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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.
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.
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.
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)
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.
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)
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.
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.
This dissertation investigates different applications of data analytics in supply chain planning. In the last years, data analytics became more important, because of the increase of computational power and the larger availability of data. Data analytics is used in various domains to improve operations performance, increase customer satisfaction and revenues. However, both the research and the application of data analytics in supply chain management is still lacking behind other industries. We1 analyze the potential of data analytics in the field of supply chain planning in three exemplary fields: demand forecasting, partial defection prediction and price discrimination. In addition, we demonstrate how to deal with three common challenges in the field of data analytics: the manual effort for method selection and hyperparameter tuning, the difficult interpretability of machine learning methods and the risks associated with data collection through randomized experiments. In the first paper, we develop a method selection approach in the field of intermittent demand prediction. Our model combines high predictive performance with automation and calculation efficiency. Unlike common practice, the prediction method gets automatically chosen for each data set without any manual selection. Our results are stable across three different data sets that come from different sources but all contain intermittent demand time series. We showcase the impact of the proposed forecasting approach with a warehouse operation simulation. We thereby prove the financial benefit with empirical data. In the second paper, we deal with partial defection prediction in a business-tobusiness environment in the logistics industry. The predictions must combine predictive performance with interpretability and profit maximization. Our model uses a large variety of customer-based and time-series-based features to predict the probability of partial defection for each customer. We use a data permutation approach to make the best performing, black-box models interpretable. Furthermore, we use a profit assessment to identify the method that leads to the highest revenue through successful retention actions. In the third paper, we study price sensitivity prediction. We do not use any randomized experiments, because the risk of loosing customers through such experiments is too high. Thereby, we address the challenge of data availability
1The term “we” refers to the authors of the respective chapters as denoted at the beginning of each chapter. For the abstract, this refers to the authors of Faber and Spinler (2019a,b,c).
Essays in cryptocurrency
(2021)
Exploring business ecosystem properties with a focus on innovation and entrepreneurship activities
(2019)
With the rising importance of the external economic environment of organisations and open and collaborative innovation, the last two decades have seen an emergence of diverse literature streams and practitioner interpretations of business ecosystems. However, most of the scholars’ attention has been on ecosystems that function in the information and telecommunication technology industries and interactions between actors driven by economic interests. Moreover, business ecosystems analysis was embedded in the strategic management and organizational design literature, which primarily investigated the ecosystem leader using examples of powerful multinational corporations such as IBM, Microsoft, Google and Apple. These new powerful corporations significantly influenced the nature of the global competitive landscape (e.g. app economy), enabled the emergence of new business models (e.g. platform business) and even new forms of entrepreneurship (e.g. blogging and self-publishing). However, little remained known about the actors at the periphery of business ecosystems – that enable the functioning of such ecosystems - and the entrepreneurship processes that take place in the ecosystem environment.
With the goal of contributing to the extant literature and creating a better understanding of the central factors that foster evolution, and the functioning and growth of today’s business ecosystems, the first project of this doctoral dissertation provides an overview of the ecosystem concept evolution in literature and in practice. Based on the findings of this project, this dissertation takes two qualitative empirical directions. To expand on the scope of the ecosystem concept, in the second project it looks at open and collaborative innovation processes in the fashion industry, analyses the role of individuals in business ecosystems and highlights the emergence of new forms of entrepreneurship. Following a worldwide growing startup trend and increased corporate engagement in diverse corporate venturing activities, the third project empirically investigates inter-organisational knowledge flows and value creation and capture processes in the innovation ecosystem environment. This dissertation contributes to the open and user innovation and entrepreneurship literature and provides practical implications for managers and entrepreneurs interested in participating in an existing business ecosystem or considering starting one on their own.
