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Politics, society and enterprises are working diligently to reduce CO2 emissions. Regardless, Europe’s transport CO2 emissions have been growing 25% since 1990, and they still are. This dissertation begins by contemplating the consumer side and proceeds to elucidate the physical delivery and finally challenge sustainable logistics with retailers’ and fleet operators’ business profitability targets. Our first study (Chapter 2) shows the impact of eCommerce logistics performance on customer lifetime value. Fifteen hypotheses on price, speed, convenience, and sustainability are validated through a literature review, expert interviews, and customer surveys. Customers are increasingly asking for customized logistics services, some demand speed, others accept waiting times and price surplus for sustainability. Alongside increasing customer lifetime value, e-Commerce companies are compelled to reduce their carbon footprint and rail becomes popular in dispatching parcels. However, rail last mile is still performed with diesel-hydraulic shunting locomotives. Fleet operators’ challenges are still not answered by science, industry, or politics. Based on real operations data we develop a techno-economical model and Total Cost of Ownership (TCO) calculation in our second study (chapter 3) evaluating the migration from diesel to battery-electric vehicles from fleet operator perspective. In our third study (Chapter 4), we apply political financial support instruments to the fleet operators’ TCO calculation. We evaluate their funding effectiveness by relating their impact on innovation diffusion and CO2 reduction to the public cost. By proposing actionable measures in this dissertation, we wish that the impact of logistics on customer value and climate change is recognized, and political funding is deployed effectively.
This dissertation explores the future of last-mile delivery and focuses on stationary and mobile delivery solutions. We1 provide a brief overview of the last-mile delivery sector and associated challenges. The high growth in e-commerce influenced the last-mile delivery sector significantly. New entrants and start-ups target emerging business segments, affecting the market dynamics in this competitive sector. Since digitization and sustainability will further shape the sector, we conduct a Delphi-based scenario study for last-mile delivery in 2040. Our expert panel evaluates 17 projections covering future consumer behavior, delivery technologies, delivery services, and regulation. Based on this data, we derive three future scenarios for the last-mile delivery sector to provide managerial and policy guidance for logistics service providers, municipalities, e-commerce retailers, and suppliers to realign their long-term strategies. According to our Delphi-based scenario study, stationary parcel lockers will be an integral part of last-mile delivery networks in the future. Thus, we examine the ecological and economic impact of optimized stationary parcel locker locations. We formulate a multinomial logit model to represent discrete customer choice for the delivery services based on recipients’ availability at home and travel distance to stationary parcel lockers. Further, we devise a mixed-integer linear programming model. The examination of various regions demonstrates that well-positioned stationary parcel lockers yield up to 11.0% of cost and 2.5% of emission savings. However, this technology leads to adverse environmental effects in more rural areas due to recipients’ different pick-up behavior. New delivery technologies might overcome the drawbacks of stationary parcel lockers. Therefore, we study the impact of integrating mobile parcel lockers into current last-mile delivery networks. As previously, a multinomial logit model represents clients’ demand. We design a mixed-integer linear programming model covering home, stationary, and mobile parcel locker delivery. Our empirical study reveals that this new technology can generate 8.7% additional cost savings and up to 5.4% extra CO2 equivalent emission savings with regards to the home and stationary parcel locker delivery network. The analysis of several regions highlights that mobile parcel lockers should be deployed in more populated cities with at least 20,000 inhabitants due to higher population densities. Our findings confirm that recipients’ diverse pick-up behavior and travel distances are crucial factors influencing the extent of emission savings.
Open innovation (OI) research has evolved widely in recent years and many facets of facilitating OI, from managing specific innovation methods, challenges of external knowledge assimilation and IP aspects, to linking organizational and managerial patterns to innovation performance, have been studied. A field that is less explored is the impact of managing and organizing the variety of OI tools and partners in an OI ecosystem, as well as in open communities, on organizational capabilities. The biopharmaceutical industry with its significantly changing innovation landscape, moving towards increasingly leveraging external knowledge sources to successfully develop new products, was selected as an appropriate research target to investigate the impacts of OI on organizational capabilities and design. While collaboration has been prominent in this knowledge-intensive sector for decades, making use of firm-external knowledge sources further gained relevance and OI approaches became increasingly popular because firms expected to overcome severe R&D productivity challenges while at the same time meeting new patient-centric demands. Three core areas that have undergone major changes in recent years are identified: New partners join the system or change their role within the innovation process; advanced OI methods and tools are becoming available; and new virtual ways of co-working are now prevalent. This observation raises the question how pharmaceutical firms and innovators react to operating in a novel innovation ecosystem, particularly regarding their organizational capabilities and –design. The first project investigates patterns of innovation partnerships through an explorative interview approach with three identified key types of partners in biopharmaceuticals: Biotech companies, academic institutes, and contract research organizations. It provides a framework to manage different partners and four identified different archetypes of OI partnerships. The second project explores an in-depth case at one of the world’s largest healthcare firms, focusing on the management and organization of various OI methods within R&D. The developed multi-dimensional framework on the determinants for organizational design for OI shows that the complexities of open innovation tools applied, and the knowledge involved to deliver OI results seem to play a major role for designing an OI organization. The third project follows an explanatory approach and tests which factors determine successful collaboration among patients and among researchers in virtual healthcare communities. The article shows that researchers rely on knowledge organization while patients rather rely on social organization. The three projects map out the evolvement towards a biopharmaceutical OI ecosystem of new partners, methods, and tools and identify effects on organizational capabilities and management patterns on ecosystem, corporate, as well as community-level.
