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Digital inclusion
(2021)
In this thesis, we tackle two social disruptions: recent refugee waves in Germany and the COVID-19 pandemic. We focus on the use of information and communication technology (ICT) as a key means of alleviating these disruptions and promoting social inclusion. As social disruptions typically lead to frustration and fragmentation, it is essential to ensure the social inclusion of individuals and societies during such times.
In the context of the social inclusion of refugees, we focus on the Syrian refugees who arrived in Germany as of 2015, as they form a large and coherent refugee community. In particular, we address the role of ICTs in refugees’ social inclusion and investigate how different ICTs (especially smartphones and social networks) can foster refugees’ integration and social inclusion. In the context of the COVID-19 pandemic, we focus on the widespread unconventional working model of work from home (WFH). Our research here centers on the main constructs of WFH and the key differences in WFH experiences based on personal characteristics such as gender and parental status.
We reveal novel insights through four well-established research methods: literature review, mixed methods, qualitative method, and quantitative method. The results of our research have been published in the form of eight articles in major information systems venues and journals. Key results from the refugee research stream include the following: Smartphones represent a central component of refugee ICT use; refugees view ICT as a source of information and power; the social connectedness of refugees is strongly correlated with their Internet use; refugees are not relying solely on traditional methods to learn the German language or pursue further education; the ability to use smartphones anytime and anywhere gives refugees an empowering feeling of global connectedness; and ICTs empower refugees on three levels (community participation, sense of control, and self-efficacy).
Key insights from the COVID-19 WFH stream include: Gender and the presence of children under the age of 18 affect workers’ control over their time, technology usefulness, and WFH conflicts, while not affecting their WFH attitudes; and both personal and technology-related factors affect an individual’s attitude toward WFH and their productivity. Further insights are being gathered at the time of submitting this thesis.
This thesis contributes to the discussion within the information systems community regarding how to use different ICT solutions to promote the social inclusion of refugees in their new communities and foster an inclusive society. It also adds to the growing body of research on COVID-19, in particular on the sudden workplace transformation to WFH. The insights gathered in this thesis reveal theoretical implications and future opportunities for research in the field of information systems, practical implications for relevant stakeholders, and social implications related to the refugee crisis and the COVID-19 pandemic that must be addressed.
The thesis assesses the contribution of technology option of Carbon Capture and Sequestration (CCS) to climate change mitigation. CCS means that CO2 is captured at large industrial facilities and sequestered in goelogical structures. The technology uses the endogenous growth model MIND. Herein the various climate change mitigation options of reducing economic growth, increasing energy efficiency, changing the energy mix and CCS are assessed simultaneously. An important question is whether CCS is a temporary or long-term solution. The results show that in the middle of the 21st century CCS has its peak contribution, which allows prolonged use of relatively cheap fossil energy carriers. However, this leads to delayed introduction of renewable energy carriers. The technology path ways are accombined with different costs of climate change mitigation. The use of CCS delays and reduces the costs of climate change mitigation. However, the delayed introduction of renewable energy carriers leads to reduced technological learning, which induces higher costs in the longer term. All in all the temporary use of CCS reduces the costs of climate change mitigation costs. The result is robust, which is tested with various uncertainty analysis.
This work analyzes the saving and consumption behavior of agents faced with the possibility of unemployment in a dynamic and stochastic life cycle model. The intertemporal optimization is based on Dynamic Programming with a backward recursion algorithm. The implemented uncertainty is not based on income shocks as it is done in traditional life cycle models but uses Markov probabilities where the probability for the next employment status of the agent depends on the current status. The utility function used is a CRRA function (constant relative risk aversion), combined with a CES function (constant elasticity of substitution) and has several consumption goods, a subsistence level, money and a bequest function.
Sustainable urban growth
(2022)
This dissertation explores the determinants for sustainable and socially optimalgrowth in a city. Two general equilibrium models establish the base for this evaluation, each adding its puzzle piece to the urban sustainability discourse and examining the role of non-market-based and market-based policies for balanced growth and welfare improvements in different theory settings. Sustainable urban growth either calls for policy actions or a green energy transition. Further, R&D market failures can pose severe challenges to the sustainability of urban growth and the social optimality of decentralized allocation decisions. Still, a careful (holistic) combination of policy instruments can achieve sustainable growth and even be first best.
