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Élite role and context
(2011)
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.
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.
Value based management
(2008)
Upper echelons theory
(2012)
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.
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)
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.
Dispersed innovation teams rely upon team members who share leadership responsibilities to attain high levels of team performance. Although this concept of team shared leadership is receiving increasing attention, this dissertation shows that especially research on team-level antecedents of shared leadership has major deficits regarding a basic framework for analyzing antecedents, depth of theory, context-specific arguments, and empirical validation. This dissertation tries to fill these research gaps, thus shedding light on the question: How can we foster the important process of shared leadership in dispersed innovation teams?
This dissertation introduces a theoretical framework into shared leadership literature to structure the antecedents of shared leadership according to their mode of functioning. As such, this dissertation argues for the first time that to establish high levels of team shared leadership the basic dimensions of motivation, opportunity, and ability for shared leadership should be addressed (motivation-opportunity-ability framework or MOA framework). Based on this notion team-level antecedents providing motivation, opportunity, and ability for shared leadership are operationalized and hypothesized as antecedents of shared leadership in dispersed innovation teams using acknowledged theories. Moreover, all discussed hypotheses are verified in a sample of 96 dispersed real work teams with innovative software tasks. Thereby, empirical results are drawn from 96 team leader responses (used to assess team-level antecedents of team shared leadership) and 337 team member responses (used to assess team shared leadership).
Motivation for Team Shared Leadership. Based on the perspective of shared leadership as a risk-taking behavior for team members in dispersed innovation teams, trustworthiness is argued as a facilitator of the willingness, thus motivation to engage in risky shared leadership actions with others. This argumentation based on trust theory was supported by empirical results showing that team member trustworthiness in terms of benevolence and integrity was positively related to team shared leadership. Surprisingly, the proposed positive relationship between ability-based trustworthiness and shared leadership could not be confirmed, thus ability-based trustworthiness could not be validated as a facilitator of shared leadership in dispersed innovation teams.
Opportunity for Team Shared Leadership. Opportunity for team shared leadership is addressed by discussing team reflexivity as an antecedent of shared leadership in dispersed
innovation teams. Team reflexivity is argued as opportunity providing antecedent of shared leadership as it gives team members a clear information basis in the complex and constantly changing environment of dispersed innovation teams, thus making leadership needs identifiable. In support of this argumentation based on goal setting theory and shared mental model theory team reflexivity was positively related to team shared leadership. Thereby, the relationship between team reflexivity and shared leadership could be shown as even stronger under conditions of high team role breadth self-efficacy and high team empowerment.
Ability for Team Shared Leadership. Ability for shared leadership is addressed in terms of social and project management skills. These two skills are argued as basic and
complementary skills needed for shared leadership in dispersed innovation teams based on socio-technical systems theory. Underscoring the importance of interpersonal competence
the empirical analysis showed that social skills were strongly positively related to team shared leadership. Contrary to the hypothesis of this study project management skills were not related to team shared leadership.
Structural Team Properties and Team Shared Leadership. Moreover, several structural team properties are discussed as team-level antecedents of shared leadership, namely female ratio, mean age, age diversity, and national diversity. Thereby, structural team properties are argued as potentially affecting team shared leadership through several
MOA dimensions. In the empirical analyses female ratio was positively related to shared leadership in dispersed innovation teams, while mean age was negatively related. Age
diversity showed no significant relationship and national diversity was marginally positively related to shared leadership in dispersed innovation teams. Based on these findings, important implications for practice, related to the three stages of a project team (establishment, forming, and performing stage), are provided. As such, team leaders of dispersed innovation teams is given a check-list of how to foster shared leadership in dispersed innovation teams based on the results of this dissertation. Future research is especially suggested regarding the “non-findings” of this dissertation, interaction effects, additional team-level antecedents, the vertical team leader’s role within shared leadership evolvement, antecedents of shared leadership in other contexts, and other levels of antecedents (e.g., organizational-level antecedents).
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.
