Filiz Mizrak

This study examines how ambiguity shapes managerial subjective value and decision commitment and evaluates a Behaviorally Calibrated Fuzzy Subjective-Value (BCFSV) representation of ambiguity responses. In a repeated-measures 2 × 2 experiment crossing risk versus ambiguity with gain versus loss framing, 525 working adults completed 12 managerial decision scenarios, yielding 6300 trial-level observations. An independent pilot (N = 42; 504 trials) was used to calibrate the fuzzy hesitation/ambiguity-response term (π) against normalized perceived ambiguity using ambiguity exposure, ambiguity width, intolerance of uncertainty, and their interaction. In the main sample, ambiguity increased perceived ambiguity and reduced the pre-choice subjective value of the uncertain option; the statistical indirect effect through perceived ambiguity was −0.405 (95% bootstrap CI [−0.439, −0.373]). Intolerance of uncertainty strengthened the ambiguity–perceived ambiguity relationship, and framing moderated the ambiguity–valuation relationship. The highest pre-choice subjective value across the two options predicted decision commitment and carried a significant indirect association between perceived ambiguity and commitment (−1.584, 95% bootstrap CI [−1.784, −1.391]). Decision confidence also predicted commitment, with a stronger confidence–commitment association among participants showing poorer Raw Brier calibration (b = 0.874, 95% CI [0.630, 1.119]). The pilot-calibrated π mapping generalized to the main sample (r = 0.620), but adding the BCFSV transformation to a same-information benchmark produced no detectable incremental predictive gain for choice or commitment. The findings indicate that ambiguity appraisal, individual differences, and framing shape valuation, while the confidence–commitment association was stronger among participants with poorer confidence calibration. BCFSV thus offers an interpretable, empirically calibrated representation of uncertainty rather than a predictively superior model.

As generative AI systems become more socially responsive, personalized, and perspective-aware, a central question is whether such features improve human–AI decision quality or simply make AI advice more influential. This study introduces the Social Intelligence Reliance Halo as a proposed social-to-epistemic spillover pattern in which perceived social understanding functions as a non-diagnostic cue for epistemic evaluation: an AI that appears to understand the user may also be granted greater epistemic authority or reliability even when the relational cue provides no evidence of factual accuracy. A randomized two-condition experiment was conducted with 462 working adults with recent generative-AI experience. Participants interacted with either a High Social-Intelligence (High-SI) relational framing package or a Neutral AI across six repeated decision trials. Post-interaction measures assessed perceived social intelligence, epistemic authority, epistemic reliability, suspension of critical judgment, verification behavior, and AI literacy; trial-level behavioral reliance was analyzed with generalized estimating equations. The randomized High-SI package increased perceived social intelligence and increased advice-taking both when participants were initially correct and the contingent AI recommendation was wrong (OR = 1.507, p < .001) and when participants were initially incorrect and the recommendation was correct (OR = 1.800, p < .001). The Condition × participant-dependent advice-correctness interaction was nonsignificant (OR = 1.195, p = .289), providing no reliable evidence that the randomized treatment effect differed across these two states. Because recommendation correctness was mechanically determined by participants’ initial accuracy rather than independently randomized, this interaction is interpreted as exploratory treatment heterogeneity across participant-dependent correctness states rather than evidence that the High-SI package altered sensitivity to AI accuracy. Post-interaction perceived social intelligence was positively associated with epistemic authority and reliability, and AI literacy was associated with lower suspension of critical judgment and greater verification; these associations do not establish temporal or causal mediation. The study therefore provides experimental evidence that a bundled relational framing package can amplify both beneficial and harmful reliance, while the proposed social-to-epistemic pathway and accuracy-sensitive reliance remain questions for stronger future designs.

