This research conducts a meticulous examination of the determinants influencing dividend payout dynamics among firms listed on the Korean Stock Exchange (KSE) from 1995 to 2021, a period characterized by profound economic fluctuations. By leveraging a dynamic panel data model and the Generalized Method of Moments (GMM) for estimation, the study addresses endogeneity concerns while exploring the effects of firm-specific and macroeconomic variables on dividend yields. The investigation delineates three distinct economic phases: normal conditions, financial crises, and the aggregate study period, facilitating a granular understanding of firms’ dividend payout adaptability under varying economic landscapes. Empirical findings underscore the persistence of dividend payments, revealing a variable adjustment speed toward target dividend yields contingent upon the economic context, with an expedited adjustment observed during crises. Crucially, firm profitability emerges as a consistent determinant of dividend yields across all examined periods, whereas the influence of macroeconomic variables is notably more pronounced during periods of economic normalcy. This research elucidates the complex interplay between internal corporate strategies and external economic pressures in shaping dividend policies, thereby enriching the discourse on dividend payout behavior in the context of Korea’s economic evolution from an emerging to a developed market.
BackgroundLittle is known about the association between fear of movement (kinesiophobia) and objectively measured physical activity (PA), the first 12 weeks after cardiac hospitalization.PurposeTo assess the longitudinal association between kinesiophobia and objectively measured PA and to assess the factor structure of kinesiophobia.MethodsWe performed a longitudinal observational study. PA was continuously measured from hospital discharge to 12 weeks using the Personal Activity Monitor. The PAM measures time spent per day in PA-intensity categories: light, moderate and heavy. Kinesiophobia was assessed with the Tampa Scale for Kinesiophobia (TSK) at four time points (hospital discharge, 3, 6 and 12 weeks). The longitudinal association between PA-intensity and kinesiophobia was studied with a random intercept cross lagged panel model (RI-CLPM). A RI-CLPM estimates effects from kinesiophobia on objectively measured PA and vice versa (cross-over effects), and autoregressive effects (e.g. kinesiophobia from one occasion to the next).ResultsIn total, 116 patients (83.6% male) with a median age of 65.5 were included in this study. On no occasion did we find an effect of kinesiophobia on PA and vice versa. Model fit for the original model was poor (X2: = 44.646 P<0.001). Best model fit was found for a model were kinesiophobia was modelled as a stable between factor (latent variable) and PA as autoregressive component (dynamic process) (X2 = 27.541 P<0.12).ConclusionKinesiophobia and objectively measured PA are not associated in the first 12 weeks after hospital discharge. This study shows that kinesiophobia remained relatively stable, 12 weeks after hospital discharge, despite fluctuations in light to moderate PA-intensity.
In the literature about web survey methodology, significant e orts have been made to understand the role of time-invariant factors (e.g. gender, education and marital status) in (non-)response mechanisms. Time-invariant factors alone, however, cannot account for most variations in (non-)responses, especially fluctuations of response rates over time. This observation inspires us to investigate the counterpart of time-invariant factors, namely time-varying factors and the potential role they play in web survey (non-)response. Specifically, we study the effects of time, weather and societal trends (derived from Google Trends data) on the daily (non-)response patterns of the 2016 and 2017 Dutch Health Surveys. Using discrete-time survival analysis, we find, among others, that weekends, holidays, pleasant weather, disease outbreaks and terrorism salience are associated with fewer responses. Furthermore, we show that using these variables alone achieves satisfactory prediction accuracy of both daily and cumulative response rates when the trained model is applied to future unseen data. This approach has the further benefit of requiring only non-personal contextual information and thus involving no privacy issues. We discuss the implications of the study for survey research and data collection.
At gas stations, tetrahydrothiophene (THT) is added to odorless biogas (and natural gas) for quick leak detection through its distinctive smell. However, for low bio and natural gas velocities, evaporation is not complete and the odorization process is compromised, causing odor fluctuations and undesired liquid accumulation on the pipeline. Inefficient odorization not only endangers the safety and well-being of gas users, but also increases gas distribution companies OPEX. To enhance THT evaporation during low bio and natural gas flow, an alternative approach involves improve the currently used atomization process. Electrohydrodynamic Atomization (EHDA), also known as Electrospray (ES), is a technology that uses strong electric fields to create nano and micro droplets with a narrow size distribution. This relatively new atomization technology can improve the odorization process as it can manipulate droplet sizes according to the natural and bio gas flow. BiomEHD aims to develop, manufacture, and test an EHDA odorization system for applying THT in biogas odorization.
