Die Emotionskluft: Wie sich die Printmediennutzung und Gratifikationserwartungen in Einstellungsstrukturen niederschlagenThe emotion gap: How print media usage and expected gratifications influence attitude structures

Regarding formal, linguistic and content-related characteristics, tabloid newspapers have a relatively high potential to emotionally arouse readers, whereas broadsheets present news in a more factual style. In the present study it is argued that the usage of the different types of newspapers affects attitude structures. Specifically, it is assumed that the frequency of usage and expected gratifications during usage have an effect on the affective-cognitive attitude basis. Results of a survey (N = 505) support the hypotheses on the emotion gap: Heavy users of broadsheets base their attitudes on cognitions, regardless of their expected gratifications. For heavy users of tabloid newspapers, expected gratifications moderate the effect of usage on attitude bases: Information-oriented tabloid recipients hold cognition-based attitudes; entertainment-oriented tabloid recipients hold affect-based attitudes.     

What you tweet is what we get?

Twitter has a high presence in our modern society, media and science. Numbers of studies with Twitter data – not only in communication research – show that tweets are a popular data source for science. This popularity can be explained by the mostly free data and its technically high availability, as well as the distinct and open communication structure. Even though much research is based on Twitter data, it is only suitable for research to a limited extent. For example, some studies have already revealed that Twitter data has a low explanatory power when predicting election outcomes. Furthermore, the rise of automated communication by bots is an urgent problem of Twitter data analysis. Although critical aspects of Twitter data have already been discussed to some extent (mostly in final remarks of studies), comprehensive evaluations of data quality are relatively rare.To contribute to a deeper understanding of problems regarding the scientific use of Twitter data leading to a more deliberate und critical handling of this data, the study examines different aspects of data quality, usability and explanatory power. Based on previous research on data quality, it takes a critical look with the following four dimensions: availability and completeness, quality (regarding authenticity, reliability and interpretability), language as well as representativeness. Based on a small case study, this paper evaluates the scientific use of Twitter data by elaborating problems in data collection, analysis and interpretation. For this illustrative purpose, the author typically gathered data via Twitter’s Streaming APIs: 73,194 tweets collected between 20–24/02/2017 (each 8pm) with the Streaming APIs (POST statuses/filter) containing the search term “#merkel”.Concerning data availability and completeness, several aspects diminish data usability. Twitter provides two types of data gateways: Streaming APIs (for real-time data) and REST APIs (for historical data). Streaming APIs only have a free available Spritzer bandwidth, that is limited to only one percent of the overall (global) tweet volume at any given time. This limit is a prevalent problem when collecting Twitter data to major events like elections and sports. The REST APIs do not usually provide data older than seven days. Furthermore, Twitter gives no information about the total or search term-related tweet volume at any time.In addition to incomplete data, several quality related aspects complicate data gathering and analysis, like the lack of user specific and verified information (age, gender, location), inconsistent hashtag usage, missing conversational context or poor data/user authenticity. Geo data on Twitter is – if available at all – rarely correct and not useful for filtering relevant tweets. Searching and filtering relevant tweets by search terms can be deceptive, because not every tweet concerning a topic contains corresponding hashtags. Furthermore, it is difficult to find a perfect search term for broader and dynamically changing topics. Besides, the missing conversational context of tweets impedes interpretation of statements (especially with regard to irony or sarcasm). In addition, the rise of social bots diminishes dataset quality enormously. In the dataset generated for this work, only three of the top 30 accounts (by tweet count) could be directly identified as genuine. One fourth of all accounts in this dataset generated about 60% of all tweets. If the high-performing accounts predominantly consist of bots, the negative impact on data quality is immense.Another problem of Twitter analysis is Internet language. While Emojis can be misinterpreted, abbreviations, neologisms, mixed languages and a lack of grammar impede text analysis. In addition to low data quality in general, the quality of tweet content and its representativeness is crucial. This work compares user statistics with research articles on SCOPUS as well as media coverage of two selected, German quality newspapers. Twitter is – compared to its user count – enormously overrepresented in media and science. Only 16% of German adults (over 18 years) are monthly active (MAUs) and merely four percent are daily active users.Considering all presented problems, Twitter can be a good data source for research, but only to a limited extent. Researchers must consider that Twitter does not guarantee complete, reliable and representative data. Ignoring those critical points can mislead data analysis. While Twitter data can be suitable for specific case studies, like the usage and spread of selected hashtags or the twitter usage of specific politicians, you cannot use it for broader, nation-based surveys like the prediction of elections or the public opinion on a specific topic. Twitter has a low representativeness and is mostly an “elite medium” with an uncertain future (concerning the stagnating number of users and financial problems).     