Public administrations confront fundamental challenges, including globalization, digitalization, and an eroding level of trust from society. By developing joint public service delivery with other stakeholders, public administrations can respond to these challenges. This increases the importance of inter-organizational governance—a development often referred to as New Public Governance, which to date has not been realized because public administrations focus on intra-organizational practices and follow the traditional “governmental chain.”
E-government initiatives, which can lead to high levels of interconnected public services, are currently perceived as insufficient to meet this goal. They are not designed holistically and merely affect the interactions of public and non-public stakeholders. A fundamental shift toward a joint public service delivery would require scrutiny of established processes, roles, and interactions between stakeholders.
Various scientists and practitioners within the public sector assume that the use of blockchain institutional technology could fundamentally change the relationship between public and non-public stakeholders. At first glance, inter-organizational, joint public service delivery could benefit from the use of blockchain. This dissertation aims to shed light on this widespread assumption. Hence, the objective of this dissertation is to substantiate the effect of blockchain on the relationship between public administrations and non-public stakeholders.
This objective is pursued by defining three major areas of interest. First, this dissertation strives to answer the question of whether or not blockchain is suited to enable New Public Governance and to identify instances where blockchain may not be the proper solution. The second area aims to understand empirically the status quo of existing blockchain implementations in the public sector and whether they comply with the major theoretical conclusions. The third area investigates the changing role of public administrations, as the blockchain ecosystem can significantly increase the number of stakeholders.
Corresponding research is conducted to provide insights into these areas, for example, combining theoretical concepts with empirical actualities, conducting interviews with subject matter experts and key stakeholders of leading blockchain implementations, and performing a comprehensive stakeholder analysis, followed by visualization of its results.
The results of this dissertation demonstrate that blockchain can support New Public Governance in many ways while having a minor impact on certain aspects (e.g., decentralized control), which account for this public service paradigm. Furthermore, the existing projects indicate changes to relationships between public administrations and non-public stakeholders, although not necessarily the fundamental shift proposed by New Public Governance. Lastly, the results suggest that power relations are shifting, including the decreasing influence of public administrations within the blockchain ecosystem. The results raise questions about the governance models and regulations required to support mature solutions and the further diffusion of blockchain for public service delivery.
The economic impact analysis contained in this book shows how irrigation farming is particularly susceptible when applying certain water management policies in the Australian Murray-Darling Basin, one of the world largest river basins and Australia’s most fertile region. By comparing different pricing and non-pricing water management policies with the help of the Water Integrated Market Model, it is found that the impact of water demand reducing policies is most severe on crops that need to be intensively irrigated and are at the same time less water productive. A combination of increasingly frequent and severe droughts and the application of policies that decrease agricultural water demand, in the same region, will create a situation in which the highly water dependent crops rice and cotton cannot be cultivated at all.
Public debate about energy relations between the EU and Russia is distorted. These distortions present considerable obstacles to the development of true partnership. At the core of the conflict is a struggle for resource rents between energy producing, energy consuming and transit countries. Supposed secondary aspects, however, are also of great importance. They comprise of geopolitics, market access, economic development and state sovereignty. The European Union, having engaged in energy market liberalisation, faces a widening gap between declining domestic resources and continuously growing energy demand. Diverse interests inside the EU prevent the definition of a coherent and respected energy policy. Russia, for its part, is no longer willing to subsidise its neighbouring economies by cheap energy exports. The Russian government engages in assertive policies pursuing Russian interests. In so far, it opts for a different globalisation approach, refusing the role of mere energy exporter. In view of the intensifying struggle for global resources, Russia, with its large energy potential, appears to be a very favourable option for European energy supplies, if not the best one. However, several outcomes of the strategic game between the two partners can be imagined. Engaging in non-cooperative strategies will in the end leave all stakeholders worse-off. The European Union should therefore concentrate on securing its partnership with Russia instead of damaging it. Stable cooperation would need the acceptance that the partner may pursue his own goals, which might be different from one’s own interests. The question is, how can a sustainable compromise be found? This thesis finds that a mix of continued dialogue, a tit for tat approach bolstered by an international institutional framework and increased integration efforts appears as a preferable solution.
