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The collection of personal information by organizations has become increasingly essential for social interactions. Nevertheless, according to the GDPR (General Data Protection Regulation), the organizations have to protect collected data. Access Control (AC) mechanisms are traditionally used to secure information systems against unauthorized access to sensitive data. The increased availability of personal sensor data, thanks to IoT-oriented applications, motivates new services to offer insights about individuals. Consequently, data mining algorithms have been proposed to infer personal insights from collected sensor data. Although they can be used for genuine purposes, attackers can leverage those outcomes, combining them with other type of data, and further breaching individuals’ privacy. Thus, bypassing AC mechanisms thanks to such insights is a concrete problem.
We propose an inference detection system based on the analysis of queries issued on a sensor database. The knowledge obtained through these queries, and the inference channels corresponding to the use of data mining algorithms on sensor data to infer individual information, are described using Raw sensor data based Inference ChannEl Model (RICE-M). The detection is carried out by RICE-M based inference detection System (RICE-Sy). RICE-Sy considers at the time of the query, the knowledge that a user obtains via a new query and has obtained via his query history, and determines whether this is sufficient to allow that user to operate a channel. Thus, privacy protection systems can take advantage of the inferences detected by RICE-Sy, taking into account individuals’ information obtained by the attackers via a database of sensors, to further protect these individuals.
The impact of urban development on local residents in the urban periphery oscillates between processes of empowerment and processes of marginalization as well as between state-controlled order and decentralized self-organization. Consequently local authorities, including urban planners and local residents need to negotiate their respective roles in the production and usage of urban space defined by transition, transformation and ambiguity. Therefore, this work examines the underlying patterns, interactions and structures which define the negotiation of state-society relations in the framework of a porous peri-urban landscape in Vietnam’s secondary cities.
In Chapter 1, peri-urban areas are defined as space of transformation where pattern of rural land use are intertwined with pattern of urban land use to create an urban-rural interface with blurred boundaries. The main characteristic of the spatial pattern in Tam Kỳ and Buôn Ma Thuột is rooted urban porosity leading to the spatial intertwining of rural, peri-urban and urban spaces. The emerging urban landscape can be defined as peri-urban city. Three main themes define this peri-urban city beyond porosity: (1) processes of transformation creating the peri-urban city, (2) networks of power and control as means to adapt to these transformations and (3) mobility as prerequisite for ability to benefit from the changing landscape in peri-urban cities.
Chapter 2 describes how state authorities, urban planners and private investors at the local level in Tam Kỳ and Buôn Ma Thuột use porous space to reproduce the city based on their aspirations. This leads to a cargo cult urbanism, where urban planning is rooted in future aspirations for the city. Plans and construction efforts reference the image of a modern urban future and produce the image of a city, which does not exists in the real urban space of Tam Kỳ and Buôn Ma Thuột. In the meantime, practices based in peri-urban space are often contradictory to the official aspirations of local state authorities
Chapter 3 explores this emerging divergence between the aspired urban space by the state and the reality of urban space rooted in material and social porosity. Traditional, newly accessible and environmental porosity provide an array of accessible space which transforms the peri-urban city into a social arena of encounters and interaction. Consequently, porosity enables local residents to maintain urban space as commons. This counters the push towards the privatization of urban space by state and private actors and creates multiple aspirations for the urban future.
Rooted in urban space as commons, porosity enables the usage of peri-urban space as spaces of resistance as discussed in Chapter 4. Mobility and interaction are means of reproduction in the porous ambiguity of urban materiality but also become means of everyday resistance. The emerging spatialities of emancipation provide opportunities for an emerging urban citizenship. Urban materiality and urban citizenship have a mutually constitutive relationship.
In summary, this cumulative dissertation investigates the application of the conjugate gradient method CG for the optimization of artificial neural networks (NNs) and compares this method with common first-order optimization methods, especially the stochastic gradient descent (SGD).