This paper develops a country-level measure of CEO discretion based on a survey of 561 strategy consultants from 35 countries. Unlike measures previously used in the literature that focus on legal constraints on managerial behavior, our measure reflects the multidimensionality of managerial discretion. Consistent with managerial discretion being associated with performance variability, our measure explains cross-country differences in variability of accounting-based and market-based measures of firm performance. In contrast, indirect measures used in prior literature do not have significant explanatory power. Taken together, these findings suggest that our measure has the potential to open up new avenues of research related to country differences of CEO discretion and their effects on firm outcomes in finance and economics. Key words: Managerial Discretion; Performance Variability; Cross-Country
Within this doctoral dissertation, I explore mergers & acquisitions (M&As) in the context of family firms. In particular, I investigate (1) the M&A performance of family firm acquirers compared to non-family firm acquirers, the strategic mechanisms that help explain the relationship between family firm acquirers and M&A performance and the influence of family board involvement on family firm acquirers’ pursuit of M&A strategies by drawing upon the socio-emotional wealth perspective, (2) the M&A motives and processes in acquiring family firms through the lens of the long-term orientation framework, and (3) the restructuring strategies, including divestments via M&As, employed by family firms to poorly performing portfolio firms by drawing upon the escalation of commitment literature coupled with the socio-emotional wealth perspective.
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.
Measuring the influence of subsidies on the sustainability of microfinance institutions is a major challenge. This dissertation shows that the Subsidy Dependence Index remains the most promising ratio to calculate the subsidy received and measure microfinance institutions’ dependence on subsidies to conduct further research on the institutions’ ability to become socially and financially sustainable. By comparing the Subsidy Dependence Index to alternative measurements, a detailed discussion, and an exemplary calculation, its applicability to addressing the research gap of microfinance institutions’ reliance on subsidies to improve their efficiency can be shown. A calculation for 224 microfinance institutions serving 23.5 million active borrowers shows its applicability for large-scale data sets. Focusing on the microfinance institution (MFI), this research project takes an institutionalist approach.
Since efficiency is the prerequisite to sustainability, the hypothesis that subsidies have a conflicting influence on microfinance institutions’ financial and social efficiency has been tested using a reduced panel of 216 microfinance institutions. A multi-input/ multi-output data envelopment analysis (DEA) is applied to determine financial and social efficiency beyond ratios. The efficiency scores that serve as a proxy for sustainability are then used as dependent variables in panel data regressions. The main finding is that subsidies have a negative but only marginal effect on efficiency. However, the effect is only strongly significant regarding social efficiency.
To the best of the author’s knowledge, there is no research done to evaluate the influence of subsidies on financial and social efficiency based on a DEA.
Delphi-Studie Zukunft 2030
(2021)
Delphi-Studie
(2021)
In my doctoral dissertation, I conduct research on family firm decision-making, performance, and valuation. In particular, I explore (i) the role stocks—in contrast to flow-based theories used by extant research (i.e., prospect theory and its derivatives)—in share repurchasing decisions of family firms by drawing on motivation-opportunity-ability theory of behavior and the developed stock-based view on family firm decision-making, (ii) the moderating effect of national culture (i.e., the degree of masculinity) on the effects of board diversity on family firm performance by drawing on upper echelons theory, and (iii) the effects of non-family-managed family firms on firm valuation in the acquisition context by drawing on signaling theory.
Background: In view of steadily rising healthcare expenditures (HCE), studies on spending distributions can provide important guidance for policy decisions. Since the majority of HCE is concentrated in a few high-cost cases, this study focusses on the spending distribution between different cost-risk groups. We show detailed allocation structures, distinguishing several categories of HCE and the survival status of insureds to gain insights regarding the share of mortality costs.
