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Disruptions regularly hit economies. Scholars and industry experts suggest many strategies to avoid disruptions or handle them effectively. Two things are repeatedly mentioned: Increasing resilience and deploying artificial intelligence (AI) technologies. In this dissertation, we1 look at both aspects and focus our efforts on production processes. To get a well-rounded view, we apply various research methods, i.e., surveys, case studies, and systematic literature reviews (SLRs). In our first paper (Chapter 2), we investigate the perceived organizational resilience of companies in the German manufacturing industry. We perform an SLR to analyze existing research on organizational resilience measures. We see that existing (qualitative) resilience measures are complex, challenging to interpret, and therefore, hard to scale and apply across multiple industries. Based on this, we develop a novel, low-threshold resilience measure consisting of six resilience items about the past perceived internal/external resilience, current perceived internal/external resilience, and anticipated need for internal/external resilience, called the Enterprise Resilience Index (ERI). Finally, we conduct an empirical study with ~200 German experts across various industries. Our survey shows that the German manufacturing industry perceives itself as relatively resilient, with significant differences between industries and company sizes. We also see that they anticipate a high need for external resilience across industries in the future. Most strikingly, the Machinery industry shows the lowest ERI levels while it anticipates a relatively high need for resilience, showing the development need for this industry in terms of resilience. To explore the aspect of AI, we focus on waste incineration plants (WIP) in Chapter 3. WIPs have various levels of automation, but they still rely on manual operations by human operators. Consequently, the combustion process is managed rather inefficiently, and steam outputs and emission levels are not optimal. Thus, we investigate how reinforcement learning (RL) can help enhance process automation and thus optimize the combustion process, e.g., by making more frequent and diverse interventions. An RL agent is trained via trial and error with a reward function that includes the optimization criteria. Since the actual equipment, i.e., the real WIP, cannot be used as the training environment, a digital twin is built using original plant data and a neural network. The RL agent is then trained in this offline environment with the deep Q-network algorithm (DQN). Our work demonstrates that a digital twin of a WIP can be built in a data-driven way. We show that the RL agent outperforms the human operator, increasing the steam output by 7.4% and reducing the oxygen level by 3.6%. Thus, applying RL might benefit the plant operator financially due to increased output and the environment in terms of reduced emission levels. Finally, we look at a practical aspect of AI: AI readiness and adoption (Chapter 4). Many companies across various sectors have adopted AI technologies. However, the supposedly high adoption rates are misleading since many applications are rather experimental and not applied in key business areas. We believe that this limited AI adoption arises from a lack of AI readiness. We conduct a case study in the waste incineration industry with over 160 clients and investigate which strategies facilitate AI adoption in not-AI-ready industries. Based on these interactions, we distill five strategies that counter typical AI readiness barriers, thus increasing AI readiness: education, trust, customer centricity, focus, and collaboration. These strategies focus on transforming businesses just as much as necessary to prepare them for the AI technology that is supposed to be implemented. With increased AI readiness, chances for AI adoption rise. We are convinced that these strategies can be applied in various environments. In summary, this dissertation gives empirical evidence and expands the literature on organizational resilience and benchmarking, reinforcement learning and digital twins, and AI readiness and adoption.
1 The term “we” refers to the authors of the respective chapters, as noted at the beginning of each chapter.
Measuring the influence of subsidies on the sustainability of microfinance institutions is a major challenge. This dissertation shows that the Subsidy Dependence Index remains the most promising ratio to calculate the subsidy received and measure microfinance institutions’ dependence on subsidies to conduct further research on the institutions’ ability to become socially and financially sustainable. By comparing the Subsidy Dependence Index to alternative measurements, a detailed discussion, and an exemplary calculation, its applicability to addressing the research gap of microfinance institutions’ reliance on subsidies to improve their efficiency can be shown. A calculation for 224 microfinance institutions serving 23.5 million active borrowers shows its applicability for large-scale data sets. Focusing on the microfinance institution (MFI), this research project takes an institutionalist approach.
Since efficiency is the prerequisite to sustainability, the hypothesis that subsidies have a conflicting influence on microfinance institutions’ financial and social efficiency has been tested using a reduced panel of 216 microfinance institutions. A multi-input/ multi-output data envelopment analysis (DEA) is applied to determine financial and social efficiency beyond ratios. The efficiency scores that serve as a proxy for sustainability are then used as dependent variables in panel data regressions. The main finding is that subsidies have a negative but only marginal effect on efficiency. However, the effect is only strongly significant regarding social efficiency.
To the best of the author’s knowledge, there is no research done to evaluate the influence of subsidies on financial and social efficiency based on a DEA.
In my doctoral dissertation, I conduct research on family firm decision-making, performance, and valuation. In particular, I explore (i) the role stocks—in contrast to flow-based theories used by extant research (i.e., prospect theory and its derivatives)—in share repurchasing decisions of family firms by drawing on motivation-opportunity-ability theory of behavior and the developed stock-based view on family firm decision-making, (ii) the moderating effect of national culture (i.e., the degree of masculinity) on the effects of board diversity on family firm performance by drawing on upper echelons theory, and (iii) the effects of non-family-managed family firms on firm valuation in the acquisition context by drawing on signaling theory.
External investors
(2023)
Within this doctoral dissertation, I explore external investments in family firms. In particular, this dissertation comprises three independent studies to contribute to and extend current literature regarding family firms and external investors. The first study extends research on external investments in family firms by fundamentally analyzing the decision criteria of family firm owner-managers for using external minority investments, thus representing a first interim step of external succession. The second study changes the point of view and contributes by analyzing the drivers of financial investors’ preference for acquiring a family firm. The third study analyzes drivers resulting in financial investors’ successful or unsuccessful acquisition of family firms.