Future oriented libraries can make use of the current start-up trend. An orientation towards new and unorthodox target groups can lead to an enhanced extension of demand and can emphasize the status of libraries. The library of the WHU – Otto Beisheim School of Management is considering to involve a new target group, start-up founders amongst their alumni. To that end, a survey was carried out and evaluated in cooperation with the Institute of Information Science at the TH Köln – University of Applied Sciences in form of a bachelor thesis, which this article is based upon. Here, a structured pre-analysis tries to determine the demand of this specific target group (founders) and develops a concept to serve the demand of this target group specifically. The example of the case study illustrates a method for target groups specific information demand and also checks the consequences for libraries and their services who venture out of their regular clientele.
2020 industry insights
(2020)
Customer-centricity, digital development and sustainability are three core aspects of cross-industry culture that will continue to be crucial as companies contend with current challenges and look ahead to longer-term strategic planning beyond COVID-19. Technological advancements, new digitally native brands and changing consumer expectations are driving uncertainty and increased competition, regardless of the industry. By understanding what cross-industry peers are focusing on for future investments and long-term strategies, businesses can stay ahead of change, embrace new opportunities to improve the customer experience and meet sustainability goals to maintain their competitive edge.
Inspire but don't interfere
(2021)
Managers play a pivotal role in the innovation process; yet, the mechanisms through which managers enhance or undermine innovation are not well understood. Drawing upon self-concordance theory, we argue that managers can augment employees’ self-concordance—defined as the congruence of goals and actions with inner values and preferences—through transformational behavior and thereby contribute to innovation. However, transformational behavior is closely coupled to another form of influence, namely process management, the attempt to directly manage innovation-related activities. This form of managerial influence reduces employees’ self-concordance and thereby undermines innovation. We test our conceptual model in a sample of 188 innovation projects using a contextualized method that asked employees to assess their self-concordance and their managers’ behavior during each project. Managers evaluated for each project the innovativeness of the outcome. Multi-level path-analysis provided support for our hypotheses. We discuss future research implications to disentangle innovation-facilitating and innovation-undermining facets of managerial influence.
Research on motivation may be alive and vital, but it is highly fragmented. This thesis discusses the most prevalent motivation theories and compares their ideas and concepts to introduce two new fields of research in this area: neurology and cardiology. The next era of motivation research will conflate concepts derived from behavioral approaches with organic substructure and elevate our understanding of motivation. A sound understanding of motivation allows organizational scholars to solve the motivational problem in the context of the organizational problem. To make proper use of promising novel insights in organizational contexts, we will revisit the current state of performance evaluation and their ability to coordinate and motivate individuals. By reconsidering how rewards crowd out intrinsic motivation via complex moods, and how superior intrinsic motivation increases performance compared to extrinsic motivation, this thesis introduces the motivation-potential model of feedback. Using a case study on a feedback-based management control system at a European e-commerce company based in Germany, we test how self-determination theory could be expanded by neurological concepts and quantitatively applied to design performance evaluation systems under the consideration of the motivation-potential model of feedback. In its entirety, this thesis attempts to build common ground for the future of motivation research and provides in-depth examples of theorization upon the aggregation of the field of research, as well as practical application and execution.