The presented research results show that CG can effectively optimize both small and very large networks. However, the default machine precision of 32 bits can lead to problems. The best results are only achieved in 64-bits computations. The research also emphasizes the importance of the initialization of the NNs’ trainable parameters and shows that an initialization using singular value decomposition (SVD) leads to drastically lower error values. Surprisingly, shallow but wide NNs, both in Transformer and CNN architectures, often perform better than their deeper counterparts. Overall, the research results recommend a re-evaluation of the previous preference for extremely deep NNs and emphasize the potential of CG as an optimization method.
People-centred reforestation is one of the ways to achieve natural climate solutions. Ghana has established a people-centred reforestation programme known as the Modified Taunya System (MTS) where local people are assigned degraded forest reserves to practice agroforestry. Given that the MTS is a people-centred initiative, socioeconomic factors are likely to have impact on the reforestation drive. This study aims to understand the role of translocal practices of remittances and visits by migrants on the MTS. Using multi-sited, sequential explanatory mixed methods and the lens of socioecological systems, the study shows that social capital and socioeconomic obligations of cash remittances from, as well as visits by migrants to their communities of origin play positive roles on reforestation under the MTS. Specifically, translocal households have access to, and use remittances to engage relatively better in the MTS than households that do not receive remittances. This shows that translocal practices can have a positive impact on the environment at the area of origin of migrants where there are people-centred environmental policies in place.
The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs.
This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70%
for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%.
From transportation to urbanization, energy and digitalization, China-backed projects of infrastructural development are increasingly common throughout Southeast Asia and the global South as both a means and outcome of development. This trend has accelerated since China’s Belt and Road Initiative (BRI) in 2013. Against this backdrop, the present ASEAS issue invites to rethink the roles infrastructure plays in forms of development that place connectivity at the center.
Contents:
Simon Rowedder, Phill Wilcox & Susanne Brandtstädter
Negotiating Chinese Infrastructures of Modern Mobilities: Insights from Southeast Asia
Current Research on Southeast Asia
Panitda Saiyarod
The Deviated Route: Navigating the Logistical Power Landscape of the Mekong Border Trade
Franziska S. Nicolaisen
The Politicization of Mobility Infrastructures in Vietnam — The Hanoi Metro Project at the Nexus of Urban Development, Fragmented Mobilities, and National Security
Arratee Ayuttacorn
Chinese Investor Networks and the Politics of Infrastructure Projects in the Eastern Economic Corridor in Thailand
Karin Dean
Belt and Road Initiative in Northern Myanmar: The Local World of China’s Global Investments
Mira Käkönen
Entangled Enclaves: Dams, Volatile Rivers, and Chinese Infrastructural Engagement in Cambodia
Research Workshop
Tim Oakes
Infrastructure Power, Circulation and Suspension
Susanne Brandtstädter
Infrastructural Fragility, Infra-Politics and Jianghu
Book Reviews
Michael Kleinod-Freudenberg
Book Review: Tappe, O., & Rowedder, S. (Eds.). (2022). Extracting Development: Contested Resource Frontiers in Mainland Southeast Asia
This book collects ten of Sandra Huebenthal’s most important contributions to the application of Social Memory Theory in Biblical studies. The volume consists of four parts, each devoted to a particular field of research. Part one addresses the general impact of Social Memory Theory for the New Testament. The second part analyzes how Social Memory Theory adds to exploring the phenomenon of (biblical) intertextuality as a strategy for negotiating Early Christian identity and the third part investigates how New Testament pseudepigraphy provides a different approach for understanding the negotiation and formation of Christian identities. Finally, part four provides an outlook how the hermeneutical approach can enhance Patristic research. The ten essays originate from discussions about Social Memory Theory and the New Testament at international conferences, three of them are translations of German contributions, while two are published for the first time in this volume.