Greening of the tax code
(2023)
This thesis examines the impact of green fiscal policies, specifically environmental taxes, on firms' decision-making and competitiveness. It aims to contribute to the taxation and financial policy literature by analyzing the influence of environmental taxes on corporate investment decisions and the distribution of the economic burden, the effect on corporate emission levels, and the shift in competitive market dynamics due to a green VAT. The findings suggest that standalone environmental taxes may not be the first best option, but their effectiveness can be improved through combining them with additional policy measures to foster firms' innovativeness. The thesis also shows that the adjustment of traditional forms of taxation, such as the VAT, can promote sector growth. The thesis thereby provides a more nuanced view on the economic consequences of environmental taxes.
Airport slot allocation
(2023)
This dissertation addresses the concept of airport slot allocation as a major regulatory directive in air transport management. In three sequential parts, today’s slot allocation procedure, addressing the assignment of time windows for departure and landing operations at coordinated airports, is being assessed and critically evaluated. The three sections evolve from the evaluation and revision of a suitable criteria set to the development and implementation of a network solution. As a key feature, a carbon emissions price including the air carriers’ CO2 emissions is being provided, serving as the allocation principle. In all three parts, explicit reference to the IATA Worldwide Slot Guidelines, representing today’s regulatory framework, is being provided, highlighting explicit deficits and drawbacks of that solution. In the first part, the Analytic Hierarchy Process (AHP), as a concept of decision making based on a comparison of criteria and alternatives, is being applied guiding the allocation decision at a single airport. In this section, a set of multiple criteria is being proposed and weighted according a set of slot coordinators’ preferences. As a key feature, the concept of Analytic Hierarchy Process (AHP) is being extended by the conduction of a Pairwise Comparison-based Preference Measurement (PCPM) representing one stage of the Analytic Hierarchy Process (AHP). In part two, the perspective of the dissertation changes from a single-point to a multiple-point allocation environment. In this part, a model is being provided that includes the allocation of slots in an airport network. As a key feature, slots are being allocated such that the two complementary cost functions are being minimized. On one hand, the developed carbon cost function includes the minimization of the carbon footprint per traveling passenger. On the other hand, the developed handling cost function is being minimized incorporating a dedicated airport perspective to the solution. In part three, the proposed model is being further extended by the incorporation of a third directive, the minimization of connection cost, related to the application of solution in a hub-and-spoke network. As a result, the study demonstrates how slot allocation can be conducted efficiently in an airport network, and how the consideration of the carbon footprint per traveling passenger serves to calculate the allocation optimum.
Linking talent management and organizational outcomes through the lens of professional football
(2023)
Essays in health economics
(2023)
In 2020, approximately 151,000 warehouses were operating worldwide, with a total annual expenditure of e300 billion, representing roughly half of total supply chain costs. Optimized warehouse management may provide competitive advantages from both cost and customer service perspectives. One way to achieve these is to leverage the abundance of data collected in supply chains in combination with powerful algorithms. This dissertation investigates how novel data sources and optimization algorithms, such as machine learning, can be applied in the context of warehouse advancement. In this research, we1 analyze the warehouse environment from two perspectives. On the one hand, we examine two available resources in warehouses—equipment and employees—and explore how predicting breakdowns and productivity, respectively, may improve warehouse performance. On the other hand, we investigate whether the warehouse concept of crossdocking can be applied virtually to allow dynamic transfers between delivery vehicles.
In our first paper, we partner with one of the largest logistics service providers to examine how master, usage and sensor data on material handling equipment can be incorporated into a predictive maintenance model. Existing literature focuses on either time- or condition-based variables, whereas we show, in a comprehensive study of statistical learning methods, that both variable types can be included simultaneously. Our predictive maintenance model is able to capture the majority of breakdowns (> 85%), while maintaining a low false-positive ratio (< 7%).
In our second paper, we work with the same logistics service provider and apply Extreme Gradient Boosting to predict the productivity of new hires. We include operator, shift and product data to show that productivity can be predicted on an individual employee basis while substantially decreasing the forecasting error (50%), which translates into cost savings.
In our third paper, we look at dynamic and synchronized transshipments during direct deliveries. This concept uses transfers between vehicles, as carried out in cross-docking, but without the need for a physical warehouse. This reduces the proportion of empty return trips by increasing the proximity of vehicles to their location of origin. Our easy-to-implement multi-algorithm reduces the total distance by 15% on average compared with simple direct deliveries, and solves large problem instances within reasonable computational times.
This dissertation with its individual research contributions highlights how novel data sources and optimization algorithms can contribute to advancing warehouse management, and highlights the managerial implications of all three topics.
1The term “we” refers to the authors of the respective chapters, as denoted at the beginning of each chapter.
The future of sportstech
(2022)
Abstract
In this dissertation, I explain how organisational and personal practices transcending the usual business context are consequential to the value creation process. Adopting the perspective that value creation is socially constructed, I show how shareholders and stakeholders draw on shared values and meta-economic resources to co-create value in a dynamic ecosystem. My model demonstrates that practices and interactions in non-traditional business spaces are central rather than peripheral to the value creation process. Through a two-part research study consisting of a historical case analysis and interview-based field work with elements of grounded theorising, I develop an explanatory model of non-profit competition using the salient case of a German cooperative banking group. My practice- and process-based model of relational value creation shows how deliberate and emergent frontline strategy in non-business spaces creates value, thereby broadening the narrow neoclassical focus on product and service ecosystems. Organisational and personal practices often go against the principles of profit maximisation, reproduce shared values, and occur outside business settings. They are consequential to product and service value propositions because they govern personal relationships and interactions among shareholders and stakeholders. In the case of cooperative banks, value creation involves establishing a local ecosystem, building personal relationships, enhancing trust and knowledge within these relationships, and fostering reciprocal behaviour which ultimately leads to value capture (i.e., the exchange and use of products and services). This enhances the holistic understanding of strategy by stressing emergent strategizing in non-business spaces. Moreover, it illustrates a relational notion of competition beyond product-driven innovation and growth.