Accurately solving classification problems nowadays is likely to be the most relevant machine learning task. Binary classification separating two classes only is algorithmically simpler but has fewer potential applications as many real-world problems are multi-class. On the reverse, separating only a subset of classes simplifies the classification task. Even though existing multi-class machine learning algorithms are very flexible regarding the number of classes, they assume that the target set Y is fixed and cannot be restricted once the training is finished. On the other hand, existing state-of-the-art production environments are becoming increasingly interconnected with the advance of Industry 4.0 and related technologies such that additional information can simplify the respective classification problems. In light of this, the main aim of this thesis is to introduce dynamic classification that generalizes multi-class classification such that the target class set can be restricted arbitrarily to a non-empty class subset M of Y at any time between two consecutive predictions.
This task is solved by a combination of two algorithmic approaches. First, classifier calibration, which transforms predictions into posterior probability estimates that are intended to be well calibrated. The analysis provided focuses on monotonic calibration and in particular corrects wrong statements that appeared in the literature. It also reveals that bin-based evaluation metrics, which became popular in recent years, are unjustified and should not be used at all. Next, the validity of Platt scaling, which is the most relevant parametric calibration approach, is analyzed in depth. In particular, its optimality for classifier predictions distributed according to four different families of probability distributions as well its equivalence with Beta calibration up to a sigmoidal preprocessing are proven. For non-monotonic calibration, extended variants on kernel density estimation and the ensemble method EKDE are introduced. Finally, the calibration techniques are evaluated using a simulation study with complete information as well as on a selection of 46 real-world data sets.
Building on this, classifier calibration is applied as part of decomposition-based classification that aims to reduce multi-class problems to simpler (usually binary) prediction tasks. For the involved fusing step performed at prediction time, a new approach based on evidence theory is presented that uses classifier calibration to model mass functions. This allows the analysis of decomposition-based classification against a strictly formal background and to prove closed-form equations for the overall combinations. Furthermore, the same formalism leads to a consistent integration of dynamic class information, yielding a theoretically justified and computationally tractable dynamic classification model. The insights gained from this modeling are combined with pairwise coupling, which is one of the most relevant reduction-based classification approaches, such that all individual predictions are combined with a weight. This not only generalizes existing works on pairwise coupling but also enables the integration of dynamic class information.
Lastly, a thorough empirical study is performed that compares all newly introduced approaches to existing state-of-the-art techniques. For this, evaluation metrics for dynamic classification are introduced that depend on corresponding sampling strategies. Thereafter, these are applied during a three-part evaluation. First, support vector machines and random forests are applied on 26 data sets from the UCI Machine Learning Repository. Second, two state-of-the-art deep neural networks are evaluated on five benchmark data sets from a relatively recent reference work. Here, computationally feasible strategies to apply the presented algorithms in combination with large-scale models are particularly relevant because a naive application is computationally intractable. Finally, reference data from a real-world process allowing the inclusion of dynamic class information are collected and evaluated. The results show that in combination with support vector machines and random forests, pairwise coupling approaches yield the best results, while in combination with deep neural networks, differences between the different approaches are mostly small to negligible. Most importantly, all results empirically confirm that dynamic classification succeeds in improving the respective prediction accuracies. Therefore, it is crucial to pass dynamic class information in respective applications, which requires an appropriate digital infrastructure.
Traditional organizations are strongly encouraged by emerging digital customer behavior and digital competition to transform their businesses for the digital age. Incumbents are particularly exposed to the field of tension between maintaining and renewing their business model. Banking is one of the industries most affected by digitalization, with a large stream of digital innovations around Fintech. Most research contributions focus on digital innovations, such as Fintech, but there are only a few studies on the related challenges and perspectives of incumbent organizations, such as traditional banks. Against this background, this dissertation examines the specific causes, effects and solutions for traditional banks in digital transformation − an underrepresented research area so far.
The first part of the thesis examines how digitalization has changed the latent customer expectations in banking and studies the underlying technological drivers of evolving business-to-consumer (B2C) business models. Online consumer reviews are systematized to identify latent concepts of customer behavior and future decision paths as strategic digitalization effects. Furthermore, the service attribute preferences, the impact of influencing factors and the underlying customer segments are uncovered for checking accounts in a discrete choice experiment. The dissertation contributes here to customer behavior research in digital transformation, moving beyond the technology acceptance model. In addition, the dissertation systematizes value proposition types in the evolving discourse around smart products and services as key drivers of business models and market power in the platform economy.