Artificial intelligence (AI) is increasingly embedded in organizational decision-making, requiring employees not only to use AI-generated recommendations but also to evaluate their quality and determine when reliance is appropriate. Although established research examines behavioral reliance on algorithmic and AI advice, fewer studies have approached performance under AI advice as an individual-differences assessment problem integrating psychometric structure, cognitive correlates, process indicators, and criterion-related evidence. This study developed and initially validated a CHC-informed, performance-based assessment of cognitive performance under AI advice using 24 organizational decision scenarios. The assessment was designed around three closely related content/performance dimensions—AI error detection, evidence integration, and cognitive control and adaptive reliance—while also capturing confidence, response time, and reliance behavior. The validation sample comprised 780 employed adults in Türkiye. Psychometric analyses included confirmatory factor analysis, multidimensional item response theory, response-time analyses, scenario-level logistic regression, measurement invariance, differential item functioning, and internal cross-validation. Results indicated a dominant general cognitive-performance component together with additional structure corresponding to the three theoretically specified dimensions. Assessment performance was positively associated with established cognitive measures, including ICAR-16 reasoning performance, working memory, processing speed, and attentional control, whereas associations with AI-related self-reports were generally weaker. Dimension-aligned analyses supported the expected associations of ICAR-16 with AI error detection and working memory with evidence integration. Performance was also moderately associated with concurrently assessed organizational decision quality (r = 0.408) and explained additional variance in this criterion beyond demographic and work characteristics, AI experience, conventional cognitive-performance measures, and AI-related self-reports (ΔR² = 0.103, p < .001). In contrast, several hypothesized scenario-specific associations involving AI confidence, time pressure, interruptions, and resistance to confidently inaccurate advice were not supported. Overall, the findings provide initial evidence for a performance-based approach to assessing how employees evaluate and respond to AI advice, while indicating that the proposed scenario-specific mechanisms and group-comparability findings require further replication before consequential applications are considered.

Self-leadership is increasingly important in service and knowledge-intensive work, yet sustained self-regulation may also create hidden psychological costs. This study introduces self-leadership overload (SLO) as a distinct self-regulatory strain state and examines whether it emerges nonlinearly, whether the self-leadership–SLO association is stronger under high work pressure, and whether SLO shows indirect associations with employee outcomes through work rumination. A two-wave survey of 408 full-time employees across seven service and knowledge-intensive sectors was analyzed using scale-development procedures, iterative PLS-SEM, two-stage nonlinear and moderation models, bootstrapped indirect-effect analysis, incremental-validity tests, and supplementary robustness checks. Self-leadership was positively associated with work happiness and sustainable performance, while its quadratic terms for these outcomes were nonsignificant. By contrast, the quadratic self-leadership–SLO term was positive and significant, indicating a progressively stronger association with overload at higher levels of self-leadership, and work pressure strengthened this association. SLO was associated with lower work happiness and sustainable performance and higher burnout and turnover intention; significant indirect associations through work rumination were observed for all four outcomes. SLO also explained additional variance beyond demographic and employment controls, conventional self-leadership, work pressure, and organizational and personal resources (ΔR² = 0.049–0.123). General organizational resources did not show the hypothesized buffering pattern, whereas health-oriented self-care was negatively associated with overload. The findings refine the self-leadership paradox by showing that a beneficial self-regulatory capability can coexist with an escalating internal burden without a corresponding direct curvilinear downturn in positive outcomes. The study contributes a theoretically bounded and empirically differentiated SLO construct and highlights workload boundaries, recovery, and health-oriented self-care as important conditions for sustainable self-leadership.