(Verlagsbeschreibung)
Technological advancements and new legal requirements are continuously changing the online data disclosure landscape in terms of both, the quantity and quality of data that firms can acquire. Nowadays, consumers are required to disclose personal data online multiple times a day and in a variety of different contexts, such as creating user profiles, online payment or using location-based services. Despite consumers’ increasing online privacy concerns, firms rely ever more strongly on consumer data that they convert into a competitive advantage through personalized product recommendations and targeted advertising. In an effort to encourage consumer data disclosure, many firms have focused on building trust as a way to counterbalance privacy concerns and mitigate risk perceptions. Correspondingly, marketing literature has continued to examine the interplay of trust and consumer privacy concerns. While the extant research has considerably advanced our understanding of the role of trust in privacy-related decision-making, the majority of studies has mainly focused on single-stage, dyadic disclosure settings and cognitive decision-making processes.
Against this background, the overarching goal of this thesis is to shed light on under-researched data disclosure contexts involving trust and to explore additional facets of the underlying decision-making processes. For example, considering pre- and post-disclosure stages when evaluating consumers’ data disclosure decisions allows for a more holistic picture of the decision-making process. Similarly, social media and sharing economy settings challenge the traditional assumption of purely dyadic consumer-firm data disclosure, thus extending traditional conceptualizations of trust. Across three independent essays, this thesis addresses the overarching research question of how the peculiarities of multi-stage and multi-actor settings shape consumers’ trust-based decision-making strategies.
CNT-PUFs: highly robust and heat-tolerant carbon-nanotube-based physical unclonable functions
(2023)
In this work, we explored a highly robust and unique Physical Unclonable Function (PUF) based on the stochastic assembly of single-walled Carbon NanoTubes (CNTs) integrated within a wafer-level technology. Our work demonstrated that the proposed CNT-based PUFs are exceptionally robust with an average fractional intra-device Hamming distance well below 0.01 both at room temperature and under varying temperatures in the range from 23 °C to 120 °C. We attributed the excellent heat tolerance to comparatively low activation energies of less than 40 meV extracted from an Arrhenius plot. As the number of unstable bits in the examined implementation is extremely low, our devices allow for a lightweight and simple error correction, just by selecting stable cells, thereby diminishing the need for complex error correction. Through a significant number of tests, we demonstrated the capability of novel nanomaterial devices to serve as highly efficient hardware security primitives.
Vanadium redox-flow batteries (VRFBs) have played a significant role in hybrid energy storage systems (HESSs) over the last few decades owing to their unique characteristics and advantages. Hence, the accurate estimation of the VRFB model holds significant importance in large-scale storage applications, as they are indispensable for incorporating the distinctive features of energy storage systems and control algorithms within embedded energy architectures. In this work, we propose a novel approach that combines model-based and data-driven techniques to predict battery state variables, i.e., the state of charge (SoC), voltage, and current. Our proposal leverages enhanced deep reinforcement learning techniques, specifically deep q-learning (DQN), by combining q-learning with neural networks to optimize the VRFB-specific parameters, ensuring a robust fit between the real and simulated data. Our proposed method outperforms the existing approach in voltage prediction. Subsequently, we enhance the proposed approach by incorporating a second deep RL algorithm—dueling DQN—which is an improvement of DQN, resulting in a 10% improvement in the results, especially in terms of voltage prediction. The proposed approach results in an accurate VFRB model that can be generalized to several types of redox-flow batteries.
The worldwide adoption of Electric Vehicles (EVs) has embraced promising advancements toward a sustainable transportation system. However, the effective charging scheduling of EVs is not a trivial task due to the increase in the load demand in the Charging Stations (CSs) and the fluctuation of electricity prices. Moreover, other issues that raise concern among EV drivers are the long waiting time and the inability to charge the battery to the desired State of Charge (SOC). In order to alleviate the range of anxiety of users, we perform a Deep Reinforcement Learning (DRL) approach that provides the optimal charging time slots for EV based on the Photovoltaic power prices, the current EV SOC, the charging connector type, and the history of load demand profiles collected in different locations. Our implemented approach maximizes the EV profit while giving a margin of liberty to the EV drivers to select the preferred CS and the best charging time (i.e., morning, afternoon, evening, or night). The results analysis proves the effectiveness of the DRL model in minimizing the charging costs of the EV up to 60%, providing a full charging experience to the EV with a lower waiting time of less than or equal to 30 min.