The second part of the thesis focuses on the effects of digital transformation on the strategy development of financial service providers, which are classified along with their firm performance levels. Standard types are derived based on fuzzy-set qualitative comparative analysis (fsQCA), with facade digitalization as one typical standard type for low performing incumbent banks that lack a holistic strategic response to digital transformation. Based on this, the contradictory impact of digitalization measures on key business figures is examined for German savings banks, confirming that the shift towards digital customer interaction was not accompanied by new revenue models diminishing bank profitability. The dissertation further contributes to the discourse on digitalized work designs and the consequences for job perceptions in banking customer advisory. The threefold impact of the IT support perceived in customer interaction on the job satisfaction of customer advisors is disentangled.
In the third part of the dissertation, solutions are developed design-oriented for core action areas of digitalized business models, i.e., data and platforms. A consolidated taxonomy for data-driven business models and a future reference model for digital banking have been developed. The impact of the platform economy is demonstrated here using the example of the market entry by Bigtech. The role-based e3-value modeling is extended by meta-roles and role segments and linked to value co-creation mapping in VDML. In this way, the dissertation extends enterprise modeling research on platform ecosystems and value co-creation using the example of banking.
This paper-based dissertation aims to contribute to the open innovation (OI) and technology management (TM) research fields by investigating their mechanisms, and potentials at the operational level. The dissertation connects the well-known concept of technology management with OI formats and applies these on specific manufacturing technologies within a clearly defined setting.
Technological breakthroughs force firms to continuously adapt and reinvent themselves. The pace of technological innovation and their impact on firms is constantly increasing due to more connected infrastructure and accessible resources (i.e. data, knowledge). Especially in the manufacturing sector it is one key element to leverage new technologies to stay competitive. These technological shifts call for new management practices.
TM supports firms with various tools to manage these shifts at different levels in the firm. It is a multifunctional and multidisciplinary field as it deals with all aspects of integrating technological issues into business decision-making and is directly relevant to a number of core business processes. Thus, it makes sense to utilize this theory and their practices as a foundation of this dissertation. However, considering the increasing complexity and number of technologies it is not sufficient anymore for firms to only rely on previous internal R&D and managerial practices. OI can expanse these practices by involving distributed innovation processes and accessing further external knowledge sources. This expansion can lead to an increasing innovation performance and thereby accelerate the time-to-market of technologies.
Research in this dissertation was based on the expectations that OI formats will support the R&D activities of manufacturing technologies on the operational level by providing access to resources, knowledge, and leading-edge technology. The dissertation represents uniqueness regarding the rich practical data sets (observations, internal documents, project reviews) drawn from a very large German high-tech firm. The researcher was embedded in an R&D unit within the operational TM department for manufacturing technologies. The analyses include 1.) an exploratory in-depth analysis of a crowdsourcing initiative to elaborate the impact on specific manufacturing technologies, 2.) a deductive approach for developing a technology evaluation score model to create a common understanding of the value of selected manufacturing technologies at the operational level, and 3.) an abductive reasoning approach in form of a longitudinal case study to derive important indicator for the in-process activities of science-based partnership university-industry collaboration format. Thereby, the dissertation contributed to research and practice 1.) linkages of TM and OI practices to assimilate technologies at the operational level, 2.) insights about the impact of CS on manufacturing technologies and a related guideline to execute CS initiatives in this specific environment 3.) introduction of manufacturing readiness levels and further criteria into the TM and OI research field to support decision-makers in the firm in gaining a common understanding of the maturity of manufacturing technologies and, 4.) context-specific important indicators for science based university-industry collaboration projects and a holistic framework to connect TM with the university-industry collaboration approach
The findings of this dissertation illustrate that OI formats can support the acceleration of time-to-market of manufacturing technologies and further improve the technical requirements of the product by leveraging external capabilities. The conclusions and implications made are intended to foster further research and improve managerial practices to evolve TM into an open collaborative context with interconnectivities between all internal and external involved technologies, individuals and organizational levels.