Licensing digital content
(2018)
This dissertation assesses the characteristics and viability of the emerging longhaul Low Cost Carriers (LCCs). In particular, the aim is to understand their business model, evaluate the cost and revenue performance, and investigate its impact on other carriers. Existing academic literature is inconclusive about characteristics and viability of the business model. To validate its defining characteristics, 37 airlines flying on North Atlantic routes are clustered using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) along a newly constructed long-haul airline business model framework. To contribute to the evaluation of business model viability, cost differences between clusters are uncovered followed by a discussion of their sustainability. Key findings include the characterization of the emerging long-haul LCC business model and its significant differences from Full-Service Network Carrier (FSNC) and leisure carrier models. On a cluster average, 33% lower unit costs compared to FSNCs are identified, of which 24 percentage points are evaluated as sustainable. As these cost advantages over FSNCs are smaller compared to the original savings of short-/medium-haul LCCs, revenue competitiveness on the longhaul becomes more critical. To assess long-haul revenue performance, a new metric for benchmarking the revenue per equivalent flight capacity is defined. Subsequently, a revenue model combining traffic, fare, load factor, and seat data from the North Atlantic is developed to determine the revenue per flight capacity across a sample of city-pairs. The results show that LCCs earn revenue per flight capacity comparable to FSNCs on shorter long-haul routes. Key factors to compensate lower direct yields are fewer low-yield connecting passengers, sales of ancillary services, higher load factors, and significantly more passengers per aircraft. Long-haul LCC market impact, and in particular their impact on incumbents’ fare levels, has not yet been assessed and short-/medium-haul LCC-related findings cannot be readily applied. To evaluate the impact of long-haul LCC presence on the incumbents’ pricing, a stylized analytical model is proposed for hypotheses development. Subsequently, Two-Steps Least Squares (2SLS) regressions with Instrumental Variables (IVs) are performed, distinguishing between economy, premium economy, and business classes, based on a sample of North Atlantic routes. Confirming the first hypothesis, LCC presence reduces incumbents’ long-haul economy and premium economy fares by -10% and -13%, respectively, ceteris paribus. LCC presence, however, does not significantly impact long-haul business class fare levels, confirming the second hypothesis. These findings are particularly relevant as the North Atlantic market represents to date one of the remaining profit pools for North American and European legacy carriers. The findings of this research implicate that the long-haul LCC model is economically viable, at least on trunk routes with high demand. FSNC management should be aware of the rising competition and fare impact on North Atlantic routes. Potential reactions could include the de-bundling of entry fares with the option of ancillary sales even on long-haul routes, a re-evaluation of the revenue impact of low-yield connecting passengers, a focus on premium passengers, and a continuous reduction of operating costs.
This dissertation investigates the application of machine learning to improve decision making in airline operations. In an introductory overview, we1 discuss the airline industry and the challenges of decision making in airline operations: e. g., complex IT infrastructure, interconnected resources, delay management, fuel price volatility and future environmental regulation. Machine learning can efficiently integrate a large volume of data from a variety of data sources and formats to generate accurate predictions. To assess the viability of using machine learning models for decision making in airline operations, we develop a model based on linear regression and gradient boosting to predict aircraft arrival time. Furthermore, we integrate cost index optimization to model the impact of aircraft speed on arrival time. While we find that machine learning can improve prediction accuracy by more than 30 %, the optimal cost index varies according to fuel cost, flight distance and delay costs. We propose an overall reduction in cost index for short-haul flights to reduce fuel cost while maintaining punctual operations. Arrival time predictions serve as input for daily operations planning and control. For network carriers accurate arrival time predictions are key for efficient hub operations. In a next step, we focus on aircraft arrival time prediction for intercontinental flights. We analyze the accuracy of en-route weather data provided by the flight plan, generate features based on en-route weather data and integrate them in our prediction model. We evaluate three machine learning models: linear regression, random forest and gradient boosting. Through our approach, we can assess the impact of en-route weather data. Overall, an increase in prediction accuracy of 25 % is achieved. By including en-route weather data, prediction accuracy is improved by 5 %. Our model outlines the essential features for intercontinental arrival time predictions and assess the value of en-route weather data. Furthermore, we outline organizational challenges in implementing predictive analytics. Future environmental regulations are a challenge for the airline industry. From 2020 onwards net growth in CO2 emissions is prohibited. Thus, airlines need to focus on initiatives to limit fuel consumption. We develop a prediction model for fuel consumption considering ten different aircraft types. Our results show that fuel consumption can be improved by more than 30 % for short and long-haul flights resulting in an annual reduction of 10.5 million e in fuel costs and a reduction on green house gas emissions of 66.000 tons. Furthermore, we assess the pilots’ willingness to integrate our prediction model in their fuel decision. Our analysis shows that high prediction accuracy, model understanding and a long testing phase are essential for acceptance of the prediction model. The main implication is that decision making in airline operations can be substantially improved through machine learning. Therefore, more prediction.