Methods: Our analyses rely on data from a large sickness fund that covers around four million insureds. We classify the population into ten equal risk groups by costs and then determine expenditure shares of total HCE and daily per-capita expenditures depending on survival status and risk group affiliation.
Results: Our results offer that the often stated dominating effect of mortality costs of HCE is only evident in lower cost-risk groups and almost exclusively attributable to inpatient care. Furthermore, HCE in the calendar year of death is the same for most cost-risk groups, with the exception of risk groups at both ends of the distribution. However, in the case of the highest cost-risk group, the difference between survivors and decedents is proportionally small. The differences in cost structure between decedents in high-risk and other risk groups are primarily attributable to pharmaceutical spending.
Conclusion: Short-term high HCE in the year of death occur equally in all cost-risk groups and are hardly avoidable. By contrast, in the extremely high cost-risk groups, the cost difference between the year before death and the year of death is much smaller. Overall, this group remains the main target to influence the rise in HCE and its characteristics should be considered with respect to future HCE projections.
External investors
(2023)
Within this doctoral dissertation, I explore external investments in family firms. In particular, this dissertation comprises three independent studies to contribute to and extend current literature regarding family firms and external investors. The first study extends research on external investments in family firms by fundamentally analyzing the decision criteria of family firm owner-managers for using external minority investments, thus representing a first interim step of external succession. The second study changes the point of view and contributes by analyzing the drivers of financial investors’ preference for acquiring a family firm. The third study analyzes drivers resulting in financial investors’ successful or unsuccessful acquisition of family firms.
Greening of the tax code
(2023)
This thesis examines the impact of green fiscal policies, specifically environmental taxes, on firms' decision-making and competitiveness. It aims to contribute to the taxation and financial policy literature by analyzing the influence of environmental taxes on corporate investment decisions and the distribution of the economic burden, the effect on corporate emission levels, and the shift in competitive market dynamics due to a green VAT. The findings suggest that standalone environmental taxes may not be the first best option, but their effectiveness can be improved through combining them with additional policy measures to foster firms' innovativeness. The thesis also shows that the adjustment of traditional forms of taxation, such as the VAT, can promote sector growth. The thesis thereby provides a more nuanced view on the economic consequences of environmental taxes.
Airport slot allocation
(2023)
This dissertation addresses the concept of airport slot allocation as a major regulatory directive in air transport management. In three sequential parts, today’s slot allocation procedure, addressing the assignment of time windows for departure and landing operations at coordinated airports, is being assessed and critically evaluated. The three sections evolve from the evaluation and revision of a suitable criteria set to the development and implementation of a network solution. As a key feature, a carbon emissions price including the air carriers’ CO2 emissions is being provided, serving as the allocation principle. In all three parts, explicit reference to the IATA Worldwide Slot Guidelines, representing today’s regulatory framework, is being provided, highlighting explicit deficits and drawbacks of that solution. In the first part, the Analytic Hierarchy Process (AHP), as a concept of decision making based on a comparison of criteria and alternatives, is being applied guiding the allocation decision at a single airport. In this section, a set of multiple criteria is being proposed and weighted according a set of slot coordinators’ preferences. As a key feature, the concept of Analytic Hierarchy Process (AHP) is being extended by the conduction of a Pairwise Comparison-based Preference Measurement (PCPM) representing one stage of the Analytic Hierarchy Process (AHP). In part two, the perspective of the dissertation changes from a single-point to a multiple-point allocation environment. In this part, a model is being provided that includes the allocation of slots in an airport network. As a key feature, slots are being allocated such that the two complementary cost functions are being minimized. On one hand, the developed carbon cost function includes the minimization of the carbon footprint per traveling passenger. On the other hand, the developed handling cost function is being minimized incorporating a dedicated airport perspective to the solution. In part three, the proposed model is being further extended by the incorporation of a third directive, the minimization of connection cost, related to the application of solution in a hub-and-spoke network. As a result, the study demonstrates how slot allocation can be conducted efficiently in an airport network, and how the consideration of the carbon footprint per traveling passenger serves to calculate the allocation optimum.
Linking talent management and organizational outcomes through the lens of professional football
(2023)
Today's global business landscape is marked by intricate technological and societal challenges, intensified by geopolitical shifts and economic disparities. The expectations for CEOs have undoubtedly evolved, now requiring a form of adaptive leadership capable of steering through unprecedented disruptions, managing vast organizational and environmental complexities, and navigating an unstable geopolitical landscape.
Added to these challenges is the urgent need to build inclusive, purpose-driven organizations that resonate with an increasingly demanding and discerning workforce.
In light of these transformative changes, a pressing question looms large for corporate boards and stakeholders alike: Are European CEOs adequately equipped and prepared for the future?
