Founder personality and entrepreneurial outcomes: A large-scale field study of technology startups.

Technology startups play an essential role in the economy-with seven of the ten largest companies rooted in technology, and venture capital investments totaling approximately $300B annually. Yet, important startup outcomes (e.g., whether a startup raises venture capital or gets acquired) remain difficult to forecast-particularly during the early stages of venture formation. Here, we examine the impact of an essential, yet underexplored, factor that can be observed from the moment of startup creation: founder personality. We predict psychological traits from digital footprints to explore how founder personality is associated with critical startup milestones. Observing 10,541 founder-startup dyads, we provide large-scale, ecologically valid evidence that founder personality is associated with outcomes across all phases of a venture's life (i.e., from raising the earliest funding round to exiting via acquisition or initial public offering). We find that openness and agreeableness are positively related to the likelihood of raising an initial round of funding (but unrelated to all subsequent conditional outcomes). Neuroticism is negatively related to all outcomes, highlighting the importance of founders' resilience. Finally, conscientiousness is positively related to early-stage investment, but negatively related to exit conditional on funding. While prior work has painted conscientiousness as a major benefactor of performance, our findings highlight a potential boundary condition: The fast-moving world of technology startups affords founders with lower or moderate levels of conscientiousness a competitive advantage when it comes to monetizing their business via acquisition or IPO.

Hiring women into senior leadership positions is associated with a reduction in gender stereotypes in organizational language.

Women continue to be underrepresented in leadership positions. This underrepresentation is at least partly driven by gender stereotypes that associate men, but not women, with achievement-oriented, agentic traits (e.g., assertive and decisive). These stereotypes are expressed and perpetuated in language, with women being described in less agentic terms than men. The present research suggests that appointing women to the top tiers of management can mitigate these deep-rooted stereotypes that are expressed in language. We use natural language processing techniques to analyze over 43,000 documents containing 1.23 billion words, finding that hiring female chief executive officers and board members is associated with changes in organizations' use of language, such that the semantic meaning of being a woman becomes more similar to the semantic meaning of agency. In other words, hiring women into leadership positions helps to associate women with characteristics that are critical for leadership success. Importantly, our findings suggest that changing organizational language through increasing female representation might provide a path for women to break out of the double bind: when female leaders are appointed into positions of power, women are more strongly associated with the positive aspects of agency (e.g., independent and confident) in language but not at the cost of a reduced association with communality (e.g., kind and caring). Taken together, our findings suggest that female representation is not merely an end, but also a means to systemically change insidious gender stereotypes and overcome the trade-off between women being perceived as either competent or likeable.

Well-being is more than happiness and life satisfaction: a multidimensional analysis of 21 countries.

BACKGROUND: Recent trends on measurement of well-being have elevated the scientific standards and rigor associated with approaches for national and international comparisons of well-being. One major theme in this has been the shift toward multidimensional approaches over reliance on traditional metrics such as single measures (e.g. happiness, life satisfaction) or economic proxies (e.g. GDP). METHODS: To produce a cohesive, multidimensional measure of well-being useful for providing meaningful insights for policy, we use data from 2006 and 2012 from the European Social Survey (ESS) to analyze well-being for 21 countries, involving approximately 40,000 individuals for each year. We refer collectively to the items used in the survey as multidimensional psychological well-being (MPWB). RESULTS: The ten dimensions assessed are used to compute a single value standardized to the population, which supports broad assessment and comparison. It also increases the possibility of exploring individual dimensions of well-being useful for targeting interventions. Insights demonstrate what may be masked when limiting to single dimensions, which can create a failure to identify levers for policy interventions. CONCLUSIONS: We conclude that both the composite score and individual dimensions from this approach constitute valuable levels of analyses for exploring appropriate policies to protect and improve well-being.

Can Psychological Traits Be Inferred From Spending? Evidence From Transaction Data.