1The term “we” refers to the authors of the respective chapters as denoted at the beginning of each chapter. For the abstract, this refers to the authors of Achenbach and Spinler (2018a,b), Achenbach et al. (2017).
The present study investigates how family firms respond to disruptive industry changes. We aim to investigate which factors prevent or support family firms` adoption of disruptive innovations in their industry and which mechanisms lead to more or less successful coping with disruptive change. Our analysis is based on 24 qualitative interviews with top executives and on secondary data from an industry in which disruptive innovations dramatically changed the way business was generated. The industry in question is the mail order industry, which, in its early days, disrupted the retail business. When the Internet and, with it, ecommerce started to disrupt the industry in the late 1990s, the industry was characterized by a high proportion of family firms and a low level of innovativeness. While incumbent firms had been very successful for decades, most of them were confronted with serious turbulence when new entrants started changing the face of the industry. Our findings show that different factors impact reactions to disruptive industry change in two different phases, namely, opportunity recognition and opportunity implementation. While some of the influencing factors are determined by industry factors, family influence may function for better or worse for incumbent firms. Specifically, we find that in firms with a family disruptor, a family member in a powerful position who drives the adoption of the new technology, hindrances can be overcome and firms tend to show more successful strategies when reacting to the disruptive industry change.
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.
By building on foundations from psychology, we aim to enhance academic understanding of the advising process in family businesses. We find evidence, based on rich qualitative data, suggesting that trust serves as a key construct in the relationship between family businesses and their advisors. In particular, we empirically show and theorize that trusting relationships evolve via a nonlinear process characterized by a constant interplay between cognitive and—increasingly important—affective assessments of family business trustors. The following types of trust emerge from these internal assessments: an intention to trust, which develops into perceived trust and finally results in behavioral trust.
With the start of digitalization and the initiation of a whole new industry of digital services, some of the well-established marketing wisdoms are now under scrutiny. This also holds for the importance of customer win-back for any firm – a belief that has actually never really been questioned. While related research focuses on mature industries and highlights the profitability of win-back, to date no study known to the authors has addressed the role of reacquired customers compared to first-lifetime customers in the interplay of acquisition and retention for digital firms. This is especially important when assessing firms’ current winback strategy and forecasting the development of acquisition and retention success of reacquired compared to first-lifetime customers. This study proposes a win-back assessment framework that enables firms to assess their win-back strategy and to detect problems in acquisition or retention of first-lifetime and reacquired customers. The authors demonstrate the use of framework on transaction data from a digital subscription service and data from a field experiment with that same service provider. They analyze the data from a new analytical perspective compared to extant research, which they call “cohort perspective” and jointly investigates acquisition and retention of first-lifetime and reacquired customers, who started their subscription around the same time (i.e. cohort). The results show the need for the cohort perspective by unveiling the contradiction that reacquired customers can “live” longer than in their previous lifetime (on average 328 days), but that they “live” shorter than first-lifetime customers of the same cohort ( ̶ 19%). The authors explain the rationale of this observation and discuss its implications.
Open versus closed organizational design options of sharing economy models and sharing communities
(2018)
With the recent emergence of the sharing economy, novel and complex organizational forms are founded on individuals and sharing communities that micro-manage and self-organize their own, private resources for social or commercial outcomes within an organizational scope.
These novel and complex forms reflect sharing economy models which are designed along a range from decentralization to centralization. In decentralized designs, individuals control and organize their own, private resources in sharing communities. In centralized designs, the organization controls and organizes the resources which are shared within communities.
This dissertation addresses the complexity of sharing economy models by exploring them on the organizational, the individual and the system level.
