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Advances in manufacturing and information technologies have made it possible for firms to satisfy consumers’ increasing demand for unique products. Although, the mass customization of products is prevalent in almost all industries today, firms’ optimal mass customization strategy is still not that clear. The initial attempt to mass customization of a number of firms failed, because it proved to be unprofitable, while others have successfully established mass customization as a product strategy. The optimal degree of mass customization solves two decision problems: first, firms’ trade-off between the coverage of consumers’ preferences to charge a premium price and cost-efficient production; second, consumers’ trade-off between tailoring a product to their needs and interaction costs. In an attempt to facilitate managerial decision making, this thesis studies a firm’s mass customization decision in a game-theoretical model that combines the decision problems faced by each player in the interaction. Based on this model of company-customer interaction, novel insights into the optimal mass customization strategy of firms depending on their market and competitive environment are gained.
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.
Hosting the Olympics or not
(2017)
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.
Tu Felix Koha
(2020)
In search of alpha
(2015)
In this study we develop a trading strategy that exploits limited investor attention. Trading signals for US S&P 500 stocks stocks are derived from Google Search Volume data, taking a long position if investor attention for the corresponding security was abnormally low in the past week. Our strategy generates 19% average annual return and thereby outperforms a simple market buy-and-hold strategy. After controlling for the well-known risk factors, a significant alpha (abnormal return) of 10% p.a. remains. Returns are sufficiently large to cover transaction costs.
This study provides novel insights to the ongoing debate how market efficiency is challenged by investor behavior. Applying search engine data we find that retail investor attention can enhance market efficiency. High attention is associated with better incorporation of idiosyncratic stock information, which we interpret as improved pricing efficiency. This effect is even more pronounced in bullish markets. In bearish markets, however, retail investor attention leads to a deterioration of pricing efficiency, which might be explained with herding behavior. Our evidence holds for a broad sample of European and US stocks.
The constant introduction of new products is of great importance for the long-term financial success of companies. Newly launched products in consumer goods and services markets show high failure rates, often reaching 50%. In order to reduce flop rates, companies can integrate innovative and knowledgeable customers, so called 'lead users', into the new product development process. However, the detection of such lead users is difficult, especially in consumer goods markets with very large customer bases. A new and potentially valuable approach for the identification of lead users are virtual stock markets, which have been proposed and applied for political and business forecasting, but not for expert identification yet. The goal of this paper is to analyze theoretically and empirically the feasibility of virtual stock markets for lead user identification. We find in our empirical study that virtual stock markets are an effective instrument to identify lead users in consumer goods markets. Using the proposed method, companies operating in these markets can identify lead users more easily and integrate them into new product development projects. Thus, they can improve the innovation processes and reduce new product flop rates.
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.
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.
Distance in work teams has become a cornerstone of today’s business world as well as a fundamental research area. It has also become a prevalent team design factor that enables managers to combine the knowledge and skills of far-flung employees together in one team and, thus, to leverage their performance. However, it is not yet clear whether and how distance affects intra-team dynamics and performance. This dissertation addresses this fundamental research gap and explores the consequences of distance in teams and how virtual teams can overcome the bur-dens of virtual collaboration. More precisely, there are three burning issues addressed with regard to dispersed team dynamics: the perception of distance versus proximity, virtual teamwork processes, and the impact of the team context. In practice, it has been shown that the inadequate management of these issues plays a crucial role when virtual teams fall behind managers’ expectations. However, practitioners to date have only limited knowledge how to handle these factors of virtual team performance effectively, due to the severe research gaps associated with these issues. Accordingly, this study’s approach to explaining virtual team performance is built on these three focused elements of virtual collaboration.
Adopting this research focus, the empirical part of this work is structured into four research papers that empirically investigate whether and how perceived distance, team processes, and the team context may become critical for virtual team performance and the extent to which practitioners can build on these three success factors to optimize distributed work. The empirical analyses to these questions are based on a sample consisting of 161 software development teams with varying degrees of geographic dispersion.
The results presented in this dissertation clearly show that team processes, the perception of distance, and the team context matter for virtual team effectiveness. The research results of this study revealed team processes to be the key drivers of virtual team performance. In fact, virtual teams with high-level collaborative team processes are able to outperform their co-located counterparts, even those with the same quality of team processes. Teams with poor team processes, in contrast, suffer heavily from dispersion and underperform co-located teams with the same (low) levels of these processes. Thus, the effect of dispersion is not necessarily detrimental to team performance but rather depends on the quality of task-related team processes.
