Open research questions in Personality Traits and Psychology

What the literature leaves open

• Future studies should expand the scope to include factors such as social influence, economic status, and students’ field of study. A qualitative approach using interviews or focus group discussions is recommended to gain deeper insights. Researchers may also explore other validated personality assessments beyond Dr. Phil’s and DISC tests to enhance accuracy. Conducting the study among males or broader age groups could also provide comparative perspectives. These steps will strengthen understanding of how personality and external factors shape individual preferences, contributing valuable insights to psychology and consumer behavior research. REFERENCES Akbari, A. (2012, May 28). Style psychology: Pointed shoes, heels and transformative footwear. StyleCaster. http://stylecaster.com/style-psychology-pointedshoes-heels-transformative-footwer/ Bagdon, M. (n.d.). Shoe preference based on subjective comfort for walking and running. National Center for Biotechnology Information. http://www.ncbi.nlm. nih.gov/pubmed/21084531 Bashford, G. M. (1996). Shoe characteristics and balance in women. Journal of the American Geriatrics Society, 44(4), 429–433. http://www.mendeley.com/research/shoecharacteristics-balance-older-women/ Belk, R. W. (2003). Shoes and self. University of Utah. http://www.acrwebsite.org/search/view-conferenceproceedings.aspx?Id=8730 Borigini, M. (2012, June 21). A woman and shoes. Psychology Today. http://www.psychologytoday. com/blog/overcoming-pain/201206/woman-andher-shoes 4 1 https://journals.e-palli.com/home/index.php/ajhp Page Am. J. Hum. Psychol. 4(2) 9-15, 2026 Cain, M. (2010, September 7). If the shoe fits: Why do women love shoes? Channel 4. http://www.channel4. com/news/if-the-shoe-fits-why-do-women-loveshoes Castillo, M. (2012, June). Looking at shoes may reveal information. CBS key demographic, personality News. http://www.cbsnews.com/8301-504763_162- 57453200-10391704/looking-at-shoes-may-revealkey-demographic-personality-information/ Chatterjee, C. (1999, July 1). Smart shoes. (Last reviewed October 12, 2010). Cooper, G. (2012, June 27). Why women are obsessed with shoes. The Telegraph. http://www.telegraph. co.uk/women/sex/divorce/9356548/Why-womenare-obsessed-with-shoas.html Daily Makeover. (2012, June 22). What your shoes say about your personality. Yahoo! Shine. http://shine. yahoo.com/fashion/shoes-personality-202700165. html FashionDesign. (2013, May 13). The shoes reflect your http://allforfashiondesign.com/thepersonality. shoes-reflect-your-personality/ Gea, F. (2012). What your choice of shoe says about you. http://research-digest.com/2012/06/what-yourchoice-of-shoe-says-about-you.html Goonetilleke, R. S., & Lee Au, E. Y. (2007). A qualitative study on the comfort and fit of ladies’ dress shoes.

  The Psychology of Footwear · 2026 · DOI

• ASAMOAH-GYAWU ET AL. / Personality Traits & Pro-Environmental Behaviours on Eco- Anxiety of Students Academic counsellors should design personalised interventions that account for individual personality traits. For instance, resilience-building programmes could be targeted at students with high levels of neuroticism to strengthen their ability to regulate emotional responses to environmental threats. For extraverted individuals, interventions might focus on directing their social energy and engagement towards constructive environmental action. In addition, mental health service providers should establish counselling and support systems that directly address eco-anxiety, with particular attention to undergraduate populations. Tailored resources can assist students in managing their emotional responses to climate change and in developing healthier coping mechanisms. Policymakers for universities should recognise eco-anxiety as a critical consideration in climate-related strategies.

  Impact of Personality Traits and Pro-Environmental Behaviours on Eco-Anxiety Among University Students · 2026 · DOI

• The present study has several limitations. First, the sample size was modest. Because the STiP- 5.1 is a semi-structured interview that requires trained assessors and considerable administration time, it is challenging to balance methodological rigor with practical time and resource constraints. Replication in other populations is also warranted. To provide a more comprehensive understanding of the relationships among the relevant variables, future studies should include samples from the general population. As noted above, university students are at a developmental stage during which identity and interpersonal relationships are still actively being shaped. As individuals approach major life milestones, such as establishing a stable career or starting a family, their perspectives on life and on relationships with others, including their parents, may also change. We also recommend testing alternative factor structures of the parental bonding scale. A threefactor or four-factor representation of the instrument may yield different findings.