The Heckscher–Ohlin model and the network structure of international trade

This paper estimates for 28 product groups a characteristic parameter that reflects the topological structure of its trading network. Using these estimates, it describes how the structure of international trade has evolved during the 1980–2000 period. Thereafter, it demonstrates the importance of networks in international trade by explicitly accounting for their scaling properties when testing the prediction of the “Heckscher–Ohlin” model that factor endowment differentials determine bilateral trade flows. The results suggest that factor endowment differentials increase bilateral trade in goods that are traded in “dispersed” networks. For goods traded in “concentrated” networks, factor endowment differentials are less important.     

Risk and Crisis Management in the Reformed European Agricultural Policy

Currently there is ample discussion among EU Institutions (European Commission, European Parliament, and Member States' governments) on the opportunity for setting up a comprehensive EU‐wide framework on risk and crises in agriculture. In the meantime, within the limits of the WTO rules on agriculture, national governments are allowed to intervene through direct compensation to farmers in case of exceptional events that cause damages to farming operations and through subsidies to crop insurance programs. Such schemes are quite expensive for domestic budgets and some Member States are trying to switch some of their cost to the Community's budget, although an expansion of financial resources devoted to agriculture in Europe is rather unlikely. Moving from the recently emanated proposal of the European Commission, this paper discusses the main issues related to public intervention for risk and crises management in agriculture. Actuellement, les institutions européennes (Commission européenne, Parlement européen et gouvernements des pays membres) discutent intensément de l'opportunité d'élaborer un cadre général pour l'ensemble de l'Union européenne sur les crises et les risques dans le secteur agricole. Entre‐temps, selon les règles de l'OMC sur l'agriculture, les gouvernements nationaux peuvent intervenir en accordant des compensations financières directes aux agriculteurs en cas de circonstances exceptionnelles causant des dommages aux exploitations agricoles ainsi que des subventions aux programmes d'assurance récolte. Ces interventions amputent considérablement les budgets nationaux, et certains pays membres tentent de transférer une partie de leurs coûts au budget de l'Union européenne, bien qu'il soit peu probable que les ressources financières consacrées à l'agriculture en Europe augmentent. A la lumière de la récente proposition de la Commission européenne, le présent article traite des principaux thèmes liés à l'intervention publique dans la gestion des risques et des crises dans le secteur agricole.     

Determinants and consequences of soft budget constraintsAn empirical analysis using enterprise‐level data in transition countries1

This paper presents empirical work grounded in the soft budget constraint (SBC) literature. A loan is soft when a bank cannot commit the enterprise to hold to a fixed initial budget and/or the timing of repayment. Using data collected by the European Bank for Reconstruction and Development (EBRD) (Business Environment and Enterprise Performance Survey (BEEPS), 2002) in 26 transition economies, we analyze the determinants of managers’ expectations of having a soft loan. In particular, we find that managers’ expectations are lower when the initial financing requires collateral, and higher for larger firms and when firms had recently experienced financial distress. We also provide evidence that managers’ expectations influence their price responsiveness.     

ENERGY EFFICIENCY EVALUATION IN MANUFACTURING THROUGH AN ONTOLOGY‐REPRESENTED KNOWLEDGE BASE

Improving energy efficiency in a manufacturing company through an energy management system requires active participation of different stakeholders and involvement of different organizational entities and technical processes. Interoperability of stakeholders and entities is the key factor to achieve a successful implementation of an energy management system. Researchers have been developing approaches in applying ontologies to address interoperability issues among humans as well as machines. Ontologies have also been used for knowledge representation in different domains, such as energy management and manufacturing. In recent years, researchers have developed knowledge‐based intelligent energy management systems in buildings, especially households, which use ontologies for knowledge representation. In the manufacturing domain, ontologies have been used for knowledge management in order to provide a common formal understanding between the stakeholders, who have different background knowledge. This paper proposes an approach to apply ontology to allow knowledge‐based energy efficiency evaluation in manufacturing companies. The ontology provides a formal knowledge representation that addresses the interoperability issues due to different human stakeholders as well as machines involved in the energy management system of the company. This paper also describes the methods used to construct and to process the ontology. Copyright © 2013 John Wiley & Sons, Ltd.     

The German bank–growth nexus revisited: savings banks and economic growth in Prussia†

This article provides evidence that smaller, regional public financial intermediaries contributed to Germany's industrial development, using a new dataset of the foundation year and location of Prussian savings banks. This extends the bank–growth nexus beyond its traditional focus on large universal banks. Since savings banks were public financial intermediaries, our results further suggest that state intervention can be successful in the financial sector, particularly at the early stages of industrial development when capital requirements are manageable, and access to international capital markets is limited.