Cemal Zehir

Effective renewable energy policy requires benchmarks that reflect countries’ structural energy characteristics rather than generic performance rankings. This study develops an explainable machine-learning framework that identifies structurally comparable peers and derives peer-relative priorities, using annual World Bank data for 37 Organisation for Economic Co-operation and Development (OECD) countries over 2010–2021. Thirteen energy, economic and demographic indicators are standardized and analysed with principal component analysis, Ward hierarchical clustering and cosine similarity, supported by bootstrap, density-based, temporal, scaling and leave-one-country-out robustness checks. Random Forest with Gini, permutation and SHAP importance is used descriptively to characterize the clusters, and directionally filtered standardized gaps between Türkiye and its closest peer form a Strategic Gap Priority Index (SGPI). Three weakly separated regimes emerge: renewable-intensive economies, larger urbanized fossil-based economies, and less urbanized lower-income economies. Under historical averages, Türkiye and the United Kingdom are mutually closest peers, and coal-based electricity generation and net energy import dependence are the two leading SGPI priorities under all weighting schemes tested. These results are conditional on the profile definition: adding annual trends makes Hungary the closest peer, and excluding hydropower makes non-hydro renewable generation the second priority.

Ethical leadership is usually examined through its consequences for employees, and it is often assumed that employees who rate their supervisor as ethical will also report lower strain and weaker intentions to leave. This study tests that assumption using a person-centred design. Survey data were collected from 247 shift-working employees of a single Turkish manufacturing organisation. Participants rated the ethical leadership of their immediate supervisor and reported their own job stress, perceived organisational support, turnover intention and career planning. Latent profile analysis identified four subgroups. Two of these are of particular interest: both report high ethical leadership and organisational support yet differ sharply in job stress (4.37 vs. 2.48) and turnover intention (4.10 vs. 2.18). This divergence helps explain why job stress is essentially unrelated to ethical leadership (r = 0.02, p = 0.820) and to perceived organisational support (r = 0.04, p = 0.492) at the bivariate level, while remaining strongly associated with turnover intention (r = 0.43, p < 0.001). Favourable evaluations of a supervisor therefore appear compatible with very different experiences of strain. Because the design is cross-sectional and all measures are self-reported by the same source, the findings describe patterns of association rather than causal processes. The results suggest that leadership-focused interventions alone may not address all sources of strain-related turnover intention and that job demands require separate assessment.

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.

The field of competitive strategy has attracted growing academic interest in recent years; however, the intellectual framework and thematic evolution of this research area remain fragmented. This study aims to systematically map the evolution of competitive strategy research using an unsupervised machine learning framework. Drawing on a dataset of approximately 3900 journal articles indexed in the Scopus database between 2015 and 2025, the study employs probabilistic topic modeling, specifically Latent Dirichlet Allocation (LDA), together with keyword co-occurrence network analysis, thematic mapping, and community detection techniques to identify the latent thematic structure of the field. The findings reveal a modular and interconnected conceptual landscape in which capability-based strategic perspectives, particularly dynamic capabilities and the resource-based view, continue to occupy central positions in the literature. At the same time, themes related to digital transformation, artificial intelligence, supply chain resilience, environmental, social, and governance (ESG)-oriented management, and sustainability-focused strategic capabilities demonstrate substantial growth and emerging prominence. Temporal analyses further indicate a gradual reconfiguration toward digitally integrated, sustainability-oriented, and capability-driven strategic frameworks. By integrating topic modeling with network-based bibliometric analysis, the study provides a comprehensive and data-driven mapping of the field’s intellectual evolution. The study contributes to competitive strategy research by synthesizing the latent thematic structure of the field and by showing how complementary computational and bibliometric techniques can support large-scale literature mapping in a rapidly evolving research domain.

