Cognitive and Social, Emotional, and Behavioral Foundations of Transformational Leadership Attitudes: Evidence from a National Sample of Brazilian Federal Public Managers

Cognitive and Social, Emotional, and Behavioral Foundations of Transformational Leadership Attitudes: Evidence from a National Sample of Brazilian Federal Public Managers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cognitive and Social, Emotional, and Behavioral Foundations of Transformational Leadership Attitudes: Evidence from a National Sample of Brazilian Federal Public Managers Telesmagno Neves-Teles, Bruno Elkfury Monticelli, Guilherme da Silva Freitas, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9418989/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Transformational leadership attitudes have been widely associated with positive organizational outcomes; however, their psychological foundations remain only partially understood, particularly in public-sector contexts. This study examined the joint contributions of executive functions (EF) and social, emotional, and behavioral (SEB) skills to transformational leadership attitudes (TLA) in a national sample of Brazilian federal public managers (N = 70). EF were assessed using a digital battery of performance-based tasks, and SEB were measured using the Brazilian version of the Behavioral, Emotional, and Social Skills Inventory (BESSI-BR). TLA were assessed with a validated Brazilian leadership scale. Hierarchical regression analyses, controlling for age and education, showed that SEB, especially emotional resilience and social engagement, were strongly associated with TLA, accounting for most of the explained variance. In contrast, EF showed limited global associations, although inhibitory control was positively related to individualized consideration in exploratory analyses. These findings suggest that transformational leadership in public organizations primarily reflects SEB competencies, complemented by specific cognitive self-regulatory processes. Practical implications highlight the relevance of incorporating SEB development and targeted cognitive training into public leadership selection, assessment, and development programs, contributing to more effective people management and public service delivery, and informing leadership development practices in public organizations. Preregistration : https://osf.io/8jz6k Cognitive Neuroscience Leadership and Ethics Psychology transformational leadership attitudes executive functions social emotional and behavioral skills inhibitory control public sector Figures Figure 1 Introduction Transformational Leadership in the Public Sector Transformational leadership has been consistently associated with positive outcomes with moderate-to-large effect sizes in organizational effectiveness, employee engagement, and psychosocial well-being, particularly in complex and high-demand contexts such as public administration (Backhaus & Vogel, 2022 ; Bao et al., 2025 ; Huang & Villadsen, 2023 ; Waldman & Balthazard, 2015 ). Leaders who articulate a compelling vision, stimulate intellectual engagement, and demonstrate individualized consideration tend to foster higher levels of motivation, trust, and adaptive performance among followers (Avolio & Bass, 2004 ; Bao et al., 2025 ; Molenberghs et al., 2017 ). Recent meta-analytic evidence in the public administration domain confirmed the robustness and consistency of these associations across institutional settings and outcome domains (Bao et al., 2025 ). Despite this solid evidence base, the psychological mechanisms underlying individual differences in transformational leadership attitudes (TLA) remain only partially understood, particularly when cognitive and social, emotional, and behavioral processes are examined jointly (Hannah et al., 2013 ; Karatosidi & Iordanoglou, 2024 ; Lindebaum & Raftopoulou, 2017 ). Cognitive and Social, Emotional, and Behavioral Foundations of Transformational Leadership Executive Functions and Transformational Leadership Theoretical and empirical advances suggested that leadership should be understood as a multidimensional phenomenon emerging from the interaction between higher-order cognitive control processes, such as executive functions (EF), and social, emotional, and behavioral (SEB) skills (Chan et al., 2021 ; Humphrey et al., 2016 ). EF support the regulation of goal-directed behavior, adaptive responding, and complex decision-making, all of which are critical for effective leadership in dynamic organizational environments (Chan et al., 2021 ; Hannah et al., 2013 ). Contemporary integrative perspectives further indicate that specific EF components —particularly working memory (WM), inhibitory control (IC), and decision-making processes under uncertainty (DM) — play a central role in shaping leaders’ capacity to sustain goal-directed behavior, regulate impulses, and make adaptive strategic choices in socially accountable contexts (Chan et al., 2021 ; Karatosidi & Iordanoglou, 2024 ). Although cognitive flexibility has also been linked to adaptive leadership in broader EF frameworks, emerging neuropsychological evidence suggests that leadership effectiveness is especially associated with executive control capacities related to response inhibition and risk-sensitive decision-making, which facilitate responsible action under uncertainty and the coordination of complex interpersonal demands (Ramchandran et al., 2016 ). Experimental and neurocomputational findings further indicate that leadership involves a distinct form of executive decision-making in socially responsible contexts, whereby individuals differ in their willingness to assume responsibility for outcomes affecting others — a process supported by higher-order control networks and associated with leadership-related behavior (Edelson et al., 2018 ). Social, Emotional, and Behavioral Skills and Transformational Leadership In parallel, research on SEB skills has increasingly been organized around the integrative framework proposed by Soto et al. ( 2022 ), which conceptualizes these skills as functional capacities to regulate emotions, maintain social relationships, and manage goal-directed behavior in demanding contexts. Within this framework, emotional resilience (ER) and social engagement (SE) emerge as particularly relevant domains for leadership functioning. ER reflects the capacity to regulate stress and mood fluctuations, supporting adaptive responses under sustained cognitive and emotional demands. SE encompasses skills related to initiating interactions, communicating effectively, and asserting ideas within groups, facilitating trust building, information exchange, and coordinated action in team environments (Fernandes et al., 2024 ; Soto et al., 2022 ). Leadership research further indicates that emotional processes operate across multiple levels of organizational life, shaping how leaders influence group climate, motivation, and interpersonal dynamics (Humphrey et al., 2016 ). Empirical findings also suggest that leaders’ capacity to regulate and express emotions constructively, as well as to engage sensitively with others, is positively associated with transformational leadership behaviors (Esteves et al., 2024 ; Gómez-Leal et al., 2022 ). These socio-emotional competencies are therefore understood as behavioral facilitators of effective leadership functioning rather than as substitutes for cognitive control mechanisms. Integrative perspectives additionally suggest that such competencies operate in concert with executive control processes in contexts requiring strategic adaptation and sustained interpersonal coordination (Karatosidi & Iordanoglou, 2024 ; Toh & Yang, 2024 ). Integrative Gaps in the Literature Although prior studies have independently linked EF and socio-emotional competencies to leadership-related outcomes, unified analytical frameworks examining their joint and incremental contributions remain scarce (Karatosidi & Iordanoglou, 2024 ). Meta-analytic syntheses have further indicated that, while a wide range of antecedents of transformational leadership has been examined, comparatively few studies explicitly integrate cognitive and social, emotional, and behavioral mechanisms within a single analytical model (Bao et al., 2025 ). Much of the empirical literature has relied either on self-report measures of cognitive abilities or on narrow operationalizations of leadership, limiting the ability to disentangle the relative contribution of cognitive versus socio-emotional domains (Karatosidi & Iordanoglou, 2024 ; Lee et al., 2012 ; Lindebaum & Raftopoulou, 2017 ). Moreover, there remains a notable gap in the literature regarding leadership processes in the public sector, where decision-making constraints, bureaucratic complexity, and social accountability place distinct demands on leaders’ cognitive control and emotional regulation capacities (Bao et al., 2025 ; Huang & Villadsen, 2023 ). The Present Study Addressing these gaps, the present study focuses on theoretically and empirically salient EF components — WM, IC, and DM — given their relevance for goal maintenance, behavioral regulation, and adaptive responding in leadership contexts. In parallel, it examines key SEB skills associated with adaptive leadership functioning, including emotional resilience (ER), social engagement (SE), cooperation (CO), self-management (SM), and empathy. Although recent studies have sought to integrate executive functions and socio-emotional constructs in the prediction of transformational leadership (e.g., Karatosidi & Iordanoglou, 2024 ), such work has primarily emphasized dispositional variables such as personality and trait emotional intelligence. In contrast, the present study advances the literature by examining SEB skills conceptualized as functional competencies, assessed within a preregistered multimethod framework and in a nationally distributed sample of experienced public-sector leaders. EF are assessed using an adapted digital battery of performance-based tasks, whereas SEB are measured through the Brazilian version of the Behavioral, Emotional, and Social Skills Inventory (BESSI-BR; Fernandes et al., 2024 ; Soto et al., 2022 ). Transformational leadership attitudes (TLA) are evaluated using a validated Brazilian scale (Fonseca & Porto, 2013 ). By integrating performance-based cognitive indicators and self-reported social, emotional, and behavioral skills, the study adopts a multimethod assessment strategy that broadens construct representation while minimizing reliance on a single measurement source. Guided by a preregistered analytical plan, the study pursues two primary objectives: (a) to describe the variability and profile of selected EF components and SEB skills among federal public managers; and (b) to test a heuristic model examining their unique and joint contributions to TLA while controlling for relevant covariates. Entering EF and SEB as separate predictor blocks in hierarchical regression models allows the evaluation of their incremental explanatory power. By integrating core executive control processes and theoretically grounded SEB skills within a preregistered and methodologically rigorous design, this study offers a preregistered multimethod test of an integrative framework linking executive control and social, emotional, and behavioral skills to leadership attitudes in the public sector. Study Hypotheses Consistent with the preregistered analytical plan, the present study tested the following hypotheses: H1 (Variability Hypothesis). Both global scores and indicator-level scores of EF components, SEB skills, and TLA will show meaningful inter-individual variability, as reflected in adequate dispersion indices and absence of substantial floor or ceiling effects within the sample. This expectation is grounded in the substantial heterogeneity of the sample with respect to age, professional background, organizational cultures, and career trajectories within the Brazilian federal public service, as well as in prior evidence indicating meaningful interindividual variability in executive decision-making, emotional regulation, and leadership-related neural and behavioral markers (Balthazard et al., 2012 ; Edelson et al., 2018 ; Fatima et al., 2020 ). H2 (EF → TLA hypothesis). Executive functions will be positively associated with transformational leadership attitudes. This hypothesis builds on both foundational and contemporary work suggesting that higher-order cognitive control processes — particularly WM, IC, and DM — support leadership-related behavior, responsibility-taking, and adaptive decision-making in complex organizational contexts (Chan et al., 2021 ; Edelson et al., 2018 ; Karatosidi & Iordanoglou, 2024 ; Ramchandran, 2011 ; Ramchandran et al., 2016 , 2020 ). Cognitive flexibility (CF) was also examined in an exploratory manner, given its relevance in broader EF frameworks. H3 (SEB → TLA Hypothesis). Social, emotional, and behavioral skills will be positively associated with transformational leadership attitudes. This hypothesis builds on evidence indicating that ER, SE, and related socio-emotional competencies such as cooperation, self-management, and empathy support effective leadership functioning, including relationship building, emotional regulation, and adaptive interpersonal behavior in organizational contexts (Bergner et al., 2022 ; Cavaness et al., 2020 ; Esteves et al., 2024 ; Frias et al., 2021 ; Gómez-Leal et al., 2022 ; Humphrey et al., 2016 ; Prezerakos, 2018 ). H4 (Exploratory Joint Hypothesis). EF components and SEB skills will show joint and potentially interactive associations with transformational leadership attitudes. In particular, WM, IC, and DM are expected to relate to TLA in combination with emotional resilience, social engagement, and related socio-emotional competencies, reflecting integrative perspectives that emphasize the coordinated role of cognitive control and socio-emotional regulation in adaptive leadership functioning (Connelly & Ruark, 2010 ; Humphrey et al., 2016 ; Molenberghs et al., 2017 ; Toh & Yang, 2024 ). Methods Preregistration and Transparency The study protocol, hypotheses, eligibility criteria, outcome measures, and primary statistical analysis plan were preregistered prior to data inspection on the Open Science Framework (OSF; https://osf.io/8jz6k ). Any deviations from the preregistered protocol are explicitly reported in the manuscript. Study Design This was a cross-sectional observational study based on random sampling of Brazilian federal public managers. No blinding or experimental manipulation was implemented, and each participant contributed a single set of scores. Primary analyses included descriptive and variability analyses, partial correlations controlling for demographic covariates, and hierarchical multiple regression models with TLA as the dependent variable. Participants and Sampling Participants were randomly selected from the official national directory of federal management positions distributed across Brazil’s 27 federative units (sampling frame = 1,100 + managers at the time of recruitment). Recruitment occurred in successive waves. After each cycle expired, individuals who had not responded were excluded from subsequent draws, and a new random selection was performed among the remaining not-yet-invited managers. Across all waves, 687 managers were invited via institutional email and official communication channels. No financial compensation was offered; participation was voluntary and target recruitment was N = 82, ensuring a final analyzed sample of at least 78 participants, based on an a priori power analysis (α = .05, 1 – β = .80, f ² = 0.15). Eligibility and Cognitive Screening Eligibility criteria included: (a) age ≥ 20 years, (b) absence of self-reported neurological or psychiatric conditions suggestive of atypical neurodevelopment, and (c) adequate global cognitive functioning. Cognitive screening was conducted via videoconference prior to enrollment and included the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and an estimate of general intellectual functioning derived from the Wechsler Adult Intelligence Scale – Third Edition (WAIS-III IQ). The MMSE served as the sole formal inclusion criterion. A cutoff score of ≥ 24 was adopted to exclude individuals with potential global cognitive impairment, following recommendations for highly educated samples (Kochhann et al., 2010 ; sensitivity = 87%, specificity = 82%). The MoCA and WAIS-III IQ were administered as complementary indicators to provide broader characterization of executive, attentional, and general intellectual functioning, supporting global cognitive comparability within the sample. MoCA performance, particularly on the MoCA-MIS, suggested that a subset of participants presented profiles consistent with possible mild cognitive impairment, despite meeting MMSE inclusion criteria. In order to ensure methodological transparency without altering predefined eligibility criteria, these findings were documented and considered in the