Door-to-door (D2D) air travel is gaining momentum for airlines, airports, and feeder traffic providers. The mobility industry and researchers are broadening their scope to include the entire travel chain, from origin to final destination. Intermodal mobility products are already on the market. At the same time, widespread trends affect transport service providers (as the suppliers) and passengers (regarding demand). Acquiring a better understanding of future D2D air travel trends is crucial for the mobility sector for long-term planning, product adaptation, the services provided and the pricing of these, and improvements in the passenger experience. Focusing on the European market, the overall objective of this doctoral thesis is to identify and understand the future trends of D2D air travel. It is divided into three parts; these provide different perspectives on trends and employ a range of methods that lead to results that develop from each other.
In Part One, the Delphi technique is utilized to identify future travel trends. The study considers projections of European air passengers and their requirements for their entire air travel chain, including airport access, a long-haul flight, and airport egress. The research focuses on 2035 and is based on a two-round Delphi survey involving 38 experts from the transport industry, academia, and consultants. The Delphi survey is supplemented with findings from a preliminary study, consisting of a literature review, interviews with 18 experts in the field of air travel, and a workshop attended by experts. Results reveal that digitalization and personalization will be the main drivers in 2035 and that passengers might demand value-added use of their travel time. In addition, environmentally friendly travel products are considered desirable but only somewhat probable by 2035. Passenger type, age, origins, and travel budget will still be influential factors in 2035. Based on the results from a hierarchical cluster analysis, Part One presents three possible future scenarios: (1) personalized D2D travel, (2) integrated D2D travel, and (3) the game-changer. A technical chapter elaborates on the Delphi technique and individual research steps.
Part Two explores the supply aspect and to what extent transport service providers consider strategically relevant trends. The scope of D2D air travel is adapted by applying multi-labeled text classification models to 52 corporate reports from a sample of transport service providers that operate in the European market. Trends identified in the first Delphi study and from an additional literature review are used to develop seven classes. Two prototype models are developed: a dictionary-based classifier and a supervised learning model using the multinomial naive Bayes and linear support vector machine classifiers. The latter yields the best model output, revealing which trends have a higher, medium, or lower relevance on the supply side. The results show that providers consider environmentally friendly air transport and related products to be highly relevant while disruption management, leveraging passengers’ data, and improving airport feeder traffic through innovative mobility initiatives are considered to be of medium relevance.
Part Three explores air passengers’ preferences and willingness to pay for ancillary services in the current transition into the new normal, brought about by the ongoing COVID-19 pandemic, high uncertainty, and changing market dynamics. A choice-based conjoint analysis is used to test six attributes within a hypothetical travel scenario for a long-haul one-way air trip. Choice data from 269 German business and leisure passengers are analyzed using a hierarchical Bayes estimator. Results reveal that the total ancillary service upgrade price influences passengers’ choices the most, followed by a seat upgrade for greater comfort and the CO2-compensation of a flight. Hygiene-related ancillaries bring low utilities. Female and senior passengers care more for environmentally friendly ancillaries. Confirming previous research, business passengers and frequent flyers care more for onboard comfort.
In the decade following the 2008 financial crisis, the coronavirus viral disease 2019 (COVID-19) pandemic and United States (US) President Trump’s Twitter account became representations of market uncertainty, attracting the financial research of (Goodell, 2020; Benton and Philips, 2020). Due to the popularity of these events and their impact on financial markets, many unanswered questions still persist, particularly, how the financial structure has changed during this unique time. The popularity of Bitcoin, one of the main cryptocurrencies, has caused a controversial topic to arise in recent academic research, namely, whether its function compares to that of conventional precious metals such as gold and platinum. This doctoral thesis aims to fill this research gap in two ways: (i) by addressing market reactions to the COVID-19 pandemic and political news by answering the question of how US legislators traded at an industry level during the ongoing COVID-19 pandemic, and how Trump’s Twitter account could shake the equity market during a trade war, and (ii) by examining the power of the gold and platinum ratio, which was first studied by (Huang and Kilic, 2019) ), in predicting Bitcoin as well as how political sentiment could drive the returns, volatility, and volume of this cryptocurrency. This thesis contributes to the empirical evidence in the areas mentioned above due to the growing attention on the financial function of cryptocurrency, the debatable effects of political news regarding the use of social media, and the eventual and unprecedented scale of the COVID-19 pandemic.