  Exploratory analysis on five-factor personality traits and parental bonding in predicting personality functioning using structural equation modeling · 2026 · DOI

• Although few studies have directly examined the relationship between five-factor personality traits and PCT, prior research has consistently associated these traits with other addictive behaviors such as gambling, internet use, and work addiction (Astarini & Yudiarso, 2020; Kun et al.

  Problematic Cryptocurrency Trading, Five-Factor Personality Traits, and Impulsivity · 2026 · DOI

• The review identified several important points that warrant further research. Few studies are based on multiple theoretical foundations. The review found that existing studies on the impact of CEO narcissism on firm performance report inconsistent results. Fifthly, studies have mostly focused on the impact of CEO narcissism on financial performance, with insufficient research on how CEO narcissism affects non-financial performance.

  CEO Narsisizmi ve Firma Performansı Arasındaki ilişkide Finansal Esnek Kaynakların ve Yönetim Kurulu Bağımsızlığının Rolü Üzerine Bir Literatür İncelemesi · 2026 · DOI

• Several limitations should be considered when interpreting the present findings. First, the study employed a cross-sectional design, which precludes causal inference. It cannot be determined whether personality traits drive subsequent harassment perpetration tenden- cies, whether harassment perpetration tendencies feed back into self- reported personality, or whether both reflect unmeasured third variables (e.g., organizational climate, leadership structure). Second, all variables were assessed by self-report at a single time point, raising the possibility of common method variance, social-desirability responding, and—particularly relevant here—underreporting of socially undesirable behaviors despite de-identification. Multi-method designs incorporating peer reports, supervisor evaluations, or behav- ioral indicators would strengthen future work. Third, the sample was recruited through the Lancers crowdsourcing platform and consisted of currently employed Japanese adults; representativeness across occu- pational sectors, hierarchical levels, employment types, and organiza- tional sizes cannot be guaranteed. Industry, organizational size, and managerial status were not systematically assessed and may moderate personality–harassment associations. Fourth, the power-harassment items used in this study (Tou et al., 2017) presuppose a supervisory or otherwise hierarchical relational context with subordinates, but mana- gerial status was not assessed at intake; it is therefore plausible that, for some respondents, the relational conditions presupposed by the items are not present. This sample–construct mismatch is a fundamental construct-validity concern that cannot be fully resolved through post- hoc adjustment within this dataset, and we acknowledge it explicitly: the power-harassment results should be read as applying to the popu- lation of currently employed Japanese adults responding to these items as written, not specifically to confirmed supervisors. Future replica- tions should either restrict the sample to confirmed supervisors or include managerial status (subordinate count, reporting line, formal authority) as a covariate or stratifying variable. Fifth, the harassment outcomes in this study are composite indices rather than pure behav- ioral-frequency counts. The power-harassment composite, in particu- lar, bundles behavioral enactment, attitudinal endorsement, and a supervisor-centered interpersonal-climate component (subordinate constriction and silence around the respondent) that conceptually reflect distinct constructs. Subscale-level analyses cannot be repro- duced from the publicly archived file because that file retains only composite scores. Findings should accordingly be read as referring to perpetration-related composites rather than to behavioral perpetra- tion in a narrow sense, and a focused replication that explicitly decom- poses the composite by subscale is warranted. Sixth, the inferential framework used here is hierarchical multiple regression on observed (manifest) variables, and the H–H ‘incremental association’ should be read as such. The claim that residual H–H variance after adjustment for the Dark Triad indexes a substantively distinct moral-personality construct (rather than absorbing other content shared with H–H but not with Dark Triad) cannot be adjudicated within this analytic framework. A latent-variable approach—e.g., a bifactor or hierarchical CFA in which a general ‘antagonism’ factor and a specific H–H factor are jointly estimated—would be required to test that specificity claim. Seventh, sex-stratified estimates differ in precision (n = 133 men vs. 220 women) and are not derived from a formal cross-sex equality test; the divergence in male versus female predictor structure should accordingly be read as a hypothesis-generating pattern rather than as a confirmed moderation effect. Finally, although the study contributes data from a Japanese employee sample, it does not permit conclusions about cross-cultural universality. Direct comparative designs using harmonized measures and matched occupational samples are needed before claims of cultural specificity or universality can be made. Taken together, these limitations underscore the need for longitudinal, multi- method, latent-variable, and cross-cultural research—and for replica- tions that include managerial status and decompose the harassment composites—to clarify the robustness and scope of the observed associations.