Brent crude oil prices are strategically important due to their sensitivity to geopolitical developments, financial market stress, and global monetary conditions. This study examines whether strategic risk indicators improve the forecasting performance of Brent crude oil returns within an integrated econometric and machine learning framework. Monthly data from January 2001 to December 2025 are employed, using the Global Geopolitical Risk Index (GPR), the CBOE Volatility Index (VIX), and the U.S. 10-year Treasury yield (DGS10) as key explanatory variables. Methodologically, the analysis first estimates benchmark econometric models, including ARIMAX (AutoRegressive Integrated Moving Average with Explanatory Variable) and ARIMAX-gjrGARCH (Glosten-Jagannathan-Runkle Generalized Autoregressive Conditional Heteroscedasticity, and then implements machine learning models, namely XGBoost (eXtreme Gradient Boosting), LightGBM (Light Gradient Boosting Machine), and Random Forest, to capture potential nonlinear relationships. Using sMAPE (Symmetric Mean Absolute Percentage Error), forecast performance is assessed over multiple forecast horizons under a rolling-origin framework. Across several forecasting horizons and train-test split configurations, the empirical results consistently show that machine learning techniques, especially LightGBM, offer superior out-of-sample forecasting accuracy. These findings suggest that the dynamics of Brent crude oil returns are influenced by complex and nonlinear relationships between macro-financial conditions, financial uncertainty, and geopolitical risk. The study concludes that flexible data-driven forecasting frameworks offer stronger predictive performance than benchmark econometric models under strategic risk conditions and provide useful implications for energy market risk management and policy decision-making.

This study examines whether corporate sustainability discourse generates uniformly positive employee reactions or whether its effects are bounded by credibility dynamics. Drawing on signaling theory and legitimacy perspectives, we propose that sustainability communication exhibits a non-linear relationship with employee evaluations. Specifically, we argue that sustainability intensity initially enhances employee evaluations but produces diminishing and eventually negative returns beyond a credibility threshold. Furthermore, we theorize that greenwashing signals reshape this non-linear relationship by altering its curvature and shifting the turning point to lower levels of sustainability intensity. Using a longitudinal firm–year panel design, we test these arguments with weighted fixed-effects models and turning-point estimations. The results reveal a significant inverted-U relationship between sustainability discourse intensity and employee evaluations. Importantly, greenwashing signals do not independently reduce evaluations; instead, they amplify the negative curvature of the relationship and significantly lower the credibility threshold at which evaluative backlash emerges. These findings extend sustainability and management research by demonstrating that sustainability communication is not a uniformly beneficial strategic tool. Rather, its effectiveness depends on perceived credibility and contextual trust conditions. By introducing the notion of a shifting credibility threshold, this study provides a more nuanced understanding of how sustainability discourse influences internal stakeholder evaluations.

Bu çalışma, hizmetkâr liderlik algısının çalışan sesliliğine etkisinde psikolojik sözleşmenin aracılık rolünü incelemektedir. Marmara Bölgesi'nde mal ve hizmet üreten işletmelerin çalışanları araştırmanın evrenini oluştururken, zaman, erişim ve kaynak kısıtları nedeniyle kolayda örnekleme yöntemiyle 419 gönüllü çalışan seçilmiştir. Nicel yöntemle yürütülen araştırmada, istatistiksel analizler IBM AMOS 24.0 yazılımı ile gerçekleştirilmiştir. Literatür ışığında geliştirilen hipotezler test edilmiş ve hem teorik hem de uygulamaya yönelik katkılar sunulmuştur. Sonuçlar, hizmetkâr liderlik algısının ve psikolojik sözleşmenin çalışan sesliliği üzerinde anlamlı bir etkisi olduğunu göstermektedir. Hizmetkâr liderlik algısının işlemsel kontrat ile anlamlı bir ilişkisi bulunmazken, ilişkisel kontrat ile pozitif ve anlamlı bir etkisi tespit edilmiştir. Ayrıca, ilişkisel kontratın hizmetkâr liderlik ile çalışan sesliliği arasındaki ilişkide kısmi aracılık rolü üstlendiği, ancak işlemsel kontratın aracılık etkisinin olmadığı belirlenmiştir. Bulgular doğrultusunda öneriler sunulmuştur.