Supply chain risk management
(2012)
Supply chain fit
(2009)
Supply chain complexity
(2019)
Supply chain management (SCM) is known as a competitive strategy in today’s business. Through increasing product variety, structures and processes, managers struggle with an increase in supply chain complexity (SCC), which leads to a negative influence on a firm’s efficiency in terms of lead times, flexibility and costs. In order to manage complexity, supply chain managers need to know its drivers or define appropriate management measures. However, managers can no longer manage SCC internally. In order to cope with this challenging issue and achieve overall benefits, it is necessary that the complete supply chain is involved in optimising supply chain processes. An aligned supply chain should ensure that supply chain partners benefit from cost savings. At the same time, supply chain processes should be agile to respond to market volatility, while also being cost-efficient to survive in the highly competitive world.
In this dissertation, three research cases are conducted with different focus areas whereby first drivers for internal and external SCC are explored in a single case study. In order to gain a representative overview of relevant drivers, not only interviews with top managers from a manufacturer are held but also with key managers from suppliers and customers. With the interviews held from the upstream to downstream supply chain, this part of the dissertation sheds light on topical SCC drivers and provides some hypotheses for managing detailed and dynamic SCC. With this case, an academic contribution is provided to better understand the relevance of SCC.
In a second step, another qualitative study addresses the requirements of a supply chain. The basis for this part is the triple-A supply chain of Hau Lee (2004), where he states from a theoretical perspective the prerequisites of a supply chain, namely agility, adaptability and alignment. In this dissertation, this approach is reviewed from a managerial perspective. It is analysed which key performance indicators of a firm are affected by the three aforementioned qualities. Finally, it is presented how agility, adaptability and alignment are linked together. In order to gain a common understanding, semi-structured interviews are conducted to extend literature through managerial experience.
In a third step, a quantitative simulation creating a discrete-event simulation model is provided to evaluate inventory and logistics costs’ effects in a company that faces significant SCC. The target is to align the supply chain by changing the existing distribution channel to direct delivery. The result shall demonstrate the financial benefit for the supply chain partners, as well as illustrating the conditions and risks when changing supply chain processes.
This dissertation shows that companies are increasingly faced by SCC. In order to respond flexibly to market volatility, supply chains must be agile to ensure that financial risk shall not alarm a company’s existence. Therefore, it is recommended to align the supply chain with a collaborative supply chain model.
Status dynamics and their effects on individual cooperation and performance in interdependent teams
(2017)
Servant Leadership in der IT
(2017)
This dissertation integrates three papers on risk management in complex new product development projects (NPD) with an integrated perspective on the automotive industry. It seeks to provide a comprehensive access to the topic by highlighting the most relevant risk management aspects in NPD – processes and methods (chapter 2), organization and coordination (chapter 3) as well as culture and strategy (chapter 4).
The focus of chapter 2 lies on the identification and assessment of risks as the initial and most critical risk management steps. It illustrates the application of the analytic hierarchy process (AHP) method as an established multi-criteria decision analysis method to create transparency on the overall risk position of a selected highly complex NPD in the automotive industry. Chapter 2 confirms the AHP method as a suitable approach for stronger preventive risk management in complex NPD with stakeholders with conflicting functional perspectives. This sets the stage for chapter 3 and a subsequent consideration of organization and coordination.
Chapter 3 considers an issue of organization and coordination in complex NPD that is mainly based on the fact that the involved stakeholders have individual targets and rely on concurrent processes. The selected automotive industry example illustrates the resulting managerial challenge of balancing the overall NPD requirements such as product characteristics and development lead time (system perspective) and the targets of single stakeholders in NPD (individual perspective). The NK model as an important setting for search builds the methodical basis to solve this issue and results in optimized organizational and coordination setups.
The implications on risk management in complex NPD as a result of newly emerging NPD types in the automotive industry are subject to chapter 4. These implications are in line with profound industry changes that include changes in the requirements of customers and markets. Chapter 4 uses complexity theory and deploys the lens of complex adaptive systems (CAS) to NPD to outline the emerging NPD types and their implications on risk framing activities. An extended risk framework is derived as basis that addresses the cultural and strategic shift in the automotive industry with regard to managing risk in emerging NPD types.