Transportation is the backbone of globalization and international trade. Moving goods over long distances enables companies to access new markets and consumers to buy products from other parts of the world. Global trade is particularly driven by sea freight due to low cost and air cargo owing to its high speed, making both transport modes key for many industries. Anticipating future developments in transportation remains a black box for many companies. The logistics sector is characterized by high price uncertainty, market volatility, and product complexity. Transport is often organized manually and only based on employees’ experience, making it prone to error. Recent trends in international trade further complicate companies’ decisionmaking. Trends include changes in global demand, particularly driven by growing wealth in Asian countries, leading to shifts in freight rates on major trade lanes. The risk of supply chain disruptions, such as delays of container vessels, has also been increasing in the last few years. More frequent extreme weather events caused by climate change and higher traffic on shipping routes make on-time arrivals more challenging than ever. Recent breakthroughs in research indicate that novel data analytics-based methods can help to increase transparency in transportation by supporting the decision-making of shipping players. It has become evident that machine learning enhances forecast accuracy, which could enable companies to reduce uncertainty in their logistics. In our first study (Chapter 2), we1 analyze the container shipping industry to predict delays of vessels. With a forecast accuracy of 77%, we identify important influencing factors for shipping delays. This primarily includes the time between ports, piracy risk, demographics, weather, traffic in maritime chokepoints, and port congestion. In our second study (Chapter 3), we present what methods need to be applied to predict spot rates in container shipping. With an accuracy of 89%, our forecasts support the decision-making of various shipping players in negotiating their transportation contracts. My dissertation journey then took me to the air cargo industry in our third study (Chapter 4). By assessing the predictability of long-term air freight rates, we show that machine learning improves forecasts, especially for trade lanes with volatile and complex price trends. As a result, we achieve an accuracy of 93%, enabling air carriers and freight forwarders to increase their operating profits by 30%. By proposing prediction solutions featuring high accuracy, robustness, and applicability in practice, this dissertation demonstrates that predictive analytics enhance transparency in transportation. It is our hope that more advanced technologies, such as machine learning, will play an increasingly important role in future decision-making on transportation.
1The term “we” in this dissertation always refers to the authors of Viellechner and Spinler (2020, 2021a,b,c)
What is it that actuates a person to really engage with their work instead of simply going through the motions? Through what means can an individual become involved and immersed in their appointed task? A textbook definition of motivation proposes it to be the sum of ‘the processes that account for an individual’s intensity, direction, and persistence of effort towards attaining a goal’ (Robbins & Judge, 2015), and adjudges it as a ‘psychological process resulting from the interaction between the individual and the environment’ (Latham & Pinder, 2005). Since the 1940s a succession of theories has interrogated the interaction of the various needs and values of the individual with those of the organisation, with a theoretical cleft lying about the origin of the motivation, hypothesised as springing from an external or from an internal source (Ryan & Deci, 2000a), with external motivation arising from incidentals associated with but outside the work itself while internal motivation emerges from the enjoyment that the worker derives from the task. A third very important type of motivation, prosocial motivation, taps into the desire to protect and care for others (Grant & Berg, 2011). However, one particular class of influential values that have the potential to motivate has hitherto been ignored: that of one’s religious beliefs. This is especially puzzling as religiosity or religiousness has experienced a recent surge, especially during the past decade. For example, in 2014 around 41% religiously affiliated American adults affirmed that they rely mainly on their religion’s tenets to guide them morally, a startling increase from the 34% who answered accordingly in 2007 (Pew Research Center, 2015). Closely associated with and reinforcing this motivational source is a second unexplored possible wellspring of motivation: one’s sense of chosenness in the religious sense. Present mainly in the Abrahamic religions (Judaism, Christianity, and Islam), chosenness is the feeling that one has been selected, or elected by a supernatural entity, often for a specific reason or a special mission.
This dissertation investigates the value of customer behavior in supply chain management through the application of (big) data analytics in demand forecasting. The use of advanced analytics in supply chain management is not novel. However, the growing expansion of data volumes provides companies with new opportunities to optimize their supply chain. Despite the rising interest from both academia and practice and the recent increase in publications in this area, empirical insights are still limited. At the same time, changes in customer expectations towards instant product delivery require companies to rethink their supply chain, where accurate demand forecasts are often at the core of enabling efficient and flexible processes. This makes the development of demand prediction models that can be used in practice especially relevant. We1 analyze the use of customer behavior in demand forecasting in three separate research papers. Leveraging data from research partners in the online fashion and construction industry, we assess the potential of the developed prediction models in three areas of application in supply chain management, namely order fulfillment, order picking, and inventory planning. In the first paper, we develop a prediction model for anticipatory shipping in the fashion industry, which predicts customers’ online purchases with the aim of shipping products in advance, and subsequently minimizing delivery times. Using various forecasting methods and data on customers’ behavior on the website, we test if, and how early, it is possible to predict online purchases. Results indicate that customer purchases are, to a certain extent, predictable, but anticipatory shipping comes at a high cost due to wrongly sent products. The second paper assesses the extent to which clickstream data can improve forecast accuracy for fashion products. Specifically, we assess which clickstream variables are most suitable for predicting demand, and identify the products that benefit most from this. Results indicate that clickstream data is especially useful for forecasting medium- and certain intermittent-demand products. A simulation of order picking for these products shows that using clickstream data in the forecast substantially decreases picking times. The third paper investigates how sequential pattern mining can be used to determine products with correlated demand, and how to leverage this as an input into forecasting for a supplier in the construction industry. We find that sequential pattern mining may be beneficial when used in combination with traditional forecasting methods, and that support vector regression models seem especially suited to forecast intermittent-demand products. An application to inventory planning shows that our developed forecasting model might reduce the company’s costs of inventory holding and lost sales by up to 6.9%. Overall, our research highlights the value of using customer behavior to enhance demand forecasting and the benefit of using improved forecasts in various applications in supply chain management.
1Referring to the authors of the respective chapters as noted at the beginning of each chapter.
Using comprehensive panel data on Chinese acquisitions in Germany in the time period from 2007 to 2016, this thesis investigates the effects of the acquisition on target firm performance and concentrates on some critical issues regarding target top management team turnover and target firm performance as well as acquirer technological capability and target innovation performance. More specifically, the thesis focuses on the following questions: (1) How do acquisitions by emerging-market firms in developed markets influence target post-acquisition firm performance? (2) Do target top management team turnover or target chief executive officer (CEO) turnover have an effect on target post-acquisition firm performance in case of emerging-market acquisitions in developed markets and what is the moderating role of appointing an acquiring firm´s manager as target CEO? (3) How does the acquirer’s technological capability relate to target post-acquisition innovation performance in case of emerging-market acquisitions in developed markets and does prior international acquisition experience of the acquiring firm play a moderating role in this relationship? The theory and findings contribute to the existing literature on emerging-market firms and their investments in developed-market firms and offer some managerial guidelines in managing target post-acquisition success.