This meta-analysis examined the impacts of artificial intelligence and robotic technologies on the labor market through 18 empirical studies. Findings were evaluated under three main categories: (1) the average automation susceptibility rate was determined to be 21.3%, with methodological approach and publication year having significant effects on results; (2) while technology had a statistically insignificant slight negative effect on total employment, developed economies showed a decrease in routine manual tasks and an increase in non-routine analytical tasks; (3) a notable shift in skill demands from STEM skills toward social-emotional skills was observed. Results indicate that technology transforms jobs rather than completely eliminating them, creates different effects in developing and developed economies, and influences the labor market in increasingly complex ways over time. These findings emp

Energy resources, particularly oil and natural gas, are vital for global development but have significant environmental impacts, including pollution and habitat destruction. Green management has become a critical concept in today’s global industries, mostly the petroleum industry. The petroleum industry is vital not only for the world but also for Türkiye’s energy needs and economic development. However, its operations significantly impact the environment through greenhouse gas emissions, water pollution, and habitat destruction. In response to both global and national environmental concerns and regulatory pressures, the Turkish petroleum industry should adopt green management practices. Despite the lack of prior studies regarding green management approaches and practices in Turkish petroleum industry, this study examines how Türkiye’s petroleum industry should integrate green management principles to minimize environmental impacts and promote sustainable development. To evaluate the environmental protection approaches and practices of petroleum enterprises based on their operational domains, a survey was conducted, and the collected data underwent statistical analysis. The survey questions were designed by the authors to determine the attitudes, approaches, and practices of managers in crude oil production and refining companies regarding green management. According to the results of the statistical data analysis, it has been determined that companies in the Turkish petroleum industry have adopted an approach known as green management or environmentally conscious entrepreneurship. The statistical analysis of the administered survey results indicates a positive relationship between firms’ operational performance scores and their green management practices scores (r = 0.247). The survey results demonstrate an increasing adoption of environmental consciousness and green management practices among managers in the Turkish petroleum sector, with 90.2% of participants providing a positive response. The survey results also indicate that green management practices have a positive impact on business operations. In this regard, 42.4% of participating managers believe that green management practices enhance corporate image, 38.0% state that they improve efficiency, 35.0% assert that they strengthen competitive advantage, and 31.5% indicate that they contribute positively to energy savings. The survey findings further indicate that 90.2% of participants recognize the contribution of green management practices to sustainable development in businesses, while an equal proportion asserts that these practices enhance clean and safe production. Moreover, 93.5% of respondents emphasize that production and processing activities carried out without environmental considerations pose a significant threat to the future of both the planet and humanity. In conclusion, based on the responses provided by the participants, it can be inferred that business managers have adopted the green management approach and recognize the significant role of green management practices in addressing environmental challenges. In line with the objectives of this study and the statistical findings obtained, a “green management model” has been proposed for enterprises in the Turkish petroleum industry, taking into consideration global practices and aligning with the principles of environmentally responsible green entrepreneurship. In this context, the study makes a significant contribution to the literature by proposing a green management model for the Turkish petroleum industry.

In the wake of the Soviet Union's collapse in 1991, the Turkic Republics, sharing a common history, languages, and cultures, embarked on establishing political and economic ties. Numerous scholarly inquiries have since delved into the economic connections and collaborations between these amicable nations. This study endeavors to contribute to the evolving landscape of research on the integration of these Turkic Republics by tracking the growth and development of academic studies concerning Türkiye's economic relations and cooperations with Azerbaijan, Kazakhstan, Kyrgyzstan, Turkmenistan, and Uzbekistan. Employing a bibliometric analysis approach, we delve into the theoretical foundations that underpin these academic investigations. Our analysis focuses on 119 publications drawn from the academic database Web of Science, spanning the years 1993 to 2023. These studies predominantly lean towards the utilization of econometric models and quantitative methods in their exploration of the economic relationships. Remarkably, the most frequently cited journals emerge from the domains of energy, environment, and sustainability, suggesting a clear thematic concentration. However, it is worth noting that the range of subjects covered in the research remains relatively limited, presenting a promising avenue for further exploration and diversification in future studies.