  Associations of HEXACO and dark traits with power and gender harassment · 2026 · DOI

• Building on the present findings and their limitations, several avenues for future research are warranted. First, longitudinal and multi-wave designs are essential for clarifying temporal ordering among personality traits and harassment perpetration tendencies. Tracking employees over time would allow researchers to examine whether stable personality characteristics prospectively relate to later harassment perpetration tendencies, or whether workplace experiences reciprocally shape self-perceptions of personality. Such designs would substantially strengthen causal inference beyond cross-sectional associations. Second, future research should incorporate multi-method assessment strategies. Combining self-reports with peer evaluations, supervisor ratings, organizational records, or experimentally derived behavioral measures would help reduce common method bias and clarify the behavioral validity of personality–harassment associations. In particular, examining whether HEXACO traits relate to externally evaluated misconduct would provide stronger evidence for practical relevance. Third, cross-cultural comparative research is necessary to determine whether the observed associations are culturally specific or more generalizable. Although the present study contributes evidence from a Japanese employee sample, parallel investigations using comparable instruments across cultural contexts would allow direct tests of invariance. Such work would clarify whether the role of Honesty– Humility and Dark Triad traits in harassment perpetration tendencies differs across societies characterized by varying norms regarding hierarchy, collectivism, and gender roles. Fourth, future studies may benefit from including additional personality constructs. Incorporating Sadism from the Dark Tetrad could help disentangle distinct forms of antagonistic motivation, while examining prosocial traits such as empathy or altruism may clarify protective mechanisms.

  Associations of HEXACO and dark traits with power and gender harassment · 2026 · DOI

• Long before personality was measured with questionnaires and statistics, it was explored through the deep, often turbulent waters of the unconscious mind. The psychoanalytic perspective, pioneered by Sigmund Freud, was the first comprehensive theory of personality. It proposed that our behavior, thoughts, and emotions are powerfully shaped by unconscious motives, internal conflicts, and the lingering effects of early childhood experiences. Universal Neuro-Genetic Matrix (INGCPT-SETEHA Compatible) This model basically can be defined as neurogenetic varna personality traits psychometric model that can be utilised in corporate setting to cluster employees based on their stress predispositions and the reactant values in calculating the stress with variable formulas, thus the below universal neurogenic axis can ideally be provided to address and cluster employeees to assign the work based on their neurogenetic profiling and and the step by step process has been explained below to foresee the entire process for company having more employees around 1 lakhs.

  Neurogenetic Psychometrics of Stress: A Multidimensional Framework for Intelligence, Personality, and Adaptability · 2026 · DOI