This dissertation investigates different applications of data analytics in supply chain planning. In the last years, data analytics became more important, because of the increase of computational power and the larger availability of data. Data analytics is used in various domains to improve operations performance, increase customer satisfaction and revenues. However, both the research and the application of data analytics in supply chain management is still lacking behind other industries. We1 analyze the potential of data analytics in the field of supply chain planning in three exemplary fields: demand forecasting, partial defection prediction and price discrimination. In addition, we demonstrate how to deal with three common challenges in the field of data analytics: the manual effort for method selection and hyperparameter tuning, the difficult interpretability of machine learning methods and the risks associated with data collection through randomized experiments. In the first paper, we develop a method selection approach in the field of intermittent demand prediction. Our model combines high predictive performance with automation and calculation efficiency. Unlike common practice, the prediction method gets automatically chosen for each data set without any manual selection. Our results are stable across three different data sets that come from different sources but all contain intermittent demand time series. We showcase the impact of the proposed forecasting approach with a warehouse operation simulation. We thereby prove the financial benefit with empirical data. In the second paper, we deal with partial defection prediction in a business-tobusiness environment in the logistics industry. The predictions must combine predictive performance with interpretability and profit maximization. Our model uses a large variety of customer-based and time-series-based features to predict the probability of partial defection for each customer. We use a data permutation approach to make the best performing, black-box models interpretable. Furthermore, we use a profit assessment to identify the method that leads to the highest revenue through successful retention actions. In the third paper, we study price sensitivity prediction. We do not use any randomized experiments, because the risk of loosing customers through such experiments is too high. Thereby, we address the challenge of data availability
1The term “we” refers to the authors of the respective chapters as denoted at the beginning of each chapter. For the abstract, this refers to the authors of Faber and Spinler (2019a,b,c).
Essays in cryptocurrency
(2021)
Exploring business ecosystem properties with a focus on innovation and entrepreneurship activities
(2019)
With the rising importance of the external economic environment of organisations and open and collaborative innovation, the last two decades have seen an emergence of diverse literature streams and practitioner interpretations of business ecosystems. However, most of the scholars’ attention has been on ecosystems that function in the information and telecommunication technology industries and interactions between actors driven by economic interests. Moreover, business ecosystems analysis was embedded in the strategic management and organizational design literature, which primarily investigated the ecosystem leader using examples of powerful multinational corporations such as IBM, Microsoft, Google and Apple. These new powerful corporations significantly influenced the nature of the global competitive landscape (e.g. app economy), enabled the emergence of new business models (e.g. platform business) and even new forms of entrepreneurship (e.g. blogging and self-publishing). However, little remained known about the actors at the periphery of business ecosystems – that enable the functioning of such ecosystems - and the entrepreneurship processes that take place in the ecosystem environment.
With the goal of contributing to the extant literature and creating a better understanding of the central factors that foster evolution, and the functioning and growth of today’s business ecosystems, the first project of this doctoral dissertation provides an overview of the ecosystem concept evolution in literature and in practice. Based on the findings of this project, this dissertation takes two qualitative empirical directions. To expand on the scope of the ecosystem concept, in the second project it looks at open and collaborative innovation processes in the fashion industry, analyses the role of individuals in business ecosystems and highlights the emergence of new forms of entrepreneurship. Following a worldwide growing startup trend and increased corporate engagement in diverse corporate venturing activities, the third project empirically investigates inter-organisational knowledge flows and value creation and capture processes in the innovation ecosystem environment. This dissertation contributes to the open and user innovation and entrepreneurship literature and provides practical implications for managers and entrepreneurs interested in participating in an existing business ecosystem or considering starting one on their own.
This dissertation assesses the characteristics and viability of the emerging longhaul Low Cost Carriers (LCCs). In particular, the aim is to understand their business model, evaluate the cost and revenue performance, and investigate its impact on other carriers. Existing academic literature is inconclusive about characteristics and viability of the business model. To validate its defining characteristics, 37 airlines flying on North Atlantic routes are clustered using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) along a newly constructed long-haul airline business model framework. To contribute to the evaluation of business model viability, cost differences between clusters are uncovered followed by a discussion of their sustainability. Key findings include the characterization of the emerging long-haul LCC business model and its significant differences from Full-Service Network Carrier (FSNC) and leisure carrier models. On a cluster average, 33% lower unit costs compared to FSNCs are identified, of which 24 percentage points are evaluated as sustainable. As these cost advantages over FSNCs are smaller compared to the original savings of short-/medium-haul LCCs, revenue competitiveness on the longhaul becomes more critical. To assess long-haul revenue performance, a new metric for benchmarking the revenue per equivalent flight capacity is defined. Subsequently, a revenue model combining traffic, fare, load factor, and seat data from the North Atlantic is developed to determine the revenue per flight capacity across a sample of city-pairs. The results show that LCCs earn revenue per flight capacity comparable to FSNCs on shorter long-haul routes. Key factors to compensate lower direct yields are fewer low-yield connecting passengers, sales of ancillary services, higher load factors, and significantly more passengers per aircraft. Long-haul LCC market impact, and in particular their impact on incumbents’ fare levels, has not yet been assessed and short-/medium-haul LCC-related findings cannot be readily applied. To evaluate the impact of long-haul LCC presence on the incumbents’ pricing, a stylized analytical model is proposed for hypotheses development. Subsequently, Two-Steps Least Squares (2SLS) regressions with Instrumental Variables (IVs) are performed, distinguishing between economy, premium economy, and business classes, based on a sample of North Atlantic routes. Confirming the first hypothesis, LCC presence reduces incumbents’ long-haul economy and premium economy fares by -10% and -13%, respectively, ceteris paribus. LCC presence, however, does not significantly impact long-haul business class fare levels, confirming the second hypothesis. These findings are particularly relevant as the North Atlantic market represents to date one of the remaining profit pools for North American and European legacy carriers. The findings of this research implicate that the long-haul LCC model is economically viable, at least on trunk routes with high demand. FSNC management should be aware of the rising competition and fare impact on North Atlantic routes. Potential reactions could include the de-bundling of entry fares with the option of ancillary sales even on long-haul routes, a re-evaluation of the revenue impact of low-yield connecting passengers, a focus on premium passengers, and a continuous reduction of operating costs.
