Monitoring,Appeal, and Investigation: The Enforcement and Legal Process

In this article, I consider a model of law enforcement in which a regulatory agency faces the following problem: To induce firms which are required to adopt a certain level of environmental care, the agency can make spot checks. This monitoring, however, measures a firms degree of protection only with error. On the basis of the observed signal, the agency then has to decide whether or not to fine a firm for taking insufficient protection. If it fines a firm which has actually met the level of environmental protection, the firm has the right to lodge an appeal. To plead the cause, the agency then is required to investigate at high cost the actual degree of the firms protection.In this context we ask how the regulatory agency allocates resources between different enforcement activities. We show how the structure of the optimal enforcement policy depends on the cost of the enforcement activities, on the uncertainty of observation, on its enforcement budget and other factors.     

Where have all the flowers gone? Forecasting green trends in the automobile industry with a choice-based conjoint adoption model

With rising gas prices, global warming, and green thinking, all-electric vehicles are currently considered the automobile technology of the future. However, besides their advantages electric drive trains also exhibit several disadvantages. Moreover, history shows several failed attempts to establish electric vehicles. Thus, a reliable forecasting model is needed that predicts if the current trend is sustainable. We develop and empirically test a choice-based conjoint adoption model that uses individual-level preferences as a basis for prediction. Predictions are mapped to the time of the next planned purchase in order to establish the adoption process. The model extends existing research in several ways. First, no prior information, e.g., historical market data or a functional form of the adoption process, has to be integrated. Second, the model allows dynamic modifications of product specifications or competition at different points in time. Third, a no-choice option can be integrated so that a technology switch is not forced by the model itself and switching costs can be considered. The empirical results reveal different critical factors for the adoption of all-electric vehicles, such as purchase price, range, timing of the market entry, or environmental evolution, which could lead to a solid base of consumers preferring this option.     

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.     

The relationship between national culture and the use of professional services: Evidence from two cross-country studies

Each year, clients spend large sums on professional services, such as accounting services, legal services and consulting services. While research has found significant cross-country differences in organizations’ spending on professional services, we do not know why they occur. Inspired by the organizational buying behavior literature, this paper investigates the influence of national culture on the use of professional services, particularly management consulting services. As the use of professional services involves considerable uncertainties—particularly for the buyer—it can be assumed to be influenced by cultural differences regarding the level of Uncertainty Avoidance, Individualism and Masculinity. By drawing on two independent cross-country studies, we show that organizations in high uncertainty avoidance and individualistic cultures use professional services less than organizations in low uncertainty avoidance and collectivist cultures. We found no relationship between Hofstede's dimension of Masculinity and the use of professional services. The findings contribute to the theorizing on how the cultural context influences organizational buying behavior and the purchasing of professional services.     

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 development of delinquency during adolescence: a comparison of missing data techniques revisited

Quality & Quantity - Conclusions about the individual development of delinquent behaviours during the life-course are often made by repeatedly interviewing the same respondents (i.e. panel...     

The development of delinquency during adolescence: a comparison of missing data techniques

Conclusions on the development of delinquent behaviour during the life-course can only be made with longitudinal data, which is regularly gained by repeated interviews of the same respondents. Missing data are a problem for the analysis of delinquent behaviour during the life-course shown with data from an adolescents’ four-wave panel. In this article two alternative techniques to cope with missing data are used: full information maximum likelihood estimation and multiple imputation. Both methods allow one to consider all available data (including adolescents with missing information on some variables) in order to estimate the development of delinquency. We demonstrate that self-reported delinquency is systematically underestimated with listwise deletion (LD) of missing data. Further, LD results in false conclusions on gender and school specific differences of the age–crime relationship. In the final discussion some hints are given for further methods to deal with bias in panel data affected by the missing process.     

Organizational expatriates and self-initiated expatriates: who adjusts better to work and life in Japan?

Expatriates are often presented in the cross-cultural adjustment literature as a homogeneous, broad population. However, recent research that makes a distinction between organizational expatriates (OEs), those who are dispatched by their home companies to international posts, and self-initiated expatriates (SIEs), those who themselves make the decision to live and work abroad, has identified differences between the two groups. The present study compares the cross-cultural adjustment of these two groups of expatriates. Survey results of 179 expatriates in Japan show that SIEs are better adjusted to general aspects of their host country and interactions with host-country nationals than OEs. Suggestions for practice are provided.     

Resource based relative value scale: a new challenge and opportunity for health care cost management

Beginning in 1992, the use of resource based relative value scale (RBRVS) will greatly change the way physicians are reimbursed for services to Medicare patients. This new system will also have implications for trust funds and corporate providers of health care benefits.