On the organizational level, this dissertation specifies the various configurations of sharing economy models. These configurations are based on representative design options which are traded off between decentralization and centralization. The Sharing Economy Spectrum is conceptualized as framework which integrates these characteristics-based design options. On the individual level, this dissertation focuses on how individuals’ resource endowment and trust in organization builders, in community members and in institutions individually influence individuals’ expectations regarding the beneficiaries of a sharing economy model. A regression model and a couple of hypotheses are developed and explored through a survey conducted at the Philippine social organization Gawad Kalinga.
On the system level, this dissertation acknowledges the sharing economy as a socioeconomic ecosystem and addresses the leadership paradox that resources are decentralized but require central leadership. Complexity leadership theory captures sharing economy models as complex adaptive systems whose leadership is organized for a common purpose. The specific roles of dynamic interaction and trust for complexity leadership are explored jointly with social entrepreneurship and sharing economy research. An ethnography with Gawad Kalinga reveals a novel complexity leadership model that fosters bottom-up leadership.
The Veblen effect revisited
(2018)
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.
This paper documents that ECB announcements on monetary policy increase stock market volatility in the euro area (EA) using several volatility measures from January 1999 to December 2019. Employing event study methods, a more pronounced impact exists following the global financial crisis starting in 2007. All assets react similarly so that no national peculiarities arise. The effects also spill over to 12 non-EA markets analyzed. Stock markets are more sensitive to negative monetary policy news than to positive ones. Further weighting the announcements by financial market reactions, stock markets behave in a more heterogeneous way.
In e-commerce, customers are usually offered a menu of home delivery time windows of which they need to select exactly one, even though at least some customers may be more exible. To exploit the exibility of such customers, we propose to introduce exible delivery time slots, defined as any combination of such regular time windows (not necessarily adjacent). In selecting a exible time slot (out of a set of windows that form the exible product), the customer agrees to be informed only shortly prior to the dispatching of the delivery vehicle in which regular time window the goods will arrive. In return for providing this exibility, the company may offer the customer a reduced delivery charge and/or highlight the environmental benefits. Our framework also can accommodate customized exible slots where customers can self-select a set of regular slots in which a delivery may take place.
The vehicle routing problem (VRP) in the presence of exible time slots bookings corresponds to a VRP with multiple time windows. We build on literature on demand management and vehicle routing for attended home delivery, as well as on exible products. These two concepts have not yet been combined, and indeed the results from the exible products literature do not carry over directly because future expected vehicle routing implications need to be taken into account. The main methodological contribution is the development of a tractable linear programming formulation that links demand management decisions and routing cost implications, whilst accounting for customer choice behavior. The output of this linear program provides information on the (approximate) opportunity cost associated with specific orders and informs a tractable dynamic pricing policy for regular and exible slots. Numerical experiments, based on realistically-sized scenarios, indicate that expected profit may increase significantly depending on demand intensity when adding exible slots rather than using only regular slots.
The profession of anaesthesia technologist is a relatively new profession in Germany. The German hospital Association published the first training guideline in 2011. Likewise the surgical technologist profession, the profession of anaesthesia technologists are not officially certified. Hence, similar disadvantages such as further career restrictions and uncertainties in case of unemployment exist. Even the hospitals need to cover the full training expenses. The training of an anaesthesia technologist lasts three years, containing of practical work experience within the anaesthesia units such as the post-anaesthesia caring unit and a theoretical education. The action site is limited to the anaesthesia units only. An anaesthesia technologist is an assistant to the doctor and takes care of the patient before, during and after the anaesthesia. Since the anaesthesia technologist profession is a very young profession group, little is known about the preferences of this group. However, hospital manager need to understand the individual preferences to be able to provide a target group tailored recruitment.
The motivation was to provide results to inform the human resource management of hospitals about the preferences of the very young profession group of anaesthesia technologist with respect to contribute to a successful development of this profession in order to cope with the current labour shortage crisis.