The analyses further show that it seems not (only) to be the actual degree of geographic dispersion that affects virtual team dynamics but rather the perceived level of distance between the members of a team. By exploring its antecedents, distance perceptions turned out to be mental states that emanate from complex and more socially-based constructions of the reality and are significantly affected by team members’ national heterogeneity. This finding illustrates the need to include the social aspects of dispersion in future research on dispersed team functioning. For executives, this finding offers new opportunities how to reap the benefits of virtual collaboration without efforts of bringing team members together face-to-face.
A third antecedent of virtual team dynamics turned out to be the organizational context. The organizational context has been shown to affect virtual team performance more indirectly by changing the conditions in which team members collaborate. In this dissertation, two paths have been identified of how the organizational context impacts virtual team functioning. First, organ-izational context variables such as the degree of formalization facilitate the perception of proximity even for team members being geographically strongly dispersed. Second, the organizational context can facilitate virtual team performance by creating an environment for superior dispersed collaboration quality. These context-related findings show that the larger organizational environment can help distributed teams to cope with the liabilities of distance more effectively. In particular, there are distinct and manageable attributes of the organizational context that can be addressed to help team members developing perceptions of interpersonal closeness as well as to perform high-level dispersed teamwork.
The results of this dissertation offer both considerable contributions to the extant literature on virtual team dynamics and guidance for the formulation of best-practices in virtual team management. In the end, all presented theoretical models and corresponding research results help to learn more about virtual teams – especially about their dynamics and the antecedents of their performance.
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.
The combined impact of changing global demand and supply dynamics, extensive trading and speculation as well as global recessionary fears, has led to an environment of unprecedented volatility in worldwide commodity markets. As a result, effective risk management has become an increasingly important topic on the agenda of top management in a broad range of industries. While practical evidence shows that successful firms integrate both operational decision making and financial hedging in a firm-wide, coordinated risk management strategy, this entails managerial challenges. On the one hand, quantifying a firm’s exposure to raw material cost risk necessitates a sound understanding of the stochastic commodity market dynamics. On the other hand, once the exposure to different sources of risk is understood, executives face an intricate optimization problem over their operational and financial decision variables with the ultimate goal to reduce profit variability, while maintaining attractive business opportunities.
In this thesis, the topic of operational and financial risk management is investigated from three different perspectives.
In Chapter 2, a four-factor maximal affine stochastic volatility model of commodity prices is developed, which is consistent with many stylized characteristics of storable commodity markets as well as the historical term structure of commodity futures and option prices. Based on this model, we provide new insights with respect to the structural dynamics of commodity markets and the pricing and hedging of commodity derivatives. As the stochastic model used to describe the uncertain evolution of commodity prices can have important implications also in the valuation and risk management of real assets, a realistic commodity price model is a prerequisite for the integrated risk management models outlined in the subsequent chapters of this dissertation.
Given the previously developed intuition for commodity market dynamics, we model the integrated operational and financial risk management problem of a stylized, single-product industrial firm in Chapter 3. The firm faces risk in the price of commodity inputs and price sensitive, stochastic demand. Within this setting, the firm seeks to maximize inter-temporal utility under downside risk aversion over a multi-period time horizon by dynamically choosing physical procurement volumes, unit selling prices, and a futures hedge. We provide a flexible, simulation-based optimization algorithm, which allows us to solve the firm’s decision problem under realistic, multi-factor commodity price dynamics involving uncertainty in the interest rate and convenience yield as well as stochastic volatility. Based on this model, we characterize the firm’s optimal operating policy and investigate a range of topics including: (a) the value of managerial flexibility and the economic cost of restrictive supply contracts; (b) the importance of accounting for the stochastic nature of costs, interest rates, convenience yields, and volatility in risk management; (c) parameter and estimation risk; (d) the impact of risk aversion and hedging on the distribution of cash flows; and (e) the sensitivity of expected performance to key input parameters.
As opposed to the case of a single-product firm, integrated procurement risk management in a large, multi-divisional organization does not only require the above mentioned cross-functional coordination between, for example, the purchasing, sales, and finance department but also involves a cross-divisional coordination of actions in order to effectively target the firm-wide net risk exposure. To capture the specific aspects of integrated risk management in this type of setting, we extend the above model to a two-product firm in Chapter 4. Within this model, each of the two divisions are subject to cost and demand risk, which can be respectively correlated. Moreover, we allow for dynamic cross-selling to capture the potential complementarity/substitutability of items. The firm has access to futures, call, and put options associated with each of the commodity input markets for financial hedging. Under an intertemporal mean-variance utility function, we are able to provide analytic solutions to the firm’s dynamic procurement, pricing, and financial hedging problem. Based on a complementary numerical study, we analyze the impact of risk correlations and unilateral changes in the market environment of one division on the entire firm. Moreover, we discuss the impact of hedging and risk aversion on optimal policy and assess the effectiveness of different operational and financial hedging strategies for risk reduction.