To address this, we carried out extensive empirical research on the 600 largest publicly listed corporations in Europe across 14 countries. We examined the life histories and career paths of approximately 1,350 CEOs. Our aim is to offer a comprehensive assessment of CEO profiles, highlighting strengths and potential challenges in the face of specific strategic imperatives. Our overarching findings underscore a duality; while there has been commendable progress in certain areas, distinct gaps remain in others, and these disparities further vary across European markets.
Essays in health economics
(2023)
In 2020, approximately 151,000 warehouses were operating worldwide, with a total annual expenditure of e300 billion, representing roughly half of total supply chain costs. Optimized warehouse management may provide competitive advantages from both cost and customer service perspectives. One way to achieve these is to leverage the abundance of data collected in supply chains in combination with powerful algorithms. This dissertation investigates how novel data sources and optimization algorithms, such as machine learning, can be applied in the context of warehouse advancement. In this research, we1 analyze the warehouse environment from two perspectives. On the one hand, we examine two available resources in warehouses—equipment and employees—and explore how predicting breakdowns and productivity, respectively, may improve warehouse performance. On the other hand, we investigate whether the warehouse concept of crossdocking can be applied virtually to allow dynamic transfers between delivery vehicles.
In our first paper, we partner with one of the largest logistics service providers to examine how master, usage and sensor data on material handling equipment can be incorporated into a predictive maintenance model. Existing literature focuses on either time- or condition-based variables, whereas we show, in a comprehensive study of statistical learning methods, that both variable types can be included simultaneously. Our predictive maintenance model is able to capture the majority of breakdowns (> 85%), while maintaining a low false-positive ratio (< 7%).
In our second paper, we work with the same logistics service provider and apply Extreme Gradient Boosting to predict the productivity of new hires. We include operator, shift and product data to show that productivity can be predicted on an individual employee basis while substantially decreasing the forecasting error (50%), which translates into cost savings.
In our third paper, we look at dynamic and synchronized transshipments during direct deliveries. This concept uses transfers between vehicles, as carried out in cross-docking, but without the need for a physical warehouse. This reduces the proportion of empty return trips by increasing the proximity of vehicles to their location of origin. Our easy-to-implement multi-algorithm reduces the total distance by 15% on average compared with simple direct deliveries, and solves large problem instances within reasonable computational times.
This dissertation with its individual research contributions highlights how novel data sources and optimization algorithms can contribute to advancing warehouse management, and highlights the managerial implications of all three topics.
1The term “we” refers to the authors of the respective chapters, as denoted at the beginning of each chapter.
The future of sportstech
(2022)
Abstract
In this dissertation, I explain how organisational and personal practices transcending the usual business context are consequential to the value creation process. Adopting the perspective that value creation is socially constructed, I show how shareholders and stakeholders draw on shared values and meta-economic resources to co-create value in a dynamic ecosystem. My model demonstrates that practices and interactions in non-traditional business spaces are central rather than peripheral to the value creation process. Through a two-part research study consisting of a historical case analysis and interview-based field work with elements of grounded theorising, I develop an explanatory model of non-profit competition using the salient case of a German cooperative banking group. My practice- and process-based model of relational value creation shows how deliberate and emergent frontline strategy in non-business spaces creates value, thereby broadening the narrow neoclassical focus on product and service ecosystems. Organisational and personal practices often go against the principles of profit maximisation, reproduce shared values, and occur outside business settings. They are consequential to product and service value propositions because they govern personal relationships and interactions among shareholders and stakeholders. In the case of cooperative banks, value creation involves establishing a local ecosystem, building personal relationships, enhancing trust and knowledge within these relationships, and fostering reciprocal behaviour which ultimately leads to value capture (i.e., the exchange and use of products and services). This enhances the holistic understanding of strategy by stressing emergent strategizing in non-business spaces. Moreover, it illustrates a relational notion of competition beyond product-driven innovation and growth.
Door-to-door (D2D) air travel is gaining momentum for airlines, airports, and feeder traffic providers. The mobility industry and researchers are broadening their scope to include the entire travel chain, from origin to final destination. Intermodal mobility products are already on the market. At the same time, widespread trends affect transport service providers (as the suppliers) and passengers (regarding demand). Acquiring a better understanding of future D2D air travel trends is crucial for the mobility sector for long-term planning, product adaptation, the services provided and the pricing of these, and improvements in the passenger experience. Focusing on the European market, the overall objective of this doctoral thesis is to identify and understand the future trends of D2D air travel. It is divided into three parts; these provide different perspectives on trends and employ a range of methods that lead to results that develop from each other.
