Refine
Year of publication
Document Type
- Part of Periodical (322)
- Doctoral Thesis (209)
- Working Paper (204)
- Book (83)
- Conference Proceeding (25)
- Article (8)
- Other (4)
- Report (3)
- Habilitation (1)
- Lecture (1)
- Master's Thesis (1)
- Moving Images (1)
Has Fulltext
- yes (862) (show_all)
Is part of the Bibliography
- no (862)
Keywords
- Lehrstuhlbericht (205)
- Deutschland (46)
- Germany (35)
- Bibliothek (30)
- Studie (28)
- Controlling (27)
- Library (27)
- Study (25)
- Familienunternehmen (24)
- Family business (22)
Institute
- Institute of Management Accounting and Control (104)
- WHU Dean's Office (74)
- WHU Library (58)
- Chair of Technology and Innovation Management (31)
- Kühne Foundation Endowed Chair of Logistics Management (28)
- Allianz Endowed Chair of Finance (22)
- Chair of Monetary Economics (22)
- WHU Financial Accounting & Tax Center (FAccT Center) (21)
- Chair of Macroeconomics and International Economics (17)
- Chair of Organization Theory (17)
Élite role and context
(2011)
Workshop Copyright
(2018)
Wissenschaftsmanagement
(2017)
Winning Frankfurt
(2017)
In diesem Beitrag wird die Expansion eines Samples von 47 HDAX-Unternehmen zwischen 1995 und 2004 hinsichtlich des Expansionsverlaufs, der Expansionsrichtung (Internationalisierung und Diversifikation) und der Expansionsumsetzung beschrieben. Die Betrachtung von 1.830 einzelnen Expansionsschritten führt zu einem detaillierten und umfassenden Bild der Expansionspfade dieser Unternehmen. Insgesamt zeigt sich, dass die Entwicklung der betrachteten Unternehmen sehr heterogen verläuft. Hieraus ergeben sich verschiedene interessante Fragestellungen und Implikationen für die weitere Forschung zur Expansion von Unternehmen. Vor allem scheint es fraglich, ob die bisherige Praxis, Internationalisierung und Produktdiversifikation getrennt zu betrachten, dem Phänomen der Expansion von Unternehmen gerecht wird.
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.
Empirical evidence suggests that there is substantial cross-firm variation in the extent of tax avoidance. However, this variation is not well understood. This paper provides a theoretical background for testing, and thus explaining, cross-firm differences in tax avoidance. We develop a formal model with two agents to analyze the incentives that lead firms to engage in tax avoidance. The tax avoidance decision is a function of moral hazard, tax-planning costs, and the potential to increase earnings. If the potential to increase earnings is low, the tax-planning decision is determined by moral-hazard problems. In contrast, when this potential is high, the tax-planning decision is mainly driven by taxplanning costs, such as reputational and political costs. One implication of our model is that moral hazard can (partly) explain why some firms do not engage in tax avoidance: Severe problems of moral hazard make tax avoidance less likely. Our model can be applied to test dierences in tax avoidance between different types of firms.
This study investigates why countries mandate accruals in the definition of corporate taxable income. Accruals alleviate timing and matching problems in cash flows, which smoothes taxable income and thus better aligns it with underlying economic performance. These accrual properties can be desirable in the tax setting as tax authorities seek more predictable corporate tax revenues. However, they can also make tax revenues procyclical by increasing the correlation between aggregate corporate tax revenues and aggregate economic activity. We argue that accruals shape the distribution of corporate tax revenues, which leads regulators to incorporate accruals into the definition of taxable income to balance the portfolio of government revenues and expenditures. Using a sample of 26 OECD countries, we find support for several theoretically motivated factors explaining the use of accruals in tax codes. We first provide evidence that corporate tax revenues are less volatile in high accrual countries, but high accrual countries collect relatively higher (lower) tax revenues when the corporate sector grows (contracts). Critically, we then show that accruals and smoother tax revenues are favored by countries with higher levels of government spending on public services and uncertain future expenditures, while countries with procyclical other tax collections favor cash rules and lower procyclicality of corporate tax revenues.
This paper empirically examines why tax avoidance differs across individuals. We use rich Swedish administrative panel data on all taxpayers, with a link between corporate and individual tax returns. Surprisingly, few individuals utilize legal and observable tax avoidance opportunities. Our results show that there are several frictions in tax avoidance participation. In addition to monetary benets from tax avoidance (incentives), the opportunity to participate in tax avoidance (access), as well as information and knowledge about these opportunities (awareness), are important factors for the individual's tax avoidance decision. We further show that information about tax avoidance opportunities spreads within informal networks.
Our paper estimates the impact of immigration on the sustainability of the Italian public finances using the methodology of Generational Accounting. We take into account socio-economic differences between the main migrants’ communities resident in Italy and we present three possible scenarios to reflect the potential economic degree of integration of foreigners in the Italian territory. Moreover, for each scenario we propose several options for migrants concerning both the length of permanence in Italy and the possible collection of retirement benefits. Our results show that the burden of current fiscal policy reduces as integration of the foreign-born increases. If migrants’ children are economically perfectly integrated, the fiscal gap is reduced from 71.9 to -15.3 percent of GDP.