This dissertation investigates the application of machine learning to improve decision making in airline operations. In an introductory overview, we1 discuss the airline industry and the challenges of decision making in airline operations: e. g., complex IT infrastructure, interconnected resources, delay management, fuel price volatility and future environmental regulation. Machine learning can efficiently integrate a large volume of data from a variety of data sources and formats to generate accurate predictions. To assess the viability of using machine learning models for decision making in airline operations, we develop a model based on linear regression and gradient boosting to predict aircraft arrival time. Furthermore, we integrate cost index optimization to model the impact of aircraft speed on arrival time. While we find that machine learning can improve prediction accuracy by more than 30 %, the optimal cost index varies according to fuel cost, flight distance and delay costs. We propose an overall reduction in cost index for short-haul flights to reduce fuel cost while maintaining punctual operations. Arrival time predictions serve as input for daily operations planning and control. For network carriers accurate arrival time predictions are key for efficient hub operations. In a next step, we focus on aircraft arrival time prediction for intercontinental flights. We analyze the accuracy of en-route weather data provided by the flight plan, generate features based on en-route weather data and integrate them in our prediction model. We evaluate three machine learning models: linear regression, random forest and gradient boosting. Through our approach, we can assess the impact of en-route weather data. Overall, an increase in prediction accuracy of 25 % is achieved. By including en-route weather data, prediction accuracy is improved by 5 %. Our model outlines the essential features for intercontinental arrival time predictions and assess the value of en-route weather data. Furthermore, we outline organizational challenges in implementing predictive analytics. Future environmental regulations are a challenge for the airline industry. From 2020 onwards net growth in CO2 emissions is prohibited. Thus, airlines need to focus on initiatives to limit fuel consumption. We develop a prediction model for fuel consumption considering ten different aircraft types. Our results show that fuel consumption can be improved by more than 30 % for short and long-haul flights resulting in an annual reduction of 10.5 million e in fuel costs and a reduction on green house gas emissions of 66.000 tons. Furthermore, we assess the pilots’ willingness to integrate our prediction model in their fuel decision. Our analysis shows that high prediction accuracy, model understanding and a long testing phase are essential for acceptance of the prediction model. The main implication is that decision making in airline operations can be substantially improved through machine learning. Therefore, more prediction.
1The term “we” refers to the authors of the respective chapters as denoted at the beginning of each chapter. For the abstract, this refers to the authors of Achenbach and Spinler (2018a,b), Achenbach et al. (2017).
The 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.
With the start of digitalization and the initiation of a whole new industry of digital services, some of the well-established marketing wisdoms are now under scrutiny. This also holds for the importance of customer win-back for any firm – a belief that has actually never really been questioned. While related research focuses on mature industries and highlights the profitability of win-back, to date no study known to the authors has addressed the role of reacquired customers compared to first-lifetime customers in the interplay of acquisition and retention for digital firms. This is especially important when assessing firms’ current winback strategy and forecasting the development of acquisition and retention success of reacquired compared to first-lifetime customers. This study proposes a win-back assessment framework that enables firms to assess their win-back strategy and to detect problems in acquisition or retention of first-lifetime and reacquired customers. The authors demonstrate the use of framework on transaction data from a digital subscription service and data from a field experiment with that same service provider. They analyze the data from a new analytical perspective compared to extant research, which they call “cohort perspective” and jointly investigates acquisition and retention of first-lifetime and reacquired customers, who started their subscription around the same time (i.e. cohort). The results show the need for the cohort perspective by unveiling the contradiction that reacquired customers can “live” longer than in their previous lifetime (on average 328 days), but that they “live” shorter than first-lifetime customers of the same cohort ( ̶ 19%). The authors explain the rationale of this observation and discuss its implications.
Open versus closed organizational design options of sharing economy models and sharing communities
(2018)
With the recent emergence of the sharing economy, novel and complex organizational forms are founded on individuals and sharing communities that micro-manage and self-organize their own, private resources for social or commercial outcomes within an organizational scope.
These novel and complex forms reflect sharing economy models which are designed along a range from decentralization to centralization. In decentralized designs, individuals control and organize their own, private resources in sharing communities. In centralized designs, the organization controls and organizes the resources which are shared within communities.
This dissertation addresses the complexity of sharing economy models by exploring them on the organizational, the individual and the system level.
On the organizational level, this dissertation specifies the various configurations of sharing economy models. These configurations are based on representative design options which are traded off between decentralization and centralization. The Sharing Economy Spectrum is conceptualized as framework which integrates these characteristics-based design options. On the individual level, this dissertation focuses on how individuals’ resource endowment and trust in organization builders, in community members and in institutions individually influence individuals’ expectations regarding the beneficiaries of a sharing economy model. A regression model and a couple of hypotheses are developed and explored through a survey conducted at the Philippine social organization Gawad Kalinga.
