Nurture over nature? The effects of inferred personality traits and structural social capital on individual resilience

Chen M., He Z.

Under the background of the COVID-19 pandemic, our paper attempts to advance a dual-process model from online social support to individual resilience via diverse social capital (bonding and bridging) and political beliefs (political efficacy, political trust and city identification). Drawing on a survey of 446 residents in Shanghai, China, the findings indicate that neither informational nor emotional social support directly affects individual resilience, but indirectly influences it through different pathways. Specifically, bridging social capital mediates informational social support and individual resilience, but bonding social capital might not follow a similar path. Notably, political efficacy and political trust are involved in the above mediation between informational social support and individual resilience. Besides, the serial mediation effect of emotional social support on individual resilience via bonding social capital and then city identification or political trust is also significant. Our findings are expected to reveal the mechanism of resilience construction and prepare for the future public health crisis.

Zhang D., Zhou L., Tao J., Zhue T., Gao G.(.

Suicide is a major cause of death among 15- to 29-year-olds globally, claiming more than 50,000 lives in the United States in 2023 alone. Despite governmental efforts to provide support, many individuals experiencing suicidal thoughts do not seek help but are increasingly turning to social media to express their feelings. This trend offers a critical opportunity for timely detection and intervention of suicidal ideation. We develop an innovative transformer-based model for suicidal ideation detection (SID) that combines domain knowledge with dynamic embedding and lexicon-based enhancements. Our model, which is tested on social media data in two languages from different platforms, outperforms existing state-of-the-art models for SID. We have also explored its applicability to detecting depression and its practical implementation in real-world scenarios. Our research contributes significantly to the field, offering new methods for timely and proactive intervention in suicidal ideation, with potential wide-reaching effects on public health, economics, and society. Methodologically, our approach advances the integration of human expertise into AI models to enhance their effectiveness.

Zarouali B., Dobber T., Schreuder J.

Based on recent technological advances, campaigners and political actors can use psychographic-based political marketing. Yet, empirical evidence about its effectiveness is still very limited. Based on self-congruity theory, a pre-registered experiment (N = 280) investigated the persuasion effects of personality-congruent political microtargeting on the attitude toward the political party and voting intentions of citizens. More precisely, the focus was on the thinking vs feeling personality dimension (MBTI), and it was tested whether this personality “interacts” with exposure to a matching advertising appeal: rational vs. emotional political ad. To do so, two different methodological approaches were used: 1) a machine learning approach; 2) a self-report survey measure of personality. Results revealed significant “congruence effects” between personality and ad appeal, and showed that perceived ad relevance was serving as the underlying mechanism (mediator). However, these results were only found when the self-report measure of personality was used. When the algorithmic approach was used, no significant results were found. These findings feed into timely societal, methodological, and theoretical contributions.

Tang X., Wei S., Chen X.

Despite recognizing the prominent effects of enterprise system (ES) use on organizations and individuals, employees frequently resort to workarounds that run counter to the intention of ES implementation. Building upon the job demands-resources (JD-R) model, we expand the challenge-hindrance stressor framework to encompass technology-driven stressors (TDS) as distinct job demands influencing employees’ workarounds. Additionally, we regard support structures as job resources and trait resilience as personal resources and then examine how the interplay between resources and demands affects workarounds. Our research is underpinned by a comprehensive two-study design. Specifically, Study 1 entailed a longitudinal survey with data gathered from 326 users within a Chinese company. The results show that technology-driven challenge stressors decrease workarounds, whereas technology-driven hindrance stressors increase it. Moreover, job and personal resources play different roles in mitigating the impact of specific TDS on workarounds. In addition, Study 2 utilized qualitative interviews to validate and supplement the findings from the Study 1. This research contributes to both theoretical and practical implications in ES research by extending the JD-R model to explore the influence of various job demands and resources on workarounds.

Zhang G., Wang H., Li M.