interpretation of the cognitive screening phase. Procedure Data collection followed a two-day sequential online protocol hosted on a secure university server and accessed via individual participant codes: Day 1: Sociodemographic questionnaire (~ 5 min) and a digital battery of seven EF tests (~ 45 min). Day 2: BESSI-BR (~ 25 min) and a Brazilian TLA scale (~ 20 min). Participants were instructed to complete all activities in a quiet, interruption-free environment without the presence of third parties. The estimated total duration was approximately 95 minutes. Although a broader set of cognitive and socio-emotional indicators was collected as part of the preregistered umbrella study, the present analyses focus on theoretically selected EF components and SEB skills. The extended dataset was retained to enable sensitivity, exploratory, and subsequent multivariate analyses. Measures Executive Function Battery (Digital Adaptation) All seven EF tasks were adapted from open-source paradigms available in PsyToolkit (Stoet, 2010 , 2017 ) and reimplemented in OpenSesame (Mathôt et al., 2012 ) for computerized administration in Brazilian Portuguese. The adaptation followed the International Test Commission (ITC) guidelines for cross-cultural test translation and adaptation (Hambleton & Zenisky, 2011 ), ensuring conceptual, linguistic, and functional equivalence. Two independent forward translations were produced and evaluated through a two-stage expert review conducted by three specialists in cognitive psychology and psychometrics. The first round focused on prototype screenshots, allowing refinement of stimuli, instructions, and interface layout. The second applied a structured checklist derived from ITC criteria. Inter-rater agreement, estimated using Fleiss’ Kappa, increased from κ = .59 to κ = .84, indicating strong linguistic and cultural adequacy. Temporal parameters for each task are provided in Supplementary Material S1 and will be archived in a public open-science repository upon publication. The digital battery assessed multiple EF domains (WM, IC, DM, and exploratory CF indicators). The present analyses focus primarily on theoretically selected components (WM, IC, and DM), whereas CF was examined in an exploratory manner. The following subsections describe the EF tasks used to measure each domain. Working Memory Working memory was assessed using two computerized tasks: the Digit Span task (WM storage) and the 2-back task (WM manipulation/updating). Digit Span (WM Storage) WM storage was assessed using a computerized Digit Span task (Wechsler, 1997 ). Digits were presented visually for 700 ms with a 300 ms interstimulus interval. Participants recalled sequences of 2–9 digits in the same order by selecting numbers on the screen. Two trials were administered for each sequence length. Performance was summarized using a standardized Digit Span composite score (storage component) integrating total correct trials and maximum span length. Both indices were z-standardized and averaged so that higher scores indicate greater storage capacity. Internal consistency for Digit Span in Brazilian adult samples is high (α = 0.88; Júlio-Costa & Haase, 2017 ). N-back (2-back; WM Manipulation) WM manipulation was assessed using a 2-back task (Hepdarcan & Can, 2025 ; Kane et al., 2007 ). Consonant letters (B, F, K, L, H, M, P, R, T, V, X, Y) were presented for 500 ms with a 2000 ms interstimulus interval. Participants indicated whether the current stimulus matched the one presented two trials earlier. The task consisted of six blocks of 36 trials with short rest intervals between blocks. Task performance was indexed by a Nback-2back composite score (manipulation component) integrating signal-detection sensitivity ( d ’) and reaction time for correct responses. Both indicators were z -standardized and averaged, with reaction time reverse-scored so that higher values indicate greater updating efficiency. Reliability estimates for N-back paradigms in Brazilian adult samples typically range between α = .80–.85 (De Nardi et al., 2013 ). Finally, the standardized scores from both tasks were averaged to create a WM composite score, representing overall working memory performance. Inhibitory Control Inhibitory control was assessed using two computerized tasks capturing complementary components of inhibition: interference suppression and response inhibition. Stroop Test (Interference Suppression) IC interference control was assessed using a computerized Stroop Color–Word task (MacLeod, 1991 ). Stimuli were presented for 1000 ms, with responses allowed up to 1500 ms and a jittered interstimulus interval (1000 ± 200 ms). Participants completed 30–40 trials per condition (congruent and incongruent). Performance was quantified using reaction-time and accuracy interference effects, calculated as the differences between incongruent and congruent trials. These indicators were z-standardized and averaged to form a Stroop composite score, ensuring balanced integration of speed- and accuracy-based interference while avoiding distortions associated with ratio-based efficiency measures (Alfers et al., 2025 ). Higher values indicated more efficient inhibitory control. Previous evidence suggests adequate reliability for Stroop performance in Brazilian adult samples (α = .80–.85; Júlio-Costa & Haase, 2017 ; Miotto et al., 2023 ). Go/NoGo (IC - Response Inhibition) Response inhibition was assessed using a Go/No-Go task (Verbruggen et al., 2019 ; Verbruggen & Logan, 2008 ). Stimuli were presented for 300–400 ms with an 800 ms response window and a jittered interstimulus interval (1000 ± 200 ms). The task comprised 160 trials, including 20% No-Go trials. Performance was operationalized as a standardized Go/NoGo composite score integrating signal-detection sensitivity ( d ’) and mean reaction time for correct Go trials. Both indices were z-standardized and averaged, with reaction time reverse-scored so that higher values indicate greater inhibitory efficiency. Previous evidence suggests adequate reliability for Go/NoGo performance in Brazilian samples (α = 0.76; Beato et al., 2012 ). Finally, the standardized scores from both IC tasks were averaged to form an IC composite score, indexing overall inhibitory control performance. Decision-Making Iowa Gambling Task (DM - Decision Learning and Risk Evaluation) DM was assessed using the Iowa Gambling Task (IGT; Bechara et al., 1994 ). Participants completed 100 selections from four decks with different reward–loss contingencies. Immediate feedback was provided after each choice. Task performance was operationalized using a DM composite score integrating two indicators: the net score, calculated as the difference between advantageous and disadvantageous deck selections [(C + D) − (A + B)], reflecting overall decision quality, and a learning index representing performance improvement across the task, computed as the difference between the final and initial blocks of trials (Block 5 − Block 1). Both indicators were z-standardized and averaged to form the DM composite score, with higher values indexing more advantageous decision-making and learning across the task. Previous evidence indicates acceptable reliability for IGT performance in Brazilian samples (α = .74; Malloy-Diniz et al., 2008 ). Cognitive Flexibility Finally, CF indicators were included as exploratory EF measures and were assessed using two computerized tasks capturing complementary aspects of flexible cognitive control: set shifting and switching efficiency. Wisconsin Card Sorting Test (CF - Set Shifting) Set-shifting ability was assessed using a computerized Wisconsin Card Sorting Test (WCST) based on the Brazilian manual for the 128-trial version (Mônego et al., 2019 ). Participants matched response cards to reference cards according to implicit rules (color, form, or number), which changed after 10 consecutive correct responses. Task performance was derived using three indices: categories completed, perseverative errors, and failures to maintain set. A composite WCST score was computed by z-standardizing each indicator and reverse-scoring error measures before averaging so that higher scores reflect greater set-shifting efficiency. Digital WCST implementations show good psychometric properties in adult samples (Steinke et al., 2021 ). Task Switching (CF – Switching Efficiency) CF was also assessed using a task-switching paradigm (Monsell, 2003 ; Vandierendonck et al., 2010 ). Participants alternated between two classification tasks guided by visual cues, with cue–stimulus intervals ranging from 300–600 ms and response windows up to 2000 ms. Performance was summarized using a Task Switching composite score integrating reaction-time switch cost, accuracy switch cost, and accuracy on switch trials. All indices were z-standardized and averaged, with cost measures reverse-scored so that higher values indicate greater switching efficiency. Composite scoring approaches are recommended to improve reliability in task-switching paradigms (Draheim et al., 2016 ; Hedge et al., 2018 ; Vandierendonck, 2021 ). Finally, the standardized scores from both tasks were averaged to form a CF composite score, indexing overall cognitive flexibility performance. Social, Emotional, and Behavioral Skills (SEB) SEB were assessed using the Brazilian version of the Behavioral, Emotional, and Social Skills Inventory (BESSI-BR), a 192-item self-report instrument originally developed by Soto et al. ( 2022 ) and adapted for use in Brazil by Fernandes et al. ( 2024 ). The inventory assesses five domains of SEB — Self-Management (SM), Social Engagement (SE), Cooperation (CO), Emotional Resilience (ER), and Innovation (IS) — comprising 32 specific skill facets. Participants rated how well they were able to enact each skill using a five-point Likert scale ranging from 1 (“not at all well”) to 5 (“extremely well”). Evidence from the Brazilian adaptation indicates high internal consistency at both the domain level (mean α = .94, range .91–.97) and the facet level (mean α = .86, range .77–.94), with comparable estimates based on McDonald’s omega (ω), supporting the robustness of the instrument’s measurement properties (Fernandes et al., 2024 ). In the present study, mean scores were computed for each domain and for a global SEB skills index representing an overall indicator of SEB competence across the five domains. Consistent with the preregistered analysis plan, only Social Engagement (SE) and Emotional Resilience (ER) were included as predictors in the primary models, as these domains show the strongest conceptual links to transformational leadership and present lower conceptual overlap with executive function constructs. SE and ER scores were subsequently standardized (z-scores) to enhance comparability with other predictors in the regression models. Transformational Leadership Attitude (TLA) TLA were assessed using the Attitudes toward Leadership Styles scale developed by Fonseca and Porto ( 2013 ). The instrument comprises items derived from internationally validated leadership measures, including the Transformational Leadership Inventory (TLI), the Multifactor Leadership Questionnaire (MLQ), and the Leader Reward and Punishment Questionnaire (LRPQ). Factor-analytic validation identified two factors corresponding to transformational leadership (24 items) and transactional leadership (8 items), with internal consistency estimates of α = .91 and α = .74, respectively (Fonseca & Porto, 2013 ). Participants rated their attitudes toward each leadership behavior using a seven-point Likert scale (1–7). For the purposes of this study, only the transformational leadership dimension was analyzed. A TLA composite score was computed by averaging responses across the transformational leadership items. This score was subsequently standardized ( z -score) to enhance comparability with other predictors in the regression models. Analytic Plan Data quality procedures were implemented to ensure valid estimation of EF performance. For computerized tasks involving RT, trials were considered valid only when a response was recorded and latency fell within a predefined temporal window (200–1500 ms). Trials with missing responses or latencies outside this range were excluded from RT-based calculations, whereas omission trials and invalid responses were treated as errors in accuracy-based indices. Potential outliers were initially flagged using the IQR criterion applied to original score distributions and subsequently inspected on a case-by-case basis. Statistical outliers were not automatically excluded. Participant-level exclusion was considered only when there was clear evidence of invalid response processes, such as random responding, systematic non-engagement, or failure to understand task instructions. Low-performing cases were otherwise interpreted as reflecting genuine interindividual variability and were retained. Domain-specific scoring procedures were adopted for EF components. For WM and CF indicators, score distributions were approximately normal and showed no evidence of extreme values indicative of invalid task engagement. For IC, composite indices were constructed by integrating standardized RT- and accuracy-based effects to avoid distortions associated with ratio-based efficiency measures. Participants with fewer than 35 valid Stroop trials (out of 80) were excluded from Stroop-based indices due to insufficient task engagement or unreliable performance estimates. For DM, assessed via the IGT, non-normal and asymmetric distributions were theoretically expected; extreme values were interpreted as meaningful indicators of advantageous or disadvantageous decision-making strategies rather than statistical outliers. Hierarchical multiple regression models were used to examine EF indicators (WM, IC, and DM) and SEB skills (SE and ER) as predictors of TLA, with age and education level included as covariates. Cognitive flexibility indicators were examined only in exploratory and sensitivity models. Although additional covariates were preregistered, they were not included in the primary models due to concerns about model overfitting given the modest sample size. Age and education were entered first (Step 0), followed by EF and SEB predictor blocks in alternative orders to examine incremental validity. Parametric statistical methods were applied to EF components showing approximate normality. For DM indicators derived from the IGT, distributional characteristics were explicitly acknowledged, and robust analyses were conducted when appropriate. Sensitivity analyses additionally examined the potential influence of sex on regression estimates. Standardized regression coefficients (β), changes in explained variance (ΔR²), and 95% confidence intervals were reported. To assess estimate stability, bootstrap confidence intervals based on 5,000 resamples were computed. No data transformations beyond scale scoring and standardization were planned. Item-level mean substitution was permitted when at least 80% of a scale was completed. EF tasks not fully completed were excluded from analyses for that specific measure only. Missing data below 5%, assumed to be missing at random, were handled using listwise deletion in regression models; higher levels of missingness prompted exploratory multiple imputation procedures. Ethical Considerations This study was approved by the Ethics Committee of the Institute of Psychology, Universidade Federal do Rio Grande do Sul (UFRGS; Protocol 7.274.390, CAAE 84134724.5.0000.5334, December 7, 2024). All participants provided digital informed consent prior to participation, and all procedures were classified as minimal risk. Results Sample Characteristics A total of 687 federal public managers were invited across five successive waves of random sampling from a national roster of approximately 1,100 officials. Of these, 87 provided informed consent, 82 met eligibility criteria (MMSE ≥ 24), and 70 completed all study procedures, constituting the final analytic sample. Further details regarding recruitment procedures and eligibility criteria are available in Supplementary Material S2. Table 1 presents the demographic characteristics of the final sample. Participants were middle-aged ( M = 48.97, SD = 7.37) and predominantly male (61.4%). The sample was highly educated, with 95.7% holding postgraduate degrees, including 27.1% with a master’s degree and 28.6% with a doctoral degree. Participants reported substantial leadership experience, with approximately 70% having more than six years of tenure and 35.7% reporting over ten years. Most were career civil servants (97.1%), and income levels reflected the seniority of the sample, with 60% reporting monthly income above R $ 20,000. (Insert Table 1 about here) Descriptive Results Descriptive statistics for the study variables are presented in Table 2. EF composite scores, SEB skills, and TLA showed adequate variability and overall acceptable distributional properties for subsequent inferential analyses. Comprehensive information on task-level validity