinal and counter-attitudinal information using the large language model GPT-3. We fail to recover evidence consistent with attitude polarization across three studies. We conclude by discussing implications for the study of political cognition and the measurement of attitudes.

Abstract

This work seeks to understand the process of sexual consent communication in the moments leading up to a sexual experience between two people. The nature of this research is exploratory and focuses on understanding how people report and perceive giving consent to their sexual partners, as well as how people report and perceive that sexual consent has been given to them. Additionally, the study explores some ways in which miscommunication can happen in the moments leading up to sexual activity. Using the framework of affirmative sexual consent, the research bridges existing scholarship in the field of sexual psychology, sexual education, and sexual violence/assault by introducing the element of sexual communication. Using a latent and manifest content analysis of participant’s survey data, the outcome of the research can help pave the way for future research that will lead to the successful implementation of affirmative sexual consent policies, sexual education curriculum, and communication practices.

Abstract

COVID-19 and the government shelter-in-place forced millions of traditional office employees to work outside their physical location, and instead, work as remote or work-from-home (WFH) employees. Even though the pandemic is over, this novel phenomenon has changed work characteristics and perceptions of employee outcomes moving forward. In addition, the mass exodus from office workers to remote workers has left a gap in the literature. The infrequency of remote workers before the pandemic is disproportionate to the many remote workers today, leaving the generalizability of WFH employee outcomes incomplete. The primary objective of this research is to examine the challenges associated with remote work, and their impact on the ability to balance professional obligations and family responsibilities. Examining the relationship between virtual work characteristics, autonomy and monitoring, and their effects on employee performance and well-being via work-home interference, we surveyed 381 full-time employees who work remotely at least one day a week. We found support for direct relationships between autonomy and performance and autonomy and work interference with family. In addition, we found direct relationships between work-home interference and performance and well-being. This study provides valuable insights into the experiences and perspectives of WFH employees.

Abstract

The rapid deployment of security robots across our society calls for further examination of their acceptance. This study explored human acceptance of security robots by theoretically extending the technology acceptance model to include the impact of autonomy and risk. To accomplish this, an online experiment involving 236 participants was conducted. Participants were randomly assigned to watch a video introducing a security robot operating at an autonomy level of low, moderate, or high, and presenting either a low or high risk to humans. This resulted in a 3 (autonomy) × 2 (risk) between-subjects design. The findings suggest that increased perceived usefulness, perceived ease of use, and trust enhance acceptance, while higher robot autonomy tends to decrease acceptance. Additionally, the physical risk associated with security robots moderates the relationship between autonomy and acceptance. Based on these results, this paper offers recommendations for future research on security robots.

Abstract

The research outlined in this dissertation provides a holistic exploration into the intricate factors influencing user expectations and trust in Automated Vehicles (AVs), both of which are central to the acceptance and adoption of such transformative technologies. AVs, with their potential to revolutionize transportation through enhanced safety, efficiency, and convenience, have generated widespread interest. However, persisting apprehensions around AV safety, performance, and reliability create a barrier to their widespread acceptance and utilization, suggesting a discrepancy between the theoretical advantages of AVs and public perceptions thereof. To unravel this discrepancy and effectively accelerate AV adoption, this dissertation undertakes a multifaceted investigation into the factors shaping public perceptions, specifically focusing on the formation of expectations and trust. My research pivots around three core research questions: What individual factors shape people's expectations of AVs? How do these expectations impact their trust in AVs? How does the gender similarity between humans and AV explanation voices affect trust, and how is this moderated by gender-role congruity? The answers to these questions elucidate the intricacies of cognitive and affective trust development in the context of AV adoption. Results from my dissertation highlight three major findings. One, individual characteristics such as demographic factors such as age, gender, and ethnicity, along with personality traits (e.g., extraversion, agreeableness, conscientiousness), significantly shape people’s preconceived expectations of AVs. Two, expectations significantly mold the level of trust in AVs, influenced by the disconfirmation effect. Three, the study demonstrated that the impact of gender similarity between users and the AV’s explanatory voice could be moderated by the expected role of the vehicle. Overall, this dissertation embarks on a profound exploration of expectations, trust, and design elements, offering critical insights that will shape the forward path for AVs development and implementation. It dissects the intricate dynamics of expectation and trust formation, essential for the user acceptance and adoption of AVs. The study also underscores the powerful role of both user-centric and voice characteristic design in influencing these factors. By bringing these components to light, this research helps navigate the complexities and potentials of AVs, paving the way for an imminent paradigm shift in transportation.