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.
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.
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.
Sowohl die Logistik als auch die Automobilzulieferindustrie gehören seit jeher zu den Branchen mit der höchsten Wachstums- und Innovationskraft in Deutschland und Europa. Insbesondere vor dem Hintergrund der sich aktuell rasant ändernden Umfeldbedingungen in der Automobilindustrie erlangt das Zusammenspiel beider Branchen eine neue Bedeutung. Die entstehenden Entwicklungs- und Produktionsnetzwerke erfordern von den Zulieferunternehmen der verschiedenen Ebenen ein Umdenken hinsichtlich der Ausgestaltung ihrer Value Chain sowie ihrer Geschäftsmodelle.
Die vorliegende Studie zeigt auf Basis einer Interviewreihe mit 41 deutschen Zulieferunternehmen, welche Fähigkeiten und Strukturen durch diese in den kommenden Jahren aufgebaut werden müssen, um erfolgreich innerhalb der sich wandelnden Wertschöpfungsstrukturen bestehen zu können.
Dabei ist es das erklärte Ziel der Studie, über die reine Informationsvermittlung hinaus, Denkanstöße für Unternehmensentscheider und Logistikverantwortliche hinsichtlich der zukünftigen Positionierung der Industrie zu liefern.
Value based management
(2008)
A fully unbundled, regulated network firm of unknown efficiency level can undertake unobservable effort to increase the likelihood of low downstream prices, e.g., by facilitating downstream competition. To incentivize such effort, the regulator can use an incentive scheme paying transfers to the firm contingent on realized downstream prices. Alternatively, the regulator can force the firm to sell the following forward contracts: the firm pays the downstream price to the owners of a contract, but receives the expected value of the contracts when selling them to a competitive financial market. We compare the two regulatory tools with respect to regulatory capture: if the regulator can be bribed to suppress information on the underlying state of the world (the basic probability of high downstream prices, or the type of the firm), optimal regulation uses forward contracts only.
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.
The purpose of this paper is to determine whether and how e-leaming technologies and e-leaming programs should be used in order to support knowledge management in multinational companies. These questions are important for two reasons. Firstly, so far only a few papers discuss the relationship between knowledge management and e-learning. Secondly, e-leaming initiatives in multinational companies are of limited success. The paper relates requirements of different types of knowledge with the characteristics of e-leaming technologies and e-leaming programs. Based on this, the paper shows how multinational companies should use e-leaming technologies and e-leaming programs. Different roles of corporate headquarters are discussed in this context.
Urheberrecht
(2018)
Upper echelons theory
(2012)
Unternehmensentwicklung
(2004)
Der Beitrag erfasst den Stand der Forschung zum Gebiet der Untemehmensentwicklung. Hierbei werden Arbeiten aus mehr als einem Jahrhundert eingeschlossen. Aus den bestehenden Forschungsstromungen wird ein Konzept fur die weitere Forschung zur Untemehmensentwicklung hergeleitet. Methodisch ist der Beitrag der konzeptionellen Forschung zuzuordnen. Die existierenden Ansatze werden anhand verwendeter Grundannahmen miteinander verglichen. Das erarbeitete Konzept fur die weitere Forschung setzt auf bestimmten Ausprägungen hinsichtlich dieser Grundannahmen auf. Der Beitrag versucht, der Diskussion der Untemehmensentwicklung neue Impulse fur die weitere empirische Forschung sowohl im Hinblick auf die inhaltliche als auch die methodische Gestaltung zu verleihen. Fur die Praxis versucht der Beitrag, ein Konzept bereitzustellen, anhand dessen Entscheidungstrager eigene Handlungen spiegeln konnen, um auf Schwachstellen in der Entwicklung des eigenen Unternehmens aufmerksam zu werden. Darüber hinaus liefert der Beitrag fur die Akteure der Kapitalmärkte Ansatzpunkte zur besseren Fundierung der Bewertung von Untemehmensentwicklungen.
A growing body of literature investigates the interaction of changes in accounting standards with institutions such as investor protection laws and corporate governance mechanisms. We examine the unintended consequences of fair value accounting in determining mandated preferred dividends. We study the case of Russian energy conglomerate UES, which had a good corporate governance track record and a consistent dividend history. Following its adoption of fair value accounting, UES reported the highest quarterly profit in world corporate history, but it subsequently omitted dividends for all its shareholders. The case analysis suggests that the transitory nature of fair value adjustments and the interaction with the investment policy were important considerations in justifying the dividend omission. The reduction in preferred dividends was not offset by any capital gains, and led to a wealth transfer from preferred to ordinary shareholders. Thus, requiring the use of fair value accounting when determining the dividend distribution base can lead to unintended consequences, and increase agency costs for minority shareholders.