criteria, scoring procedures, and additional distributional diagnostics is available in Supplementary Material S3. (Insert Table 2 about here) Associations between Executive Functions, Social, Emotional and Behavioral Skills, and Transformational Leadership Attitudes Correlation Analysis Partial correlations controlling for age and education level among EF indicators, SE and ER, and TLA are presented in Table 3. Overall, both SEB indicators (SE and ER) showed moderate positive associations with TLA ( r = .53, p < .001 for both). In contrast, global EF indicators showed consistently small and non-significant associations with TLA. (Insert Table 3 about here) As expected, SE and ER were strongly intercorrelated ( r = .69, p < .001), suggesting shared social, emotional, and behavioral regulatory mechanisms. A modest negative association was also observed between IC and SE ( r = − .30, p < .05), although this secondary pattern should be interpreted cautiously given the study’s primary focus. Taken together, these findings suggest a differential pattern in which social, emotional, and behavioral skills are more closely related to transformational leadership attitudes than performance-based executive function indicators. Hierarchical Regression Models Hierarchical multiple regression analyses were conducted to examine the incremental contributions of EF and SEB indicators to TLA, controlling for age and education level. Age and education level accounted for a small proportion of variance in TLA (R² = .06). In the first preregistered model, the inclusion of the SEB indicators (SE and ER) substantially improved model fit, increasing the explained variance to R² = .33 (ΔR² = .27). In the final step, EF indicators (WM, IC, and DM) were added, producing only a modest additional increase in explained variance (R² = .39, ΔR² = .06). In the second preregistered hierarchical model, EF indicators (WM, IC, and DM) were entered before the SEB indicators (SE and ER). Their inclusion did not meaningfully increase the explained variance (R² = .07, ΔR² = .01). In the final step, the addition of SE and ER substantially improved model fit, increasing the explained variance to R² = .39 (ΔR² = .32). The results for both preregistered models are presented in Table 4. Across both preregistered models, SEB skills accounted for the largest incremental proportion of explained variance in TLA, whereas EF indicators showed negligible incremental contribution. ER emerged as the only significant predictor (β = .39, p = .031), while SE was positively but not significantly associated with TLA (β = .29, p = .122). No EF indicators meaningfully predicted TLA (all p > .05). Bootstrap analyses (5,000 resamples) yielded highly consistent parameter estimates, supporting the stability of the regression findings despite the modest sample size. (Insert Table 4 about here) Exploratory associations (IC X Individualized consideration) To further explore potential links between EF indicators and specific dimensions of TLA, partial correlations controlling for age and education level were examined between IC and the Providing Individualized Support (PIS) dimension. A significant positive association was observed ( r = .32, p = .018), and bootstrap confidence intervals based on 5,000 resamples confirmed the robustness of this effect (95% CI [.08, .51]; see Table 5). (Insert Table 5 about here) Importantly, PIS was not significantly associated with SE ( r = − .06, p > .05) or ER ( r = .04, p > .05), suggesting that this exploratory finding reflects a more specific link between inhibitory control and leadership attitudes related to individualized consideration rather than a broader association with social, emotional, and behavioral skills. Overall, although EF indicators were not associated with global TLA scores, inhibitory control may play a more circumscribed, domain-specific role in leadership attitudes involving interpersonal support and follower-focused behaviors. Discussion Overview of the Study and Main Findings Overall, the findings revealed substantial variability across the examined constructs and indicated that SEB skills — including social engagement (SE) and emotional resilience (ER) — were consistently associated with TLA, whereas EF showed a more selective and domain-specific pattern of associations. Among the EF components (WM, IC, and DM), only IC was positively related to leadership attitudes reflecting individualized support. Taken together, these results suggest that SEB skills — particularly ER — may represent more robust correlates of TLA than performance-based cognitive control indicators in experienced public-sector leaders. By examining component-specific EF indicators and SEB skills conceptualized as functional competencies within a preregistered multimethod design, the present study advances prior integrative research on the psychological foundations of transformational leadership. An important limitation concerns the potential influence of shared-method variance on the observed associations between SEB skills and TLA, as both variables were assessed using self-report measures. Although the multimethod design reduced common-method bias in the assessment of EF through performance-based indicators, the use of the same response format for SEB and TLA may have inflated their observed relationships. Accordingly, the stronger predictive role of SEB skills should be interpreted with appropriate methodological caution. H1 — Variability Hypothesis The first hypothesis predicted that EF (WM, IC, and DM), SEB skills (SE and ER), and TLA would display sufficient variability at both global and indicator levels. The findings supported this expectation, with descriptive statistics indicating substantial dispersion across the examined constructs. This pattern suggests that the sample captured meaningful individual differences in cognitive functioning, social, emotional, and behavioral skills — including SE and ER — and leadership attitudes. Such variability likely reflects the heterogeneous composition of the Brazilian federal public service, which includes professionals with diverse educational backgrounds, career trajectories, and organizational experiences. Prior research has linked this type of heterogeneity to differences in decision-making styles, emotional regulation, and leadership behavior (Balthazard et al., 2012 ; Edelson et al., 2018 ; Fatima et al., 2020 ). Similarly, meta-analytic evidence indicates that institutional environments, administrative traditions, and organizational cultures contribute to variability in leadership patterns within the public sector (Backhaus & Vogel, 2022 ). In the Brazilian context, such variability may be further amplified by the country’s continental dimensions and pronounced sociocultural and economic differences across regions (IBGE, 2024 ), which shape organizational resources, administrative challenges, and leadership demands in distinct ways. These structural disparities reinforce the relevance of examining individual differences in EF and SEB skills among federal public-sector leaders. From a methodological standpoint, adequate variability across EF, SEB (including SE and ER), and TLA strengthens the interpretability of the correlational and regression findings, as it ensures sufficient dispersion to detect meaningful associations among variables. Most constructs also showed approximately normal distributions, with only mild deviations observed for TLA and DM. Overall, these distributional properties support the suitability of the data for the inferential analyses conducted in this study. H2 — Executive Functions and Transformational Leadership Attitudes The second hypothesis predicted that EF would be positively associated with TLA. The findings provided selective support for this expectation. Regression analyses including WM, IC, and DM did not reveal consistent associations with global leadership attitudes. However, exploratory analyses indicated that IC was positively associated with leadership attitudes related to individualized consideration. This pattern suggests that specific executive processes, rather than EF more broadly, may be particularly relevant for certain leadership-related behaviors. Individualized consideration involves attending to followers’ needs, supporting their development, and managing complex interpersonal dynamics (Avolio & Bass, 2004 ), all of which may require the regulation of automatic responses and sustained goal-directed engagement in socially demanding contexts. IC supports impulse regulation, response suppression, and reflective behavior (Diamond, 2013 ; Friedman & Miyake, 2017 ; Miyake & Friedman, 2012 ). Accordingly, leaders with stronger inhibitory control may be better able to modulate immediate reactions, consider alternative perspectives, and engage in more deliberate and adaptive interpersonal decision-making. These findings align with theoretical accounts proposing that executive control contributes to adaptive leadership through reflective judgment and strategic regulation in complex organizational settings (Chan et al., 2021 ; Edelson et al., 2018 ). At the same time, prior neuropsychological research suggests that EF–leadership associations may be component-specific and context-dependent (Ramchandran et al., 2016 ), reinforcing the view that the cognitive foundations of leadership attitudes operate through selective executive mechanisms rather than uniformly across EF domains. H3 — Social, Emotional, and Behavioral Skills and Transformational Leadership Attitudes The third hypothesis predicted that SEB skills would be positively associated with TLA, particularly through the SE and ER dimensions. The findings provided strong support for this expectation, as both SE and ER showed moderate positive associations with leadership attitudes, with ER emerging as the only significant predictor in the regression models. These results are consistent with a substantial body of research highlighting the importance of socio-emotional competencies — such as empathy, emotional regulation, and interpersonal effectiveness — for adaptive leadership behavior (Cavaness et al., 2020 ; Gómez-Leal et al., 2022 ; Humphrey et al., 2016 ). Transformational leadership involves motivating followers, fostering trust, and demonstrating individualized consideration (Avolio & Bass, 2004 ), all of which require the ability to regulate emotional responses and maintain constructive interpersonal relationships. Overall, the present findings reinforce the view that social, emotional, and behavioral skills represent proximal foundations of transformational leadership attitudes in organizational contexts, particularly among experienced public-sector leaders. H4 — Joint Contributions of Executive and Social, Emotional, and Behavioral Processes The fourth hypothesis proposed that EF and SEB skills would jointly relate to transformational leadership attitudes through a potentially complex pattern of associations. The findings provided partial support for this integrative perspective. SEB skills showed consistent and robust associations with TLA, whereas EF demonstrated a more selective and domain-specific pattern, primarily involving inhibitory control. This asymmetrical pattern suggests that leadership attitudes may reflect complementary regulatory processes rather than equivalent contributions from cognitive and social, emotional, and behavioral domains. SEB skills appear to provide proximal interpersonal foundations of transformational leadership, supporting constructive engagement with followers and effective emotional regulation. In contrast, executive control processes — particularly inhibitory control — may play a more circumscribed role by facilitating reflective judgment and the regulation of automatic responses in demanding organizational contexts. These findings align with integrative leadership models emphasizing the interaction between cognitive control and socio-emotional functioning in shaping adaptive leadership behavior (Connelly & Ruark, 2010 ; Humphrey et al., 2016 ; Molenberghs et al., 2017 ). Building on this perspective, Fig. 1 presents a heuristic integrative model in which cognitive control processes and SEB skills contribute to TLA through complementary pathways. Although partially supported by the present findings, this framework should be interpreted as a conceptual guide for future research. (Insert Fig. 1 about here) Particularly, longitudinal and experimental investigations adopting multimethod approaches similar to the present design may help clarify the dynamic and potentially interactive effects of EF and SEB on leadership development and performance. Taken together, the results highlight the importance of advancing integrative investigations of cognitive and social, emotional, and behavioral mechanisms in leadership research while recognizing that their contributions may differ in magnitude and specificity. Strengths and Limitations This study presents several strengths. First, it examined the associations between EF, SEB, and TLA within a real-world population of federal public managers, providing ecologically valid insights into leadership-related processes in the public sector. To the best of our knowledge, this is among the first studies in Brazil to investigate transformational leadership using performance-based cognitive measures derived from cognitive neuroscience in a nationally distributed sample of public-sector leaders. Second, the preregistered analytical plan enhanced transparency, reduced researcher degrees of freedom, and strengthened the methodological rigor of the findings. Third, the integration of performance-based cognitive measures and validated self-report SEB instruments provided a multimethod perspective on leadership-related processes, broadening construct representation and partially mitigating shared-method bias. Nevertheless, several limitations should be acknowledged. First, the cross-sectional design precludes causal inference regarding the associations between EF, SEB, and TLA. Longitudinal and experimental studies are needed to clarify the temporal dynamics and potential causal pathways linking cognitive control, social, emotional, and behavioral skills, and leadership behavior. Second, the final sample size was slightly smaller than originally anticipated in the preregistration. However, the observed effect sizes exceeded those assumed in the a priori power estimation (estimated f ² = .15 vs. observed f ² = .39), suggesting that the study was adequately powered to detect the effects identified. Third, performance-based EF tasks — particularly digital adaptations — may involve task impurity, as individual tasks can recruit multiple cognitive processes beyond the targeted executive component. Fourth, the sample showed limited variability in educational background, which may constrain generalizability to populations with broader educational diversity, although this characteristic reflects the typical profile of senior public managers in the Brazilian federal administration. Finally, the reliance on self-report measures for both SEB indicators and TLA may have inflated observed associations due to shared-method variance. Directions for Future Research Future research may advance the understanding of the associations between EF, SEB skills, and leadership by addressing several directions suggested by the present findings. First, longitudinal designs are needed to clarify the temporal dynamics linking cognitive control processes, social, emotional, and behavioral skills, and leadership development. Such approaches could examine whether improvements in EF and SEB precede changes in leadership attitudes or whether these associations evolve reciprocally over time. Second, experimental and intervention-based studies may investigate whether targeted training programs aimed at strengthening EF, particularly IC, and SEB skills contribute to the development of leadership-related capabilities. Cognitive training interventions focusing on inhibitory control, alongside programs designed to enhance emotional regulation and interpersonal effectiveness, may help clarify the malleability of leadership-related psychological processes. Third, future studies could extend the present heuristic integrative framework illustrated in Fig. 1 by incorporating neurophysiological and neuroimaging measures, as well as behavioral indicators of leadership performance. Such approaches may help elucidate the neural and cognitive mechanisms underlying leadership behavior and provide a more comprehensive understanding of how cognitive control and SEB skills jointly contribute to adaptive leadership in complex organizational environments. Finally, examining these associations across more diverse organizational contexts and populations — including private-sector leaders and cross-cultural samples — will be important to assess the generalizability of the present findings and identify potential contextual moderators. Practical Implications These findings have direct implications for management development and leadership training in public-sector organizations. The consistent associations between SEB skills and TLA indicate that training initiatives should prioritize the development of emotional regulation, interpersonal awareness, and social engagement. Strengthening these competencies may foster leadership behaviors that enhance motivation, trust, and team effectiveness. The selective association between inhibitory control and individualized consideration suggests that cognitive self-regulation supports reflective and adaptive interpersonal leadership. Accordingly, leadership development programs may benefit from incorporating targeted cognitive training strategies aimed at strengthening executive control processes, particularly inhibitory control and working memory, to support decision-making in complex organizational environments. In addition, performance-based cognitive assessments, such as the digital EF battery used in this study, may provide practical tools for identifying regulatory demands and informing targeted interventions to support leadership effectiveness in public organizations. Conclusion The study advances the understanding of transformational leadership by integrating performance-based executive function indicators and social, emotional, and behavioral skills within a preregistered multimethod framework applied to a national sample of public-sector leaders. 