The effects of leader narcissism on employee outcomes have yielded inconsistent results. Scholars suggest that its benefits or costs may depend on situational moderators that explain when and how leaders’ narcissistic traits are activated. In response to this, the present research draws on trait activation theory and proposes that leaders’ perceived goal congruence with their followers can be a situational cue that is likely to activate narcissistic leaders’ behavioral tendencies, as captured by employees’ perceived leader narcissism. We further suggest that employees’ perceived leader narcissism negatively affects their perception of leader effectiveness, employee performance, and employee creativity. Multilevel analysis of multisource and multiwave data collected from 55 leaders and 214 followers support our hypotheses. The results serve as a response to the contradictory findings of the effects of leader narcissism and suggest implications for theory and practice.

Deng S., Cheng X., Hu R.

PurposeAs convenience and anonymity, people with mental illness are increasingly willing to communicate and share information through social media platforms to receive emotional and spiritual support. The purpose of this paper is to identify the degree of depression based on people's behavioral patterns and discussion content on the Internet.Design/methodology/approachBased on the previous studies on depression, the severity of depression is divided into four categories: no significant depressive symptoms, mild MDD, moderate MDD and severe MDD, and defined each of them. Next, in order to automatically identify the severity, the authors proposed social media digital cues to identify the severity of depression, which include textual lexical features, depressive language features and social behavioral features. Finally, the authors evaluate a system that is developed based on social media digital cues in the experiment using social media data.FindingsThe social media digital cues including textual lexical features, depressive language features and social behavioral features (F1, F2 and F3) is the relatively best one to classify four different levels of depression.Originality/valueThis paper innovatively proposes a social media data-based framework (SMDF) to identify and predict different degrees of depression through social media digital cues and evaluates the accuracy of the detection through social media data, providing useful attempts for the identification and intervention of depression.

Fan J., Sun T., Liu J., Zhao T., Zhang B., Chen Z., Glorioso M., Hack E.

The present study explores the plausibility of measuring personality indirectly through an artificial intelligence (AI) chatbot. This chatbot mines various textual features from users' free text responses collected during an online conversation/interview and then uses machine learning algorithms to infer personality scores. We comprehensively examine the psychometric properties of the machine-inferred personality scores, including reliability (internal consistency, split-half, and test-retest), factorial validity, convergent and discriminant validity, and criterion-related validity. Participants were undergraduate students (n = 1,444) enrolled in a large southeastern public university in the United States who completed a self-report Big Five personality measure (IPIP-300) and engaged with an AI chatbot for approximately 20-30 min. In a subsample (n = 407), we obtained participants' cumulative grade point averages from the University Registrar and had their peers rate their college adjustment. In an additional sample (n = 61), we obtained test-retest data. Results indicated that machine-inferred personality scores (a) had overall acceptable reliability at both the domain and facet levels, (b) yielded a comparable factor structure to self-reported questionnaire-derived personality scores, (c) displayed good convergent validity but relatively poor discriminant validity (averaged convergent correlations = .48 vs. averaged machine-score correlations = .35 in the test sample), (d) showed low criterion-related validity, and (e) exhibited incremental validity over self-reported questionnaire-derived personality scores in some analyses. In addition, there was strong evidence for cross-sample generalizability of psychometric properties of machine scores. Theoretical implications, future research directions, and practical considerations are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Hu Q., Pan Z.

The recent COVID-19 pandemic has raised concerns about individual resilience in the face of adversity. The abundant research suggested that artificial intelligence (AI) can help organizations handle changes during this challenging period. However, little empirical research has explored whether the presence of AI enhances individual resilience against adversities. Drawing on the reciprocal determinism theory, this study considers the formation of two typical post-adoption behaviors and their subsequent results in individual resilience. The structural equation modeling shows that AI factors (usability and sociability) and personal factors (self-efficacy) determine usage behaviors (routine and infusion use), in turn affecting individual resilience. The OLS results suggest the right half of the U-shaped relationships between infusion use and resilience. Two-step fsQCA offers three configurations resulting in high resilience under the different presence of AI factors and also suggests the roles of user behaviors. The study provides new theoretical enlightenment for the impact of digital service technology on individuals and enriches the existing literature on the usage of digital service technology. The findings provide practical implications for practitioners to design AI products better to improve smart service experience.