On the system level, this dissertation acknowledges the sharing economy as a socioeconomic ecosystem and addresses the leadership paradox that resources are decentralized but require central leadership. Complexity leadership theory captures sharing economy models as complex adaptive systems whose leadership is organized for a common purpose. The specific roles of dynamic interaction and trust for complexity leadership are explored jointly with social entrepreneurship and sharing economy research. An ethnography with Gawad Kalinga reveals a novel complexity leadership model that fosters bottom-up leadership.
The Veblen effect revisited
(2018)
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.
Hosting the Olympics or not
(2017)
This dissertation investigates capacity and technology choice decisions in maritime container shipping under demand and regulatory uncertainty. In an introductory overview, we discuss the industry and the challenges that complicate investment decisions in shipping: e. g., the multitude of decisions, market volatility, excess capacities and the trend of new environmental regulation. Real option valuation methods can account for strategic options and the uncertainties in capacity decisions in shipping. To assess the impact of chartering on maritime investment, we analyze investment and charter options individually in a continuous-time model. We combine both in a discrete-time approach taking into account key features of the industry: investment with time to build, divestment, chartering, an endogenous charter rate, layup, and demand uncertainty. While we find demand volatility to increase optimal capacities if only investment with time to build is possible, chartering reduces this effect. It adds value to the overall project, should be mainly applied to compensate unexpected capacity shortages and needs to be considered in decision-making. Uncertainty about future eco-regulation is a further challenge for the industry. In an approximate dynamic programming model extension, we account for a stochastic introduction of operating cost-increasing regulation to assess optimal capacity choice under regulatory uncertainty. Regulation can allow for grandfathering, affecting only newly acquired vessels. We find uncertainty about future regulation with grandfathering to induce heavy up-front investment to secure a low cost base even in regulated markets. Such uncertainty may increase excess capacities and industry emissions. Uncertainty without grandfathering, however, reduces overall investment and emissions. In this case, the market may contract as investors resort to chartering until uncertainty is resolved. To also assess technology choice in light of uncertain future regulation, we develop a two-phase regime-switching model. We derive analytical solutions and study the effects of regulatory uncertainty on technology choice in a numerical extension that relaxes restrictive assumptions. We find that such uncertainty can increase optimal capacities and that a single-technology strategy is preferable over a fleet of mixed technologies in most cases. We further extend the model and compare the effectiveness of two different regimes—an emissions cap and an emissions tax. Results suggest that an emissions cap is more effective at reducing overall emissions while a tax causes lower regulation cost. The regime choice also determines the optimal technological fleet composition. The main implication is that chartering and regulatory uncertainty have a strong effect on optimal investment in shipping and should be considered in project valuation. Further, regulatory uncertainty can lead to unintended investment behavior that undermines regulatory goals. Lastly, regulatory regimes are not equally effective/efficient in reducing the environmental footprint of shipping and constitute varying incentives for investing in eco-friendly technology.
Essays on health economics
(2019)
Advances in manufacturing and information technologies have made it possible for firms to satisfy consumers’ increasing demand for unique products. Although, the mass customization of products is prevalent in almost all industries today, firms’ optimal mass customization strategy is still not that clear. The initial attempt to mass customization of a number of firms failed, because it proved to be unprofitable, while others have successfully established mass customization as a product strategy. The optimal degree of mass customization solves two decision problems: first, firms’ trade-off between the coverage of consumers’ preferences to charge a premium price and cost-efficient production; second, consumers’ trade-off between tailoring a product to their needs and interaction costs. In an attempt to facilitate managerial decision making, this thesis studies a firm’s mass customization decision in a game-theoretical model that combines the decision problems faced by each player in the interaction. Based on this model of company-customer interaction, novel insights into the optimal mass customization strategy of firms depending on their market and competitive environment are gained.
In 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.
Servant Leadership in der IT
(2017)
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.
Status dynamics and their effects on individual cooperation and performance in interdependent teams
(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 assesses investment decisions in container shipping. To understand the current state of the industry, key characteristics and challenges, such as overcapacity, eroding margins due to low freight rates, long investment lead times, and frequent changes in alliance structure are introduced.
The nature of the industry motivates the application of real options, hence a real options investment model in oligopolistic competition is presented. An analytic solution in continuous time as well as a dynamic programming solution in discrete time are derived. The model takes into account an endogenous price function, fuel-efficient investment, endogenous lead times, and endogenous price formation in the secondary vessel market. This allows to study the impact of competitive intensity, number of players, volatility, fuel-efficiency, lead time, and variable cost on optimal capacity. An investigation of optimal investment policies shows that strategic action increases firm value and strategic alliances might help alleviate some of the industry’s challenges.
Since the container shipping market is characterized by frequent alliance changes, the performance of the real options model in the context of a cooperative shipping game is assessed. Extending the coalition structure value concept it can be shown that, compared with discounted cash flow, the real options trigger performs better, especially in light of high competitive intensity and freight rate volatility while not exhibiting substantial disadvantages in other settings. A further assessment of a number of drivers for alliance instability finds that alliance complexity cost, freight rate volatility, and competitive intensity increase alliance changes.
To verify the investment approach, a characterization of the container freight rate is provided with an empirical Autoregressive Integrated Moving Average (ARIMA) model. It can be observed that the freight rate exhibits a negative relationship with capacity deployment; hence the oligopoly price function is confirmed. Based on the freight rate characterization, a back testing of the real options investment approach is provided. It shows that if players had applied the presented approach, capacities would have decreased and rates improved. A number of limitations of the real options approach are identified,
i.e. substantial impact of volatility expectation, potentially induced cyclicality from trigger approaches, and the timing impact of investment and divestment lead times.