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Harcourt Brace & Co Tables Table 1 Demographic Characteristics of the Final Sample (N = 70) Variable N (%) or M (SD) Age 48.97 (7.37) Sex Male Female 43 (61.4%) 27 (38.6%) Race Black Brown White 3 (4.3%) 14 (20.0%) 53 (75.7%) Education Bachelor’s degree Postgraduate specialization Master’s degree Doctoral degree 3 (4.3%) 28 (40.0%) 19 (27.1%) 20 (28.6%) Marital Status Married Separated Single 60 (85.7%) 2 (2.9%) 8 (11.4%) Leadership tenure 1-2 years 3-5 years 6-10 years > 10 years 3 (4.3%) 18 (25.7%) 24 (34.3%) 25 (35.7%) Administrative span of control No direct reports 1-5 employees 6-10 employees 11-20 employees 21-50 employees 51-100 employees 101-200 employees > 200 employees 1 (1.5%) 7 (10.0%) 12 (17.1%) 14 (20.0%) 19 (27.1%) 6 (8.6%) 5 (7.1%) 6 (8.6%) Employment type Career civil servant Comissioned 68 (97.1%) 2 (2.9%) Income R$ 4,001.00-R$ 8,000.00 R$ 8,001.00-R$ 12,000.00 R$ 12,001.00-R$ 16,000.00 R$ 16,001.00-R$ 20,000.00 > R$ 20,000.00 4 (5.7%) 4 (5.7%) 6 (8.6%) 14 (20.0%) 42 (60.0%) Table 2 Descriptive Statistics of Study Variables Variable N Mean SD Skewness Kurtosis Reliability Age 70 48.97 7.37 0.50 0.60 -- Education level a 70 2.80 0.91 0.06 -1.19 -- WM composite 61 -0.02 0.60 -0.23 -0.41 -- IC composite 56 0.00 0.47 -0.57 0.86 -- DM composite 70 0.00 0.91 0.38 -0.44 -- CF b composite 70 0.00 0.32 -0.21 -0.49 -- SE 70 0.00 1.00 -0.57 0.34 α = .93 (ω = .93) ER 70 0.00 1.00 -0.20 -0.49 α = .96 (ω = .97) TLA 70 0.00 1.00 -0.53 -0.46 α = .82 (ω = .86) Note . WM = working memory; IC = inhibitory control; DM = decision-making; CF = cognitive flexibility; SE = social engagement; ER = emotional resilience; TLA = transformational leadership attitudes. All EF variables are composite scores derived from multiple performance-based EF task indicators (see supplementary material S3). a Education level is coded from 1 (bachelor’s degree) to 4 (doctoral degree). b CF was examined as an exploratory EF component. Table 3 Partial Correlations among EF, Social, Emotional, and Behavioral Skills, and Transformational Leadership Attitudes, Controlling for Age and Education Level WM IC DM SE ER TLA WM — IC -0.11 — DM 0.13 -0.24 — SE 0.07 -0.30* 0.14 — ER 0.06 -0.26 -0.02 0.69*** — TLA 0.03 0.02 0.06 0.53*** 0.53*** — Note . Partial correlations controlling for age and education level. WM = working memory; IC = inhibitory control; DM = decision making; SE = social engagement; ER = emotional resilience; TLA = transformational leadership attitudes. * p < .05, ** p < .01, *** p < .001. Table 4 Hierarchical Multiple Regression Predicting Transformational Leadership Attitudes Predictor β 95% CI p Covariates Age -.11 [-0.37, 0.16] .43 Education -.19 [-0.33, 0.19] .58 SEB indicators SE .29 [-0.08, 0.64] .12 ER .39 [0.04, 0.74] .03 EF indicators WM .06 [-0.21, 0.34] .64 IC .25 [-0.03, 0.52] .08 DM -.02 [-0.30, 0.25] .86 Model summary R² ΔR² Step 1 (covariates) .058 -- Model 1 (SEB → EF) Step 2 (SEB) .330 .272 Step 3 (EF) .385 .055 Model 2 (EF → SEB) Step 2 (EF) .065 .007 Step 3 (SEB) .385 .320 Note . β = standardized coefficient; CI = confidence interval; SEB = social, emotional, and behavioral skills; EF = executive functions; SE = social engagement; ER = emotional resilience; WM = working memory; IC = inhibitory control; DM = decision-making under risk and uncertainty. Table 5 Bootstrap Partial Correlation between Inhibitory Control and Providing Individualized Support (TLA Dimension), Controlling for Age and Education Level Association n Pearson’s r p 95% CI Effect size Fisher’s z SE (z) IC X PIS 56 .32 .018 [.075, .513] .33 .14 Note . Partial correlation controlling for age and education level. Confidence intervals based on 5,000 bootstrap resamples. IC = inhibitory control composite score; PIS = Providing Individualized Support. Additional Declarations The authors declare no competing interests. Supplementary Files S1TemporalconfigurationEFdigitaltools.docx S1 – Temporal Configuration of EF Digital Tools S2Participantrecruitmentwaveseligibilitystatusprotocolcompletionandanalyticalinclusion.xlsx S2 - Participant recruitment waves, eligibility status, protocol completion, and analytical inclusion S3Taskleveldescriptivediagnosticsandvaliditycriteria.docx S3 — Task-level descriptive diagnostics and validity criteria Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Neves-Teles","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIie3RsUrDQBjA8e/4oFkSul6p4iscBHQpyavkOHCSKnQRHLwQSJY+QMSXcDpxS8jQJQ+QscUXSMEhgYCmilLBa3ATvP/0fcOP4+MATKY/Gcq95QpgDGS9GwMdsIF8kX5iABOJ7JeEZQPEt8Jw01543qMlrXXNZr5bidH2pYP5mfyZ2HYeuY4S4mmZkTBl51xVAu+PY1gcZRpCeTwlCgWrAhLZrAhOq8sCJxJ4qrvlZJO0rbr9IB179d1UINLuAKEkBkcV3jvpbycPVCCpRwdIyaOpo1YBK/PwbskET8tnRBLTBdUQKynybatufLaKsrq59vxx0r/SdLO5jnzG5d4HAdowBPr/+baRZhCYTCbTP+oNpZBbafRIZIkAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4234-3218","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":true,"prefix":"","firstName":"Telesmagno","middleName":"","lastName":"Neves-Teles","suffix":""},{"id":623175035,"identity":"debdd799-1a6a-4c78-84be-bd425cca6c0c","order_by":1,"name":"Bruno Elkfury Monticelli","email":"","orcid":"https://orcid.org/0000-0002-6768-7375","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"Elkfury","lastName":"Monticelli","suffix":""},{"id":623175529,"identity":"5692598c-6df2-4941-a1b4-049224b08b34","order_by":2,"name":"Guilherme da Silva Freitas","email":"","orcid":"https://orcid.org/0009-0004-4002-3175","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":false,"prefix":"","firstName":"Guilherme","middleName":"da Silva","lastName":"Freitas","suffix":""},{"id":623175530,"identity":"e3e143d5-f7c6-4dce-8325-53a57eb2cd38","order_by":3,"name":"Cristian Zanon","email":"","orcid":"https://orcid.org/0000-0003-3822-5275","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":false,"prefix":"","firstName":"Cristian","middleName":"","lastName":"Zanon","suffix":""},{"id":623175531,"identity":"e8d3a8de-37ea-4cf4-83dc-18357d23b87c","order_by":4,"name":"Rosa Maria Martins de Almeida","email":"","orcid":"https://orcid.org/0000-0002-2450-2238","institution":"Universidade Federal do Rio Grande do Sul","correspondingAuthor":false,"prefix":"","firstName":"Rosa","middleName":"Maria Martins","lastName":"de Almeida","suffix":""}],"badges":[],"createdAt":"2026-04-14 18:59:36","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9418989/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9418989/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107481038,"identity":"a85fc4d4-77ba-4b5e-a717-92d6be36bd23","added_by":"auto","created_at":"2026-04-22 02:15:28","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":320179,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eConceptual Model Linking Executive Functions, Social, Emotional, and Behavioral Skills,\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eand Transformational Leadership Attitudes\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9418989/v1/1ed58bf0c028a133898dbcb7.jpeg"},{"id":107705000,"identity":"ed7627b7-c3b0-44cc-af09-5854c0cd41ef","added_by":"auto","created_at":"2026-04-24 09:06:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":863426,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9418989/v1/41fe8b76-22e7-427d-a081-bd160689abd1.pdf"},{"id":107098535,"identity":"b86a80f4-f08d-4849-b65f-7a3e82211ffe","added_by":"auto","created_at":"2026-04-16 18:09:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19960,"visible":true,"origin":"","legend":"\u003cp\u003eS1 – Temporal Configuration of EF Digital Tools\u003c/p\u003e","description":"","filename":"S1TemporalconfigurationEFdigitaltools.docx","url":"https://assets-eu.researchsquare.com/files/rs-9418989/v1/3ca7a96ab78d2b8dd5e636a3.docx"},{"id":107481014,"identity":"88502f03-2572-41aa-9b20-4579a690715f","added_by":"auto","created_at":"2026-04-22 02:15:11","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14358,"visible":true,"origin":"","legend":"\u003cp\u003eS2 - Participant recruitment waves, eligibility status, protocol completion, and analytical inclusion\u003c/p\u003e","description":"","filename":"S2Participantrecruitmentwaveseligibilitystatusprotocolcompletionandanalyticalinclusion.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9418989/v1/6be0e1e34ce940e5eacf3b71.xlsx"},{"id":107098538,"identity":"81f54bda-025f-441a-b597-bf3c3eefb681","added_by":"auto","created_at":"2026-04-16 18:09:48","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":22923,"visible":true,"origin":"","legend":"\u003cp\u003eS3 — Task-level descriptive diagnostics and validity criteria\u003c/p\u003e","description":"","filename":"S3Taskleveldescriptivediagnosticsandvaliditycriteria.docx","url":"https://assets-eu.researchsquare.com/files/rs-9418989/v1/a38d2f1eb7690e8025025df7.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eCognitive and Social, Emotional, and Behavioral Foundations of Transformational Leadership Attitudes: Evidence from a National Sample of Brazilian Federal Public Managers\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eTransformational Leadership in the Public Sector\u003c/h2\u003e \u003cp\u003eTransformational leadership has been consistently associated with positive outcomes with moderate-to-large effect sizes in organizational effectiveness, employee engagement, and psychosocial well-being, particularly in complex and high-demand contexts such as public administration (Backhaus \u0026amp; Vogel, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bao et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Huang \u0026amp; Villadsen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Waldman \u0026amp; Balthazard, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Leaders who articulate a compelling vision, stimulate intellectual engagement, and demonstrate individualized consideration tend to foster higher levels of motivation, trust, and adaptive performance among followers (Avolio \u0026amp; Bass, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bao et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Molenberghs et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent meta-analytic evidence in the public administration domain confirmed the robustness and consistency of these associations across institutional settings and outcome domains (Bao et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Despite this solid evidence base, the psychological mechanisms underlying individual differences in transformational leadership attitudes (TLA) remain only partially understood, particularly when cognitive and social, emotional, and behavioral processes are examined jointly (Hannah et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Karatosidi \u0026amp; Iordanoglou, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lindebaum \u0026amp; Raftopoulou, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCognitive and Social, Emotional, and Behavioral Foundations of Transformational Leadership\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eExecutive Functions and Transformational Leadership\u003c/h2\u003e \u003cp\u003eTheoretical and empirical advances suggested that leadership should be understood as a multidimensional phenomenon emerging from the interaction between higher-order cognitive control processes, such as executive functions (EF), and social, emotional, and behavioral (SEB) skills (Chan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Humphrey et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). EF support the regulation of goal-directed behavior, adaptive responding, and complex decision-making, all of which are critical for effective leadership in dynamic organizational environments (Chan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hannah et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eContemporary integrative perspectives further indicate that specific EF components \u0026mdash;particularly working memory (WM), inhibitory control (IC), and decision-making processes under uncertainty (DM) \u0026mdash; play a central role in shaping leaders\u0026rsquo; capacity to sustain goal-directed behavior, regulate impulses, and make adaptive strategic choices in socially accountable contexts (Chan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Karatosidi \u0026amp; Iordanoglou, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Although cognitive flexibility has also been linked to adaptive leadership in broader EF frameworks, emerging neuropsychological evidence suggests that leadership effectiveness is especially associated with executive control capacities related to response inhibition and risk-sensitive decision-making, which facilitate responsible action under uncertainty and the coordination of complex interpersonal demands (Ramchandran et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExperimental and neurocomputational findings further indicate that leadership involves a distinct form of executive decision-making in socially responsible contexts, whereby individuals differ in their willingness to assume responsibility for outcomes affecting others \u0026mdash; a process supported by higher-order control networks and associated with leadership-related behavior (Edelson et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eSocial, Emotional, and Behavioral Skills and Transformational Leadership\u003c/h3\u003e\n\u003cp\u003eIn parallel, research on SEB skills has increasingly been organized around the integrative framework proposed by Soto et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which conceptualizes these skills as functional capacities to regulate emotions, maintain social relationships, and manage goal-directed behavior in demanding contexts. Within this framework, emotional resilience (ER) and social engagement (SE) emerge as particularly relevant domains for leadership functioning. ER reflects the capacity to regulate stress and mood fluctuations, supporting adaptive responses under sustained cognitive and emotional demands. SE encompasses skills related to initiating interactions, communicating effectively, and asserting ideas within groups, facilitating trust building, information exchange, and coordinated action in team environments (Fernandes et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Soto et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLeadership research further indicates that emotional processes operate across multiple levels of organizational life, shaping how leaders influence group climate, motivation, and interpersonal dynamics (Humphrey et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Empirical findings also suggest that leaders\u0026rsquo; capacity to regulate and express emotions constructively, as well as to engage sensitively with others, is positively associated with transformational leadership behaviors (Esteves et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; G\u0026oacute;mez-Leal et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These socio-emotional competencies are therefore understood as behavioral facilitators of effective leadership functioning rather than as substitutes for cognitive control mechanisms. Integrative perspectives additionally suggest that such competencies operate in concert with executive control processes in contexts requiring strategic adaptation and sustained interpersonal coordination (Karatosidi \u0026amp; Iordanoglou, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Toh \u0026amp; Yang, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eIntegrative Gaps in the Literature\u003c/h3\u003e\n\u003cp\u003eAlthough prior studies have independently linked EF and socio-emotional competencies to leadership-related outcomes, unified analytical frameworks examining their joint and incremental contributions remain scarce (Karatosidi \u0026amp; Iordanoglou, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meta-analytic syntheses have further indicated that, while a wide range of antecedents of transformational leadership has been examined, comparatively few studies explicitly integrate cognitive and social, emotional, and behavioral mechanisms within a single analytical model (Bao et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Much of the empirical literature has relied either on self-report measures of cognitive abilities or on narrow operationalizations of leadership, limiting the ability to disentangle the relative contribution of cognitive versus socio-emotional domains (Karatosidi \u0026amp; Iordanoglou, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lindebaum \u0026amp; Raftopoulou, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, there remains a notable gap in the literature regarding leadership processes in the public sector, where decision-making constraints, bureaucratic complexity, and social accountability place distinct demands on leaders\u0026rsquo; cognitive control and emotional regulation capacities (Bao et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Huang \u0026amp; Villadsen, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eThe Present Study\u003c/h3\u003e\n\u003cp\u003eAddressing these gaps, the present study focuses on theoretically and empirically salient EF components \u0026mdash; WM, IC, and DM \u0026mdash; given their relevance for goal maintenance, behavioral regulation, and adaptive responding in leadership contexts. In parallel, it examines key SEB skills associated with adaptive leadership functioning, including emotional resilience (ER), social engagement (SE), cooperation (CO), self-management (SM), and empathy.