• 808 This study has several important limitations. First, although the longitudinal design improves 809 on cross-sectional approaches by establishing temporal ordering and allowing us to distinguish within- 810 from between-person variation, the observational nature of the data precludes causal conclusions. The 811 findings are consistent with selection and socialization processes, but time-varying within-person 812 confounders cannot be ruled out. For example, changes in health or family responsibilities may 813 simultaneously shape personality development and opportunities for philanthropic engagement. Future 814 research could move closer to causal inference by specifying the broader causal network of these 815 associations more explicitly and, where feasible, combining repeated assessments of theoretically 816 relevant contextual changes with longitudinal causal inference approaches (Chatton & Rohrer, 2024; 35 PERSONALITY TRAITS AND PHILANTHROPY ACROSS TIME 817 Loh & Ren, 2023). Complementary quasi-experimental or intervention-based designs, such as 818 programs that facilitate volunteering or encourage charitable giving, would provide further evidence. 819 Second, the 2-year observation period has limited our ability to capture long-term personality 820 development. A previous examination of trait change in this sample found comparatively high rank- 821 order stability of personality domains and aspects (Haehner, Krämer, Schaefer, et al., 2025). 822 Personality change often unfolds gradually and may be difficult to detect reliably across relatively 823 short intervals (Bleidorn et al., 2022; Roberts et al., 2006). As a result, the present design may have 824 limited sensitivity to personality changes associated with philanthropic engagement that occur on 825 longer time scales. Future research should therefore examine these processes with longer study 826 durations to better capture long-term developmental trajectories. 827 Third, both personality traits and philanthropic behaviors were assessed via self-reports. Self- 828 reports of philanthropic engagement, particularly estimates of volunteering time or monetary 829 contributions, may be imprecise due to recall bias. Future research could benefit from incorporating 830 more objective indicators of philanthropic behavior, such as register-based donation records (Elinder 831 & Erixson, 2025). For personality traits, prior research suggests that self-reports can be constrained by 832 response biases and imperfect self-insight, and that informant reports from close others provide 833 complementary and uniquely predictive information (e.g., Connolly et al., 2007; Vazire & Mehl, 834 2008). A multi-method approach combining self-reports with informant ratings and behavioral data 835 may thus yield complementary insights into transactions of personality and philanthropic behaviors. 836 Finally, the present data support generalizability within the Swiss context but only to a limited 837 extent beyond it. Levels of philanthropic engagement are comparatively high in Switzerland (also 838 reflected in the present sample), potentially reflecting context-specific features such as a dense 839 network of civic organizations, high wealth, or a strong humanitarian tradition. More generally, 840 engagement rates vary substantially across countries and contexts (Nakamura, Gibson, et al., 2025; 841 Nakamura, Węziak-Białowolska, et al., 2025). Such variation may affect the strength of associations 842 between personality traits and philanthropic behaviors. Additionally, cross-cultural variation in 843 developmental trajectories of personality traits may also alter personality-philanthropy links (Bleidorn 844 et al., 2013). Future research should examine associations between personality traits and philanthropy 36 PERSONALITY TRAITS AND PHILANTHROPY ACROSS TIME 845 across diverse contexts to assess how they are shaped by contextual factors and whether they 846 generalize across countries and cultures.

  Personality Traits and Philanthropy Across Time · 2026 · DOI

• Our work focuses exclusively on fine-tuning models for regression- based personality prediction and does not evaluate performance on generative tasks. Consequently, issues commonly associated with gen- erative language models, such as hallucination or generative bias, were not examined. Future research can address this limitation by extending the framework to generative or hybrid settings. Incorporating strategies such as retrieval augmentation, adaptive context selection or fine-tuning before generative tasks could further improve context utilization in extended narratives and enhance our evaluation pipeline. Careful dataset curation, fairness evaluation, and transparent reporting of model behavior are therefore important to mitigate bias in real-world deployments, particularly where automated assessments could influence decisions related to hiring, evaluation, or psychological profiling. Another known limitation of large language models is hallucina- tion, where models may produce plausible but incorrect outputs. Although the present work focuses on fine-tuning language models for structured personality prediction rather than open-ended genera- tion, hallucination risks remain relevant for broader LLM-based sys- tems, particularly in high-stakes domains such as clinical or pharmaceutical decision-support where incorrect outputs could lead to misleading interpretations. Responsible deployment of LLM-based systems also requires appropriate governance and monitoring practices. Emerging LLMOps frameworks emphasize model documentation, evaluation protocols, and monitoring pipelines to detect drift or unintended behavior during deployment. Such governance mechanisms support transpar- ency and accountability by enabling auditing, monitoring, and correc- tive interventions when unexpected outcomes occur in operational environments. Researchers and practitioners should therefore apply automated personality assessment systems responsibly and carefully consider ethical implications in real-world applications.

  Language-based personality assessment from life narratives: a focus on model interpretability and efficiency · 2026 · DOI

• existing trait models • Personalizing AI at work • Determining the and in other contexts to sources, development, optimize the benefitsand malleability of to-harms ratio at the personality factors individual and societal linked to AI • Determining the levels • Investigating how multiple consequences of aspects of culture shape a personality factors for person’s interactions usage and engagement with AI…

  Personality, identity, and Artificial Intelligence: a grand challenge · 2026 · DOI

• The paper identifies dual-use risks (political targeting, discriminatory filtering in employment/finance, manipulation of vulnerable populations) but does not propose technical safeguards, detection mechanisms, or adversarial robustness testing for the PSO-CNN model against malicious personality profiling or adversarial text inputs.