The implications of this research are that strategic action in the container shipping industry is worthwhile and understanding the market specifics (such as competitive intensity, volatility, and freight rate characterization) is very important. Container carriers should add a real options approach to their investment toolkit and keep an eye on potential overcapacity. Finally, entering strategic alliances is suggested, but complexity should be avoided.
FMCG marketing and sales
(2015)
Any innovation process naturally goes hand in hand with a strong potential for failure. Up to as many as 90% of all innovation projects fall to termination before their successful completion, with even higher termination rates in the pharmaceutical industry. Such terminations can have detrimental effects on the project members involved. Yet, surprisingly, as project members make up the most important reason for the success of innovations, research has hitherto scarcely noted the human side of innovation project terminations. Consequently, this study aims to analyze how to retain the innovative functioning of project members after such setbacks.
This dissertation develops a framework to study the processes involved and analyze the influence of termination characteristics on project members. Resilience, or positive adaptation despite adversity, serves as the dissertation’s underlying framework and is adapted to the innovation context. To measure the resilience of project members after termination and to analyze the influence of termination characteristics on resilience, this dissertation develops the resilience construct innovator resilience potential (IRP), embedded in the innovator resilience (IR) framework. Herein, IRP stands for the potential in individuals for future innovative functioning and coping with future setbacks. The IRP construct consists of six malleable facets: self-efficacy, outcome expectancy, hope, optimism, self-esteem, and risk propensity.
This dissertation theoretically develops and tests this definition and operationalization of IRP in a qualitative and a quantitative study. The qualitative study was conducted in a world-wide operating hospitality company that underwent a large-scale innovation project termination. The quantitative field study took place as an online questionnaire that 238 innovators completed entirely (180 innovators had experienced an innovation project termination). A total of 326 innovators completed the first part of the questionnaire regarding IRP. After developing IRP in the qualitative study, it became operationalized, tested, and validated in the quantitative study. Furthermore, the quantitative field study tested the relationship of IRP with future innovative functioning, which has great importance for the success of future innovation projects. A second step identifies factors influencing IRP.
The outcomes of the quantitative field study show that IRP strongly relates to project commitment, which represents future innovative functioning of project members, as project commitment is important for the success of innovation projects. The strong relationship hinges on having experienced a termination beforehand. Therefore, IRP is important in the ongoing work of innovators following an innovation project termination. It is thus important to strengthen IRP after a termination, as this has a strong relationship with project commitment and as such future innovative functioning. Furthermore, this dissertation identifies social support as an influencing factor on IRP. More precisely, emotional support of the leader and organizational support provided through a climate of psychological safety strongly relate to IRP. In contrast, the support from family and friends does not relate to IRP. The dissertation found only work-related social support to strongly influence IRP. These findings result in theoretical and practical implications on how best to support project members during and after an innovation project termination.
In addition, the general practical discussion of this dissertation presents five steps leading to IRP and guidelines for a post termination debriefing to provide leaders with practical advice. The theoretical findings suggest that IRP can be used to measure the influence of innovation project terminations on project members and that IRP is important for future innovative functioning and for future coping of project members. The findings build the basis for future research in this nascent research field.
Innovators' careers
(2013)
The success or failure of most organizations is heavily dependent on innovation. However, this innovation is only possible through a special group of employees: the socalled innovators. These scientists, researchers, and developers are vital for the innovativeness of the organization. In my dissertation, I examine innovators’ careers from their organization’s point of view. In particular, my objective is to develop a more balanced approach to innovators’ careers than is offered in the contemporary career management literature. Some researchers argue that organizational career management has become more important because of the war for talent, whereas others postulate that organizations withdraw from career management because of the changing definitions of a career. These contrasting viewpoints leave organizations without a clear guideline for managing their key players’ careers. In order to provide such a guideline, in this thesis I address three central research questions by drawing on a cross-level sample of 625 innovators and 37 HR (human resources) managers from 37 organizations.
First, I analyze why some innovators leave their technical field of specialization to become managers. The results reveal that new career attitudes and organizational culture have an influence. More specifically, innovators with a self-directed career attitude, a boundaryless mindset, and a preference for organizational mobility often have a high managerial orientation, whereas the opposite is the case for innovators with a more valuesoriented career attitude. Furthermore, it is more likely in organizations with a supportive and collaborative culture that innovators will want to advance their career in a management position than in organizations with a culture in favor of innovativeness, power sharing, and
learning and development.
Second, I investigate whether organizational career management can still influence innovators’ careers. My results reveal that this is the case. In particular, they show that organizations should apply their organizational career management intensively, but should refrain from using too many practices and from further diversifying an already intensively applied organizational career management. Moreover, my study indicates that organizations profit from increasing innovators’ perceived career management support because it boosts the match between individual and organizational career plans.
Third, I examine how organizations can use dual ladders to raise innovators’ organizational commitment and career satisfaction. I identify the perceived recognition and transparency of the dual ladder as factors integral to the success of this career management instrument. My analyses illustrate that both are positively related to innovators’ organizational commitment and career satisfaction. In addition, my results reveal that selfdirected career management weakens the positive relationships between the perceived recognition of the dual ladder and organizational commitment and career satisfaction. Based on the conceptual arguments and empirical results, I provide recommendations for organizations on how to handle innovators’ careers in the era of the new career. Therefore, I provide easy-to-use guidelines on how to keep innovators in their technical
positions and roles, how to use organizational career management, and how to make dual ladders work. Last, but not least, I focus on the limitations of my study and point to future research possibilities.
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).