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This work analyzes the saving and consumption behavior of agents faced with the possibility of unemployment in a dynamic and stochastic life cycle model. The intertemporal optimization is based on Dynamic Programming with a backward recursion algorithm. The implemented uncertainty is not based on income shocks as it is done in traditional life cycle models but uses Markov probabilities where the probability for the next employment status of the agent depends on the current status. The utility function used is a CRRA function (constant relative risk aversion), combined with a CES function (constant elasticity of substitution) and has several consumption goods, a subsistence level, money and a bequest function.
Media artists have been struggling for financial survival ever since media art came into being. The non-material value of the artwork, a provocative attitude towards the traditional arts world and originally anti-capitalist mindset of the movement makes it particularly difficult to provide a constructive solution. However, a cultural entrepreneurial approach can be used to build a framework in order to find a balance between culture and business while ensuring that the cultural mission remains the top priority.
The present thesis introduces an iterative expert-based Bayesian approach for assessing greenhouse gas (GHG) emissions from the 2030 German new vehicle fleet and quantifying the impacts of their main drivers. A first set of expert interviews has been carried out in order to identify technologies which may help to lower car GHG emissions and to quantify their emission reduction potentials. Moreover, experts were asked for their probability assessments that the different technologies will be widely adopted, as well as for important prerequisites that could foster or hamper their adoption. Drawing on the results of these expert interviews, a Bayesian Belief Network has been built which explicitly models three vehicle types: Internal Combustion Engine Vehicles (which include mild and full Hybrid Electric Vehicles), Plug-In Hybrid Electric Vehicles, and Battery Electric Vehicles. The conditional dependencies of twelve central variables within the BBN - battery energy, fuel and electricity consumption, relative costs, and sales shares of the vehicle types - have been quantified by experts from German car manufacturers in a second series of interviews. For each of the seven second-round interviews, an expert's individually specified BBN results. The BBN have been run for different hypothetical 2030 scenarios which differ, e.g., in regard to battery development, regulation, and fuel and electricity GHG intensities. The present thesis delivers results both in regard to the subject of the investigation and in regard to its method. On the subject level, it has been found that the different experts expect 2030 German new car fleet emission to be at 50 to 65% of 2008 new fleet emissions under the baseline scenario. They can be further reduced to 40 to 50% of the emissions of the 2008 fleet though a combination of a higher share of renewables in the electricity mix, a larger share of biofuels in the fuel mix, and a stricter regulation of car CO$_2$ emissions in the European Union. Technically, 2030 German new car fleet GHG emissions can be reduced to a minimum of 18 to 44% of 2008 emissions, a development which can not be triggered by any combination of measures modeled in the BBN alone but needs further commitment. Out of a wealth of existing BBN, few have been specified by individual experts through elicitation, and to my knowledge, none of them has been employed for analyzing perspectives for the future. On the level of methods, this work shows that expert-based BBN are a valuable tool for making experts' expectations for the future explicit and amenable to the analysis of different hypothetical scenarios. BBN can also be employed for quantifying the impacts of main drivers. They have been demonstrated to be a valuable tool for iterative stakeholder-based science approaches.
Within our research group Bayesian Risk Solutions we have coined the idea of a Bayesian Risk Management (BRM). It claims (1) a more transparent and diligent data analysis as well as (2)an open-minded incorporation of human expertise in risk management. In this dissertation we formulize a framework for BRM based on the two pillars Hardcore-Bayesianism (HCB) and Softcore-Bayesianism (SCB) providing solutions for the claims above. For data analysis we favor Bayesian statistics with its Markov Chain Monte Carlo (MCMC) simulation algorithm. It provides a full illustration of data-induced uncertainty beyond classical point-estimates. We calibrate twelve different stochastic processes to four years of CO2 price data. Besides, we calculate derived risk measures (ex ante/ post value-at-risks, capital charges, option prices) and compare them to their classical counterparts. When statistics fails because of a lack of reliable data we propose our integrated Bayesian Risk Analysis (iBRA) concept. It is a basic guideline for an expertise-driven quantification of critical risks. We additionally review elicitation techniques and tools supporting experts to express their uncertainty. Unfortunately, Bayesian thinking is often blamed for its arbitrariness. Therefore, we introduce the idea of a Bayesian due diligence judging expert assessments according to their information content and their inter-subjectivity.