In Part One, the Delphi technique is utilized to identify future travel trends. The study considers projections of European air passengers and their requirements for their entire air travel chain, including airport access, a long-haul flight, and airport egress. The research focuses on 2035 and is based on a two-round Delphi survey involving 38 experts from the transport industry, academia, and consultants. The Delphi survey is supplemented with findings from a preliminary study, consisting of a literature review, interviews with 18 experts in the field of air travel, and a workshop attended by experts. Results reveal that digitalization and personalization will be the main drivers in 2035 and that passengers might demand value-added use of their travel time. In addition, environmentally friendly travel products are considered desirable but only somewhat probable by 2035. Passenger type, age, origins, and travel budget will still be influential factors in 2035. Based on the results from a hierarchical cluster analysis, Part One presents three possible future scenarios: (1) personalized D2D travel, (2) integrated D2D travel, and (3) the game-changer. A technical chapter elaborates on the Delphi technique and individual research steps.
Part Two explores the supply aspect and to what extent transport service providers consider strategically relevant trends. The scope of D2D air travel is adapted by applying multi-labeled text classification models to 52 corporate reports from a sample of transport service providers that operate in the European market. Trends identified in the first Delphi study and from an additional literature review are used to develop seven classes. Two prototype models are developed: a dictionary-based classifier and a supervised learning model using the multinomial naive Bayes and linear support vector machine classifiers. The latter yields the best model output, revealing which trends have a higher, medium, or lower relevance on the supply side. The results show that providers consider environmentally friendly air transport and related products to be highly relevant while disruption management, leveraging passengers’ data, and improving airport feeder traffic through innovative mobility initiatives are considered to be of medium relevance.
Part Three explores air passengers’ preferences and willingness to pay for ancillary services in the current transition into the new normal, brought about by the ongoing COVID-19 pandemic, high uncertainty, and changing market dynamics. A choice-based conjoint analysis is used to test six attributes within a hypothetical travel scenario for a long-haul one-way air trip. Choice data from 269 German business and leisure passengers are analyzed using a hierarchical Bayes estimator. Results reveal that the total ancillary service upgrade price influences passengers’ choices the most, followed by a seat upgrade for greater comfort and the CO2-compensation of a flight. Hygiene-related ancillaries bring low utilities. Female and senior passengers care more for environmentally friendly ancillaries. Confirming previous research, business passengers and frequent flyers care more for onboard comfort.
Jahresbericht
(2012)
Leveraging Wikipedia
(2018)
Guide to reference
(2014)
ISO/TR 19814 : information and documentation - collections management for archives and libraries
(2017)
Modern pathfinders
(2015)
Meaningful metrics
(2015)
Google search secrets
(2014)
Library security
(2015)
Building the digital branch
(2009)
In the decade following the 2008 financial crisis, the coronavirus viral disease 2019 (COVID-19) pandemic and United States (US) President Trump’s Twitter account became representations of market uncertainty, attracting the financial research of (Goodell, 2020; Benton and Philips, 2020). Due to the popularity of these events and their impact on financial markets, many unanswered questions still persist, particularly, how the financial structure has changed during this unique time. The popularity of Bitcoin, one of the main cryptocurrencies, has caused a controversial topic to arise in recent academic research, namely, whether its function compares to that of conventional precious metals such as gold and platinum. This doctoral thesis aims to fill this research gap in two ways: (i) by addressing market reactions to the COVID-19 pandemic and political news by answering the question of how US legislators traded at an industry level during the ongoing COVID-19 pandemic, and how Trump’s Twitter account could shake the equity market during a trade war, and (ii) by examining the power of the gold and platinum ratio, which was first studied by (Huang and Kilic, 2019) ), in predicting Bitcoin as well as how political sentiment could drive the returns, volatility, and volume of this cryptocurrency. This thesis contributes to the empirical evidence in the areas mentioned above due to the growing attention on the financial function of cryptocurrency, the debatable effects of political news regarding the use of social media, and the eventual and unprecedented scale of the COVID-19 pandemic.