This dissertation examines the role tangible resources play in innovation projects. Specifically, it is distinguished between two mindsets when it comes to tangible resource constraints in innovation projects. One mindset regarding the relationship between tangible resources and innovation project performance can be described best as “resource driven”, meaning that innovation project performance is strongly linked to the presence of adequate tangible resource input. Counter-intuitively, a vast number of case examples suggest the exact opposite, though. The underlying “less is more” mindset suggests that innovation project performance could also be achieved because (not despite) of constrained tangible resources. Those views reflect the two extremes of the continuous variable “tangible resource orientation” which expresses the perceived dependency on tangible resources in order to achieve a high level of performance in innovation projects.
Since conceptual and empirical evidence on the antecedents and consequences of varying degrees of tangible resource adequacy in extant literature is inconclusive, this dissertation aims at disentangling the ambiguous tangible resources-innovation project performance relationship. The arguments I put forward in this dissertation are based on conceptual evidence as a result of synthesizing literature from neighboring research fields like cognitive psychology, entrepreneurship, and corporate strategy pointing to mechanisms which cause material resource constraints to facilitate creative and innovative performance in innovation projects. These literature streams are then brought together within the framework of the resource based view (RBV) of the firm. The empirical analyses to test the hypothesized relationships are based on a sample consisting of 121 innovation project teams from the electronics industry.
The results presented in this dissertation show that the ambiguity of the relationship between tangible resources and innovation project performance observed in the literature may be a result of moderating variables that determine the direction and strength of tangible resources’ influence on innovation project performance. Specifically, the analyses reveal an interaction effect in that perceptions of material resource adequacy indeed may entail diverging effects on innovation project outcomes in family and non-family businesses, depending on differences in these firms’ organizational culture regarding the orientation toward material resources. In particular, innovation projects in non-family firms do Extended summary III significantly better when being provided with more adequate material resources, while in family firms innovation projects do not show significant differences in conditions of more or less adequate tangible resources and are shown to perform better (relative to those in nonfamily firms) under conditions of perceived material resource inadequacy.
Moreover the results of this dissertation point to the influence of socio-cognitive factors on a team’s evaluation of material resource adequacy. These socio-cognitive influences are further shown to root both on innovation project team attributes and capabilities, as well as on characteristics of organizational culture. Specifically, regarding team-level factors, it is found that innovation project team members’ perception of material resource adequacy is influenced by a team’s action repertoire, the belief of a team being able to accomplish a task, and the team task’s work volume. Regarding organizational-level factors, this research reveals that innovation project teams in family firms perceived their material resource provisions generally as being more adequate. Further, this relationship is shown to be mediated by an organization’s cultural characteristic of material resource orientation, representing an important link that transmits family influence to innovation project teams’ perceptions of material resource adequacy.
Finally, the results reveal that depending on which outcome dimension is operationalized as the main project focus, i.e., whether the quality or innovativeness of the developed products is used as outcome measure, converse results may emerge. Specifically, it is found that perceptions of material resource adequacy have a positive effect on new product quality and a negative effect on new product novelty. In a similar vein, relative team size is shown to be negatively related with new product novelty while showing a positive relationship with new product quality. Additionally, relative team size is also found to have a positive relationship with project efficiency.
From these findings, key implications for the management of innovation projects are derived in order to ensure the effectiveness of companies’ investments in R&D, showing that adequate tangible resources per se do not have a direct effect (neither positive nor negative) on innovation project performance. Rather, the role of tangible resources in innovation projects is contingent upon how adequate tangible resources are perceived to be, on certain contingency variables, and on the outcome dimension focused on. Thus, sometimes less may indeed turn out to be more and this thesis points to ways how to tap this potential.
Private banking rating
(2013)
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).