Generative artificial intelligence (GenAI) is increasingly embedded in consulting and professional-service work, yet employees may not always disclose its contribution to work outputs. This study examines hidden GenAI use as a social-cognitive, organizational, and human–AI interaction phenomenon and investigates how individual and organizational factors are associated with responsible AI disclosure and concealment. Survey data were collected from 564 consulting and professional-service employees in Türkiye. The analytical framework combined PLS-SEM with covariance-based SEM robustness checks, cross-validated predictive modeling with out-of-fold SHAP interpretation, and exploratory Choquet decision support. The structural results showed that AI disclosure anxiety and competence penalty fear were positively associated with hidden GenAI use, whereas social intelligence, official AI tool adequacy, and responsible AI disclosure were negatively associated with concealment. AI metacognition, human–AI trust, AI policy clarity, social intelligence, and official AI tool adequacy were positively associated with responsible AI disclosure. In the predictive analysis, Ridge regression achieved the strongest average cross-validated performance, while XGBoost was used for nonlinear interpretation through out-of-fold SHAP values; AI disclosure anxiety and competence penalty fear emerged as the strongest predictors. The exploratory Choquet analysis consistently prioritized a non-punitive AI-use disclosure system under alternative weighting and uncertainty scenarios. The study contributes by shifting attention from whether professionals adopt GenAI to whether, why, and under what organizational and social conditions they disclose or conceal its substantive contribution to professional work. The cross-sectional design supports association-based rather than causal interpretation.

Airports face growing pressure to decarbonize energy-intensive infrastructure while integrating digital twins, artificial intelligence, real-time monitoring, electrification, renewable-energy systems, and resilient operational controls. This study develops a reliability-aware Bayesian framework for assessing readiness for digital-twin-enabled smart-energy systems across 20 major international airports. The preferred latent model uses 15 airport-level capability indicators organized into four theory-defined domains: smart-energy infrastructure, digital twins and intelligent systems, managerial and organizational readiness, and governance and resilience. A sixteenth indicator, evidence transparency, is retained separately as an auxiliary reporting-quality measure. Eight additional variables describe the airport scale and country- or economy-level enabling environment. Evidence was compiled from official airport reports, sustainability disclosures, regulatory records, project documents, and international datasets, with explicit distinctions between operational systems, pilots, planned investments, and group-level claims. Observation-level reliability incorporates source quality, verification, temporal relevance, consistency, and completeness, while missing observations contribute no direct likelihood to the Bayesian measurement model. The preferred PyMC/NUTS specification produced stable posterior estimates with no divergent transitions, a maximum overall readiness R-hat of 1.003, and acceptable posterior predictive performance (RMSE = 0.094; MAE = 0.057). Singapore Changi, Hong Kong, Amsterdam Schiphol, and Copenhagen formed the highest-readiness group. Sensitivity analyses showed that the broad ordering was robust to alternative reliability assumptions, variance-floor choices, prior specifications, and high-information restrictions, although stronger informative-missingness assumptions affected several evidence-sparse airports. A complementary rule-based threshold screen identified capability shortfalls and evidence gaps without interpreting them as causal necessary conditions. The framework provides an uncertainty-aware basis for benchmarking, investment sequencing, procurement, and integrated digital and energy transition planning.

Artificial intelligence (AI)-supported decision systems can improve managerial decision-making, but their cognitive implications may depend on how information, explanations, alerts, time pressure, uncertainty, autonomy, and human control are configured. This study develops an Adaptive Cognitive Load Twin using data from 420 logistics and supply-chain professionals in Türkiye who completed four AI-supported managerial decision scenarios, yielding 1680 participant–scenario observations. The framework models information overload, cognitive load, decision fatigue, perceived explainability, trust in AI, automation bias, perceived human control, perceived decision quality, and objective decision accuracy as distinct but related dimensions. Participant-clustered path analysis showed that higher information overload was associated with higher cognitive load, and higher cognitive load was strongly associated with higher decision fatigue; in turn, higher cognitive load and decision fatigue were associated with lower perceived decision quality. Higher perceived explainability was associated with lower cognitive load, greater trust, higher perceived decision quality, and lower automation-bias tendency, while greater perceived human control was associated with higher perceived decision quality and lower automation bias. Higher cognitive load was associated with lower objective decision accuracy. Participant-grouped machine learning models showed that cognitive load was more predictable than decision fatigue or objective accuracy, with information density and time pressure emerging as leading predictive contributors. Model-based simulations indicated that lower information density and progressive disclosure offered the clearest opportunities for adaptive support. The framework extends digital-twin logic toward multidimensional, human-centered monitoring and adaptation of cognitive conditions in AI-assisted managerial work.