Transportation is the backbone of globalization and international trade. Moving goods over long distances enables companies to access new markets and consumers to buy products from other parts of the world. Global trade is particularly driven by sea freight due to low cost and air cargo owing to its high speed, making both transport modes key for many industries. Anticipating future developments in transportation remains a black box for many companies. The logistics sector is characterized by high price uncertainty, market volatility, and product complexity. Transport is often organized manually and only based on employees’ experience, making it prone to error. Recent trends in international trade further complicate companies’ decisionmaking. Trends include changes in global demand, particularly driven by growing wealth in Asian countries, leading to shifts in freight rates on major trade lanes. The risk of supply chain disruptions, such as delays of container vessels, has also been increasing in the last few years. More frequent extreme weather events caused by climate change and higher traffic on shipping routes make on-time arrivals more challenging than ever. Recent breakthroughs in research indicate that novel data analytics-based methods can help to increase transparency in transportation by supporting the decision-making of shipping players. It has become evident that machine learning enhances forecast accuracy, which could enable companies to reduce uncertainty in their logistics. In our first study (Chapter 2), we1 analyze the container shipping industry to predict delays of vessels. With a forecast accuracy of 77%, we identify important influencing factors for shipping delays. This primarily includes the time between ports, piracy risk, demographics, weather, traffic in maritime chokepoints, and port congestion. In our second study (Chapter 3), we present what methods need to be applied to predict spot rates in container shipping. With an accuracy of 89%, our forecasts support the decision-making of various shipping players in negotiating their transportation contracts. My dissertation journey then took me to the air cargo industry in our third study (Chapter 4). By assessing the predictability of long-term air freight rates, we show that machine learning improves forecasts, especially for trade lanes with volatile and complex price trends. As a result, we achieve an accuracy of 93%, enabling air carriers and freight forwarders to increase their operating profits by 30%. By proposing prediction solutions featuring high accuracy, robustness, and applicability in practice, this dissertation demonstrates that predictive analytics enhance transparency in transportation. It is our hope that more advanced technologies, such as machine learning, will play an increasingly important role in future decision-making on transportation.
1The term “we” in this dissertation always refers to the authors of Viellechner and Spinler (2020, 2021a,b,c)
Dieses Handbuch stellt in Beiträgen der führenden Experten qualitative und quantitative Forschungsmethoden des Faches vor. Behandelt werden sowohl fachspezifische Methoden wie auch Methoden der Sozialwissenschaften und der Informatik: Entwicklung von Forschungsdesigns, Befragungen, Nutzungsmessung von Websites, Benutzerforschung, Ethnomethodologie, Methoden der Informetrie, Evaluation von Informationssystemen, Inhaltsanalyse, Diskursanalyse, hermeneutische Methoden, Delphi-Methode, Methoden der buchwissenschaftlichen Forschung, Forschungsmethoden für historische Fragestellungen, Methoden der Lese- und Mediennutzungsforschung. Auch neue Möglichkeiten der Unterstützung durch Online-Tools (z.B. Online-Befragungen) werden erklärt.
What is it that actuates a person to really engage with their work instead of simply going through the motions? Through what means can an individual become involved and immersed in their appointed task? A textbook definition of motivation proposes it to be the sum of ‘the processes that account for an individual’s intensity, direction, and persistence of effort towards attaining a goal’ (Robbins & Judge, 2015), and adjudges it as a ‘psychological process resulting from the interaction between the individual and the environment’ (Latham & Pinder, 2005). Since the 1940s a succession of theories has interrogated the interaction of the various needs and values of the individual with those of the organisation, with a theoretical cleft lying about the origin of the motivation, hypothesised as springing from an external or from an internal source (Ryan & Deci, 2000a), with external motivation arising from incidentals associated with but outside the work itself while internal motivation emerges from the enjoyment that the worker derives from the task. A third very important type of motivation, prosocial motivation, taps into the desire to protect and care for others (Grant & Berg, 2011). However, one particular class of influential values that have the potential to motivate has hitherto been ignored: that of one’s religious beliefs. This is especially puzzling as religiosity or religiousness has experienced a recent surge, especially during the past decade. For example, in 2014 around 41% religiously affiliated American adults affirmed that they rely mainly on their religion’s tenets to guide them morally, a startling increase from the 34% who answered accordingly in 2007 (Pew Research Center, 2015). Closely associated with and reinforcing this motivational source is a second unexplored possible wellspring of motivation: one’s sense of chosenness in the religious sense. Present mainly in the Abrahamic religions (Judaism, Christianity, and Islam), chosenness is the feeling that one has been selected, or elected by a supernatural entity, often for a specific reason or a special mission.