Abstract

With the wide availability of Large Language Models and generative AI, there are four primary paradigms for Human-AI collaboration: human only, AI only (ChatGPT-4), augmented human (where a human making the final decision with AI output as a reference), or augmented AI (where the AI making the final decision with human output as a reference). In partnership with one of the world’s leading consulting firms, we enlist professional content creators and ChatGPT-4 to create advertising content for products and persuasive content for campaigns following the aforementioned paradigms. First, we find that, contrary to the expectations of existing algorithm aversion literature on conventional predictive AI, content generated by generative AI and augmented AI is perceived as of higher quality than that produced by human experts and augmented human experts. Second, revealing the source of content production reduces – but does not reverse – the perceived quality gap between human- and AI-generated content. This bias in evaluation is predominantly driven by human favoritism rather than AI aversion: knowing the same content is created by a human expert increases its (reported) perceived quality, but knowing that AI is involved in the creation process does not affect its perceived quality. Further analysis suggests this bias is not due to a “quality prime” as knowing the content they are about to evaluate comes from competent creators (e.g., industry professionals and state-of-the-art AI) without knowing exactly the creator of each piece of content does not increase participants’ perceived quality.

Abstract

Introducing automated vehicles (AVs) on public roads may challenge established norms as drivers in human-driven vehicles (HVs) learn to interact with AVs. Our study utilizes a game theory framework to investigate how driver and vehicle driving styles (aggressive vs. conservative), interaction types (HV-HV vs. HV-AV), scenario types (chicken game vs. public goods game scenarios), and time constraints (high vs. low) influence human drivers’ decision-making in mixed-traffic environments. According to an online survey study, drivers with aggressive driving styles and high time constraints were more likely to take aggressive actions. More importantly, there were significant interaction effects between vehicle driving styles and scenario types, between scenario types and time constraints, and between driver driving styles and interaction types on driver decision-making. Our findings provide essential insights into the design of AVs and promote the development of related laws and policies to facilitate human-machine cooperation in mixed-traffic environments.

Abstract

People seem to have stronger disapproving reactions when they have unfairly suffered from bias than when they have unfairly benefited from it (i.e., they seem less concerned when they have experienced positive bias). Is this because people do not care about the consequences of bias if it has positively affected them, or is it because they fail to notice positive bias? We argue that it is the latter, and that increasing awareness of a victim who has been harmed can “remove the blinders” of the beneficiary of bias. Across seven pre-registered studies of American participants, we tested the effect of a salient victim on people who have experienced positive bias. Our results show that when a victim has been made salient, beneficiaries of bias are more likely to recognize and condemn the positive bias, and they are also more likely to act to correct it. We found this salient victim effect when people reflected on their own positive treatment in society, when they benefited from favoritism in interpersonal interactions, and when they imagined benefiting from nepotism. The effect emerged with both direct and indirect manipulations of the victim. Moreover, the presence of a salient victim spurred more action in those who experienced positive bias even when there was a personal cost. We discuss the contributions of our research to the fairness, morality, and bias literatures.

Abstract

Social scientists rely heavily on data collected from human participants via surveys or experiments. To obtain these data, many social scientists recruit study participants from online panels that provide access to large numbers of people willing to do tasks for modest compensation. In a large study (total N= 13,053), we explore nine non-probability samples of American respondents drawn from panels used in social science research, comparing them on three dimensions: data quality (attention, effort, lying, speeding, and attrition), representativeness (demographics, attitude typicality, and responding to experimental treatments), and professionalism (number of studies taken, frequency of taking studies, and modality of device the study is taken on). We document substantial variation across these samples on each dimension. Most notably, there is a clear tradeoff between the representativeness and data quality of samples, such that samples with higher data quality tend to be less representative, and vice versa. Prolific, a Mechanical Turk panel maintained by a research group at Stanford, Cloud Research’s Connect panel, and Cloud Research’s Mechanical Turk panel were highest on data quality, but the least representative. Cloud Research’s Connect panel with demographic quotas and Prolific’s “Nationally representative” quota option maintained the same level of data quality and were somewhat more representative. Bovitz-Forthright and Lucid were the most representative samples but had lower data quality (with data quality on Lucid being particularly low). Interestingly, this tension between data quality and representativeness can be somewhat mitigated by using the most representative samples and screening for attentiveness: filters can allow Bovitz-Forthright and (via much heavier filtering) Lucid to achieve data quality parity comparable with the higher scoring samples, with doing so preserving representativeness on Bovitz-Forthright (but reducing representativeness on Lucid). Finally, recruiting participants directly from Mechanical Turk was the worst sample on all measured dimensions. Our findings provide a guide to help researchers decide which sample is optimal given one’s research question and constraints.

