This book chapter provides an analysis of cabinet formation after the German election of 22 September 2013. It starts with the observation that the election of 2013 brought a radical change to the German postwar party system as the FDP disappeared from the Bundestag. Given the likely need for more coalitions across the traditional divide between centre-left and centre-right (for example, grand coalitions of CDU/CSU and SPD or new coalitions between CDU/CSU and Greens), the chapter focuses on mechanisms of conflict management. Based on the comparative literature on the issue, he analyses coalition governance in the new grand coalition under Merkel. After the traditional bargaining between party elites, the SPD conducted a vote of all party members for the ratification of the bargaining result. This was an insurance policy for the party leadership vis-à-vis its rank and file, a reassurance for the CDU/CSU that the SPD was really committed to the new coalition, and a relatively successful bargaining tactic on the part of the SPD as it lent credibility to the various 'red lines' formulated by the SPD prior to the negotiations. One key aspect of coalition governance was the allocation of portfolios. Both parties secured control of those ministries whose jurisdictions were crucial to their attempts to claim issue ownership vis-à-vis core voters. After some initial support for the grand coalition, the negotiations were accompanied by growing public scepticism. One key point was that the legislative strength of the new coalition would be overwhelming (with around 80 per cent of the seats in the Bundestag), and that the two opposition parties, the Greens and the Left Party, would be too weak to mount a sustained and effective parliamentary opposition. In addition, media commentators missed the ‘big projects’ which the new government, Merkel III, would tackle. However, the deal the parties agreed on in 2013 seemed more like a package of small, if socially costly, policy measures reminiscent of the collective result of log-rolling. It is also argued that the new coalition is in a precarious situation. Whether it will last its entire term (until 2017) will depend on a number of factors, especially any 'exogenous shocks' that it may be subjected to. In particular, however, it will depend on the parties' constant evaluation of the electoral risks arising from the coalition.
This introduction to the edited volume "Germany After the 2013 Elections: Breaking the Mould of German Politics?" provides a brief factual account of the election and its result. This part is followed by a summary and contextualisation of the individual contributions to this volume.
Many stakeholders are involved in process operation and, consequently, also in process improvement and innovation. For the coordination of all stakeholders, an effective governance model with clearly defined roles and tasks can support process-oriented decision-making, which drives improvement and innovation. In this chapter, such a Business Process Management governance model is introduced. The role-based model FAR+ (Framework for Assignment of Responsibilities) provides precise assignment of process accountabilities and responsibilities. In the following, we apply FAR+ to an exemplary process at Lufthansa Technik. Based on this application, we derive implications for research and practice.
Smart electricity meters allow capturing consumption load profiles of residential buildings. Besides several other applications, the retrieved data renders it possible to reveal household characteristics including the number of persons per apartment, age of the dwelling, etc., which helps to develop targeted energy conservation services. The goal of this chapter is to develop further related methods of smart meter data analytics that infer such household characteristics using weekly load curves. The contribution of this chapter to the state of the art is threefold. The authors first quadruplicate the number of defined features that describe electricity load curves to preserve relevant structures for classification. Then, they suggest feature filtering techniques to reduce the dimension of the input to a set of a few significant ones. Finally, the authors redefine class labels for some properties. As a result, the classification accuracy is elevated up to 82%, while the runtime complexity is significantly reduced.