  <p>Particle Swarm Optimization-Convolutional Neural Network (PSO-CNN): An Optimized Deep Learning Model for Personality Recognition From Social Media Profiles</p> · 2026 · DOI

• The dataset comprises Twitter profiles preprocessed with anonymization and personally identifiable information removal, but the specific size of the training/validation/test split, class distribution across 16 MBTI types, and representativeness of the Twitter user population relative to general demographics are not disclosed. Dataset bias and potential skew toward specific personality types or user demographics are unaddressed.

  <p>Particle Swarm Optimization-Convolutional Neural Network (PSO-CNN): An Optimized Deep Learning Model for Personality Recognition From Social Media Profiles</p> · 2026 · DOI

• PSO-CNN achieved competitive or superior performance against transformer-based models and fine-tuned DenseNet architectures (Table 9, 2017-2025 comparison), yet the paper does not investigate why systematic PSO-based architectural optimization outperforms attention mechanisms. The theoretical mechanisms explaining PSO effectiveness for personality recognition CNN design remain unexplored.

  <p>Particle Swarm Optimization-Convolutional Neural Network (PSO-CNN): An Optimized Deep Learning Model for Personality Recognition From Social Media Profiles</p> · 2026 · DOI

• The paper demonstrates PSO optimization of CNN hyperparameters for MBTI personality classification but does not specify PSO parameter configurations (inertia weight, cognitive/social coefficients, number of particles, iteration count) used during optimization. Ablation studies isolating the contribution of PSO optimization versus baseline CNN architecture tuning are absent.

  <p>Particle Swarm Optimization-Convolutional Neural Network (PSO-CNN): An Optimized Deep Learning Model for Personality Recognition From Social Media Profiles</p> · 2026 · DOI

• While PSO-CNN demonstrates 40% fewer parameters and 2.3 ms inference time compared to BERT-base (5.7 ms), the model has only been validated on Twitter datasets. Real-time deployment performance across heterogeneous social media platforms (Facebook, Instagram, TikTok) with varying text characteristics, user demographics, and content modalities remains unvalidated.

  <p>Particle Swarm Optimization-Convolutional Neural Network (PSO-CNN): An Optimized Deep Learning Model for Personality Recognition From Social Media Profiles</p> · 2026 · DOI

• The PSO-CNN model achieved 84.5% accuracy using single-modality text-only data from Twitter profiles for 16-class MBTI classification. The paper acknowledges that multimodal frameworks typically yield higher accuracy through complementary data sources, but does not explore the specific integration of image, metadata, or temporal features with the PSO-CNN architecture for personality recognition.

  <p>Particle Swarm Optimization-Convolutional Neural Network (PSO-CNN): An Optimized Deep Learning Model for Personality Recognition From Social Media Profiles</p> · 2026 · DOI

• Reference [72] compares human versus machine performance in personality-based deception detection, but lacks specification of which personality dimensions most contribute to deceptive communication detection and whether personality factors interact differently with machine learning classifiers versus human judges.

  Twenty Years of Personality Computing: Threats, Challenges and Future Directions · 2026 · DOI

• Reference [76] demonstrates personality trait prediction using physiological data and driving behavior with machine learning, but the generalizability across diverse driving contexts (highway vs. urban), driver populations (age, experience level), and vehicle types remains untested in personality computing applications.

  Twenty Years of Personality Computing: Threats, Challenges and Future Directions · 2026 · DOI

• References [84-87] demonstrate personality prediction from Instagram picture features using visual and content-based approaches, but the relative contribution and interaction effects of visual features versus textual content features remain unresolved; multimodal feature fusion strategies for Instagram-based personality computing need explicit comparison and optimization.

  Twenty Years of Personality Computing: Threats, Challenges and Future Directions · 2026 · DOI

• The literature on mobile phone-based personality metrics [64] and psychographic user modeling from mobile phone usage [65] lacks direct comparative validation; future work should systematically compare personality predictions from mobile behavioral data against laboratory-administered personality assessments to establish criterion validity.

  Twenty Years of Personality Computing: Threats, Challenges and Future Directions · 2026 · DOI

• Reference [62] identifies that using short measures of the Big Five personality traits has unexamined consequences for personality computing applications; the specific impact of trait measurement length on computational personality recognition accuracy across different modalities (text, audio, visual) needs systematic evaluation.