\u003c/p\u003e \u003cp\u003eAlthough recent studies have sought to integrate executive functions and socio-emotional constructs in the prediction of transformational leadership (e.g., Karatosidi \u0026amp; Iordanoglou, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), such work has primarily emphasized dispositional variables such as personality and trait emotional intelligence. In contrast, the present study advances the literature by examining SEB skills conceptualized as functional competencies, assessed within a preregistered multimethod framework and in a nationally distributed sample of experienced public-sector leaders.\u003c/p\u003e \u003cp\u003eEF are assessed using an adapted digital battery of performance-based tasks, whereas SEB are measured through the Brazilian version of the Behavioral, Emotional, and Social Skills Inventory (BESSI-BR; Fernandes et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Soto et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Transformational leadership attitudes (TLA) are evaluated using a validated Brazilian scale (Fonseca \u0026amp; Porto, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). By integrating performance-based cognitive indicators and self-reported social, emotional, and behavioral skills, the study adopts a multimethod assessment strategy that broadens construct representation while minimizing reliance on a single measurement source.\u003c/p\u003e \u003cp\u003eGuided by a preregistered analytical plan, the study pursues two primary objectives: (a) to describe the variability and profile of selected EF components and SEB skills among federal public managers; and (b) to test a heuristic model examining their unique and joint contributions to TLA while controlling for relevant covariates. Entering EF and SEB as separate predictor blocks in hierarchical regression models allows the evaluation of their incremental explanatory power.\u003c/p\u003e \u003cp\u003eBy integrating core executive control processes and theoretically grounded SEB skills within a preregistered and methodologically rigorous design, this study offers a preregistered multimethod test of an integrative framework linking executive control and social, emotional, and behavioral skills to leadership attitudes in the public sector.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Hypotheses\u003c/h2\u003e \u003cp\u003eConsistent with the preregistered analytical plan, the present study tested the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1 (Variability Hypothesis).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBoth global scores and indicator-level scores of EF components, SEB skills, and TLA will show meaningful inter-individual variability, as reflected in adequate dispersion indices and absence of substantial floor or ceiling effects within the sample. This expectation is grounded in the substantial heterogeneity of the sample with respect to age, professional background, organizational cultures, and career trajectories within the Brazilian federal public service, as well as in prior evidence indicating meaningful interindividual variability in executive decision-making, emotional regulation, and leadership-related neural and behavioral markers (Balthazard et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Edelson et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fatima et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2 (EF \u0026rarr; TLA hypothesis).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eExecutive functions will be positively associated with transformational leadership attitudes. This hypothesis builds on both foundational and contemporary work suggesting that higher-order cognitive control processes \u0026mdash; particularly WM, IC, and DM \u0026mdash; support leadership-related behavior, responsibility-taking, and adaptive decision-making in complex organizational contexts (Chan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Edelson et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Karatosidi \u0026amp; Iordanoglou, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ramchandran, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ramchandran et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Cognitive flexibility (CF) was also examined in an exploratory manner, given its relevance in broader EF frameworks.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3 (SEB \u0026rarr; TLA Hypothesis).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSocial, emotional, and behavioral skills will be positively associated with transformational leadership attitudes. This hypothesis builds on evidence indicating that ER, SE, and related socio-emotional competencies such as cooperation, self-management, and empathy support effective leadership functioning, including relationship building, emotional regulation, and adaptive interpersonal behavior in organizational contexts (Bergner et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cavaness et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Esteves et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Frias et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; G\u0026oacute;mez-Leal et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Humphrey et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Prezerakos, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4 (Exploratory Joint Hypothesis).\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEF components and SEB skills will show joint and potentially interactive associations with transformational leadership attitudes. In particular, WM, IC, and DM are expected to relate to TLA in combination with emotional resilience, social engagement, and related socio-emotional competencies, reflecting integrative perspectives that emphasize the coordinated role of cognitive control and socio-emotional regulation in adaptive leadership functioning (Connelly \u0026amp; Ruark, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Humphrey et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Molenberghs et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Toh \u0026amp; Yang, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePreregistration and Transparency\u003c/h2\u003e \u003cp\u003eThe study protocol, hypotheses, eligibility criteria, outcome measures, and primary statistical analysis plan were preregistered prior to data inspection on the Open Science Framework (OSF; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/8jz6k\u003c/span\u003e\u003cspan address=\"https://osf.io/8jz6k\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). Any deviations from the preregistered protocol are explicitly reported in the manuscript.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis was a cross-sectional observational study based on random sampling of Brazilian federal public managers. No blinding or experimental manipulation was implemented, and each participant contributed a single set of scores. Primary analyses included descriptive and variability analyses, partial correlations controlling for demographic covariates, and hierarchical multiple regression models with TLA as the dependent variable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and Sampling\u003c/h2\u003e \u003cp\u003eParticipants were randomly selected from the official national directory of federal management positions distributed across Brazil\u0026rsquo;s 27 federative units (sampling frame\u0026thinsp;=\u0026thinsp;1,100\u0026thinsp;+\u0026thinsp;managers at the time of recruitment). Recruitment occurred in successive waves. After each cycle expired, individuals who had not responded were excluded from subsequent draws, and a new random selection was performed among the remaining not-yet-invited managers. Across all waves, 687 managers were invited via institutional email and official communication channels.\u003c/p\u003e \u003cp\u003eNo financial compensation was offered; participation was voluntary and target recruitment was \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;82, ensuring a final analyzed sample of at least 78 participants, based on an a priori power analysis (α\u0026thinsp;=\u0026thinsp;.05, 1 \u0026ndash; β\u0026thinsp;=\u0026thinsp;.80, \u003cem\u003ef\u003c/em\u003e\u0026sup2; = 0.15).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEligibility and Cognitive Screening\u003c/h2\u003e \u003cp\u003eEligibility criteria included: (a) age\u0026thinsp;\u0026ge;\u0026thinsp;20 years, (b) absence of self-reported neurological or psychiatric conditions suggestive of atypical neurodevelopment, and (c) adequate global cognitive functioning. Cognitive screening was conducted via videoconference prior to enrollment and included the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and an estimate of general intellectual functioning derived from the Wechsler Adult Intelligence Scale \u0026ndash; Third Edition (WAIS-III IQ).\u003c/p\u003e \u003cp\u003eThe MMSE served as the sole formal inclusion criterion. A cutoff score of \u0026ge;\u0026thinsp;24 was adopted to exclude individuals with potential global cognitive impairment, following recommendations for highly educated samples (Kochhann et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; sensitivity\u0026thinsp;=\u0026thinsp;87%, specificity\u0026thinsp;=\u0026thinsp;82%). The MoCA and WAIS-III IQ were administered as complementary indicators to provide broader characterization of executive, attentional, and general intellectual functioning, supporting global cognitive comparability within the sample.\u003c/p\u003e \u003cp\u003eMoCA performance, particularly on the MoCA-MIS, suggested that a subset of participants presented profiles consistent with possible mild cognitive impairment, despite meeting MMSE inclusion criteria. In order to ensure methodological transparency without altering predefined eligibility criteria, these findings were documented and considered in the interpretation of the cognitive screening phase.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eData collection followed a two-day sequential online protocol hosted on a secure university server and accessed via individual participant codes:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDay 1: Sociodemographic questionnaire (~\u0026thinsp;5 min) and a digital battery of seven EF tests (~\u0026thinsp;45 min).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDay 2: BESSI-BR (~\u0026thinsp;25 min) and a Brazilian TLA scale (~\u0026thinsp;20 min).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e Participants were instructed to complete all activities in a quiet, interruption-free environment without the presence of third parties. The estimated total duration was approximately 95 minutes.\u003c/p\u003e \u003cp\u003eAlthough a broader set of cognitive and socio-emotional indicators was collected as part of the preregistered umbrella study, the present analyses focus on theoretically selected EF components and SEB skills. The extended dataset was retained to enable sensitivity, exploratory, and subsequent multivariate analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eExecutive Function Battery (Digital Adaptation)\u003c/h2\u003e \u003cp\u003eAll seven EF tasks were adapted from open-source paradigms available in \u003cem\u003ePsyToolkit\u003c/em\u003e (Stoet, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and reimplemented in \u003cem\u003eOpenSesame\u003c/em\u003e (Math\u0026ocirc;t et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) for computerized administration in Brazilian Portuguese. The adaptation followed the International Test Commission (ITC) guidelines for cross-cultural test translation and adaptation (Hambleton \u0026amp; Zenisky, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), ensuring conceptual, linguistic, and functional equivalence.\u003c/p\u003e \u003cp\u003eTwo independent forward translations were produced and evaluated through a two-stage expert review conducted by three specialists in cognitive psychology and psychometrics. The first round focused on prototype screenshots, allowing refinement of stimuli, instructions, and interface layout. The second applied a structured checklist derived from ITC criteria. Inter-rater agreement, estimated using Fleiss\u0026rsquo; Kappa, increased from \u003cem\u003eκ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.59 to \u003cem\u003eκ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.84, indicating strong linguistic and cultural adequacy. Temporal parameters for each task are provided in Supplementary Material S1 and will be archived in a public open-science repository upon publication.\u003c/p\u003e \u003cp\u003eThe digital battery assessed multiple EF domains (WM, IC, DM, and exploratory CF indicators). The present analyses focus primarily on theoretically selected components (WM, IC, and DM), whereas CF was examined in an exploratory manner. The following subsections describe the EF tasks used to measure each domain.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWorking Memory\u003c/h2\u003e \u003cp\u003eWorking memory was assessed using two computerized tasks: the Digit Span task (WM storage) and the 2-back task (WM manipulation/updating).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDigit Span (WM Storage)\u003c/h2\u003e \u003cp\u003eWM storage was assessed using a computerized Digit Span task (Wechsler, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Digits were presented visually for 700 ms with a 300 ms interstimulus interval. Participants recalled sequences of 2\u0026ndash;9 digits in the same order by selecting numbers on the screen. Two trials were administered for each sequence length.\u003c/p\u003e \u003cp\u003ePerformance was summarized using a standardized Digit Span composite score (storage component) integrating total correct trials and maximum span length. Both indices were z-standardized and averaged so that higher scores indicate greater storage capacity. Internal consistency for Digit Span in Brazilian adult samples is high (α\u0026thinsp;=\u0026thinsp;0.88; J\u0026uacute;lio-Costa \u0026amp; Haase, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eN-back (2-back; WM Manipulation)\u003c/h2\u003e \u003cp\u003eWM manipulation was assessed using a 2-back task (Hepdarcan \u0026amp; Can, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kane et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Consonant letters (B, F, K, L, H, M, P, R, T, V, X, Y) were presented for 500 ms with a 2000 ms interstimulus interval. Participants indicated whether the current stimulus matched the one presented two trials earlier. The task consisted of six blocks of 36 trials with short rest intervals between blocks.