The automatic assessment of psychological traits from digital footprints allows researchers to study psychological traits at unprecedented scale and in settings of high ecological validity. In this research, we investigated whether spending records-a ubiquitous and universal form of digital footprint-can be used to infer psychological traits. We applied an ensemble machine-learning technique (random-forest modeling) to a data set combining two million spending records from bank accounts with survey responses from the account holders (N = 2,193). Our predictive accuracies were modest for the Big Five personality traits (r = .15, corrected ρ = .21) but provided higher precision for specific traits, including materialism (r = .33, corrected ρ = .42). We compared the predictive accuracy of these models with the predictive accuracy of alternative digital behaviors used in past research, including those observed on social media platforms, and we found that the predictive accuracies were relatively stable across socioeconomic groups and over time.

Money Buys Happiness When Spending Fits Our Personality.

In contrast to decades of research reporting surprisingly weak relationships between consumption and happiness, recent findings suggest that money can indeed increase happiness if it is spent the "right way" (e.g., on experiences or on other people). Drawing on the concept of psychological fit, we extend this research by arguing that individual differences play a central role in determining the "right" type of spending to increase well-being. In a field study using more than 76,000 bank-transaction records, we found that individuals spend more on products that match their personality, and that people whose purchases better match their personality report higher levels of life satisfaction. This effect of psychological fit on happiness was stronger than the effect of individuals' total income or the effect of their total spending. A follow-up study showed a causal effect: Personality-matched spending increased positive affect. In summary, when spending matches the buyer's personality, it appears that money can indeed buy happiness.

Large language models can infer psychological dispositions of social media users.

Large language models (LLMs) demonstrate increasingly human-like abilities across a wide variety of tasks. In this paper, we investigate whether LLMs like ChatGPT can accurately infer the psychological dispositions of social media users and whether their ability to do so varies across socio-demographic groups. Specifically, we test whether GPT-3.5 and GPT-4 can derive the Big Five personality traits from users' Facebook status updates in a zero-shot learning scenario. Our results show an average correlation of r = 0.29 ( range = [ 0.22 , 0.33 ] ) between LLM-inferred and self-reported trait scores-a level of accuracy that is similar to that of supervised machine learning models specifically trained to infer personality. Our findings also highlight heterogeneity in the accuracy of personality inferences across different age groups and gender categories: predictions were found to be more accurate for women and younger individuals on several traits, suggesting a potential bias stemming from the underlying training data or differences in online self-expression. The ability of LLMs to infer psychological dispositions from user-generated text has the potential to democratize access to cheap and scalable psychometric assessments for both researchers and practitioners. On the one hand, this democratization might facilitate large-scale research of high ecological validity and spark innovation in personalized services. On the other hand, it also raises ethical concerns regarding user privacy and self-determination, highlighting the need for stringent ethical frameworks and regulation.

The role of negative affect in shaping populist support: Converging field evidence from across the globe.

Support for populism has grown substantially during the past 2 decades, a development that has coincided with a marked increase in the experience of negative affect around the world. We use a multimodal, multimethod empirical approach, with data from a diverse set of geographical and political contexts, to investigate the extent to which the rising electoral demand for populism can be explained by negative affect. We demonstrate that negative affect-measured via (a) self-reported emotions in surveys as well as (b) automated text analyses of Twitter data-predicts individual-level populist attitudes in two global surveys (Studies 1a and 1b), longitudinal changes in populist party vote shares at general elections in Europe (Study 2), district-level Brexit voting in the 2016 U.K. referendum (Study 3), and county-level vote shares for Donald Trump in the 2016 and 2020 U.S. presidential elections (Studies 4a and 4b). We find that negative emotions-such as fear and anger as well as more often overlooked low-arousal negative emotions like depression and sadness-are predictive of populist beliefs as well as voting and election results at scale. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Psychological well-being in Europe after the outbreak of war in Ukraine.

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

The impact of the early stages of COVID-19 on mental health in the United States, Germany, and the United Kingdom.