The public encounter
(2019)
This thesis puts the citizen-state interaction at its center. Building on a comprehensive model incorporating various perspectives on this interaction, I derive selected research gaps. The three articles, comprising this thesis, tackle these gaps. A focal role plays the citizens’ administrative literacy, the relevant competences and knowledge necessary to successfully interact with public organizations. The first article elaborates on the different dimensions of administrative literacy and develops a survey instrument to assess these. The second study shows that public employees change their behavior according to the competences that citizens display during public encounters. They treat citizens preferentially that are well prepared and able to persuade them of their application’s potential. Thereby, they signal a higher success potential for bureaucratic success criteria which leads to the employees’ cream-skimming behavior. The third article examines the dynamics of employees’ communication strategies when recovering from a service failure. The study finds that different explanation strategies yield different effects on the client’s frustration. While accepting the responsibility and explaining the reasons for a failure alleviates the frustration and anger, refusing the responsibility leads to no or even reinforcing effects on the client’s frustration. The results emphasize the different dynamics that characterize the nature of citizen-state interactions and how they establish their short- and long-term outcomes.
Within our research group Bayesian Risk Solutions we have coined the idea of a Bayesian Risk Management (BRM). It claims (1) a more transparent and diligent data analysis as well as (2)an open-minded incorporation of human expertise in risk management. In this dissertation we formulize a framework for BRM based on the two pillars Hardcore-Bayesianism (HCB) and Softcore-Bayesianism (SCB) providing solutions for the claims above. For data analysis we favor Bayesian statistics with its Markov Chain Monte Carlo (MCMC) simulation algorithm. It provides a full illustration of data-induced uncertainty beyond classical point-estimates. We calibrate twelve different stochastic processes to four years of CO2 price data. Besides, we calculate derived risk measures (ex ante/ post value-at-risks, capital charges, option prices) and compare them to their classical counterparts. When statistics fails because of a lack of reliable data we propose our integrated Bayesian Risk Analysis (iBRA) concept. It is a basic guideline for an expertise-driven quantification of critical risks. We additionally review elicitation techniques and tools supporting experts to express their uncertainty. Unfortunately, Bayesian thinking is often blamed for its arbitrariness. Therefore, we introduce the idea of a Bayesian due diligence judging expert assessments according to their information content and their inter-subjectivity.
The first goal of the present work focuses on the need for different rationing methods of the The Global Change and Financial Transition (GFT) work- ing group at the Potsdam Institute for Climate Impact Research (PIK): I provide a toolbox which contains a variety of rationing methods to be ap- plied to micro-economic disequilibrium models of the lagom model family. This toolbox consists of well known rationing methods, and of rationing methods provided specifically for lagom. To ensure an easy application the toolbox is constructed in modular fashion. The second goal of the present work is to present a micro-economic labour market where heterogenous labour suppliers experience consecu- tive job opportunities and need to decide whether to apply for employ- ment. The labour suppliers are heterogenous with respect to their qualifi- cations and their beliefs about the application behaviour of their competi- tors. They learn simultaneously – in Bayesian fashion – about their individ- ual perceived probability to obtain employment conditional on application (PPE) by observing each others’ application behaviour over a cycle of job opportunities.
African states are often called corrupt indicating that the political system in Africa differs from the one prevalent in the economically advanced democracies. This however does not give us any insight into what makes corruption the ruling norm of African statehood. Thus we must turn to the overly neglected theoretical work on the political economy of Africa in order to determine how the poverty of governance in Africa is firmly anchored both in Africa’s domestic socioeconomic reality, as well as in the region’s role in the international economic order. Instead of focusing on increased monitoring, enforcement and formal democratic procedures, this book integrates economic analysis with political theory in order to arrive at a better understanding of the political-economic roots of corruption in Sub-Saharan Africa.
Current business organizations want to be more efficient and constantly evolving to find ways to retain talent. It is well established that visionary leadership plays a vital role in organizational success and contributes to a better working environment. This study aims to determine the effect of visionary leadership on employees' perceived job satisfaction. Specifically, it investigates whether the mediators meaningfulness at work and commitment to the leader impact the relationship. I take support from job demand resource theory to explain the overarching model used in this study and broaden-and-build theory to leverage the use of mediators.
To test the hypotheses, evidence was collected in a multi-source, time-lagged design field study of 95 leader-follower dyads. The data was collected in a three-wave study, each survey appearing after one month. Data on employee perception of visionary leadership was collected in T1, data for both mediators were collected in T2, and employee perception of job satisfaction was collected in T3. The findings display that meaningfulness at work and commitment to the leader play positive intervening roles (in the form of a chain) in the indirect influence of visionary leadership on employee perceptions regarding job satisfaction.
