Geothermische Stromerzeugung in Deutschland — Stiefkind oder schlafender Riese?

This contribution analyses the development of power generation from geothermal energy in Germany. It considers research funding schemes, the support through the german Renewable Energy Sources Act (EEG) and the role of decisive actors as well as technical, economic and societal framework conditions. Compared to other renewable energy sources research funding for geothermal energy had played an inferior role in the past. After the potentials for geothermal power generation had been underlined by a study in 2003, geothermal power gained more political and institutional advocates. Subsequently it was equipped with a higher tariff in the EEG 2004 and led to realization of demonstration projects. Yet, the appliance of technologies for electrical power transformation, ORC and Kalina, are at the very beginning. Presently, the geothermal energy industry consists of a small amount of medium-sized businesses. Regional and nationwide energy providers, acting as operators and investors, do not exert much pressure in view of limited returns. Despite the compatibility with existing power supply systems, constraints to the expansion of geothermal energy use — above all exploration risks and high drilling costs- are likely to lead to a step-by step enhancement rather than a rush.     

Is all turnover intent the same? Exploring future job preference and environmental considerations

This study approaches turnover intent in a novel way by incorporating both environmental and internal organizational factors together to create a more nuanced view of what drives turnover. The analytical focus is on senior-level employees in four agencies within the US Department of Health and Human Services. The findings show that internal organizational factors partially explain decisions to change jobs, but agency and time differences remain even after controlling for those factors. It also finds that the decision to leave government is driven by different factors than the decision to move to other jobs within government.     

Effecting change in colleges and universities

Institutional leaders often try to use theories to rethink or resolve organizational challenges. Bolman and Deal (2003) describe four organizational frames (structural, human resource, political, and symbolic) to understand and diagnose problems within any organization. A classic organizational challenge in higher education is the status quo or conservatism relating to change of faculty involvement in institutional decision making. Leaders in higher education recognize the need for change but are often uncertain how to facilitate it or lack an understanding of how to implement it. The use of multiple theoretical frameworks can assist leaders in diagnosing dilemmas and instigating change.     

Social Media Analytics

In this contribution, we introduce “social media analytics” (SMA) as an emerging interdisciplinary research field that, in our view, will have a significant impact on social media-related future research from across different academic disciplines. Despite a number of challenges, we argue that SMA can provide other disciplines – including IS – with methodological foundations for research that focuses on social media. Furthermore, we believe that SMA can help IS research to develop decision-making or decision-aiding frameworks by tackling the issue of social media-related performance measurement, which has been challenging until now. Moreover, SMA can provide architectural designs and solution frameworks for new social media-based applications and information systems. Finally, we call for an interdisciplinary SMA research agenda as well as a significantly increased level of interdisciplinary research co-operation, which must aim to generate significant advancements in scientific methods for analyzing social media, as well as to answer research questions from across different disciplines.     

Intertemporal propensity to consume

Cross-sectionally estimated Engel curves tend to exhibit shapes that imply ultimately decreasing marginal propensities to consume as income rises. This suggests at first sight a tendency to slow down in private consumption. This tendency has to be reconsidered taking into account the fact that Engel curves usually shift significantly over time. We introduce the notion of “intertemporal propensity to consume” to shed more light on the drivers and patterns of the evolution of Engel curves. Using this notion, we are able to identify and measure the direction of the expansion path of consumption as time goes by, while controlling for household characteristics and price levels. Using German household data, we show that, over time, the intertemporal propensity to consume systematically offsets the consumption paths implied by the marginal propensity to consume that is derived cross-sectionally at one point in time. We claim that this difference between marginal and intertemporal propensity to consume should be ascribed to variations in the socioeconomic structures that occur over time and that may be linked both to the supply side (innovation and technical change) and the demand side (learning dynamics of consumers). We also show that changes in relative prices play a minor role in the evolution of Engel curves.     

Leading indicators of fiscal distress: evidence from extreme bounds analysis

Early warning systems (EWSs) are widely used to assess a country’s vulnerability to fiscal distress. A fiscal distress episode is identified as a period when government experiences extreme funding difficulties. Most EWSs employ a specific set of only fiscal leading indicators predetermined by the researchers, which casts doubt on their robustness. We revisit this issue using extreme bounds analysis, which allows identifying robust leading indicators of fiscal distress from a large set. A robust leading indicator’s effect does not strongly depend on the model specification. Consistent with the theoretical predictions of latest generation crisis models, we find that both fiscal and non-fiscal leading indicators are robust. In addition, we find that a fiscal vulnerability indicator based on fiscal and non-fiscal leading indicators offers a 29% gain in predictive power compared to a traditional one based only on fiscal leading indicators. This suggests that both fiscal and non-fiscal leading indicators should be taken into account when assessing country’s vulnerability to fiscal distress.     

Meta‐Regression Models and Observational Research

Meta‐regression models were originally developed for the synthesis of experimental research where randomization ensures unbiased and consistent estimation of the effect of interest. Most economics research is, however, observational and specification searches may often result in estimates that are biased and inconsistent, for example, due to omitted‐variable biases. We show that if the authors of primary studies search for statistically significant estimates in observational research, meta‐regression models tend to make false‐positive findings of genuine empirical effects. More research is needed to better understand how meta‐regression models need to be specified to help identifying genuine empirical effects in observational research.     

Lag length selection and p -hacking in Granger causality testing: prevalence and performance of meta-regression models

The academic system incentivizes p-hacking, where researchers select estimates and statistics with statistically significant p-values for publication. We a