Abstract

Purpose The purpose of this Real Impact Research Article is to empirically explore one of the most controversial and elusive concepts in knowledge management research – practical wisdom. It develops a 10-dimensional practical wisdom construct and tests it within the nomological network of counterproductive and productive knowledge behavior. Design/methodology/approach A survey instrument was created based on the extant literature. A model was developed and tested by means of Partial Least Squares with data obtained from 200 experienced employees recruited from CloudResearch Connect crowdsourcing platform. Findings Practical wisdom is a multidimensional construct that may be operationalized and measured like other well-established knowledge management concepts. Practical wisdom guides employee counterproductive and productive knowledge behavior: it suppresses knowledge sabotage and knowledge hiding (whether general, evasive, playing dumb, rationalized or bullying) and promotes knowledge sharing. While all proposed dimensions contribute to employee practical wisdom, particularly salient are subject matter expertise, moral purpose in decision-making, self-reflection in the workplace and external reflection in the workplace. Unexpectedly, practical wisdom facilitates knowledge hoarding instead of reducing it. Practical implications Managers should realize that possessing practical wisdom is not limited to a group of select, high-level executives. Organizations may administer the practical wisdom questionnaire presented in this study to their workers to identify those who score the lowest, and invest in employee training programs that focus on the development of those attributes pertaining to the practical wisdom dimensions. Originality/value The concept of practical wisdom is a controversial topic that has both detractors and supporters. To the best of the author’s knowledge, this is the first large-scale empirical study of practical wisdom in the knowledge management domain.

Abstract

Names are frequently used in social science research to manipulate identities such as race and gender. However, names may signal unintended identities or downplay intended identities. Three studies (N= 1,100 US participants) examined the gendered evaluations of names from five racial groups: Chinese, Indian, Black, Hispanic, and White. Studies 1 and 2 consistently found that Chinese and Indian female names were perceived as less feminine and more masculine than the three other racialized female names, which contradicts extant findings using Asian female faces. Chinese and Indian male names, on the other hand, were considered more feminine and less masculine than the other racialized male names. Study 3 found that participants expressed greater uncertainty and lower confidence about the gender of Chinese and Indian names compared to other racialized names. This research raises potential methodological concerns regarding the effectiveness of racialized names in signaling the gender of Asian ethnic groups.

Abstract

With the rapid evolution of artificial intelligence (AI), this study aims to examine the interplay among the perceived usability, perceived enjoyment, perceived responsiveness, and intention to continue using ChatGPT. Structural equation modeling (SEM) was used to investigate our proposed model. We recruited 446 ChatGPT users through an online survey conducted on the Connect platform, powered by CloudResearch. Perceived usability (β = 0.254) and enjoyment (β = 0.438) significantly influence satisfaction with ChatGPT. However, perceived responsiveness is not significantly related to perceived attachment or satisfaction. Furthermore, we established that perceived attachment (β = 0.405) and satisfaction (β = 0.447) are pivotal in shaping users’ intentions to continue using ChatGPT, providing insights into human–AI interactions. The practical implications of our findings suggest that generative AI chatbots should be crafted with a focus on enjoyable, user-centered interfaces that foster long-term user satisfaction and engagement. AI developers should design AI conversation flows and chatbot personas to optimize the user experience.

Abstract

Our tendency to be curious about our environment and our capacity to effectively explore it are central to human intelligence. This dissertation takes a functional approach to investigating curiosity and exploration, examining the systematic, and at times approximate, manner humans seek information in service of their varied goals. In chapter one, I examine exploratory behavior in the service of a prescribed goal using a paradigm that allows for tight experimental control. Comparing participants’ choices to the optimal strategy for this task, I show that humans explore as if balancing seeking goal-relevant information with managing their limited cognitive resources. Chapters two and three focus on epistemic information-seeking driven by curiosity in more naturalistic contexts. In chapter two I chart the relationship between information seeking and motivation, asking how an acute change to motivation impacts information seeking. By measuring the profound changes to motivation affected by the onset of the COVID-19 pandemic in the United States, as well as information seeking behavior and expectations of usefulness, I show that motivation both directs information seeking to goal-relevant information, as well as energizing the seeking of information of any kind. In chapter three, I examine how fine-scale changes to motivation, brought about by previous learning experiences, influence curiosity in a continual manner. I find that learning satisfying information both enhances curiosity for similar information and decreases curiosity for dissimilar information. Combined, these three chapters demonstrate the sophistication of human information-seeking strategies, which are efficient, flexible, and adapted to the constraints of human cognition.