Tätigkeitsbericht
(2010)
Tätigkeitsbericht
(2011)
Tätigkeitsbericht
(2009)
Tätigkeitsbericht
(2009)
Tätigkeitsbericht
(2010)
Tätigkeitsbericht
(2008)
Tätigkeitsbericht
(2011)
Tätigkeitsbericht
(2010)
Tu Felix Koha
(2020)
The aim of this study is to measure total transaction costs as well as the cost components for six German investment management firms from August 1st until October 31st 2001. The investigation is based on a unique order-level data set that includes all relevant information (e.g. time of investment decision, order-release to the broker and trade execution). For computing transaction costs we use tick by tick price data for the 75 stocks of the Euro Stoxx 50- and Stoxx 50-universe.
Volume weighted, i.e. effectively paid, one way transaction costs sum up to 66.76 bp. Market pact is the highest cost component and amounts to 27.93 bp for volume weighted averages. According to a regression analysis market impact is driven by high volatilities and bid ask spreads; market momentum induces market impact to fall, indicating mean reversion of stock prices. High free float and market activity do not influence the market impact, but lead – just as high volatilities and bid ask spreads - to a reduction in trading aggressiveness. Trading duration decreases with high volatility and diminishing bid ask spreads. Overall, the results illustrate that to some extent German investment management firms trade strategically. Nevertheless, a further reduction in market impact, and therefore an increase in investment performance, seems to be possible, if traders pay greater attention on the liquidity indicators known already at order release.
Research on strategic agenda building has traditionally emphasized individual agency, thereby neglecting organizational context. Developing a contextual model of strategic agenda building, we address this limitation. Based on the evolutionary framework we show that an organization’s core elements, that is, strategy, culture, structure and top management team, heavily influence what issues are considered in organizational agenda building processes. Moreover, the strategic agenda building process, apart from the role of capabilities and cognition, provides an alternative explanation for path dependency.
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.
The present dissertation applies findings from the field of cognitive psychology to the business context. Specifically, it examines the influence of different types of similarity on decisions in the areas of innovation and strategy. Similarity and related concepts, such as strategic fit, play an important role in these fields, such as when generating or assessing the value of ideas. However, recent findings in cognitive psychology, indicating that a purely taxonomic, traditional model of similarity does not capture the entire picture of similarity, have widely been ignored in the business context.
Two entities are taxonomically similar if they belong to the same category based on features they share (e.g., dog and cat). In contrast, entities are thematically similar if they co-occur or interact in the same scenario or event (e.g., dog and bone). Thematic thinking builds on the latter type of similarity. The present dissertation focuses on the role thematic similarity plays in managerial decision making and takes the first steps toward establishing thematic thinking as a business-relevant concept, using a multi-study approach. After explaining the conceptual basis of thematic thinking, hypotheses are derived and tested, using four different samples of field data and applying different methods of data collection. The main body of the dissertation comprises four empirical studies.
The first study that is presented examines individual antecedents and outcomes of thematic thinking based on a sample using survey data from 199 individuals. Positive affect and experience are shown to be positively related to thematic thinking. A negative relationship is postulated for thematic thinking and formal education; the relationship found is indeed negative, yet not significant. The empirical findings related to the outcomes of thematic thinking turn out to be the opposite of the postulated relationships: creativity is found to be significantly negatively related to thematic thinking, while adaptation is significantly positively related to it.
The second empirical study investigates the relationship between thematic thinking and individual performance within the research and development (R&D) context. The findings are based on a sample of 172 R&D professionals. As hypothesized, a significant positive relationship between thematic thinking and innovativeness as well as job performance are hown. The relationship between thematic thinking and job performance is mediated by innovativeness. Furthermore, post-hoc analyses reveal that the relationship between thematic thinking and job performance is moderated by political skill.
The Veblen effect revisited
(2018)
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.
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 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.
This paper examines the role of uncertainty in the context of the business cycle in the Eurozone. To gain a more granular perspective on uncertainty, the paper decomposes uncertainty along two dimensions: First, we construct the four different moments of uncertainty, including the point estimate, the standard deviation, the skewness and the kurtosis. The second dimension of uncertainty spans along three distinct groups of economic agents, including consumers, corporates and financial markets. Based on this taxonomy, we construct uncertainty indices and assess the impact on real GDP via impulse response functions and further investigate their informational value in rolling out-of-sample GDP forecasts. The analysis lends evidence to the hypothesis that higher uncertainty expressed through the point estimate, a larger standard deviation among confidence estimates, positive skewness and a higher kurtosis are all negatively correlated with the business cycle. The impulse response functions reveal that in particular the first and the second moment of uncertainty cause a permanent effect on GDP with an initial decline and a subsequent overshoot. We find uncertainty in the corporate sector to be the main driver behind this observation, followed by financial markets’ uncertainty whose initial effect on GDP is comparable but receding much faster. While the first two moments of uncertainty improve GDP forecasts significantly, both the skewness and the kurtosis do not augment the forecast quality any further.
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)
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.
The future of sportstech
(2022)