This research offers contributions to literature and theory by first broadening the existing knowledge on the effects of visionary leadership on employees. Second, it contributes to the literature on constructs meaningfulness at work, commitment to the leader, and job satisfaction. Third, it sheds light on the mediation mechanism dealing with study variables in line with the proposed model. Fourth, it integrates two theories, job demand resource theory and broaden-and-build theory providing further evidence. Additionally, the study provides practical implications for business leaders and HR practitioners.
Overall, my study discusses the potential of visionary leadership behavior to elevate employee outcomes. The study aligns with previous research and answers several calls for further research on visionary leadership, job satisfaction, and mediation mechanism with meaningfulness at work and commitment to the leader.
Digital transformation (DT) has not only been a major challenge in recent years, it is also supposed to continue to enormously impact our society and economy in the forthcoming decade. On the one hand, digital technologies have emerged, diffusing and determining our private and professional lives. On the other hand, digital platforms have leveraged the potentials of digital technologies to provide new business models. These dynamics have a massive effect on individuals, companies, and entire ecosystems. Digital technologies and platforms have changed the way persons consume or interact with each other. Moreover, they offer companies new opportunities to conduct their business in terms of value creation (e.g., business processes), value proposition (e.g., business models), or customer interaction (e.g., communication channels), i.e., the three dimensions of DT. However, they also can become a threat for a company's competitiveness or even survival. Eventually, the emergence, diffusion, and employment of digital technologies and platforms bear the potential to transform entire markets and ecosystems.
Against this background, IS research has explored and theorized the phenomena in the context of DT in the past decade, but not to its full extent. This is not surprising, given the complexity and pervasiveness of DT, which still requires far more research to further understand DT with its interdependencies in its entirety and in greater detail, particularly through the IS perspective at the confluence of technology, economy, and society. Consequently, the IS research discipline has determined and emphasized several relevant research gaps for exploring and understanding DT, including empirical data, theories as well as knowledge of the dynamic and transformative capabilities of digital technologies and platforms for both organizations and entire industries.
Hence, this thesis aims to address these research gaps on the IS research agenda and consists of two streams. The first stream of this thesis includes four papers that investigate the impact of digital technologies on organizations. In particular, these papers study the effects of new technologies on firms (paper II.1) and their innovative capabilities (II.2), the nature and characteristics of data-driven business models (II.3), and current developments in research and practice regarding on-demand healthcare (II.4). Consequently, the papers provide novel insights on the dynamic capabilities of digital technologies along the three dimensions of DT. Furthermore, they offer companies some opportunities to systematically explore, employ, and evaluate digital technologies to modify or redesign their organizations or business models.
The second stream comprises three papers that explore and theorize the impact of digital platforms on traditional companies, markets, and the economy and society at large. At this, paper III.1 examines the implications for the business of traditional insurance companies through the emergence and diffusion of multi-sided platforms, particularly in terms of value creation, value proposition, and customer interaction. Paper III.2 approaches the platform impact more holistically and investigates how the ongoing digital transformation and "platformization" in healthcare lastingly transform value creation in the healthcare market. Paper III.3 moves on from the level of single businesses or markets to the regulatory problems that result from the platform economy for economy and society, and proposes appropriate regulatory approaches for addressing these problems. Hence, these papers bring new insights on the table about the transformative capabilities of digital platforms for incumbent companies in particular and entire ecosystems in general.
Altogether, this thesis contributes to the understanding of the impact of DT on organizations and markets through the conduction of multiple-case study analyses that are systematically reflected with the current state of the art in research. On this empirical basis, the thesis also provides conceptual models, taxonomies, and frameworks that help describing, explaining, or predicting the impact of digital technologies and digital platforms on companies, markets and the economy or society at large from an interdisciplinary viewpoint.
Inequalities in health are a prevalent feature of societies. And as societies, we condemn inequalities that are rooted in immutable circumstances such as gender, race, and parental background. Consequently, policy makers are interested in measuring and understanding the causes of health inequalities rooted in circumstances. However, identifying causal estimates of these relationships is very ambitious for reasons such as the presence of confounders or measurement error in the data. This thesis contributes to this ambitious endeavour by addressing these challenges in four chapters.
