Suchergebnisse

In the decades following World War I and even more so during and after World War II, the major powers disposed of massive quantities of captured, damaged, and obsolete unexploded ordnance (UXO) by dumping them into the oceans. Various projects such as UDEMM, R.E.D C.O.D., HELCOM or CHEMSEA were implemented around the world in order to study and understand the impact of UXO on the marine ecosystems. The UXO dumpsites have the potential to become a serious public health hazard by negatively impacting different areas of human activities with serious environmental consequences. Due to a continuous increase in shipping traffic, as well as development of offshore oil industry, infrastructures associated with pipelines, wind farms and aquaculture it is required that a greater urgency is attributed to studying and developing strategies to clean up munitions from the seafloor. The Black Sea is lacking in information regarding dumping zones of UXO, this is because following WW I and II records of such activities were poorly kept, incomplete, lost or held as a military secret. Recent research projects and contracts implemented by Romanian geoscientists, such as "Submarine Archaeological Heritage on the Western Black Sea Shelf – HERAS" (Caraivan et al., 2015), or "Implementation of a geophysical investigation and monitoring tool of the Romanian maritime space security – MAR-S" (Dimitriu et al., 2019) gathered information that led to a better understanding of the situation of UXO contaminations in the western Black Sea.

Word of mouth disseminates across Twitter by means of retweeting; however the antecedents of retweeting have not received much attention. This study uses the CHAID decision tree predictive method (Kass, 1980) with readily available Twitter data, and manually coded sentiment and content data, to identify why some tweets are more likely to be retweeted than others in a (political) marketing context. The analysis includes four CHAID models: (i) using message structure variables only, (ii) source variables only, (iii) message content and sentiment variables only and (iv) a combined model using source, message structure, message content and sentiment variables. The aggregated predictive model correctly classified retweeting behavior with a 76.7% success rate. Retweeting tends to occur when the originator has a high number of Twitter followers and the sentiment of the tweet is negative, contradicting previous research (East, Hammond, & Wright, 2007; Wu, 2013) but concurring with others (Hennig-Thurau, Wiertz, & Feldhaus, 2014). Additionally, particular types of tweet content are associated with high levels of retweeting, in particular those tweets including fear appeals or expressing support for others, whilst others are associated with very low levels of retweeting, such as those mentioning the sender's personal life. Managerial implications and research directions are presented. The study makes a methodological contribution by illustrating how CHAID predictive modelling can be used for Twitter data analysis and a theoretical contribution by providing insights into why retweeting occurs in a (political) marketing context.

This paper, issued from the final report of the work package 3 (migrants and borders), presents Europe's representations among different types of Moldovian migrants. While some plan to migrate, some are asylum claimers, and other ones already live in Romania. Based on qualitative interviews realised in the framework of the EuroBroadMap project, this paper demonstrates that Romania became an important access point to European Union for migrants but that, most of the time, it is also considered as a first step rather than as a final destination.