Past research has identified innovation portfolio management as a key success factor for a firm’s new product development process. However, the relevant literature falls short in considering the strategic scope of innovation portfolio decision-making. To take a first step in this direction, Marcel Coulon defines the construct of ‘portfolio orientation’ as a strategic orientation. He develops a conceptual framework to identify performance effects of portfolio-oriented decision-making for new product development programs. Based on data of more than 200 German companies, he presents three papers. First, he addresses contingencies for portfolio orientation effectiveness to help understand interactions of formal innovation management with new product development issues inside and outside firm boundaries. Second, he identifies best performing portfolio types for specific levels of portfolio management formalization. Third, a variety of antecedents and their impact on a firm’s portfolio orientation are presented. Accordingly, the book is equally relevant for practitioners and academics in the fields of research and development management and innovation management, as well as strategic marketing management.
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.
Disruptions regularly hit economies. Scholars and industry experts suggest many strategies to avoid disruptions or handle them effectively. Two things are repeatedly mentioned: Increasing resilience and deploying artificial intelligence (AI) technologies. In this dissertation, we1 look at both aspects and focus our efforts on production processes. To get a well-rounded view, we apply various research methods, i.e., surveys, case studies, and systematic literature reviews (SLRs). In our first paper (Chapter 2), we investigate the perceived organizational resilience of companies in the German manufacturing industry. We perform an SLR to analyze existing research on organizational resilience measures. We see that existing (qualitative) resilience measures are complex, challenging to interpret, and therefore, hard to scale and apply across multiple industries. Based on this, we develop a novel, low-threshold resilience measure consisting of six resilience items about the past perceived internal/external resilience, current perceived internal/external resilience, and anticipated need for internal/external resilience, called the Enterprise Resilience Index (ERI). Finally, we conduct an empirical study with ~200 German experts across various industries. Our survey shows that the German manufacturing industry perceives itself as relatively resilient, with significant differences between industries and company sizes. We also see that they anticipate a high need for external resilience across industries in the future. Most strikingly, the Machinery industry shows the lowest ERI levels while it anticipates a relatively high need for resilience, showing the development need for this industry in terms of resilience. To explore the aspect of AI, we focus on waste incineration plants (WIP) in Chapter 3. WIPs have various levels of automation, but they still rely on manual operations by human operators. Consequently, the combustion process is managed rather inefficiently, and steam outputs and emission levels are not optimal. Thus, we investigate how reinforcement learning (RL) can help enhance process automation and thus optimize the combustion process, e.g., by making more frequent and diverse interventions. An RL agent is trained via trial and error with a reward function that includes the optimization criteria. Since the actual equipment, i.e., the real WIP, cannot be used as the training environment, a digital twin is built using original plant data and a neural network. The RL agent is then trained in this offline environment with the deep Q-network algorithm (DQN). Our work demonstrates that a digital twin of a WIP can be built in a data-driven way. We show that the RL agent outperforms the human operator, increasing the steam output by 7.4% and reducing the oxygen level by 3.6%. Thus, applying RL might benefit the plant operator financially due to increased output and the environment in terms of reduced emission levels. Finally, we look at a practical aspect of AI: AI readiness and adoption (Chapter 4). Many companies across various sectors have adopted AI technologies. However, the supposedly high adoption rates are misleading since many applications are rather experimental and not applied in key business areas. We believe that this limited AI adoption arises from a lack of AI readiness. We conduct a case study in the waste incineration industry with over 160 clients and investigate which strategies facilitate AI adoption in not-AI-ready industries. Based on these interactions, we distill five strategies that counter typical AI readiness barriers, thus increasing AI readiness: education, trust, customer centricity, focus, and collaboration. These strategies focus on transforming businesses just as much as necessary to prepare them for the AI technology that is supposed to be implemented. With increased AI readiness, chances for AI adoption rise. We are convinced that these strategies can be applied in various environments. In summary, this dissertation gives empirical evidence and expands the literature on organizational resilience and benchmarking, reinforcement learning and digital twins, and AI readiness and adoption.
1 The term “we” refers to the authors of the respective chapters, as noted at the beginning of each chapter.
The 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.
Open-ended property funds
(2011)
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.
Managerial Intentionality
(2013)