FMCG marketing and sales
(2015)
Dancing with the dragon
(2017)
Delphi-Studie Zukunft 2030
(2021)
Delphi-Studie
(2021)
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.
ERM für Koha mit CORAL
(2020)
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.
Nursing without caring?
(2019)
We know that existing professions in the health care sector value work environment and job conditions to a great extent. However, we are also witnessing an expansion of new roles into the health care sector, many of which substitutie the tasks of existing professions. This may be efficient, in that it releases professionals’ time. However, there is little understanding of what motivates these new professions in entering or remaining in these newly created roles. This study tries to evaluate the preference structure of one of these new staff groups, surgical technologist, through examining the preferences of trainees, defined over a number of attributes, in this group. The DCE study covers 80% of the target population. The results show a vigorous disfavour towards any perceived nursing job characteristics such as caring activities, hierarchical work environment or shift types. The results inform policy makers and hospital manager about the importance to focus not only on the nursing profession but also to take into account the existence of a group of people who is willing to work within the health care system however, associated with strong preferences against nursing activities, especially caring. Implementing and further development of new and specialised profession through reallocating former nursing tasks- should be considered while coping with labour shortage.
Essays on health economics
(2019)
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.
Datenmigration nach Koha
(2020)
Do FOMC members herd?
(2011)
Twice a year FOMC members submit forecasts for growth, unemployment and inflation to be published in the Humphrey-Hawkins Report to Congress. In this paper we use individual FOMC forecasts to assess whether these forecasts exhibit herding behavior, a pattern often found in private sector forecasts. While growth and unemployment forecast do not show herding behavior, the inflation forecasts show strong evidence of anti-herding, i.e. FOMC members intentionally scatter their forecasts around the consensus. Interestingly, anti-herding is more important for nonvoting members than for voters.
Using a large international data set we analyze whether business cycle forecasters tend to herd or anti-herd. Applying different measures of economic crises, we distinguish between normal economic circumstances and times of crises. We fnd evidence for anti-herding behavior for most industrial economies, i.e. forecasters eliberately stick out their neck with extreme forecasts for strategic reasons. For a set of emerging market economies, by contrast, we find evidence for herding behavior. We relate this finding to the high incidence of economic and financial crises in these countries. A test for herding behavior during economic crises confirms that forecasters tend to herd in times of high forecast uncertainty.
Central bank projections have gained considerable attention for monetary policy modeling. However, less is known about the nature of central bank projections. This letter explores the unbiasedness and rationality of more than 2; 000 growth and in ation projections published by 15 major central banks. The results indicate that central bank projections are in most cases rational and unbiased. Interestingly,
in ation projections are more biased than growth projections.
In this paper, we contrast more than 6,000 private sector forecasts to projections of the German council of economic experts (Sachverständigenrat). Although the forecasts are submitted simultaneously, we find that the council's real economy forecasts, i.e. their growth, unemployment and fiscal forecasts have a higher forecast accuracy compared to the private sector forecasts. We also document that private sector forecasters deliberately place their real economy forecasts away from the council's projection. This strategic forecasting behavior explains why the private sector performs worse than the council. This result is robust over time but splitting the private sector in different groups reveals that the forecasts of banks compared to research institutes deviate more from the council's forecast.
Meaningful metrics
(2015)
We use oil price forecasts from the Consensus Economic Forecast poll to analyze how forecaster build their expectations. Our findings point into the direction that the extrapolative as well as the regressive expectation formation hypothesis play a role. Standard measures of forecast accuracy reveal forecasters' underperformance relative to the random-walk benchmark. However, it seems that this result might be biased due to peso problems.
This dissertation assesses investment decisions in container shipping. To understand the current state of the industry, key characteristics and challenges, such as overcapacity, eroding margins due to low freight rates, long investment lead times, and frequent changes in alliance structure are introduced.
The nature of the industry motivates the application of real options, hence a real options investment model in oligopolistic competition is presented. An analytic solution in continuous time as well as a dynamic programming solution in discrete time are derived. The model takes into account an endogenous price function, fuel-efficient investment, endogenous lead times, and endogenous price formation in the secondary vessel market. This allows to study the impact of competitive intensity, number of players, volatility, fuel-efficiency, lead time, and variable cost on optimal capacity. An investigation of optimal investment policies shows that strategic action increases firm value and strategic alliances might help alleviate some of the industry’s challenges.