\u003c/p\u003e \u003cp\u003eTask performance was indexed by a Nback-2back composite score (manipulation component) integrating signal-detection sensitivity (\u003cem\u003ed\u003c/em\u003e\u0026rsquo;) and reaction time for correct responses. Both indicators were \u003cem\u003ez\u003c/em\u003e-standardized and averaged, with reaction time reverse-scored so that higher values indicate greater updating efficiency. Reliability estimates for N-back paradigms in Brazilian adult samples typically range between α\u0026thinsp;=\u0026thinsp;.80\u0026ndash;.85 (De Nardi et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, the standardized scores from both tasks were averaged to create a WM composite score, representing overall working memory performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eInhibitory Control\u003c/h2\u003e \u003cp\u003eInhibitory control was assessed using two computerized tasks capturing complementary components of inhibition: interference suppression and response inhibition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStroop Test (Interference Suppression)\u003c/h2\u003e \u003cp\u003eIC interference control was assessed using a computerized Stroop Color\u0026ndash;Word task (MacLeod, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Stimuli were presented for 1000 ms, with responses allowed up to 1500 ms and a jittered interstimulus interval (1000\u0026thinsp;\u0026plusmn;\u0026thinsp;200 ms). Participants completed 30\u0026ndash;40 trials per condition (congruent and incongruent).\u003c/p\u003e \u003cp\u003ePerformance was quantified using reaction-time and accuracy interference effects, calculated as the differences between incongruent and congruent trials. These indicators were z-standardized and averaged to form a Stroop composite score, ensuring balanced integration of speed- and accuracy-based interference while avoiding distortions associated with ratio-based efficiency measures (Alfers et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Higher values indicated more efficient inhibitory control. Previous evidence suggests adequate reliability for Stroop performance in Brazilian adult samples (α\u0026thinsp;=\u0026thinsp;.80\u0026ndash;.85; J\u0026uacute;lio-Costa \u0026amp; Haase, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Miotto et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eGo/NoGo (IC - Response Inhibition)\u003c/h2\u003e \u003cp\u003eResponse inhibition was assessed using a Go/No-Go task (Verbruggen et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Verbruggen \u0026amp; Logan, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Stimuli were presented for 300\u0026ndash;400 ms with an 800 ms response window and a jittered interstimulus interval (1000\u0026thinsp;\u0026plusmn;\u0026thinsp;200 ms). The task comprised 160 trials, including 20% No-Go trials.\u003c/p\u003e \u003cp\u003ePerformance was operationalized as a standardized Go/NoGo composite score integrating signal-detection sensitivity (\u003cem\u003ed\u003c/em\u003e\u0026rsquo;) and mean reaction time for correct Go trials. Both indices were z-standardized and averaged, with reaction time reverse-scored so that higher values indicate greater inhibitory efficiency. Previous evidence suggests adequate reliability for Go/NoGo performance in Brazilian samples (α\u0026thinsp;=\u0026thinsp;0.76; Beato et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, the standardized scores from both IC tasks were averaged to form an IC composite score, indexing overall inhibitory control performance.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eDecision-Making\u003c/h2\u003e \u003cdiv id=\"Sec24\" class=\"Section4\"\u003e \u003ch2\u003eIowa Gambling Task (DM - Decision Learning and Risk Evaluation)\u003c/h2\u003e \u003cp\u003eDM was assessed using the Iowa Gambling Task (IGT; Bechara et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Participants completed 100 selections from four decks with different reward\u0026ndash;loss contingencies. Immediate feedback was provided after each choice.\u003c/p\u003e \u003cp\u003eTask performance was operationalized using a DM composite score integrating two indicators: the net score, calculated as the difference between advantageous and disadvantageous deck selections [(C\u0026thinsp;+\u0026thinsp;D) \u0026minus; (A\u0026thinsp;+\u0026thinsp;B)], reflecting overall decision quality, and a learning index representing performance improvement across the task, computed as the difference between the final and initial blocks of trials (Block 5\u0026thinsp;\u0026minus;\u0026thinsp;Block 1).\u003c/p\u003e \u003cp\u003eBoth indicators were z-standardized and averaged to form the DM composite score, with higher values indexing more advantageous decision-making and learning across the task. Previous evidence indicates acceptable reliability for IGT performance in Brazilian samples (α\u0026thinsp;=\u0026thinsp;.74; Malloy-Diniz et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCognitive Flexibility\u003c/h2\u003e \u003cp\u003eFinally, CF indicators were included as exploratory EF measures and were assessed using two computerized tasks capturing complementary aspects of flexible cognitive control: set shifting and switching efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eWisconsin Card Sorting Test (CF - Set Shifting)\u003c/h2\u003e \u003cp\u003eSet-shifting ability was assessed using a computerized Wisconsin Card Sorting Test (WCST) based on the Brazilian manual for the 128-trial version (M\u0026ocirc;nego et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Participants matched response cards to reference cards according to implicit rules (color, form, or number), which changed after 10 consecutive correct responses.\u003c/p\u003e \u003cp\u003eTask performance was derived using three indices: categories completed, perseverative errors, and failures to maintain set. A composite WCST score was computed by z-standardizing each indicator and reverse-scoring error measures before averaging so that higher scores reflect greater set-shifting efficiency. Digital WCST implementations show good psychometric properties in adult samples (Steinke et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eTask Switching (CF \u0026ndash; Switching Efficiency)\u003c/h2\u003e \u003cp\u003eCF was also assessed using a task-switching paradigm (Monsell, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Vandierendonck et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Participants alternated between two classification tasks guided by visual cues, with cue\u0026ndash;stimulus intervals ranging from 300\u0026ndash;600 ms and response windows up to 2000 ms.\u003c/p\u003e \u003cp\u003ePerformance was summarized using a Task Switching composite score integrating reaction-time switch cost, accuracy switch cost, and accuracy on switch trials. All indices were z-standardized and averaged, with cost measures reverse-scored so that higher values indicate greater switching efficiency. Composite scoring approaches are recommended to improve reliability in task-switching paradigms (Draheim et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hedge et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vandierendonck, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, the standardized scores from both tasks were averaged to form a CF composite score, indexing overall cognitive flexibility performance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eSocial, Emotional, and Behavioral Skills (SEB)\u003c/h2\u003e \u003cp\u003eSEB were assessed using the Brazilian version of the Behavioral, Emotional, and Social Skills Inventory (BESSI-BR), a 192-item self-report instrument originally developed by Soto et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and adapted for use in Brazil by Fernandes et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The inventory assesses five domains of SEB \u0026mdash; Self-Management (SM), Social Engagement (SE), Cooperation (CO), Emotional Resilience (ER), and Innovation (IS) \u0026mdash; comprising 32 specific skill facets.\u003c/p\u003e \u003cp\u003eParticipants rated how well they were able to enact each skill using a five-point Likert scale ranging from 1 (\u0026ldquo;not at all well\u0026rdquo;) to 5 (\u0026ldquo;extremely well\u0026rdquo;). Evidence from the Brazilian adaptation indicates high internal consistency at both the domain level (mean α\u0026thinsp;=\u0026thinsp;.94, range .91\u0026ndash;.97) and the facet level (mean α\u0026thinsp;=\u0026thinsp;.86, range .77\u0026ndash;.94), with comparable estimates based on McDonald\u0026rsquo;s omega (ω), supporting the robustness of the instrument\u0026rsquo;s measurement properties (Fernandes et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, mean scores were computed for each domain and for a global SEB skills index representing an overall indicator of SEB competence across the five domains. Consistent with the preregistered analysis plan, only Social Engagement (SE) and Emotional Resilience (ER) were included as predictors in the primary models, as these domains show the strongest conceptual links to transformational leadership and present lower conceptual overlap with executive function constructs. SE and ER scores were subsequently standardized (z-scores) to enhance comparability with other predictors in the regression models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eTransformational Leadership Attitude (TLA)\u003c/h2\u003e \u003cp\u003eTLA were assessed using the Attitudes toward Leadership Styles scale developed by Fonseca and Porto (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The instrument comprises items derived from internationally validated leadership measures, including the Transformational Leadership Inventory (TLI), the Multifactor Leadership Questionnaire (MLQ), and the Leader Reward and Punishment Questionnaire (LRPQ). Factor-analytic validation identified two factors corresponding to transformational leadership (24 items) and transactional leadership (8 items), with internal consistency estimates of α\u0026thinsp;=\u0026thinsp;.91 and α\u0026thinsp;=\u0026thinsp;.74, respectively (Fonseca \u0026amp; Porto, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Participants rated their attitudes toward each leadership behavior using a seven-point Likert scale (1\u0026ndash;7). For the purposes of this study, only the transformational leadership dimension was analyzed. A TLA composite score was computed by averaging responses across the transformational leadership items. This score was subsequently standardized (\u003cem\u003ez\u003c/em\u003e-score) to enhance comparability with other predictors in the regression models.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalytic Plan\u003c/h3\u003e\n\u003cp\u003eData quality procedures were implemented to ensure valid estimation of EF performance. For computerized tasks involving RT, trials were considered valid only when a response was recorded and latency fell within a predefined temporal window (200\u0026ndash;1500 ms). Trials with missing responses or latencies outside this range were excluded from RT-based calculations, whereas omission trials and invalid responses were treated as errors in accuracy-based indices.\u003c/p\u003e \u003cp\u003ePotential outliers were initially flagged using the IQR criterion applied to original score distributions and subsequently inspected on a case-by-case basis. Statistical outliers were not automatically excluded. Participant-level exclusion was considered only when there was clear evidence of invalid response processes, such as random responding, systematic non-engagement, or failure to understand task instructions. Low-performing cases were otherwise interpreted as reflecting genuine interindividual variability and were retained.\u003c/p\u003e \u003cp\u003eDomain-specific scoring procedures were adopted for EF components. For WM and CF indicators, score distributions were approximately normal and showed no evidence of extreme values indicative of invalid task engagement. For IC, composite indices were constructed by integrating standardized RT- and accuracy-based effects to avoid distortions associated with ratio-based efficiency measures. Participants with fewer than 35 valid Stroop trials (out of 80) were excluded from Stroop-based indices due to insufficient task engagement or unreliable performance estimates. For DM, assessed via the IGT, non-normal and asymmetric distributions were theoretically expected; extreme values were interpreted as meaningful indicators of advantageous or disadvantageous decision-making strategies rather than statistical outliers.\u003c/p\u003e \u003cp\u003eHierarchical multiple regression models were used to examine EF indicators (WM, IC, and DM) and SEB skills (SE and ER) as predictors of TLA, with age and education level included as covariates. Cognitive flexibility indicators were examined only in exploratory and sensitivity models. Although additional covariates were preregistered, they were not included in the primary models due to concerns about model overfitting given the modest sample size. Age and education were entered first (Step 0), followed by EF and SEB predictor blocks in alternative orders to examine incremental validity.\u003c/p\u003e \u003cp\u003eParametric statistical methods were applied to EF components showing approximate normality. For DM indicators derived from the IGT, distributional characteristics were explicitly acknowledged, and robust analyses were conducted when appropriate. Sensitivity analyses additionally examined the potential influence of sex on regression estimates. Standardized regression coefficients (β), changes in explained variance (ΔR\u0026sup2;), and 95% confidence intervals were reported. To assess estimate stability, bootstrap confidence intervals based on 5,000 resamples were computed.\u003c/p\u003e \u003cp\u003eNo data transformations beyond scale scoring and standardization were planned. Item-level mean substitution was permitted when at least 80% of a scale was completed. EF tasks not fully completed were excluded from analyses for that specific measure only. Missing data below 5%, assumed to be missing at random, were handled using listwise deletion in regression models; higher levels of missingness prompted exploratory multiple imputation procedures.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e This study was approved by the Ethics Committee of the Institute of Psychology, Universidade Federal do Rio Grande do Sul (UFRGS; Protocol 7.274.390, CAAE 84134724.5.0000.5334, December 7, 2024). All participants provided digital informed consent prior to participation, and all procedures were classified as minimal risk.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003eSample Characteristics\u003c/h2\u003e \u003cp\u003eA total of 687 federal public managers were invited across five successive waves of random sampling from a national roster of approximately 1,100 officials. Of these, 87 provided informed consent, 82 met eligibility criteria (MMSE\u0026thinsp;\u0026ge;\u0026thinsp;24), and 70 completed all study procedures, constituting the final analytic sample. Further details regarding recruitment procedures and eligibility criteria are available in Supplementary Material S2.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;1 presents the demographic characteristics of the final sample. Participants were middle-aged (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;48.97, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.37) and predominantly male (61.4%). The sample was highly educated, with 95.7% holding postgraduate degrees, including 27.1% with a master\u0026rsquo;s degree and 28.6% with a doctoral degree.\u003c/p\u003e \u003cp\u003eParticipants reported substantial leadership experience, with approximately 70% having more than six years of tenure and 35.7% reporting over ten years. Most were career civil servants (97.1%), and income levels reflected the seniority of the sample, with 60% reporting monthly income above R\u003cspan\u003e$\u003c/span\u003e20,000.\u003c/p\u003e \u003cp\u003e(Insert Table\u0026nbsp;1 about here)\u003c/p\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eDescriptive Results\u003c/h2\u003e \u003cp\u003eDescriptive statistics for the study variables are presented in Table\u0026nbsp;2. EF composite scores, SEB skills, and TLA showed adequate variability and overall acceptable distributional properties for subsequent inferential analyses. Comprehensive information on task-level validity criteria, scoring procedures, and additional distributional diagnostics is available in Supplementary Material S3.\u003c/p\u003e \u003cp\u003e(Insert Table\u0026nbsp;2 about here)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eAssociations between Executive Functions, Social, Emotional and Behavioral Skills, and Transformational Leadership Attitudes\u003c/h3\u003e\n\u003cdiv id=\"Sec36\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Analysis\u003c/h2\u003e \u003cp\u003ePartial correlations controlling for age and education level among EF indicators, SE and ER, and TLA are presented in Table\u0026nbsp;3. Overall, both SEB indicators (SE and ER) showed moderate positive associations with TLA (\u003cem\u003er\u003c/em\u003e = .53, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001 for both). In contrast, global EF indicators showed consistently small and non-significant associations with TLA.