Be Creative, Now!
(2018)
Purpose – This thesis set out to explore, describe, and evaluate the reality behind the rhetoric of freedom and control in the context of creativity. The overarching subject is concerned with the relationship between creativity, freedom, and control, considering freedom is also seen as an element of control to manage creativity.
Design/methodology/approach – In-depth qualitative data gathered from at two innovative start-ups. Two ethnographic studies were conducted. The data are based on participatory observations, interviews, and secondary sources, each of which included a three months field study and a total of 41 interviews from both organizations.
Findings – The thesis provides explanations for the practice of freedom and the control of creativity within organizations and expands the existing theory of neo-normative control. The findings indicate that organizations use complex control systems that allow a high degree of freedom that paradoxically leads to more control. Freedom is a cover of control, which in turn leads to creativity. Covert control even results in the responsibility to be creative outside working hours.
Practical implications – Organizations, which rely on creativity might use the results of this thesis. Positive workplace control of creativity provides both freedom and structure for creative work. While freedom leads to organizational members being more motivated and committing themselves more strongly to their and the organization’s goals, and a specific structure also helps to provide the requirements for creativity.
Originality/value – The thesis provides an insight into an approach to workplace control, which has mostly neglected in creativity research and proposes a modified concept of neo-normative control. It serves to provide a further understanding of freedom for creativity and to challenge the liberal claims of new control forms.
Entrepreneurship is known to be a main driver of economic growth. Hence, governments have an interest in supporting and promoting entrepreneurial activities. Start-up subsidies, which have been analyzed extensively, only aim at mitigating the lack of financial capital. However, some entrepreneurs also lack in human, social, and managerial capital. One way to address these shortcomings is by subsidizing coaching programs for entrepreneurs. However, theoretical and empirical evidence about business coaching and programs subsidizing coaching is scarce. This dissertation gives an extensive overview of coaching and is the first empirical study for Germany analyzing the effects of coaching programs on its participants. In the theoretical part of the dissertation the process of a business start-up is described and it is discussed how and in which stage of the company’s evolvement coaching can influence entrepreneurial success. The concept of coaching is compared to other non-monetary types of support as training, mentoring, consulting, and counseling. Furthermore, national and international support programs are described. Most programs have either no or small positive effects. However, there is little quantitative evidence in the international literature. In the empirical part of the dissertation the effectiveness of coaching is shown by evaluating two German coaching programs, which support entrepreneurs via publicly subsidized coaching sessions. One of the programs aims at entrepreneurs who have been employed before becoming self-employed, whereas the other program is targeted at former unemployed entrepreneurs. The analysis is based on the evaluation of a quantitative and a qualitative dataset. The qualitative data are gathered by intensive one-on-one interviews with coaches and entrepreneurs. These data give a detailed insight about the coaching topics, duration, process, effectiveness, and the thoughts of coaches and entrepreneurs. The quantitative data include information about 2,936 German-based entrepreneurs. Using propensity score matching, the success of participants of the two coaching programs is compared with adequate groups of non-participants. In contrast to many other studies also personality traits are observed and controlled for in the matching process. The results show that only the program for former unemployed entrepreneurs has small positive effects. Participants have a larger survival probability in self-employment and a larger probability to hire employees than matched non-participants. In contrast, the program for former employed individuals has negative effects. Compared to individuals who did not participate in the coaching program, participants have a lower probability to stay in self-employment, lower earned net income, lower number of employees and lower life satisfaction. There are several reasons for these differing results of the two programs. First, former unemployed individuals have more basic coaching needs than former employed individuals. Coaches can satisfy these basic coaching needs, whereas former employed individuals have more complex business problems, which are not very easy to be solved by a coaching intervention. Second, the analysis reveals that former employed individuals are very successful in general. It is easier to increase the success of former unemployed individuals as they have a lower base level of success than former employed individuals. An effect heterogeneity analysis shows that coaching effectiveness differs by region. Coaching for previously unemployed entrepreneurs is especially useful in regions with bad labor market conditions. In summary, in line with previous literature, it is found that coaching has little effects on the success of entrepreneurs. The previous employment status, the characteristics of the entrepreneur and the regional labor market conditions play a crucial role in the effectiveness of coaching. In conclusion, coaching needs to be well tailored to the individual and applied thoroughly. Therefore, governments should design and provide coaching programs only after due consideration.
