Re-written by machine and new technology: Did the Internet kill the Video Star

The traditional way of understanding television content consumption and viewer reactions may be simply summarised: information about the program, viewing at airing time, and interpersonal discussion after the program. In our digital media environment due to crossmedia consumption and platform shifts, the actual trend in audiovisual, and traditionally television content consumption is changing, the viewer’s journey is different across contents and platforms. Content is becoming independent from the platform and the medium is increasingly in the hands of technologically empowered viewers. Our objective is to uncover how traditional content expressly manufactured for television (series, reality shows, sports) can now be consumed via other platforms, and how and to what extent audiovisual content consumption is complemented or replaced by other forms (text, audio). In our exploratory research we identify the typical patterns of interaction and synergies of consumption across classical media content. In this study we used a multimethodology qualitative research design with three research phases including focus groups, online content analysis, and viewers’ narratives. Overall, the Video Star stays alive, but has to deal with immediate reactions and has to change according to his or her audiences’ wishes

Aligning supplier evaluation with manufacturing competitive priorities. Results of an international survey in manufacturing and assembly industries

The aim of our research is to investigate a company’s purchasing and manufacturing competitive priorities jointly in order to uncover any relationships between them and also their effect on the supply chain tools implemented by the company. We assume that the supplier selection criteria of the companies reflect the true goals of the purchasing function and we take the purchasing function’s point of view to examine the aforementioned relationships. We use a multinational database for our empirical analysis which contains almost 700 companies from 20 countries and data that were acquired in 2009. We identified four different clusters which reflect a different level of supply chain tool implementation.