\u003c/p\u003e \u003cp\u003e(Insert Table\u0026nbsp;3 about here)\u003c/p\u003e \u003cp\u003eAs expected, SE and ER were strongly intercorrelated (\u003cem\u003er\u003c/em\u003e = .69, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), suggesting shared social, emotional, and behavioral regulatory mechanisms. A modest negative association was also observed between IC and SE (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.30, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05), although this secondary pattern should be interpreted cautiously given the study\u0026rsquo;s primary focus.\u003c/p\u003e \u003cp\u003eTaken together, these findings suggest a differential pattern in which social, emotional, and behavioral skills are more closely related to transformational leadership attitudes than performance-based executive function indicators.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003eHierarchical Regression Models\u003c/h2\u003e \u003cp\u003eHierarchical multiple regression analyses were conducted to examine the incremental contributions of EF and SEB indicators to TLA, controlling for age and education level.\u003c/p\u003e \u003cp\u003eAge and education level accounted for a small proportion of variance in TLA (R\u0026sup2; = .06). In the first preregistered model, the inclusion of the SEB indicators (SE and ER) substantially improved model fit, increasing the explained variance to R\u0026sup2; = .33 (ΔR\u0026sup2; = .27). In the final step, EF indicators (WM, IC, and DM) were added, producing only a modest additional increase in explained variance (R\u0026sup2; = .39, ΔR\u0026sup2; = .06).\u003c/p\u003e \u003cp\u003eIn the second preregistered hierarchical model, EF indicators (WM, IC, and DM) were entered before the SEB indicators (SE and ER). Their inclusion did not meaningfully increase the explained variance (R\u0026sup2; = .07, ΔR\u0026sup2; = .01). In the final step, the addition of SE and ER substantially improved model fit, increasing the explained variance to R\u0026sup2; = .39 (ΔR\u0026sup2; = .32). The results for both preregistered models are presented in Table\u0026nbsp;4.\u003c/p\u003e \u003cp\u003eAcross both preregistered models, SEB skills accounted for the largest incremental proportion of explained variance in TLA, whereas EF indicators showed negligible incremental contribution. ER emerged as the only significant predictor (β\u0026thinsp;=\u0026thinsp;.39, \u003cem\u003ep\u003c/em\u003e = .031), while SE was positively but not significantly associated with TLA (β\u0026thinsp;=\u0026thinsp;.29, \u003cem\u003ep\u003c/em\u003e = .122). No EF indicators meaningfully predicted TLA (all \u003cem\u003ep\u003c/em\u003e \u0026gt; .05).\u003c/p\u003e \u003cp\u003eBootstrap analyses (5,000 resamples) yielded highly consistent parameter estimates, supporting the stability of the regression findings despite the modest sample size.\u003c/p\u003e \u003cp\u003e(Insert Table\u0026nbsp;4 about here)\u003c/p\u003e \u003cdiv id=\"Sec38\" class=\"Section3\"\u003e \u003ch2\u003eExploratory associations (IC X Individualized consideration)\u003c/h2\u003e \u003cp\u003eTo further explore potential links between EF indicators and specific dimensions of TLA, partial correlations controlling for age and education level were examined between IC and the Providing Individualized Support (PIS) dimension. A significant positive association was observed (\u003cem\u003er\u003c/em\u003e = .32, \u003cem\u003ep\u003c/em\u003e = .018), and bootstrap confidence intervals based on 5,000 resamples confirmed the robustness of this effect (95% CI [.08, .51]; see Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003e(Insert Table\u0026nbsp;5 about here)\u003c/p\u003e \u003cp\u003eImportantly, PIS was not significantly associated with SE (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.06, \u003cem\u003ep\u003c/em\u003e \u0026gt; .05) or ER (\u003cem\u003er\u003c/em\u003e = .04, \u003cem\u003ep\u003c/em\u003e \u0026gt; .05), suggesting that this exploratory finding reflects a more specific link between inhibitory control and leadership attitudes related to individualized consideration rather than a broader association with social, emotional, and behavioral skills. Overall, although EF indicators were not associated with global TLA scores, inhibitory control may play a more circumscribed, domain-specific role in leadership attitudes involving interpersonal support and follower-focused behaviors.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec40\" class=\"Section2\"\u003e \u003ch2\u003eOverview of the Study and Main Findings\u003c/h2\u003e \u003cp\u003eOverall, the findings revealed substantial variability across the examined constructs and indicated that SEB skills \u0026mdash; including social engagement (SE) and emotional resilience (ER) \u0026mdash; were consistently associated with TLA, whereas EF showed a more selective and domain-specific pattern of associations.\u003c/p\u003e \u003cp\u003eAmong the EF components (WM, IC, and DM), only IC was positively related to leadership attitudes reflecting individualized support. Taken together, these results suggest that SEB skills \u0026mdash; particularly ER \u0026mdash; may represent more robust correlates of TLA than performance-based cognitive control indicators in experienced public-sector leaders. By examining component-specific EF indicators and SEB skills conceptualized as functional competencies within a preregistered multimethod design, the present study advances prior integrative research on the psychological foundations of transformational leadership.\u003c/p\u003e \u003cp\u003eAn important limitation concerns the potential influence of shared-method variance on the observed associations between SEB skills and TLA, as both variables were assessed using self-report measures. Although the multimethod design reduced common-method bias in the assessment of EF through performance-based indicators, the use of the same response format for SEB and TLA may have inflated their observed relationships. Accordingly, the stronger predictive role of SEB skills should be interpreted with appropriate methodological caution.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eH1 — Variability Hypothesis\u003c/h3\u003e\n\u003cp\u003eThe first hypothesis predicted that EF (WM, IC, and DM), SEB skills (SE and ER), and TLA would display sufficient variability at both global and indicator levels. The findings supported this expectation, with descriptive statistics indicating substantial dispersion across the examined constructs. This pattern suggests that the sample captured meaningful individual differences in cognitive functioning, social, emotional, and behavioral skills \u0026mdash; including SE and ER \u0026mdash; and leadership attitudes.\u003c/p\u003e \u003cp\u003eSuch variability likely reflects the heterogeneous composition of the Brazilian federal public service, which includes professionals with diverse educational backgrounds, career trajectories, and organizational experiences. Prior research has linked this type of heterogeneity to differences in decision-making styles, emotional regulation, and leadership behavior (Balthazard et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Edelson et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fatima et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, meta-analytic evidence indicates that institutional environments, administrative traditions, and organizational cultures contribute to variability in leadership patterns within the public sector (Backhaus \u0026amp; Vogel, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the Brazilian context, such variability may be further amplified by the country\u0026rsquo;s continental dimensions and pronounced sociocultural and economic differences across regions (IBGE, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which shape organizational resources, administrative challenges, and leadership demands in distinct ways. These structural disparities reinforce the relevance of examining individual differences in EF and SEB skills among federal public-sector leaders.\u003c/p\u003e \u003cp\u003eFrom a methodological standpoint, adequate variability across EF, SEB (including SE and ER), and TLA strengthens the interpretability of the correlational and regression findings, as it ensures sufficient dispersion to detect meaningful associations among variables. Most constructs also showed approximately normal distributions, with only mild deviations observed for TLA and DM. Overall, these distributional properties support the suitability of the data for the inferential analyses conducted in this study.\u003c/p\u003e\n\u003ch3\u003eH2 — Executive Functions and Transformational Leadership Attitudes\u003c/h3\u003e\n\u003cp\u003eThe second hypothesis predicted that EF would be positively associated with TLA. The findings provided selective support for this expectation. Regression analyses including WM, IC, and DM did not reveal consistent associations with global leadership attitudes. However, exploratory analyses indicated that IC was positively associated with leadership attitudes related to individualized consideration.\u003c/p\u003e \u003cp\u003eThis pattern suggests that specific executive processes, rather than EF more broadly, may be particularly relevant for certain leadership-related behaviors. Individualized consideration involves attending to followers\u0026rsquo; needs, supporting their development, and managing complex interpersonal dynamics (Avolio \u0026amp; Bass, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), all of which may require the regulation of automatic responses and sustained goal-directed engagement in socially demanding contexts.\u003c/p\u003e \u003cp\u003eIC supports impulse regulation, response suppression, and reflective behavior (Diamond, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Friedman \u0026amp; Miyake, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Miyake \u0026amp; Friedman, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Accordingly, leaders with stronger inhibitory control may be better able to modulate immediate reactions, consider alternative perspectives, and engage in more deliberate and adaptive interpersonal decision-making.\u003c/p\u003e \u003cp\u003eThese findings align with theoretical accounts proposing that executive control contributes to adaptive leadership through reflective judgment and strategic regulation in complex organizational settings (Chan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Edelson et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At the same time, prior neuropsychological research suggests that EF\u0026ndash;leadership associations may be component-specific and context-dependent (Ramchandran et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), reinforcing the view that the cognitive foundations of leadership attitudes operate through selective executive mechanisms rather than uniformly across EF domains.\u003c/p\u003e\n\u003ch3\u003eH3 — Social, Emotional, and Behavioral Skills and Transformational Leadership Attitudes\u003c/h3\u003e\n\u003cp\u003eThe third hypothesis predicted that SEB skills would be positively associated with TLA, particularly through the SE and ER dimensions. The findings provided strong support for this expectation, as both SE and ER showed moderate positive associations with leadership attitudes, with ER emerging as the only significant predictor in the regression models.\u003c/p\u003e \u003cp\u003eThese results are consistent with a substantial body of research highlighting the importance of socio-emotional competencies \u0026mdash; such as empathy, emotional regulation, and interpersonal effectiveness \u0026mdash; for adaptive leadership behavior (Cavaness et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; G\u0026oacute;mez-Leal et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Humphrey et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Transformational leadership involves motivating followers, fostering trust, and demonstrating individualized consideration (Avolio \u0026amp; Bass, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), all of which require the ability to regulate emotional responses and maintain constructive interpersonal relationships.\u003c/p\u003e \u003cp\u003eOverall, the present findings reinforce the view that social, emotional, and behavioral skills represent proximal foundations of transformational leadership attitudes in organizational contexts, particularly among experienced public-sector leaders.\u003c/p\u003e\n\u003ch3\u003eH4 — Joint Contributions of Executive and Social, Emotional, and Behavioral Processes\u003c/h3\u003e\n\u003cp\u003eThe fourth hypothesis proposed that EF and SEB skills would jointly relate to transformational leadership attitudes through a potentially complex pattern of associations. The findings provided partial support for this integrative perspective. SEB skills showed consistent and robust associations with TLA, whereas EF demonstrated a more selective and domain-specific pattern, primarily involving inhibitory control.\u003c/p\u003e \u003cp\u003eThis asymmetrical pattern suggests that leadership attitudes may reflect complementary regulatory processes rather than equivalent contributions from cognitive and social, emotional, and behavioral domains. SEB skills appear to provide proximal interpersonal foundations of transformational leadership, supporting constructive engagement with followers and effective emotional regulation. In contrast, executive control processes \u0026mdash; particularly inhibitory control \u0026mdash; may play a more circumscribed role by facilitating reflective judgment and the regulation of automatic responses in demanding organizational contexts.\u003c/p\u003e \u003cp\u003eThese findings align with integrative leadership models emphasizing the interaction between cognitive control and socio-emotional functioning in shaping adaptive leadership behavior (Connelly \u0026amp; Ruark, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Humphrey et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Molenberghs et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Building on this perspective, Fig.\u0026nbsp;1 presents a heuristic integrative model in which cognitive control processes and SEB skills contribute to TLA through complementary pathways. Although partially supported by the present findings, this framework should be interpreted as a conceptual guide for future research.\u003c/p\u003e \u003cp\u003e(Insert Fig.\u0026nbsp;1 about here)\u003c/p\u003e \u003cp\u003eParticularly, longitudinal and experimental investigations adopting multimethod approaches similar to the present design may help clarify the dynamic and potentially interactive effects of EF and SEB on leadership development and performance. Taken together, the results highlight the importance of advancing integrative investigations of cognitive and social, emotional, and behavioral mechanisms in leadership research while recognizing that their contributions may differ in magnitude and specificity.\u003c/p\u003e\n\u003ch3\u003eStrengths and Limitations\u003c/h3\u003e\n\u003cp\u003eThis study presents several strengths. First, it examined the associations between EF, SEB, and TLA within a real-world population of federal public managers, providing ecologically valid insights into leadership-related processes in the public sector. To the best of our knowledge, this is among the first studies in Brazil to investigate transformational leadership using performance-based cognitive measures derived from cognitive neuroscience in a nationally distributed sample of public-sector leaders.\u003c/p\u003e \u003cp\u003eSecond, the preregistered analytical plan enhanced transparency, reduced researcher degrees of freedom, and strengthened the methodological rigor of the findings. Third, the integration of performance-based cognitive measures and validated self-report SEB instruments provided a multimethod perspective on leadership-related processes, broadening construct representation and partially mitigating shared-method bias.\u003c/p\u003e \u003cp\u003eNevertheless, several limitations should be acknowledged. First, the cross-sectional design precludes causal inference regarding the associations between EF, SEB, and TLA. Longitudinal and experimental studies are needed to clarify the temporal dynamics and potential causal pathways linking cognitive control, social, emotional, and behavioral skills, and leadership behavior. Second, the final sample size was slightly smaller than originally anticipated in the preregistration. However, the observed effect sizes exceeded those assumed in the a priori power estimation (estimated \u003cem\u003ef\u003c/em\u003e\u0026sup2; = .15 vs. observed \u003cem\u003ef\u003c/em\u003e\u0026sup2; = .39), suggesting that the study was adequately powered to detect the effects identified.