In the first Chapter, I use 25 years of rich health information to describe three features of intergenerational health mobility in Germany. First, we describe the joint permanent health distribution of the parents and their children. A ten percentile increase in parental permanent health is associated with a 2.3 percentile increase in their child’s health. Second, a percentile point increase in permanent health ranks is associated with a 0.8% to 1.4% increase in permanent income for, both, children, and parents, respectively. Non-linearities in the association between permanent health and income create incentives to escape the bottom of the permanent health distribution. Third, upward mobility in permanent health varies with parental socio-economic status.
In the second Chapter, we estimate the effect of maternal schooling on children’s mental health in adulthood. Using the Socio-Economic Panel and the mental health measure based on the SF-12 questionnaire, we exploit a compulsory schooling law reform to identify the causal effect of maternal schooling on children’s mental health. While the theoretical considerations are not clear, we do not find that the mother’s schooling has an effect on the mental health of the children. However, we find a positive effect on children’s physical health operating mainly through physical functioning. In addition, albeit with the absence of a reduced-form effect on mental health, we find evidence that the number of friends moderates the relationship between maternal schooling and their children’s mental health.
In the third Chapter, against a background of increasing violence against non-natives, we estimate the effect of hate crime on refugees’ mental health in Germany. For this purpose, we combine two datasets: administrative records on xenophobic crime against refugee shelters by the Federal Criminal Office and the IAB-BAMF-SOEP Survey of Refugees. We apply a regression discontinuity design in time to estimate the effect of interest. Our results indicate that hate crime has a substantial negative effect on several mental health indicators, including the Mental Component Summary score and the Patient Health Questionnaire-4 score. The effects are stronger for refugees with closer geographic proximity to the focal hate crime and refugees with low country-specific human capital. While the estimated effect is only transitory, we argue that negative mental health shocks during the critical period after arrival have important long-term consequences.
In the last Chapter of this thesis, we investigate how the economic consequences of the pandemic and the government-mandated measures to contain its spread affect the self-employed – particularly women– in Germany. For our analysis, we use representative, real-time survey data in which respondents were asked about their situation during the COVID-19 pandemic. Our findings indicate that among the self-employed, who generally face a higher likelihood of income losses due to COVID-19 than employees, women are 35% more likely to experience income losses than their male counterparts. We do not find a comparable gender gap among employees. Our results further suggest that the gender gap among the self-employed is largely explained by the fact that women disproportionately work in industries that are more severely affected by the COVID-19 pandemic. Our analysis of potential mechanisms reveals that women are significantly more likely to be impacted by government-imposed restrictions, e.g., the regulation of opening hours. We conclude that future policy measures intending to mitigate the consequences of such shocks should account for this considerable variation in economic hardship.
Creative intensive processes
(2023)
Creativity – developing something new and useful – is a constant challenge in the working world. Work processes, services, or products must be sensibly adapted to changing times. To be able to analyze and, if necessary, adapt creativity in work processes, a precise understanding of these creative activities is necessary. Process modeling techniques are often used to capture business processes, represent them graphically and analyze them for adaptation possibilities. This has been very limited for creative work. An accurate understanding of creative work is subject to the challenge that, on the one hand, it is usually very complex and iterative. On the other hand, it is at least partially unpredictable as new things emerge. How can the complexity of creative business processes be adequately addressed and simultaneously manageable? This dissertation attempts to answer this question by first developing a precise process understanding of creative work. In an interdisciplinary approach, the literature on the process description of creativity-intensive work is analyzed from the perspective of psychology, organizational studies, and business informatics. In addition, a digital ethnographic study in the context of software development is used to analyze creative work. A model is developed based on which four elementary process components can be analyzed: Intention of the creative activity, Creation to develop the new, Evaluation to assess its meaningfulness, and Planning of the activities arising in the process – in short, the ICEP model. These four process elements are then translated into the Knockledge Modeling Description Language (KMDL), which was developed to capture and represent knowledge-intensive business processes. The modeling extension based on the ICEP model enables creative business processes to be identified and specified without the need for extensive modeling of all process details. The modeling extension proposed here was developed using ethnographic data and then applied to other organizational process contexts. The modeling method was applied to other business contexts and evaluated by external parties as part of two expert studies. The developed ICEP model provides an analytical framework for complex creative work processes. It can be comprehensively integrated into process models by transforming it into a modeling method, thus expanding the understanding of existing creative work in as-is process analyses.