The thesis assesses the contribution of technology option of Carbon Capture and Sequestration (CCS) to climate change mitigation. CCS means that CO2 is captured at large industrial facilities and sequestered in goelogical structures. The technology uses the endogenous growth model MIND. Herein the various climate change mitigation options of reducing economic growth, increasing energy efficiency, changing the energy mix and CCS are assessed simultaneously. An important question is whether CCS is a temporary or long-term solution. The results show that in the middle of the 21st century CCS has its peak contribution, which allows prolonged use of relatively cheap fossil energy carriers. However, this leads to delayed introduction of renewable energy carriers. The technology path ways are accombined with different costs of climate change mitigation. The use of CCS delays and reduces the costs of climate change mitigation. However, the delayed introduction of renewable energy carriers leads to reduced technological learning, which induces higher costs in the longer term. All in all the temporary use of CCS reduces the costs of climate change mitigation costs. The result is robust, which is tested with various uncertainty analysis.
Digital transformation (DT) has not only been a major challenge in recent years, it is also supposed to continue to enormously impact our society and economy in the forthcoming decade. On the one hand, digital technologies have emerged, diffusing and determining our private and professional lives. On the other hand, digital platforms have leveraged the potentials of digital technologies to provide new business models. These dynamics have a massive effect on individuals, companies, and entire ecosystems. Digital technologies and platforms have changed the way persons consume or interact with each other. Moreover, they offer companies new opportunities to conduct their business in terms of value creation (e.g., business processes), value proposition (e.g., business models), or customer interaction (e.g., communication channels), i.e., the three dimensions of DT. However, they also can become a threat for a company's competitiveness or even survival. Eventually, the emergence, diffusion, and employment of digital technologies and platforms bear the potential to transform entire markets and ecosystems.
Against this background, IS research has explored and theorized the phenomena in the context of DT in the past decade, but not to its full extent. This is not surprising, given the complexity and pervasiveness of DT, which still requires far more research to further understand DT with its interdependencies in its entirety and in greater detail, particularly through the IS perspective at the confluence of technology, economy, and society. Consequently, the IS research discipline has determined and emphasized several relevant research gaps for exploring and understanding DT, including empirical data, theories as well as knowledge of the dynamic and transformative capabilities of digital technologies and platforms for both organizations and entire industries.
Hence, this thesis aims to address these research gaps on the IS research agenda and consists of two streams. The first stream of this thesis includes four papers that investigate the impact of digital technologies on organizations. In particular, these papers study the effects of new technologies on firms (paper II.1) and their innovative capabilities (II.2), the nature and characteristics of data-driven business models (II.3), and current developments in research and practice regarding on-demand healthcare (II.4). Consequently, the papers provide novel insights on the dynamic capabilities of digital technologies along the three dimensions of DT. Furthermore, they offer companies some opportunities to systematically explore, employ, and evaluate digital technologies to modify or redesign their organizations or business models.
The second stream comprises three papers that explore and theorize the impact of digital platforms on traditional companies, markets, and the economy and society at large. At this, paper III.1 examines the implications for the business of traditional insurance companies through the emergence and diffusion of multi-sided platforms, particularly in terms of value creation, value proposition, and customer interaction. Paper III.2 approaches the platform impact more holistically and investigates how the ongoing digital transformation and "platformization" in healthcare lastingly transform value creation in the healthcare market. Paper III.3 moves on from the level of single businesses or markets to the regulatory problems that result from the platform economy for economy and society, and proposes appropriate regulatory approaches for addressing these problems. Hence, these papers bring new insights on the table about the transformative capabilities of digital platforms for incumbent companies in particular and entire ecosystems in general.
Altogether, this thesis contributes to the understanding of the impact of DT on organizations and markets through the conduction of multiple-case study analyses that are systematically reflected with the current state of the art in research. On this empirical basis, the thesis also provides conceptual models, taxonomies, and frameworks that help describing, explaining, or predicting the impact of digital technologies and digital platforms on companies, markets and the economy or society at large from an interdisciplinary viewpoint.