Basiswissen RDA
(2017)
Wissenschaftsmanagement
(2017)
Buchgeschichte
(2019)
This dissertation investigates the value of customer behavior in supply chain management through the application of (big) data analytics in demand forecasting. The use of advanced analytics in supply chain management is not novel. However, the growing expansion of data volumes provides companies with new opportunities to optimize their supply chain. Despite the rising interest from both academia and practice and the recent increase in publications in this area, empirical insights are still limited. At the same time, changes in customer expectations towards instant product delivery require companies to rethink their supply chain, where accurate demand forecasts are often at the core of enabling efficient and flexible processes. This makes the development of demand prediction models that can be used in practice especially relevant. We1 analyze the use of customer behavior in demand forecasting in three separate research papers. Leveraging data from research partners in the online fashion and construction industry, we assess the potential of the developed prediction models in three areas of application in supply chain management, namely order fulfillment, order picking, and inventory planning. In the first paper, we develop a prediction model for anticipatory shipping in the fashion industry, which predicts customers’ online purchases with the aim of shipping products in advance, and subsequently minimizing delivery times. Using various forecasting methods and data on customers’ behavior on the website, we test if, and how early, it is possible to predict online purchases. Results indicate that customer purchases are, to a certain extent, predictable, but anticipatory shipping comes at a high cost due to wrongly sent products. The second paper assesses the extent to which clickstream data can improve forecast accuracy for fashion products. Specifically, we assess which clickstream variables are most suitable for predicting demand, and identify the products that benefit most from this. Results indicate that clickstream data is especially useful for forecasting medium- and certain intermittent-demand products. A simulation of order picking for these products shows that using clickstream data in the forecast substantially decreases picking times. The third paper investigates how sequential pattern mining can be used to determine products with correlated demand, and how to leverage this as an input into forecasting for a supplier in the construction industry. We find that sequential pattern mining may be beneficial when used in combination with traditional forecasting methods, and that support vector regression models seem especially suited to forecast intermittent-demand products. An application to inventory planning shows that our developed forecasting model might reduce the company’s costs of inventory holding and lost sales by up to 6.9%. Overall, our research highlights the value of using customer behavior to enhance demand forecasting and the benefit of using improved forecasts in various applications in supply chain management.
1Referring to the authors of the respective chapters as noted at the beginning of each chapter.
Maltfabrikken
(2021)
Handbuch Bibliothek 2.0
(2010)
Unter Bibliothek 2.0 verstehen die Herausgeber eine Einrichtung, die die Prinzipien des Web 2.0 wie Offenheit, Wiederverwendung (ReUse), Kollaboration und Interaktion in der Gesamtorganisation anwendet. Bibliotheken erweitern Serviceangebote und Arbeitsabläufe um die Möglichkeiten der Web 2.0-Technologien. Dies verändert Berufsbild und Selbstverständnis der Bibliothekare. Der Sammelband bietet einen kompletten Überblick zum Thema Bibliothek 2.0 und den aktuellen Stand der Entwicklungen aus technologischer, soziologischer, informationstheoretischer sowie praxisorientierter Sicht.
Using comprehensive panel data on Chinese acquisitions in Germany in the time period from 2007 to 2016, this thesis investigates the effects of the acquisition on target firm performance and concentrates on some critical issues regarding target top management team turnover and target firm performance as well as acquirer technological capability and target innovation performance. More specifically, the thesis focuses on the following questions: (1) How do acquisitions by emerging-market firms in developed markets influence target post-acquisition firm performance? (2) Do target top management team turnover or target chief executive officer (CEO) turnover have an effect on target post-acquisition firm performance in case of emerging-market acquisitions in developed markets and what is the moderating role of appointing an acquiring firm´s manager as target CEO? (3) How does the acquirer’s technological capability relate to target post-acquisition innovation performance in case of emerging-market acquisitions in developed markets and does prior international acquisition experience of the acquiring firm play a moderating role in this relationship? The theory and findings contribute to the existing literature on emerging-market firms and their investments in developed-market firms and offer some managerial guidelines in managing target post-acquisition success.
Datendienste haben für die Forschung wichtige Funktionen. Bei fortschreitender Digitalisierung der wissenschaftlichen Arbeit werden sie zu unverzichtbaren Grundvoraussetzungen für den Erkenntnisgewinn. Sie ermöglichen Zugang zu Daten, verknüpfen, verarbeiten (z.B. analysieren) und archivieren Daten. Dabei können Datendienste unterschiedlichen Zielsetzungen und Zwecken dienen beziehungsweise für unterschiedliche Zielgruppen (wissenschaftliche Fachgemeinschaften, Domänen aber auch öffentliche und private Unternehmen) aufgebaut sein und verwendet werden.