  Twenty Years of Personality Computing: Threats, Challenges and Future Directions · 2026 · DOI

• Unobtrusive measures, such as the CEO Narcissism Index or signature size, produce mixed or inconsistent results and may reflect different correlates rather than narcissism.

  The measurement trap: a meta-analytic review of measures in CEO narcissism research · 2025 · DOI

• This meta-analysis of individual participant data aimed to examine differences in personality traits between VP/VLBW ( n = 568) and term-born ( n = 1,060) adults, and the role of neonatal characteristics and neurosensory impairments in childhood, which have not been previously investigated.

  The effect of very preterm birth on the Five-Factor Model of personality traits: A meta-analysis of individual participant data · 2024 · DOI

• What is the effect of trait narcissism on creative performance? Although both constructs share an emphasis on uniqueness and novelty, prior investigations of the narcissism–creative performance relationship have produced inconsistent findings and failed to provide conclusive answers to this question.

  Paradoxical effects of narcissism on creative performance: Roles of leader–follower narcissism (in)congruence and follower identification with the leader · 2024 · DOI

Most-cited papers in Personality Traits and Psychology

• From mice to men: What can we learn about personality from animal research? · Psychological Bulletin · 2001 · 1,226 citations
• Bifactor Models and Rotations: Exploring the Extent to Which Multidimensional Data Yield Univocal Scale Scores · Journal of Personality Assessment · 2010 · 927 citations
• PERSONALITY MEASURES AS PREDICTORS OF JOB PERFORMANCE: A META‐ANALYTIC REVIEW · Personnel Psychology · 1991 · 892 citations
• Big Five factors and facets and the prediction of behavior. · Journal of Personality and Social Psychology · 2001 · 650 citations
• Narcissism and Leadership: A Meta‐Analytic Review of Linear and Nonlinear Relationships · Personnel Psychology · 2013 · 480 citations
• Pathogens, personality, and culture: Disease prevalence predicts worldwide variability in sociosexuality, extraversion, and openness to experience. · Journal of Personality and Social Psychology · 2008 · 478 citations
• RECONCILING PROCESSING DYNAMICS AND PERSONALITY DISPOSITIONS · Annual Review of Psychology · 1998 · 473 citations
• Comparing Clinical and Social‐Personality Conceptualizations of Narcissism · Journal of Personality · 2008 · 454 citations
• Personality, mood, and cognitive processing of emotional information: Three conceptual frameworks. · Psychological Bulletin · 1998 · 433 citations
• The implications of Big Five standing for the distribution of trait manifestation in behavior: Fifteen experience-sampling studies and a meta-analysis. · Journal of Personality and Social Psychology · 2009 · 427 citations

Most recent work

• Epigenetic aging and personality differences: Latent change analyses of twin data · European Journal of Personality · 2026
• Why some managers might positively evaluate subordinates with dark personality traits · Journal of Managerial Psychology · 2026
• Dark Tetrad traits and problematic generative artificial intelligence use: the serial mediating role of individualism and intolerance of uncertainty · BMC Psychology · 2026
• Unpacking the personality–cognitive ability link: a cross-national facet-level analysis of the Big Five · Large-scale Assessments in Education · 2026
• Team narcissism and team learning: the role of team envy and team leader humility · Baltic Journal of Management · 2026
• Twenty Years of Personality Computing: Threats, Challenges and Future Directions · ACM Computing Surveys · 2026
• <p>Particle Swarm Optimization-Convolutional Neural Network (PSO-CNN): An Optimized Deep Learning Model for Personality Recognition From Social Media Profiles</p> · Cureus Journal of Computer Science. · 2026
• A Hybrid CNN–LSTM Model for Personality Trait Classification from Textual Data · International Journal of Creative and Open Research in Engineering and Management · 2026
• Personality, identity, and Artificial Intelligence: a grand challenge · Frontiers in Psychology · 2026
• Personality Over Demographics: Hierarchical Evidence for Big Five Predictors of Emotional Exhaustion, Cynicism, and Professional Efficacy in Vietnamese Primary School Teachers · North American journal of psychology · 2026

Related topics in Psychology