The impact of COVID-19 on people's physical health is well documented. But how did COVID-19-with all the uncertainty and disruption of daily life it entailed-impact people's mental health? We used ecologically momentary assessments from 22,562 individuals (largely young adults) across the United States, Germany, and the United Kingdom to study the impact of the early stages of COVID-19 on mental health. Exploring within-person trajectories of mood (4,471,810 observations) and depression (274,911 observations) between January 1 and September 30 of 2020, and comparing them to those observed for the same time period in 2019, we provide evidence that people-on average-show high levels of resilience. While the United States saw momentary decreases in mood and increases in depression that quickly returned to baseline, Germany and the United Kingdom did not experience observable negative effects on mental health. In a small subsample of U.S. users, we show that the mental health trajectories appear to be relatively consistent across different sociodemographics groups. Investigating the impact of social distancing on people's mental health within-person, we demonstrate that social distancing-on average-was associated with a decline in mental health. However, our findings also highlight that not all COVID-19 experiences were created equal. While individuals who experienced social distancing as burdensome reported lower levels of mental health, those who did not, indicated normal or even elevated levels of mental health. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Leveraging psychological fit to encourage saving behavior.

Despite their best intentions, most people fail to save enough for the future. In this research, we demonstrate that people are more successful at saving when their savings goals are aligned with their Big Five personality traits. Study 1 uses a nationally representative sample of 2,447 U.K. citizens to test whether people whose self-declared savings goals more closely match their Big Five personality also report higher levels of savings. We apply specification curve analyses to minimize the risk of having arbitrary analytical decisions produce false-positive results. As our findings show, person-goal fit significantly predicted savings across all 48 specifications. Study 2 expands these findings by testing whether psychological fit can influence savings even if the saving goals are not formulated by the individuals themselves but instead suggested by a technology service designed to help them save. In a field experiment with 6,056 U.S.-based low-income users of a nonprofit Fintech app (with < $100 in current savings), we show that people who were encouraged to save $100 over the course of a month were more likely to achieve this target if they were encouraged to save toward personality-matched goals. Our research provides support for the theory of psychological fit, showing that an alignment between an individual's Big Five personality traits and the personality appeal of a saving goal can help increase savings, even among those who struggle the most. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Using machine learning to predict student retention from socio-demographic characteristics and app-based engagement metrics.

Student attrition poses a major challenge to academic institutions, funding bodies and students. With the rise of Big Data and predictive analytics, a growing body of work in higher education research has demonstrated the feasibility of predicting student dropout from readily available macro-level (e.g., socio-demographics or early performance metrics) and micro-level data (e.g., logins to learning management systems). Yet, the existing work has largely overlooked a critical meso-level element of student success known to drive retention: students' experience at university and their social embeddedness within their cohort. In partnership with a mobile application that facilitates communication between students and universities, we collected both (1) institutional macro-level data and (2) behavioral micro and meso-level engagement data (e.g., the quantity and quality of interactions with university services and events as well as with other students) to predict dropout after the first semester. Analyzing the records of 50,095 students from four US universities and community colleges, we demonstrate that the combined macro and meso-level data can predict dropout with high levels of predictive performance (average AUC across linear and non-linear models = 78%; max AUC = 88%). Behavioral engagement variables representing students' experience at university (e.g., network centrality, app engagement, event ratings) were found to add incremental predictive power beyond institutional variables (e.g., GPA or ethnicity). Finally, we highlight the generalizability of our results by showing that models trained on one university can predict retention at another university with reasonably high levels of predictive performance.

Personality Science in the Digital Age: The Promises and Challenges of Psychological Targeting for Personalized Behavior-Change Interventions at Scale.

