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Disruptions regularly hit economies. Scholars and industry experts suggest many strategies to avoid disruptions or handle them effectively. Two things are repeatedly mentioned: Increasing resilience and deploying artificial intelligence (AI) technologies. In this dissertation, we1 look at both aspects and focus our efforts on production processes. To get a well-rounded view, we apply various research methods, i.e., surveys, case studies, and systematic literature reviews (SLRs). In our first paper (Chapter 2), we investigate the perceived organizational resilience of companies in the German manufacturing industry. We perform an SLR to analyze existing research on organizational resilience measures. We see that existing (qualitative) resilience measures are complex, challenging to interpret, and therefore, hard to scale and apply across multiple industries. Based on this, we develop a novel, low-threshold resilience measure consisting of six resilience items about the past perceived internal/external resilience, current perceived internal/external resilience, and anticipated need for internal/external resilience, called the Enterprise Resilience Index (ERI). Finally, we conduct an empirical study with ~200 German experts across various industries. Our survey shows that the German manufacturing industry perceives itself as relatively resilient, with significant differences between industries and company sizes. We also see that they anticipate a high need for external resilience across industries in the future. Most strikingly, the Machinery industry shows the lowest ERI levels while it anticipates a relatively high need for resilience, showing the development need for this industry in terms of resilience. To explore the aspect of AI, we focus on waste incineration plants (WIP) in Chapter 3. WIPs have various levels of automation, but they still rely on manual operations by human operators. Consequently, the combustion process is managed rather inefficiently, and steam outputs and emission levels are not optimal. Thus, we investigate how reinforcement learning (RL) can help enhance process automation and thus optimize the combustion process, e.g., by making more frequent and diverse interventions. An RL agent is trained via trial and error with a reward function that includes the optimization criteria. Since the actual equipment, i.e., the real WIP, cannot be used as the training environment, a digital twin is built using original plant data and a neural network. The RL agent is then trained in this offline environment with the deep Q-network algorithm (DQN). Our work demonstrates that a digital twin of a WIP can be built in a data-driven way. We show that the RL agent outperforms the human operator, increasing the steam output by 7.4% and reducing the oxygen level by 3.6%. Thus, applying RL might benefit the plant operator financially due to increased output and the environment in terms of reduced emission levels. Finally, we look at a practical aspect of AI: AI readiness and adoption (Chapter 4). Many companies across various sectors have adopted AI technologies. However, the supposedly high adoption rates are misleading since many applications are rather experimental and not applied in key business areas. We believe that this limited AI adoption arises from a lack of AI readiness. We conduct a case study in the waste incineration industry with over 160 clients and investigate which strategies facilitate AI adoption in not-AI-ready industries. Based on these interactions, we distill five strategies that counter typical AI readiness barriers, thus increasing AI readiness: education, trust, customer centricity, focus, and collaboration. These strategies focus on transforming businesses just as much as necessary to prepare them for the AI technology that is supposed to be implemented. With increased AI readiness, chances for AI adoption rise. We are convinced that these strategies can be applied in various environments. In summary, this dissertation gives empirical evidence and expands the literature on organizational resilience and benchmarking, reinforcement learning and digital twins, and AI readiness and adoption.
1 The term “we” refers to the authors of the respective chapters, as noted at the beginning of each chapter.
The trucking industry is at the beginning of a radical change due to several megatrends which will reshape the industry significantly. Based on the targets of the Paris Climate Agreement, the German government adopted its own Climate Action Plan 2050 which includes sector- specific reduction targets to reach a greenhouse gas neutral society by the middle of the 21st century. By 2030, the German Climate Action Plan specifies a reduction target of 40% from transportation compared to the reference year 1990. While emissions from other sectors such as energy or industry have decreased significantly since then, emissions from transportation remained stable. Among the various modes of transportation, passenger cars and commercial vehicles are by far the largest emitters of greenhouse gas emissions. As of January 2019, 99.7% of heavy-duty trucks registered in Germany run on diesel while the number of alternative fuel- powered passenger cars increases steadily. Apart from rising emissions, the industry faces a severe shortage of qualified truck drivers. According to the German Association of Freight Forwarders and Logistics Companies, the industry was facing a shortage of 45,000 drivers in Germany in 2017 with increasing tendency due to higher trade volumes and e-commerce.
This dissertation aims to discuss the transition of road transport in Germany toward innovative heavy-duty trucks. The main body of this dissertation consists of three research papers each of them focusing on autonomous and/or alternative fuel-powered heavy-duty trucks. The first research paper presents the results of a Delphi study with experts from industry and academia on factors affecting the purchasing decision and operation of alternative fuel-powered heavy-duty trucks in Germany. In the second study, a choice-based conjoint experiment with employees from freight companies was conducted to test how customers value the main attributes of innovative heavy-duty trucks. The Generalized Bass diffusion model was applied in the third study to investigate the future diffusion of battery electric heavy-duty trucks considering total-cost-of-ownership reduction effects.