Der RfII unterscheidet Zwecke der Nutzung, der Verwertung und der Vermarktung von Daten, die Einfluss auf Strukturen, Zugänge und letztlich auf Betriebsund Geschäftsmodelle der Dienste haben. Je nach Ausgestaltung der Dienste, ihrer Geschäftsmodelle und ihrer Angebote können sich aus derartigen Infrastrukturen für die Wissenschaft unterschiedliche Folgewirkungen ergeben. Der Zusammenhang der konkreten Ausgestaltung einschließlich des „Wirtschaftens“ von Datendiensten mit der Entwicklung digitaler Forschung wird im gegenwärtigen Forschungshandeln noch kaum reflektiert. Um an dieser Stelle mehr Licht ins Dunkel zu bringen, hat sich der RfII gut vierzig Datendienste an den Schnittstellen zwischen Wissenschaft, Wirtschaft, Zivilgesellschaft und Verwaltung angesehen. Exemplarisch kamen dabei sechs breitgefächerte Domänen in den Blick: die Erd- und Umweltwissenschaften, die Naturwissenschaften, die Lebenswissenschaften, die Sozial-, Verhaltens- und Wirtschaftswissenschaften, die Geistes- und Kulturwissenschaften sowie heterogene Datenspeicher, die mit den sogenannten „Long-Tail-Daten“ umgehen. Die in den Domänen ausgewählten Dienste wurden als Fallbeispiele – ohne Anspruch auf Repräsentativität – dahingehend analysiert, ob ihre jeweiligen Betriebs- und Geschäftsmodelle Auswirkungen auf das wissenschaftliche Arbeiten mit „ihren“ Daten haben – z.B. (kosten-)freie Zugänglichkeit oder Archivierungsmöglichkeiten, die dauerhaft einen offenen und qualitätsgesicherten Zugang für Wissenschaftlerinnen und Wissenschaftler ermöglichen.
Im Ergebnis kann festgestellt werden, dass sich die Frage danach, wie wissenschaftskonform oder auch der Forschung zuträglich spezifische Betriebs- oder Geschäftsmodelle von Datendiensten sind, nicht pauschal beantworten lässt – weder für einzelne Domänen, noch für die Wissenschaft insgesamt. Auch auf die Frage, ob im Sinne eines nachhaltigen Datenökosystems für die Wissenschaft Dienste besser öffentlich, besser in privater Trägerschaft (also: kommerziell ausgerichtet) oder besser in Public-Private-Partnership angeboten werden sollten, gibt es keine einfache Antwort.
Für eine angemessene Bewertung kommt es vor allem auf den Nutzungszweck an, mit welchem die Forschung auf einen Dienst zugreift. Ebenso zählt – gerade aus der Sicht auf das gesamte Wissenschaftssystem und seine Datenumwelt – die Relevanz und konkrete Beurteilung von Nachhaltigkeitsdimensionen. In der Summe ist das Prinzip der besten Eignung zielführend. Mischformen, in denen öffentlich geförderte Angebote mit kommerziellen Angeboten verzahnt werden können, haben nach Einschätzung des RfII ein großes Potential für die Wissenschaft. Die Partizipation von Fachgemeinschaften und öffentliche Aufmerksamkeit können den Bestand solcher Arrangements auch „politisch“ sichern. Zentral für ein Gelingen der Auswahl nach bester Eignung bleibt eine große Verantwortung der öffentlichen Hand. Sie muss ein dauerhaftes und
nachhaltiges Basisangebot für die Wissenschaft, das mindestens die Funktionalitäten des Suchens und Findens sowie des Bewahrens (Langzeitspeicherung auf maschinenlesbaren Medien) abdeckt, aktiv sicherstellen.
Basierend auf dieser Bewertung der Befunde, gibt der RfII Empfehlungen
- zur staatlichen Regulierung des Marktes für kommerzielle und öffentlich finanzierte Datendienste,
- zur Gestaltung der Kooperationsbeziehungen zwischen wissenschaftlichen Akteuren und externen Anbietern von Diensten und
- zur (Selbst-)Organisation von Handlungsfähigkeit auf Seiten der Wissenschaft.
Mit seinen Empfehlungen steckt der RfII einen Rahmen ab, welcher Wettbewerb und Pluralität unter den Datendiensten und ihrer jeweiligen Geschäftsmodelle zum größtmöglichen Nutzen der Wissenschaft gewährleistet. Einseitige Abhängigkeiten durch Lock-Ins und Monopolstellungen von Anbietern oder Angeboten gehen zu Lasten der wissenschaftlichen Souveränität. Das gilt es auszuschließen. Nicht zuletzt sieht der RfII die Nationale Forschungsdateninfrastruktur (NFDI) und ihre Konsortien im Bereich der Nutzung und Verwertung von Forschungsdaten in einer Schlüsselposition, wenn es im Bereich der Datendienste um die
Vermittlung zwischen der Rationalität der wissenschaftlichen und der ökonomischen Sphäre geht.
This paper proposes a method for deriving the joint asymptotic distribution of sample proportions when the population and sample size increase jointly to infinity. The joint distribution of the sample proportions is derived by reducing the multi- variate to a univariate problem and applying the Cramer-Wold device. Knowing the asymptotic distribution we are in a position to conduct inference on linear or non-linear functions of the population proportions such as a ratio or the log-odds. We motivate and develop our method by means of a topical epidemiological application, namely the infection fatality rate of a virus such as SARS-CoV-2. We demonstrate that our method can be extended to more general settings of interest.