With the rapidly growing availability of scalable psychological assessments, personality science holds great promise for the scientific study and applied use of customized behavior-change interventions. To facilitate this development, we propose a classification system that divides psychological targeting into two approaches that differ in the process by which interventions are designed: audience-to-content matching or content-to-audience matching. This system is both integrative and generative: It allows us to (a) integrate existing research on personalized interventions from different psychological subdisciplines (e.g., political, educational, organizational, consumer, and clinical and health psychology) and to (b) articulate open questions that generate promising new avenues for future research. Our objective is to infuse personality science into intervention research and encourage cross-disciplinary collaborations within and outside of psychology. To ensure the development of personality-customized interventions aligns with the broader interests of individuals (and society at large), we also address important ethical considerations for the use of psychological targeting (e.g., privacy, self-determination, and equity) and offer concrete guidelines for researchers and practitioners.

Regional personality differences predict variation in early COVID-19 infections and mobility patterns indicative of social distancing.

The early stages of the COVID-19 pandemic revealed stark regional variation in the spread of the virus. While previous research has highlighted the impact of regional differences in sociodemographic and economic factors, we argue that regional differences in social and compliance behaviors-the very behaviors through which the virus is transmitted-are critical drivers of the spread of COVID-19, particularly in the early stages of the pandemic. Combining self-reported personality data that capture individual differences in these behaviors (3.5 million people) with COVID-19 prevalence and mortality rates as well as behavioral mobility observations (29 million people) in the United States and Germany, we show that regional personality differences can help explain the early transmission of COVID-19; this is true even after controlling for a wide array of important sociodemographic, economic, and pandemic-related factors. We use specification curve analyses to test the effects of regional personality in a robust and unbiased way. The results indicate that in the early stages of COVID-19, Openness to experience acted as a risk factor, while Neuroticism acted as a protective factor. The findings also highlight the complexity of the pandemic by showing that the effects of regional personality can differ (a) across countries (Extraversion), (b) over time (Openness), and (c) from those previously observed at the individual level (Agreeableness and Conscientiousness). Taken together, our findings support the importance of regional personality differences in the early spread of COVID-19, but they also caution against oversimplified answers to phenomena as complex as a global pandemic. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Personality similarity predicts synchronous neural responses in fMRI and EEG data.

Successful communication and cooperation among different members of society depends, in part, on a consistent understanding of the physical and social world. What drives this alignment in perspectives? We present evidence from two neuroimaging studies using functional magnetic resonance imaging (fMRI; N = 66 with 2145 dyadic comparisons) and electroencephalography (EEG; N = 225 with 25,200 dyadic comparisons) to show that: (1) the extent to which people's neural responses are synchronized when viewing naturalistic stimuli is related to their personality profiles, and (2) that this effect is stronger than that of similarity in gender, ethnicity and political affiliation. The localization of the fMRI results in combination with the additional eye tracking analyses suggest that the relationship between personality similarity and neural synchrony likely reflects alignment in the interpretation of stimuli and not alignment in overt visual attention. Together, the findings suggest that similarity in psychological dispositions aligns people's reality via shared interpretations of the external world.

Correcting Sociodemographic Selection Biases for Population Prediction from Social Media.

Social media is increasingly used for large-scale population predictions, such as estimating community health statistics. However, social media users are not typically a representative sample of the intended population - a "selection bias". Within the social sciences, such a bias is typically addressed with restratification techniques, where observations are reweighted according to how under- or over-sampled their socio-demographic groups are. Yet, restratifaction is rarely evaluated for improving prediction. In this two-part study, we first evaluate standard, "out-of-the-box" restratification techniques, finding they provide no improvement and often even degraded prediction accuracies across four tasks of esimating U.S. county population health statistics from Twitter. The core reasons for degraded performance seem to be tied to their reliance on either sparse or shrunken estimates of each population's socio-demographics. In the second part of our study, we develop and evaluate Robust Poststratification, which consists of three methods to address these problems: (1) estimator redistribution to account for shrinking, as well as (2) adaptive binning and (3) informed smoothing to handle sparse socio-demographic estimates. We show that each of these methods leads to significant improvement in prediction accuracies over the standard restratification approaches. Taken together, Robust Poststratification enables state-of-the-art prediction accuracies, yielding a 53.0% increase in variance explained (R 2) in the case of surveyed life satisfaction, and a 17.8% average increase across all tasks.

