Abstract This study examines the temporal evolution and cross-country interdependence of public search interest in “Online Learning” using a balanced monthly panel covering 20 countries from January 2004 to October 2021. The dataset contains 214 observations per country... This study examines the temporal evolution and cross-country interdependence of public search interest in “Online Learning” using a balanced monthly panel covering 20 countries from January 2004 to October 2021. The dataset contains 214 observations per country and 4,280 country month observations in total, with no missing values. The analysis combines univariate and multivariate time series procedures to characterize stationarity, autocorrelation, dynamic dependence, long run relationships, and the propagation of shocks. Augmented Dickey Fuller (ADF) and Kwiatkowski Phillips Schmidt Shin (KPSS) tests were applied to all series in levels and first differences. Eight series satisfied the combined stationarity criterion in levels, whereas 18 series satisfied it after first differencing. Autocorrelation and partial autocorrelation functions were examined up to 12 lags, followed by ARIMA and monthly seasonal SARIMA modeling for focal nonstationary series. For multivariate analysis, a parsimonious six country system comprising Argentina, India, Indonesia, Iran, Mexico, and the United States was selected to avoid excessive parameterization. Informationcriterion selection supported a VAR(3) specification, while the Johansen procedure indicated a cointegration rank of five, motivating estimation of a VECM. Granger causality testing with false discovery rate adjustment identified 14 significant directional relationships. Impulse response functions show that most shocks are strongest at short horizons and generally attenuate over time, while forecast error variance decomposition reveals heterogeneous dependence on own and cross country innovations. Overall, the findings demonstrate that online learning search interest is both country specific and dynamically interconnected, with meaningful short run transmission and substantial long run linkage among the focal series. Read More Read Less

Abstract Aviation operations are highly sensitive to rapidly changing meteorological conditions, yet existing research often focuses on isolated airports or specific weather hazards. This study addresses these gaps by developing and analyzing a high resolution, global Meteorological Aerodrome... Aviation operations are highly sensitive to rapidly changing meteorological conditions, yet existing research often focuses on isolated airports or specific weather hazards. This study addresses these gaps by developing and analyzing a high resolution, global Meteorological Aerodrome Report (METAR) dataset comprising 958,338 observations from 26 major cities across 15 countries between 2024 and 2026. By integrating meteorological characterization, adverse-weather identification, and machine learning, the study quantifies the temporal and geographical variability of aviation weather conditions. Results reveal a severe class imbalance, with adverse weather events accounting for only 4.29% of observations. While classification models achieved high overall accuracy (up to 98.1%), minority class precision and recall remained low, though fog detection demonstrated strong ranking ability (ROC-AUC = 0.860). Conversely, regression modeling for a continuous severity score proved highly challenging (R² = -1.43), indicating that temperature alone is insufficient for severity prediction. Spatiotemporal analyses highlighted distinct diurnal peaks in adverse weather (11:00–14:00 UTC) and significant geographic heterogeneity. Ultimately, this study provides a comprehensive, multi hazard framework that underscores the need for advanced feature engineering and cost sensitive learning to support risk based operational decision making in global aviation. Read More Read Less

Abstract As social media ecosystems grow increasingly complex, understanding content strategies requires moving beyond traditional demographic or industry based segmentation toward multidimensional behavioral configurations. This study presents a multivariate clustering analysis of social media content strategies, utilizing a dataset... As social media ecosystems grow increasingly complex, understanding content strategies requires moving beyond traditional demographic or industry based segmentation toward multidimensional behavioral configurations. This study presents a multivariate clustering analysis of social media content strategies, utilizing a dataset of 20 industry specific strategic profiles. By employing Multiple Correspondence Analysis (MCA), hierarchical clustering, Gower distance clustering, text embedding analysis, and K-modes clustering, we identify latent strategic archetypes based on combinations of platform, content format, primary goals, and audience engagement mechanisms. The analysis reveals four distinct exploratory archetypes: Professional Trust Building, High Energy Product Discovery, Visual Lifestyle and Brand Affinity, and Informational Consideration and Conversion. Notably, the findings demonstrate that strategic similarities frequently transcend conventional industry boundaries, and that structured categorical choices provide stronger strategic differentiation than semantic textual descriptions. Ultimately, this research conceptualizes social media content strategy as a multidimensional configuration, offering a data driven framework for marketers and organizations to design more effective, archetype-aligned communication strategies. Read More Read Less