Airports face increasing pressure to reduce their climate impacts while maintaining mobility, connectivity, operational capacity, and service quality. However, many airport sustainability assessments rely on expert judgments, narrow environmental indicators, or conventional multi-criteria decision-making models that treat heterogeneous public data as equally reliable. This study evaluates the sustainable mobility and climate-transition performance of 20 European airports using 18 criteria across six dimensions: airport access sustainability, low-carbon mobility, carbon transition, energy and resource efficiency, circularity, and operational pressure. Data from sustainability, annual, and integrated reports; Airport Carbon Accreditation records; official airport websites; public transport sources; and traffic statistics were integrated through a standardized airport–criterion data structure recording the source type, reporting year, unit, organizational boundary, calculation status, and assurance level of each observation. The data were then harmonized to common definitions and units before analysis. A Z-number fuzzy WENSLO–ALWAS framework was applied to represent each airport–criterion observation through two components: a fuzzy performance value and a fuzzy reliability value. This mechanism allows airport-specific, externally assured observations to exert stronger evidential influence than calculated, group-level, incomplete, or proxy-based values, thereby avoiding the equal treatment of data with different levels of credibility. The results provide reliability-adjusted airport rankings, dimension-level performance profiles, sensitivity analyses, robustness comparisons, and airport clusters. The findings indicate that airport sustainability performance depends not only on carbon reduction and accreditation maturity, but also on public transport accessibility, low-carbon mobility, resource efficiency, circularity, operational pressure, and reporting reliability. The empirical findings are specific to the selected European airports, while the proposed framework is transferable to other regions, subject to the recalibration of criteria, benchmarks, reporting conventions, and reliability rules. The study demonstrates how heterogeneous public sustainability data can be converted into transparent and defensible, reliability-aware inputs for airport benchmarking.

This study develops a secondary-data-driven decision support framework for prioritizing strategic energy investment readiness across countries. The empirical application covers 36 countries and 18 criteria grouped under macroeconomic feasibility, institutional capacity, energy security, sustainability and decarbonization, market and demand conditions, and technical resource potential. The study responds to a methodological gap in energy investment prioritization by moving from expert-only linguistic scoring toward a reproducible, explainable, stakeholder-sensitive, and validation-oriented multi-criteria decision support structure. In the implemented empirical model, public secondary data are transformed into a normalized country-level decision matrix, baseline readiness scores are calculated using equal weights, and entropy, Criteria Importance Through Intercriteria Correlation (CRITIC), and hybrid entropy-CRITIC configurations are used as objective weighting benchmarks. Large language model (LLM)-assisted extraction is used as a documented criterion-discovery and screening aid, not as an autonomous scoring, weighting, or ranking mechanism. The fuzzy component is specified as an uncertainty-sensitive extension and illustrated through panel-based fuzzy interval construction for selected time-series indicators, while the main baseline ranking is based on the latest available normalized data matrix. The results show meaningful cross-country variation, with top-10 readiness scores ranging from 0.675 to 0.537 and the lowest five scores ranging from 0.364 to 0.338. Persona-based results indicate that country priorities differ across public planners, private investors, grid operators, sustainability policymakers, and infrastructure funds, while robustness checks show broad ranking stability, with Spearman correlations between 0.892 and 0.986 and a median simulated-agent correlation of 0.932. Fairness and partial-correlation diagnostics suggest that the model captures multidimensional readiness rather than simply reproducing wealth or existing renewable capacity. Post-hoc validation against observable investment- and transition-related benchmarks further supports the convergent validity of the readiness index. The framework should therefore be interpreted as an early-stage country-level screening tool for strategic energy investment prioritization, not as a final project-level investment appraisal model.