Since the launch of the BRI, particular modes of movement are integral to its vision of what it means to be a modern world citizen. Nowhere is this more apparent than in Southeast Asia, where China-backed infrastructure projects expand, and at great speed. Such infrastructure projects are carriers of particular versions of modernity, promising rapid mobility to populations better connected than ever before. Yet, until now, little attention has been paid to how mobility and promises of mobility intersect with local understandings of development. In the introduction to this special issue, we argue that it is essential to think about the role infrastructure plays in forms of development that place connectivity at the center. We suggest that considering development, mobility and mo-dernity together is enlightening because it interrogates the connections between these interlocking themes. Through an introduction to five ethnographically grounded papers and two commentaries, all of which engage with infrastructures in different contexts throughout Southeast Asia, we demonstrate that there are significant gaps between of-ficial policy and lived experience. This makes the need to interrogate what infrastructure, mobilities, and global China really mean all the more pressing.
ChatGPT and similar generative AI models have attracted hundreds of millions of users and have become part of the public discourse. Many believe that such models will disrupt society and lead to significant changes in the education system and information generation. So far, this belief is based on either colloquial evidence or benchmarks from the owners of the models—both lack scientific rigor. We systematically assess the quality of AI-generated content through a large-scale study comparing human-written versus ChatGPT-generated argumentative student essays. We use essays that were rated by a large number of human experts (teachers). We augment the analysis by considering a set of linguistic characteristics of the generated essays. Our results demonstrate that ChatGPT generates essays that are rated higher regarding quality than human-written essays. The writing style of the AI models exhibits linguistic characteristics that are different from those of the human-written essays. Since the technology is readily available, we believe that educators must act immediately. We must re-invent homework and develop teaching concepts that utilize these AI models in the same way as math utilizes the calculator: teach the general concepts first and then use AI tools to free up time for other learning objectives.
Systems-focused error prevention efforts are internationally recognized in the healthcare industry, and industry efforts to identify and correct organizational defects through the process of CRM are well established in the U.S. and Germany. However, in both countries, there is no clear corresponding liability for healthcare organizations who fail to engage in systems-based learning through the process of clinical risk management (CRM). Although both jurisdictions do recognize organization-based theories of liability, liability for negligent CRM has not been explicitly recognized by courts in either jurisdiction to date. German legal scholars, recognizing this gap in liability for healthcare organizations, have written in support of finding liability for negligent CRM under existing tort law; however, there is no corresponding discussion in the American legal literature. This dissertation fills that gap with a comparative analysis of medical negligence law in the U.S. and Germany through the international lens of modern medical error prevention science and policy to articulate a legal basis and sketch the evidentiary framework for tort liability based on negligent CRM.
In the constrained planarity setting, we ask whether a graph admits a crossing-free drawing that additionally satisfies a given set of constraints. These constraints are often derived from very natural problems; prominent examples are Level Planarity, where vertices have to lie on given horizontal lines indicating a hierarchy, Partially Embedded Planarity, where we extend a given drawing without modifying already-drawn parts, and Clustered Planarity, where we additionally draw the boundaries of clusters which recursively group the vertices in a crossing-free manner. In the last years, the family of constrained planarity problems received a lot of attention in the field of graph drawing. Efficient algorithms were discovered for many of them, while a few others turned out to be NP-complete. In contrast to the extensive theoretical considerations and the direct motivation by applications, only very few of the found algorithms have been implemented and evaluated in practice.
The goal of this thesis is to advance the research on both theoretical as well as practical aspects of constrained planarity. On the theoretical side, we consider two types of constrained planarity problems. The first type are problems that individually constrain the rotations of vertices, that is they restrict the counter-clockwise cyclic orders of the edges incident to vertices. We give a simple linear-time algorithm for the problem Partially Embedded Planarity, which also generalizes to further constrained planarity variants of this type.