Abstract

What makes faces seem trustworthy? We investigated how racial prejudice predicts the extent to which perceivers employ racially prototypical cues to infer trustworthiness from faces. We constructed participant-level computational models of trustworthiness and White-to-Black prototypicality from U.S. college students’ judgments of White (Study 1, N = 206) and Black–White morphed (Study 3, N = 386) synthetic faces. Although the average relationships between models differed across stimuli, both studies revealed that as participants’ anti-Black prejudice increased and/or intergroup contact decreased, so too did participants’ tendency to conflate White prototypical features with trustworthiness and Black prototypical features with untrustworthiness. Study 2 (N = 324) and Study 4 (N = 397) corroborated that untrustworthy faces constructed from participants with pro-White preferences appeared more Black prototypical to naive U.S. adults, relative to untrustworthy faces modeled from other participants. This work highlights the important role of racial biases in shaping impressions of facial trustworthiness.

Abstract

Does parents’ status motivation affect their educational product choices for their children? Across seven studies, we find that when parents believe that society provides enough opportunities for individuals to achieve higher social status through hard work (i.e., high social mobility), they prioritize the status advancement goal and prefer products to help maximize a child’s strengths. However, when parents believe that even if one works hard, the opportunity to climb up the social ladder is limited (i.e., low social mobility), they focus on maintaining their current status and prefer products that help remedy a child’s weaknesses. Moreover, the research demonstrates that this effect diminishes when the strength and weakness are in the domains that have low relevance to status. Finally, we find that when parents believe that children with specialties are more likely to succeed, they prefer products for maximizing strengths; whereas when they believe that well-rounded children are more likely to succeed, they prefer products for remedying weaknesses, regardless of their perception of social mobility.

Abstract

Review-based recommender systems (RS) have shown great potential in helping users manage information overload and find suitable items. However, a lack of trust still impedes the widespread acceptance of RS. To increase users’ trust, research proposes various methods to generate justifications or explanations. Furthermore, online customer reviews (OCRs) are found to be a trustworthy and reliable source of information. However, it is still unclear how justifications compare to explanations in their influence on users’ trust and whether basing them on OCRs additionally adds trust. Hence, we conduct an online experiment with 531 participants and find that explanations exceed justifications in increasing users’ trust, while basing them on OCRs directly increases users’ intentions to use the system and adopt recommendations without increasing trust in the RS themselves. Unifying different research streams from review-based RS and Explainable Artificial Intelligence, we provide an overarching, holistic view on the conception of justifications and explanations.

Abstract

Are public and private employees different? And is this difference due to the fact that different people are attracted to work for government rather than companies? It has been proposed that individuals with certain characteristics, such as having high levels of risk aversion or public service motivation (PSM), are more likely to self-select into public service. This study argues that this sector attraction effect depends on the amount of job-related information available to job seekers at different stages of the job search process. We test our hypotheses using a three-stage conjoint experimental design. The employment sector only matters to job seekers when little direct information is provided. Once more job-related information is available to them, it diminishes. This effect is especially strong for people with high levels of PSM. These findings suggest that the sector attraction effect is situational, which has important implications for theory and practice of public sector attraction.

Abstract

Due to increasing calls to reduce the gender pay gap, many US cities and states have passed or are debating laws requiring salary range disclosures in job postings. We use an experiment to examine the effects of such disclosures on labor market participants’ application likelihood and negotiation likelihood if offered the position. We manipulate pay range disclosure at three levels: no pay range, broad pay range, and narrow pay range. We find that female participants are more likely than male participants to apply to positions with a narrow range compared to no range or a broad range. We also find that female participants are more likely to negotiate salary when pay ranges are present than when they are absent, and that this effect is driven by narrow pay ranges. Results of our study are not only useful for policymakers but also for employers who seek to create more equitable workplaces.