Edinger A., Valdez D., Walsh-Buhi E., Trueblood J.S., Lorenzo-Luaces L., Rutter L.A., Bollen J.

Background Shortly after the worst of the COVID-19 pandemic, an outbreak of mpox introduced another critical public health emergency. Like the COVID-19 pandemic, the mpox outbreak was characterized by a rising prevalence of public health misinformation on social media, through which many US adults receive and engage with news. Digital misinformation continues to challenge the efforts of public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the mpox outbreak to map the tension between rapidly diffusing misinformation and public health communication. Objective This study aims to observe topical themes occurring in a large-scale collection of tweets about mpox using deep learning. Methods We leveraged a data set comprised of all mpox-related tweets that were posted between May 7, 2022, and July 23, 2022. We then applied Sentence Bidirectional Encoder Representations From Transformers (S-BERT) to the content of each tweet to generate a representation of its content in high-dimensional vector space, where semantically similar tweets will be located closely together. We projected the set of tweet embeddings to a 2D map by applying principal component analysis and Uniform Manifold Approximation Projection (UMAP). Finally, we group these data points into 7 topical clusters using k-means clustering and analyze each cluster to determine its dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal thematic changes. Results Our deep-learning pipeline revealed 7 distinct clusters of content: (1) cynicism, (2) exasperation, (3) COVID-19, (4) men who have sex with men, (5) case reports, (6) vaccination, and (7) World Health Organization (WHO). Clusters that largely communicated erroneous or irrelevant information began earlier and grew faster, reaching a wider audience than later communications by official instances and health officials. Conclusions Within a few weeks of the first reported mpox cases, an avalanche of mostly false, misleading, irrelevant, or damaging information started to circulate on social media. Official institutions, including the WHO, acted promptly, providing case reports and accurate information within weeks, but were overshadowed by rapidly spreading social media chatter. Our results point to the need for real-time monitoring of social media content to optimize responses to public health emergencies.

Zarouali B., Schreuder J.

In recent years, we have witnessed a sharp increase in consumers’ use of various digital devices. This has revealed large amounts of digital trace data, i.e., data on consumers’ online activities, contributions, interactions and communications. Concurrent with the ubiquity of digital devices, technological advances in computing and data analytics such as Natural Language Processing (NLP) and the application of Machine Learning (ML) have arisen, making it possible to discover patterns in the massive quantities of digital data available for marketing purposes.

Yao X., Chen S., Yu G.

The online depression community (ODC) has become a popular resource for people with depression to manage their mental health during the COVID-19 pandemic. This study proposed a novel perspective based on response style theory to investigate whether depression individuals’ distractive and ruminative behaviors in ODC were related to social support received and co-rumination. Furthermore, we explored the influences of social support and co-rumination on suicidal behaviors using panel data set. We collected text data from 22,286 depressed users of a large ODC in China from March 2020 to July 2021, and conducted text mining and econometrics analyses to test our research questions. The results showed that depression users’ online ruminative behaviors had a positive relationship with the co-rumination and had a negative relationship with social support received. Besides, constructive distractive behaviors (i.e., providing social support to others) increased the support users received from others but had a negative relationship with co-rumination. Depression users' future suicidal behaviors are influenced by past received social support and co-rumination. The received social supports and co-rumination have a negative and positive influence on depression users' future suicidal behaviors, respectively. Our results enrich the application of response style theory in online medicine. They provide meaningful insights into behaviors that influence the acquisition of online social support and the incidence of online co-rumination in ODCs. This study helps relevant institutions to conduct more targeted online suicide interventions for depression patients.

Liu A., Yu Y., Sun S.

This study explored the relationship between the Big Five personality traits , rumination, resilience, and anxiety in a sample of Chinese undergraduates ( N = 323) from a cross-sectional perspective. Results showed that neuroticism , conscientiousness, and agreeableness significantly predicted anxiety of college students and that rumination mediated the association between neuroticism and anxiety, and between agreeableness and anxiety. In addition, resilience had a significant moderating effect on neuroticism and rumination, agreeableness and anxiety. Overall, higher level of resilience led to less rumination and anxiety in individuals with low neuroticism and high agreeableness, but there were adverse effects in those with high neuroticism and low agreeableness. This study is valuable to understand the complex mechanisms of the relationship between personality traits and anxiety and provides a direction for reducing anxiety of college students.