In recent years, entire industries and their participants have been affected by disruptive technologies, resulting in dramatic market changes and challenges to firm’s business logic and thus their business models (BMs). Firms from mature industries are increasingly realizing that BMs that worked successfully for years have become insufficient to stay on track in today’s “move fast and break things” economy. Firms must scrutinize the core logic that informs how they do business, which means exploring novel ways to engage customers and get them to pay. This can lead to a complete renewal of existing BMs or innovating completely new BMs.
BMs have emerged as a popular object of research within the last decade. Despite the popularity of the BM, the theoretical and empirical foundation underlying the concept is still weak. In particular, the innovation process for BMs has been developed and implemented in firms, but understanding of the mechanisms behind it is still lacking. Business model innovation (BMI) is a complex and challenging management task that requires more than just novel ideas. Systematic studies to generate a better understanding of BMI and support incumbents with appropriate concepts to improve BMI development are in short supply. Further, there is a lack of knowledge about appropriate research practices for studying BMI and generating valid data sets in order to meet expectations in both practice and academia.
This paper-based dissertation aims to contribute to research practice in the field of BM and BMI and foster better understanding of the BM concept and BMI processes in incumbent firms from mature industries. The overall dissertation presents three main results. The first result is a new perspective, or the systems thinking view, on the BM and BMI. With the systems thinking view, the fuzzy BM concept is clearly structured and a BMI framework is proposed. The second result is a new research strategy for studying BMI. After analyzing current research practice in the areas of BMs and BMI, it is obvious that there is a need for better research on BMs and BMI in terms of accuracy, transparency, and practical orientation. Thus, the action case study approach combined with abductive methodology is proposed and proven in the research setting of this thesis. The third result stems from three action case studies in incumbent firms from mature industries employed to study how BMI occurs in practice. The new insights and knowledge gained from the action case studies help to explain BMI in such industries and increase understanding of the core of these processes.
By studying these issues, the articles complied in this thesis contribute conceptually and empirically to the recently consolidated but still increasing literature on the BM and BMI. The conclusions and implications made are intended to foster further research and improve managerial practices for achieving BMI in a dramatically changing business environment.
Foresight in networks
(2021)
The goal of this dissertation is to contribute to the corporate foresight research field by investigating capabilities, practices, and challenges particularly in the context of interorganizational settings and networked organizations informed by the theoretical perspectives of the relational view and dynamic capabilities.
Firms are facing an increasingly complex environment and highly complex product and service landscapes that often require multiple organizations to collaborate for innovation and offerings. Public-private partnerships that are targeted at supporting this have been introduced by policy-makers in the recent past. One example for such a partnership is the European Institute of Innovation and Technology (EIT) with multiple Knowledge and Innovation Communities (KICs). The EIT has been initiated by the European Commission in 2008 with the ambition of addressing grand societal challenges, driving innovativeness of European companies, and supporting systemic change. The resulting network organizations are managed similarly to corporations with managers, boards, and firm-like governance structures. EIT Digital as one of the EIT KICs are a central case of this work.
Research in this dissertation was based on the expectation that corporate foresight activities will increasingly be embedded in such interorganizational settings and a) can draw on such settings for the benefit of themselves and b) may contribute to shared visions, trust building and planning in these network organizations. In this dissertation the EIT Digital (formerly EIT ICT Labs) is a central case, supplemented with insights from three additional cases. I draw on the rich theoretical understanding of the resource-based view, dynamic capabilities, and particularly the relational view to further the discussion in the field of corporate foresight—defined as foresight in organizations in contrast to foresight with a macro-economical perspective—towards a relational understanding. Further, I use and revisit Rohrbeck’s Maturity Model for the Future Orientation of Firms as conceptual frame for corporate foresight in interorganizational settings. The analyses—available as four individual publications complemented by on additional chapter—are designed as exploratory case studies based on multiple data sources including an interview series with 49 persons, two surveys (N=54, n=20), three supplementary interviews, access to key documents and presentations, and observation through participation in meetings and activities of the EIT Digital. This research setting allowed me to contribute to corporate foresight research and practice by 1) integrating relational constructs primarily drawn from the relational view and dynamic capabilities research into the corporate foresight research stream, 2) exploring and understanding capabilities that are required for corporate foresight in interorganizational and networked organizations, 3) discussing and extending the Maturity Model for network organizations, and 4) to support individual organizations to tie their foresight systems effectively to networked foresight systems.