Abstract

Past literature examining dissociation beyond white populations is sparse. The few studies that have diversified their sampling report higher rates of dissociative symptomatology amongst black participants as compared to other racial groups. However, much of this research has failed to extend their findings beyond the general conclusion of an observable racial discrepancy in experienced dissociation. The present study incorporates theoretical models of racial traumatic stress and maladaptive dissociative coping in its investigation of perceived exposure to racial abuse as a potential factor for racial differences in dissociation—specifically of the nonpathological variety. Findings reveal a significantly higher rate of nonpathological dissociation amongst black individuals as compared to white individuals. Less statistically conclusive results were obtained regarding the degree of exposure to racial abuse and related dissociative tendencies. Yet, intrinsic limitations within the study were largely due to the difficulty in quantifying the vast complexities of experienced racism, suggesting the tentative importance of the findings. Discussions on racial trauma, dissociative phenomena, and hierarchical networks—as well as suggested directions for future research—are included.

Abstract

Attitudes are summary evaluations of objects in memory, and much has been done to understand how they are formed and changed. There are two broad approaches to attitude change: direct and indirect. Direct change involves changing an attitude by targeting the attitude itself, while indirect change involves modifying one attitude by targeting another related one with a persuasive attempt. Additionally, there are two broad perspectives on the mechanism of indirect change: linkage models, in which attitude change spreads from the targeted attitude to similar attitudes to varying degrees based on the strength of the associations, and hierarchical models, in which attitude change is more likely to spread from more abstract categorical targets to less abstract category member attitudes than in reverse. In this project, I proposed two studies to serve as the first to demonstrate evidence of the varying contexts in which the different models of indirect attitude change are better or worse predictors of change in attitudes. Study 1 conceptually replicated previous work and found no evidence of hierarchical organization, as change in attitudes showed no directional difference based on hypotheses drawn from a hierarchical perspective. Study 2 examined the contribution of hierarchy accessibility to the effects within the hierarchical structure and found that accessibility does play a role in indirect attitude change. Indirect attitude change is stronger when relations among objects are more accessible. Results have implications for furthering our understanding of how to produce the most significant changes in attitudes in more varied contexts and further our understanding of the distinction between direct and indirect attitude change.

Abstract

Language understanding plays a crucial role in psychological measurement and so it is important that semantic cues should be studied for more effective and accurate measurement practices. With advancements in computer science, natural language processing (NLP) techniques have emerged as efficient methods for analyzing textual data and have been used to improve psychological measurement. This dissertation investigates the application of NLP embeddings to address fundamental methodological challenges in psychological measurement, specifically scale development and validation. In Study 1, a word embedding-based approach was used to develop a corporate personality measure, which resulted in a three-factor solution closely mirroring three dimensions out of the Big Five framework (ie, Extraversion, Agreeableness, and Conscientiousness). This research furthers our conceptual understanding of corporate personality by identifying similarities and differences between human and organizational personality traits. In Study 2, the sentence-based embedding model was applied to predict empirical pairwise item response relationships, comparing its performance with human ratings. This study also demonstrated the effectiveness of fine-tuned NLP models for classifying item pair relationships into trivial/low or moderate/high empirical relationships, which provides preliminary validity evidence without collecting human responses. The research seeks to enhance psychological measurement practices by leveraging NLP techniques, fostering innovation and improved understanding in the field of social sciences.

Abstract

Researchers have used signal-detection theory-based approach to show that when police use proper practices with eyewitnesses, highly confident witnesses will be highly accurate even when viewing conditions may be suboptimal (Wixted & Wells, 2017). This is referred to as the pristine conditions hypothesis. There have been multiple, and often contradictory, studies that have investigated the relationship between viewing conditions and memory degradation (Giacona et al., 2021; Grabman et al., 2019; Lockamyeir et al, 2020; Semmler et al., 2018). In the current study, I systematically manipulated five estimator variables (lighting, distance, retention interval, exposure duration, and race) as either suboptimal or optimal to further investigate this relationship. I found that, as expected, overall memory strength decreased as the number of suboptimal estimator variables increased. Next, I assessed CAC curves for the number of suboptimal estimator variables and found that the pristine conditions hypothesis holds, except when all five variables are suboptimal, at which point high confidence does not equal high accuracy. Additionally, these results did not hold for when base rates were low. Similarly, when collapsing across viewing type, it was found that under low base rates, high confidence did not equal high accuracy when the conditions were suboptimal. While this research found a lot of support for the pristine conditions hypothesis, it also established important boundary conditions for when this hypothesis is not valid. Further research is still needed to continue to address the confidence-accuracy relationship.