Personal echo chambers: Openness-to-experience is linked to higher levels of psychological interest diversity in large-scale behavioral data.

This research investigates the extent to which the personality trait of Openness-to-Experience can protect individuals from living in personal echo chambers that create and reinforce a narrow view on the world. Analyses of 2 large-scale data sets with over 80,000 participants and more than 40,000,000 behavioral observations demonstrate that individuals scoring high on Openness show higher variability in the psychological profiles associated with their personal interests-a novel concept termed psychological interest diversity. Study 1 examines the Facebook profiles of 57,185 users to demonstrate that a person's Openness level predicts the degree to which the pages they like are diverse in the political ideologies, personal values, and personality traits of their followers. Study 2 replicates the findings of Study 1 using over 28,000,000 GPS-tracked event attendances collected via people's smartphones. Specifically, the results show that individuals (N = 22,953) with higher levels of Openness also show higher levels of psychological interest diversity in the events they attend, and that this effect is incremental to county-level variation in psychological interest diversity. The findings empirically support the theoretical conceptualization of Openness as a preference for variety and exploration and corroborate the role of personal dispositions in the creation of personal echo chambers. The discussion highlights the need to further explore psychological interest diversity as the initial basis of algorithmic filter bubbles-for example, recommendation systems or targeted advertising-which further amplify and reinforce existing interests and preferences. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Personality-place transactions: Mapping the relationships between Big Five personality traits, states, and daily places.

People actively select their environments, and the environments they select can alter their psychological characteristics in the moment and over time. Such dynamic person-environment transactions are likely to play out in the context of daily life via the places people spend time in (e.g., home, work, or public places like cafes and restaurants). This article investigates personality-place transactions at 3 conceptual levels: stable personality traits, momentary personality states, and short-term personality trait expressions. Three 2-week experience sampling studies (2 exploratory and 1 confirmatory with a total N = 2,350 and more than 63,000 momentary assessments) were used to provide the first large-scale evidence showing that people's stable Big Five traits are associated with the frequency with which they visit different places on a daily basis. For example, extraverted people reported spending less time at home and more time at cafés, bars, and friends' houses. The findings also show that spending time in a particular place predicts people's momentary personality states and their short-term trait expression over time. For example, people reported feeling more extraverted in the moment when spending time at bars/parties, cafés/restaurants, or friends' houses, compared with when at home. People who showed preferences for spending more time in these places also showed higher levels of short-term trait extraversion over the course of 2 weeks. The findings make theoretical contributions to environmental psychology, personality dynamics, as well as the person-environment transactions literature, and highlight practical implications for a world in which the places people visit can be easily captured via GPS sensors. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Depression predictions from GPS-based mobility do not generalize well to large demographically heterogeneous samples.

Depression is one of the most common mental health issues in the United States, affecting the lives of millions of people suffering from it as well as those close to them. Recent advances in research on mobile sensing technologies and machine learning have suggested that a person's depression can be passively measured by observing patterns in people's mobility behaviors. However, the majority of work in this area has relied on highly homogeneous samples, most frequently college students. In this study, we analyse over 57 million GPS data points to show that the same procedure that leads to high prediction accuracy in a homogeneous student sample (N = 57; AUC = 0.82), leads to accuracies only slightly higher than chance in a U.S.-wide sample that is heterogeneous in its socio-demographic composition as well as mobility patterns (N = 5,262; AUC = 0.57). This pattern holds across three different modelling approaches which consider both linear and non-linear relationships. Further analyses suggest that the prediction accuracy is low across different socio-demographic groups, and that training the models on more homogeneous subsamples does not substantially improve prediction accuracy. Overall, the findings highlight the challenge of applying mobility-based predictions of depression at scale.