Since the container shipping market is characterized by frequent alliance changes, the performance of the real options model in the context of a cooperative shipping game is assessed. Extending the coalition structure value concept it can be shown that, compared with discounted cash flow, the real options trigger performs better, especially in light of high competitive intensity and freight rate volatility while not exhibiting substantial disadvantages in other settings. A further assessment of a number of drivers for alliance instability finds that alliance complexity cost, freight rate volatility, and competitive intensity increase alliance changes.
To verify the investment approach, a characterization of the container freight rate is provided with an empirical Autoregressive Integrated Moving Average (ARIMA) model. It can be observed that the freight rate exhibits a negative relationship with capacity deployment; hence the oligopoly price function is confirmed. Based on the freight rate characterization, a back testing of the real options investment approach is provided. It shows that if players had applied the presented approach, capacities would have decreased and rates improved. A number of limitations of the real options approach are identified,
i.e. substantial impact of volatility expectation, potentially induced cyclicality from trigger approaches, and the timing impact of investment and divestment lead times.
The implications of this research are that strategic action in the container shipping industry is worthwhile and understanding the market specifics (such as competitive intensity, volatility, and freight rate characterization) is very important. Container carriers should add a real options approach to their investment toolkit and keep an eye on potential overcapacity. Finally, entering strategic alliances is suggested, but complexity should be avoided.
Modern pathfinders
(2015)
We used the oil-price forecasts of the 'Survey of Professional Forecasters' published by the European Central Bank to analyze whether oil-price forecasters herd or anti-herd. Oil-price forecasts are consistent with herding (anti-herding) of forecasters if forecasts are biased towards (away from) the consensus forecast. Based on a new empirical test developed by Bernhardt et al. (J. Financ. Econ. 80: 657-675, 2006), we found strong evidence of anti-herding among oil-price forecasters.
On the international consistency of short-term, medium-term, and long-term oil price forecasts
(2011)
We derive internal consistency restrictions on short-term, mediumterm, and long-term oil price forecasts. We then analyze whether oil price forecasts extracted from the Survey of Professional Forecasters conducted by the European Central Bank satisfy these internal consistency restrictions. We find that neither short-term forecasts are consistent with medium-term forecasts nor that medium-term forecasts are consistent with long-term forecasts. Using a more complex expectation formation structure featuring a distributed lag structure, however, we find stronger evidence of internal consistency of mediumterm forecasts with long-term forecasts.
In this thesis, we(1) use operations research methods to provide insights into three areas associated with health care operations management. In Chapter 2, we use a discreteevent supply chain simulation to asses if coordination among partners is beneficial in a supply chain with the characteristics of the German pharmaceutical market. We find that the greatest cost savings and service levels could be achieved through a highly integrated collaboration although most of its impact could already be achieved through sharing point-of-sales demand information. Results suggest that coordination is most beneficial in situations where product shelf life is short and demand variation is high.
In Chapter 3 we consider quality-of-life maximizing sequences of prophylactic surgeries for female carriers of a BRCA1/2 genetic mutation, who face a significantly elevated breast and ovarian cancer risk. Using a Markov Decision Process model, we determine the optimal surgery sequence that maximizes the carrier’s expected lifetime qualityadjusted life years (QALYs). Baseline results demonstrate that a QALY-maximizing sequence recommends a bilateral mastectomy between ages 30 and 60 and bilateral salpingo-oophorectomy after age 40 for BRCA1 carriers. Surgeries are recommended later for BRCA2 carriers, as their cancer risk is lower. The model’s structural properties show that when one surgery has already been completed, there exists an optimal control limit after which performing the other surgery is always QALY-maximizing.
In Chapter 4, we develop a two-stage model for optimizing when and where to assign Ebola treatment unit (ETU) beds—across geographic regions—during an infectious disease outbreak’s early phase. The first stage includes a dynamic transmission model that forecasts occurrence of new cases at the regional level, thus capturing connectivity among regions; in this stage we introduce a coefficient for behavioral adaptation to changing epidemic conditions. The second stage includes two approaches to efficiently allocate intervention resources across affected regions. Such an allocation could have prevented up to 3,434 infections over an 18-week period during the 2014 Ebola outbreak in West Africa, a 58% improvement compared with the actual allocation.
(1) In Chapter 2, 3, and 4, the term ’we’ refers to the authors of Nohdurft & Spinler (2016), Nohdurft et al. (2016a), and Nohdurft et al. (2016b), respectively.