We examine the effects of the COVID-19 pandemic on the economic decline expected in Germany in 2020. The magnitude of the economic slump that will occur in 2020 depends on the extent of the slump during the shutdown, on the point in time, at which a significant easing of shutdown occurs, and on the length of adjustment process towards the structures that prevailed before the pandemic. We derive several scenarios and find that the shutdown will only remain in the single-digit percentage range if we apply very optimistic assumptions about the extent of the initial decline in GDP during the shutdown and the speed of adjustment after opening up of the economy. However, assuming that the economic crisis cannot end before the medical crisis ends, which medical experts project not to happen before the end of 2020, such optimistic assumptions do not appear realistic. Hence, we find it more likely that the percentage decline of GDP in Germany will be two-digit in 2020. Our findings are in contrast to the growth projections recently issued by the German Council of Economic Experts or by the Federal Ministry of Economic Affairs and Energy of Germany.
Urheberrecht
(2018)
At the beginning of their career civil servants in Germany can choose between the social health insurance (SHI) system and a private plan combined with a direct reimbursement of the government of up to 70 percent. Most civil servants chose the latter, not only but also because they have to cover all contribution payments in the social system themselves, while normal employees get nearly 50 percent from their employers. The city state of Hamburg decided to change the system by paying a share of the contributions if civil servants choose the social plan. We use a stochastic microsimulation model to analyse which socio-economic types of civil servants could benefit from the Hamburg plan and if this changes the mix of insured persons in the SHI system. Our results show that low income and high morbidity types as well as families have a substantially higher incentive to choose SHI. This reform might thereby increase the adverse selection of high risk cases towards SHI.
Der Bericht präsentiert empirische Befunde zum wissenschaftlichen Nachwuchs in Deutschland. Schwerpunktthema des Berichtes ist die Vereinbarkeit von Familie und akademischer Karriere. Weitere Themen sind Arbeits- und Beschäftigungsbedingungen, Qualifizierungsbedingungen in der Promotionsphase sowie Karrierewege und -perspektiven, insbesondere in der Post-Doc-Phase. Das Mobilitätsverhalten und der Beitrag zu Forschung, Lehre und Transfer des wissenschaftlichen Nachwuchses sowie Bildungsrenditen der Promotion werden ebenfalls in den Blick genommen.
Basis des Berichtes sind primär Daten aus der amtlichen Statistik sowie aus regelmäßig durchgeführten Befragungen. Bei der Aufbereitung der Daten liegt der Fokus stärker als bisher auf der Vergleichbarkeit und Einordnung der Befunde. Damit schafft der Bericht eine Wissensbasis für Hochschulen und Forschungseinrichtungen, Interessenvertretungen, Förderorganisationen und Entscheidungsträger/innen in Bund und Ländern.
In diesem Beitrag wird die Expansion eines Samples von 47 HDAX-Unternehmen zwischen 1995 und 2004 hinsichtlich des Expansionsverlaufs, der Expansionsrichtung (Internationalisierung und Diversifikation) und der Expansionsumsetzung beschrieben. Die Betrachtung von 1.830 einzelnen Expansionsschritten führt zu einem detaillierten und umfassenden Bild der Expansionspfade dieser Unternehmen. Insgesamt zeigt sich, dass die Entwicklung der betrachteten Unternehmen sehr heterogen verläuft. Hieraus ergeben sich verschiedene interessante Fragestellungen und Implikationen für die weitere Forschung zur Expansion von Unternehmen. Vor allem scheint es fraglich, ob die bisherige Praxis, Internationalisierung und Produktdiversifikation getrennt zu betrachten, dem Phänomen der Expansion von Unternehmen gerecht wird.
The paper analyzes firms' expansion paths within and across industries and specifically different patterns along these paths. Using a procedure previously applied in statistical process control and employing longitudinal data on the expansion of 91 German firms, we analyzed firms' clustering behavior regarding different characteristics. Results reveal that expansion is a heterogeneous process: the clustering behavior along the expansion path does not only differ between firms, but also within one firm regarding different characteristics of its expansion path.
Changes in the regulatory framework allow German business schools to select an important part of their students by themselves for the first time through entrance exams. We analyze if the students participating in these entrance exams differ from average first-year business students and which factors influence their choice of a business school. A separate analysis for a cluster of very highly motivated students reveals that for these students a short study time and a practical Orientation are of utmost importance as decision criteria. We derive suggestions for German business schools that want to target this specific segment of students.
Corporate Raider
(2002)
This paper looks at the value-relevance of accounting data and measures of web-traffic for Internet firms listed on the Neuer Markt. In particular, the objective is to identify value drivers during the period from October 1999 to May 2000. In doing so, the study attempts to contribute to the understanding of the investment behaviour of market participants during that time, in a market environment characterised by rapid technological change and growth. The study subdivides Internet companies into Ecommerce and Enabler firms and analyses the value-relevance accordingly. It emerged that, across both samples, no significant value-relevance of traditionally applied financial valuation metrics such as earnings and cashflow could be evidenced. However, a positive association of total sales with market capitalisation can be shown for both samples, and in addition, sales and marketing expenses (Ecommerce) as well as research and development costs (Enabler) can also be identified as value drivers. Furthermore, the paper finds a number of webmetrics to be highly value-relevant and positively associated with market capitalisation, viz. customer loyalty, reach, page impressions and unique visitors. Combining and comparing the information content and value-relevance supports the notion that webmetrics, which are not part of standardised reporting regulations, did provide at least as much explanatory power for variations in market value as standardised accounting data.
Business angels in Germany
(2000)
Workshop Copyright
(2018)
Élite role and context
(2011)