The rapid digitalization of manufacturing has introduced new psychosocial risks alongside productivity gains, particularly in the form of technostress. This study investigates how technostress influences worker mental health, burnout, engagement, and safety outcomes in automated manufacturing environments, and whether job resources and psychosocial safety climate (PSC) serve as protective factors. Drawing on the Job Demands–Resources (JD-R) model, a two-wave panel design was employed with data collected from 605 employees at baseline and 450 employees at a four-week follow-up across five automated manufacturing plants. Multilevel and moderation analyses revealed that while direct effects of technostress on outcomes were directionally consistent with theoretical expectations, they were not statistically significant. Instead, job resources—including supervisory ICT support, recovery opportunities, and digital literacy—emerged as significant buffers, mitigating the potential negative effects of technostress. Furthermore, PSC at the crew and organizational level predicted improved engagement, better mental health, and stronger safety behaviors, underscoring its role as a higher-order organizational resource. These findings suggest that technostress in manufacturing is contingent on contextual factors: it becomes harmful when resources are insufficient and protective climates are absent. The study contributes to technostress and occupational health literature by integrating psychosocial and technological stressors in a multilevel framework and offers practical guidance for managers seeking to balance digitalization with worker well-being.

Örgütlerde iş sağlığı ve güvenliğine yönelik alınan önlemler ve uygulamalar sağlıklı ve güvenli bir çalışma ortamının oluşturulmasını amaçlamaktadır. İş güvenliği kültürü de bu ortamın yaratılmasında önemli bir görev üstlenmektedir. Örgüt kültürünün bir alt bileşeni olarak kabul edilen güvenlik kültürü bir kurumda çalışanların güvenlikle ilgili paylaştığı değerler, inançlar, tutumlar ve davranışlar bütünüdür. Bu araştırmanın amacı, otel işletmelerinde çalışanların güvenlik kültürü algı düzeylerini tespit etmek ve demografik değişkenler açısından bir farklılık olup olmadığını belirlemektir. Nicel araştırma yönteminin tercih edildiği bu çalışmada betimsel ve ilişkisel tarama deseni kullanılmıştır. Veriler anket tekniği ile toplanmış ve SPSS 25.0 istatistik programında analiz edilmiştir. Yapılan analiz sonucunda güvenlik kültürü algı düzeyinin yüksek seviyede olduğu ve güvenlik kültürü genel algısının yaş ve eğitim değişkenine göre farklılık gösterdiği belirlenmiştir.

Sustainable development has become a strategic priority for countries seeking long-term economic prosperity while balancing social, environmental, and institutional objectives. According to the April 2026 International Monetary Fund World Economic Outlook projections, Türkiye is expected to become the largest economy among the selected major Muslim economies in terms of nominal gross domestic product. Motivated by this development, this study benchmarks Türkiye’s sustainable development performance against nine comparable economies and examines the relationships between selected Sustainable Development Goal (SDG) indicators and economic performance. The analytical framework integrates benchmarking, correlation, SDG network analysis, regression modeling, and explainable machine learning using SDG and macroeconomic data. The findings reveal both synergies and trade-offs among the selected SDGs. Strong positive relationships emerge between SDG12 and SDG13 and between SDG4 and SDG16, confirming the interconnected structure of sustainable development. Türkiye ranks second in overall SDG performance and performs particularly strongly in clean energy, innovation and infrastructure, and international partnerships. Regression results identify education, clean energy, innovation, and climate action as statistically associated with economic performance. Under a strict year-based chronological holdout and rolling-origin validation framework, the tuned Random Forest model achieved the strongest out-of-sample predictive performance. SHapley Additive exPlanations analysis indicates that one-year lagged economic performance is the dominant predictor, while current macroeconomic conditions and selected SDG indicators provide additional predictive information. The findings demonstrate substantial temporal persistence in economic performance and show that sustainability indicators complement prior economic information in forecasting. The study contributes to sustainability research by integrating comparative assessment, systems-oriented analysis, econometric modeling, and explainable machine learning while clearly distinguishing statistical association from predictive contribution.

Modern iş dünyasında iş sağlığı ve güvenliği artık yalnızca yasal bir zorunluluk değil, kurumsal sürdürülebilirliğin, çalışan refahının ve marka itibarının temel yapı taşlarından biridir. Dr. Umut Elbir, bu kitapta iş güvenliği kültürünün nasıl inşa edilebileceğini; liderlik, stratejik yönetim ve örgüt kültürü boyutlarıyla derinlemesine ele alıyor. Gerçek vaka örnekleri, bilimsel araştırmalar ve uygulanabilir stratejilerle desteklenen bu eser, iş kazalarını önlemenin ötesinde güvenli, verimli ve motive edici iş ortamlarının nasıl yaratılacağını gösteriyor. Dijital dönüşüm, yapay zekâ, büyük veri ve yeni nesil teknolojilerin İSG yönetiminde nasıl dönüştürücü bir rol oynayabileceğini anlaşılır ve yol gösterici bir dille ortaya koyuyor. İster yönetici, ister İK profesyoneli, isterse akademisyen olun; bu kitap size sadece güvenli bir iş kültürü kurmanın yollarını değil, aynı zamanda geleceğin liderlik anlayışını da sunuyor. İş güvenliğini bir maliyet değil, stratejik bir değer olarak görenler için vazgeçilmez bir rehber.

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.

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.