\u003c/p\u003e \u003cp\u003eThird, performance-based EF tasks \u0026mdash; particularly digital adaptations \u0026mdash; may involve task impurity, as individual tasks can recruit multiple cognitive processes beyond the targeted executive component. Fourth, the sample showed limited variability in educational background, which may constrain generalizability to populations with broader educational diversity, although this characteristic reflects the typical profile of senior public managers in the Brazilian federal administration. Finally, the reliance on self-report measures for both SEB indicators and TLA may have inflated observed associations due to shared-method variance.\u003c/p\u003e\n\u003ch3\u003eDirections for Future Research\u003c/h3\u003e\n\u003cp\u003eFuture research may advance the understanding of the associations between EF, SEB skills, and leadership by addressing several directions suggested by the present findings. First, longitudinal designs are needed to clarify the temporal dynamics linking cognitive control processes, social, emotional, and behavioral skills, and leadership development. Such approaches could examine whether improvements in EF and SEB precede changes in leadership attitudes or whether these associations evolve reciprocally over time.\u003c/p\u003e \u003cp\u003eSecond, experimental and intervention-based studies may investigate whether targeted training programs aimed at strengthening EF, particularly IC, and SEB skills contribute to the development of leadership-related capabilities. Cognitive training interventions focusing on inhibitory control, alongside programs designed to enhance emotional regulation and interpersonal effectiveness, may help clarify the malleability of leadership-related psychological processes.\u003c/p\u003e \u003cp\u003eThird, future studies could extend the present heuristic integrative framework illustrated in Fig.\u0026nbsp;1 by incorporating neurophysiological and neuroimaging measures, as well as behavioral indicators of leadership performance. Such approaches may help elucidate the neural and cognitive mechanisms underlying leadership behavior and provide a more comprehensive understanding of how cognitive control and SEB skills jointly contribute to adaptive leadership in complex organizational environments.\u003c/p\u003e \u003cp\u003eFinally, examining these associations across more diverse organizational contexts and populations \u0026mdash; including private-sector leaders and cross-cultural samples \u0026mdash; will be important to assess the generalizability of the present findings and identify potential contextual moderators.\u003c/p\u003e\n\u003ch3\u003ePractical Implications\u003c/h3\u003e\n\u003cp\u003eThese findings have direct implications for management development and leadership training in public-sector organizations. The consistent associations between SEB skills and TLA indicate that training initiatives should prioritize the development of emotional regulation, interpersonal awareness, and social engagement. Strengthening these competencies may foster leadership behaviors that enhance motivation, trust, and team effectiveness.\u003c/p\u003e \u003cp\u003eThe selective association between inhibitory control and individualized consideration suggests that cognitive self-regulation supports reflective and adaptive interpersonal leadership. Accordingly, leadership development programs may benefit from incorporating targeted cognitive training strategies aimed at strengthening executive control processes, particularly inhibitory control and working memory, to support decision-making in complex organizational environments.\u003c/p\u003e \u003cp\u003eIn addition, performance-based cognitive assessments, such as the digital EF battery used in this study, may provide practical tools for identifying regulatory demands and informing targeted interventions to support leadership effectiveness in public organizations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study advances the understanding of transformational leadership by integrating performance-based executive function indicators and social, emotional, and behavioral skills within a preregistered multimethod framework applied to a national sample of public-sector leaders. The findings indicate that SEB skills represent proximal foundations of leadership attitudes, whereas executive control processes contribute in a more selective manner.\u003c/p\u003e \u003cp\u003eThis pattern supports integrative perspectives suggesting that effective leadership emerges from complementary regulatory mechanisms rather than uniform effects across psychological domains. By highlighting the distinct and potentially interactive roles of cognitive control and SEB functioning, the study contributes to a more nuanced and practice-relevant understanding of leadership in public-sector contexts.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlfers T, Gittler G, Ulitzsch E, Pohl S (2025) Assessing the Speed\u0026ndash;Accuracy Tradeoff in Psychological Testing Using Experimental Manipulations. 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Harcourt Brace \u0026amp; Co\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDemographic Characteristics of the Final Sample (N = 70)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eN (%) or M (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e48.97 (7.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e43 (61.4%)\u003c/p\u003e\n \u003cp\u003e27 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003cp\u003eBrown\u003c/p\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (4.3%)\u003c/p\u003e\n \u003cp\u003e14 (20.0%)\u003c/p\u003e\n \u003cp\u003e53 (75.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e\n \u003cp\u003ePostgraduate specialization\u003c/p\u003e\n \u003cp\u003eMaster\u0026rsquo;s degree\u003c/p\u003e\n \u003cp\u003eDoctoral degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (4.3%)\u003c/p\u003e\n \u003cp\u003e28 (40.0%)\u003c/p\u003e\n \u003cp\u003e19 (27.1%)\u003c/p\u003e\n \u003cp\u003e20 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003cp\u003eSeparated\u003c/p\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e60 (85.7%)\u003c/p\u003e\n \u003cp\u003e2 (2.9%)\u003c/p\u003e\n \u003cp\u003e8 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eLeadership tenure\u003c/p\u003e\n \u003cp\u003e1-2 years\u003c/p\u003e\n \u003cp\u003e3-5 years\u003c/p\u003e\n \u003cp\u003e6-10 years\u003c/p\u003e\n \u003cp\u003e\u0026gt; 10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e3 (4.3%)\u003c/p\u003e\n \u003cp\u003e18 (25.7%)\u003c/p\u003e\n \u003cp\u003e24 (34.3%)\u003c/p\u003e\n \u003cp\u003e25 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eAdministrative span of control\u003c/p\u003e\n \u003cp\u003eNo direct reports\u003c/p\u003e\n \u003cp\u003e1-5 employees\u003c/p\u003e\n \u003cp\u003e6-10 employees\u003c/p\u003e\n \u003cp\u003e11-20 employees\u003c/p\u003e\n \u003cp\u003e21-50 employees\u003c/p\u003e\n \u003cp\u003e51-100 employees\u003c/p\u003e\n \u003cp\u003e101-200 employees\u003c/p\u003e\n \u003cp\u003e\u0026gt; 200 employees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1 (1.5%)\u003c/p\u003e\n \u003cp\u003e7 (10.0%)\u003c/p\u003e\n \u003cp\u003e12 (17.1%)\u003c/p\u003e\n \u003cp\u003e14 (20.0%)\u003c/p\u003e\n \u003cp\u003e19 (27.1%)\u003c/p\u003e\n \u003cp\u003e6 (8.6%)\u003c/p\u003e\n \u003cp\u003e5 (7.1%)\u003c/p\u003e\n \u003cp\u003e6 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eEmployment type\u003c/p\u003e\n \u003cp\u003eCareer civil servant\u003c/p\u003e\n \u003cp\u003eComissioned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68 (97.1%)\u003c/p\u003e\n \u003cp\u003e2 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003cp\u003eR$ 4,001.00-R$ 8,000.00\u003c/p\u003e\n \u003cp\u003eR$ 8,001.00-R$ 12,000.00\u003c/p\u003e\n \u003cp\u003eR$ 12,001.00-R$ 16,000.00\u003c/p\u003e\n \u003cp\u003eR$ 16,001.00-R$ 20,000.00\u003c/p\u003e\n \u003cp\u003e\u0026gt; R$ 20,000.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4 (5.7%)\u003c/p\u003e\n \u003cp\u003e4 (5.7%)\u003c/p\u003e\n \u003cp\u003e6 (8.6%)\u003c/p\u003e\n \u003cp\u003e14 (20.0%)\u003c/p\u003e\n \u003cp\u003e42 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDescriptive Statistics of Study Variables\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eReliability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e48.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eEducation level\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eWM composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eIC composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eDM composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eCF\u003cem\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/em\u003e composite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026alpha; = .93 (\u0026omega; = .93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026alpha; = .96 (\u0026omega; = .97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eTLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026alpha; = .82 (\u0026omega; = .86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. WM = working memory; IC = inhibitory control; DM = decision-making; CF = cognitive flexibility; SE = social engagement; ER = emotional resilience; TLA = transformational leadership attitudes. All EF variables are composite scores derived from multiple performance-based EF task indicators (see supplementary material S3). \u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eEducation level is coded from 1 (bachelor\u0026rsquo;s degree) to 4 (doctoral degree). \u003cem\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/em\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eCF was examined as an exploratory EF component.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePartial Correlations among EF, Social, Emotional, and Behavioral Skills, and Transformational Leadership Attitudes, Controlling for Age and Education Level\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.30*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eER\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.69***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.53***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.53***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Partial correlations controlling for age and education level. WM = working memory; IC = inhibitory control; DM = decision making; SE = social engagement; ER = emotional resilience; TLA = transformational leadership attitudes. * \u003cem\u003ep\u003c/em\u003e \u0026lt; .05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; .01, *** \u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHierarchical Multiple Regression Predicting Transformational Leadership Attitudes\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eCovariates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[-0.37, 0.16]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[-0.33, 0.19]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eSEB indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[-0.08, 0.64]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[0.04, 0.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eEF indicators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eWM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[-0.21, 0.34]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[-0.03, 0.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 71px;\"\u003e\n \u003cp\u003e-.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e[-0.30, 0.25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eModel summary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003eR\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026Delta;R\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eStep 1 (covariates)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eModel 1 (SEB\u0026nbsp;\u0026rarr;\u0026nbsp;EF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eStep 2 (SEB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eStep 3 (EF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eModel 2 (EF\u0026nbsp;\u0026rarr;\u0026nbsp;SEB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eStep 2 (EF)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003eStep 3 (SEB)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003e.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 5px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. \u0026beta; = standardized coefficient; CI = confidence interval; SEB = social, emotional, and behavioral skills; EF = executive functions; SE = social engagement; ER = emotional resilience; WM = working memory; IC = inhibitory control; DM = decision-making under risk and uncertainty.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBootstrap Partial Correlation between Inhibitory Control and Providing Individualized Support (TLA Dimension), Controlling for Age and Education Level\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003eAssociation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003ePearson\u0026rsquo;s r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 102px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 211px;\"\u003e\n \u003cp\u003eEffect size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eFisher\u0026rsquo;s z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003eSE (z)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eIC X PIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e[.075, .513]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Partial correlation controlling for age and education level. Confidence intervals based on 5,000 bootstrap resamples. IC = inhibitory control composite score; PIS = Providing Individualized Support.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Federal University of Rio Grande do Sul","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"transformational leadership attitudes, executive functions, social, emotional, and behavioral skills, inhibitory control, public sector","lastPublishedDoi":"10.21203/rs.3.rs-9418989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9418989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTransformational leadership attitudes have been widely associated with positive organizational outcomes; however, their psychological foundations remain only partially understood, particularly in public-sector contexts. This study examined the joint contributions of executive functions (EF) and social, emotional, and behavioral (SEB) skills to transformational leadership attitudes (TLA) in a national sample of Brazilian federal public managers (N\u0026thinsp;=\u0026thinsp;70). EF were assessed using a digital battery of performance-based tasks, and SEB were measured using the Brazilian version of the Behavioral, Emotional, and Social Skills Inventory (BESSI-BR). TLA were assessed with a validated Brazilian leadership scale. Hierarchical regression analyses, controlling for age and education, showed that SEB, especially emotional resilience and social engagement, were strongly associated with TLA, accounting for most of the explained variance. In contrast, EF showed limited global associations, although inhibitory control was positively related to individualized consideration in exploratory analyses. These findings suggest that transformational leadership in public organizations primarily reflects SEB competencies, complemented by specific cognitive self-regulatory processes. Practical implications highlight the relevance of incorporating SEB development and targeted cognitive training into public leadership selection, assessment, and development programs, contributing to more effective people management and public service delivery, and informing leadership development practices in public organizations.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePreregistration\u003c/b\u003e: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/8jz6k\u003c/span\u003e\u003cspan address=\"https://osf.io/8jz6k\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e","manuscriptTitle":"Cognitive and Social, Emotional, and Behavioral Foundations of Transformational Leadership Attitudes: Evidence from a National Sample of Brazilian Federal Public Managers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 18:09:40","doi":"10.21203/rs.3.rs-9418989/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"75d02658-207e-47ac-994d-8d37e0842925","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66321158,"name":"Cognitive Neuroscience"},{"id":66321159,"name":"Leadership and Ethics"},{"id":66321160,"name":"Psychology"}],"tags":[],"updatedAt":"2026-04-16T18:09:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 18:09:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9418989","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9418989","identity":"rs-9418989","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}