Jiang Q., Zhang Y., Pian W.

• Empathy work as a key mechanism of information processing in human-AI interaction. • Five types of Replika experiences among Chinese female users are found. • Varying degrees of cognitive empathy, affective empathy and empathic response are involved in human-AI interaction. • Mediated empathy facilitates resilience processes and enhance well-being. • Implications for global recovery from the COVID-19 pandemic are discussed. As a global health crisis, the COVID-19 pandemic has also made heavy mental and emotional tolls become shared experiences of global communities, especially among females who were affected more by the pandemic than males for anxiety and depression. By connecting multiple facets of empathy as key mechanisms of information processing with the communication theory of resilience, the present study examines human-AI interactions during the COVID-19 pandemic in order to understand digitally mediated empathy and how the intertwining of empathic and communicative processes of resilience works as coping strategies for COVID-19 disruption. Mixed methods were adopted to explore the using experiences and effects of Replika, a chatbot companion powered by AI, with ethnographic research, in-depth interviews, and grounded theory-based analysis. Findings of this research extend empathy theories from interpersonal communication to human-AI interactions and show five types of digitally mediated empathy among Chinese female Replika users with varying degrees of cognitive empathy, affective empathy, and empathic response involved in the information processing processes, i.e., companion buddy, responsive diary, emotion-handling program, electronic pet, and tool for venting. When processing information obtained from AI and collaborative interactions with the AI chatbot, multiple facets of mediated empathy become unexpected pathways to resilience and enhance users’ well-being. This study fills the research gap by exploring empathy and resilience processes in human-AI interactions. Practical implications, especially for increasing individuals’ psychological resilience as an important component of global recovery from the pandemic, suggestions for future chatbot design, and future research directions are also discussed.

Pérez-Fernández H., Cacciotti G., Martín-Cruz N., Delgado-García J.B.

Entrepreneurial intention plays a key role in entrepreneurship. Over the years, scholars have explained it using personality traits, cognitive models and, to a lesser extent, the role of social environment. Since this role has been underestimated, we build on trait activation theory to explore how social networks are especially relevant and can trigger the activation of individuals’ need for achievement to predict entrepreneurial intention. We test our hypotheses on a sample of 597 university students from Spain using partial least squares (PLS). Our results confirm that social network size positively influences the entrepreneurial information obtained in social networks, which in turn, positively impacts entrepreneurial intention. Additionally, we found that need for achievement is activated in the context of social networks, enhancing the influence of this information on entrepreneurial intention. Through fuzzy-set qualitative comparative analysis (fsQCA), we also identify alternative configurations of the previous variables that lead to greater entrepreneurial intention.

Nieto M., Visier M.E., Silvestre I.N., Navarro B., Serrano J.P., Martínez‐Vizcaíno V.

Resilience refers to the process by which individuals use the ability to cope with challenges to successfully adapt to adverse situations, inclining towards the future and hope. The main aim of this study was to analyze the relation between resilience, personality traits, and hopelessness. Furthermore, we conducted comparisons between two age groups: young and older adults. The sample comprised 439 Spanish participants (66.7% women; M = 43.73, SD = 26.41; age range = 18–98 years). The Connor–Davidson Resilience Scale, NEO-Five Factor Inventory, and Beck Hopelessness Scale were used to measure the main study variables. The results revealed a negative relation between resilience and neuroticism, and a positive association with the other personality traits. Additionally, levels of resilience were found to be negatively related to hopelessness. The group of older adults showed significantly lower resilience levels than the young adults, although age was not a significant predictor of resilience. Neuroticism, extraversion, openness, and hopelessness were the only predictors of resilience for the current study. This work contributes to the study of resilience and related factors, by attempting to understand the role of resilience and resistance to risk and how individuals tackle challenges over time, with important implications for mental health.