The second type of constrained planarity problem concerns more involved planarity variants that come down to the question whether there are embeddings of one or multiple graphs such that the rotations of certain vertices are in sync in a certain way. Clustered Planarity and a variant of the Simultaneous Embedding with Fixed Edges Problem (Connected SEFE-2) are well-known problems of this type. Both are generalized by our Synchronized Planarity problem, for which we give a quadratic algorithm. Through reductions from various other problems, we provide a unified modelling framework for almost all known efficiently solvable constrained planarity variants that also directly provides a quadratic-time solution to all of them.
For both our algorithms, a key ingredient for reaching an efficient solution is the usage of the right data structure for the problem at hand. In this case, these data structures are the SPQR-tree and the PC-tree, which describe planar embedding possibilities from a global and a local perspective, respectively. More specifically, PC-trees can be used to locally describe the possible cyclic orders of edges around vertices in all planar embeddings of a graph. This makes it a key component for our algorithms, as it allows us to test planarity while also respecting further constraints, and to communicate constraints arising from the surrounding graph structure between vertices with synchronized rotation.
Bridging over to the practical side, we present the first correct implementation of PC-trees. We also describe further improvements, which allow us to outperform all implementations of alternative data structures (out of which we only found very few to be fully correct) by at least a factor of 4. We show that this yields a simple and competitive planarity test that can also yield an embedding to certify planarity. We also use our PC-tree implementation to implement our quadratic algorithm for solving Synchronized Planarity. Here, we show that our algorithm greatly outperforms previous attempts at solving related problems like Clustered Planarity in practice. We also engineer its running time and show how degrees of freedom in the theoretical algorithm can be leveraged to yield an up to tenfold speed-up in practice.
The nationalism-patriotism distinction is one of the most influential distinctions in the field of political psychology. While frequently used, the distinction suffers from a number of shortcomings that have hitherto been devoted little attention to. This dissertation aims to contribute to fill this research gap by systematically addressing these pitfalls. Notably, it does not abandon the binary distinction as such, but aims to further refine it. Thoroughly revisiting the nationalism-patriotism distinction, it synthesises the field's two predominant research traditions, i.e. the work of Kosterman and Feshbach (1989) in the U.S. and the one of Blank and Schmidt (2003) in Germany, that have not been brought into dialogue. In so doing, and engaging with research on attachment, it calls for a more nuanced triad of attachments: nationalism, that revolves around the nation; patriotism, that refers to the homeland; and democratic patriotism with democracy as its object of attachment. In line with this triad, it introduces a novel three-factor measurement model that has been validated in three studies in Germany. Overall, the dissertation underlines the need to approach ambiguous and complex concepts such as nationalism and patriotism in a more theoretically consistent way before operationalizing them in a rigorous manner.
Data has become a necessary resource for firm operations in the modern digital world, explaining their growing data gathering efforts. Due to this development, consumers are confronted with decisions to disclose personal data on a daily basis, and have become increasingly intentional about data sharing. While this reluctance to disclose personal data poses challenges for firms, at the same time, it also creates new opportunities for improving privacy-related interactions with customers. This dissertation advocates for a more holistic perspective on consumers’ privacy-related decision-making and introduces the consumer privacy journey consisting of three subsequent phases: pre data disclosure, data disclosure, post data disclosure. In three independent essays, I stress the importance of investigating data requests (i.e., the first step of this journey) as they represent a largely neglected, yet, potentially powerful means to influence consumers’ decision-making and decision-evaluation processes. Based on dual-processing models of decision-making, this dissertation focuses on both consumers’ cognitive and affective evaluations of privacy-related information: First, Essay 1 offers novel conceptualizations and operationalizations of consumers’ perceived behavioral control over personal data (i.e., cognitive processing) in the context of Artificial Intelligence (AI)-based data disclosure processes. Next, Essay 2 examines consumers’ cognitive and affective processing of a data request that entails relevance arguments as well as relevance-illustrating game elements. Finally, Essay 3 categorizes affective cues that trigger consumers’ affective processing of a data request and proposes that such cues need to fit with a specific data disclosure situation to foster long-term decision satisfaction. Collectively, my findings provide research and practice with new insights into consumers’ privacy perceptions and behaviors, which are particularly valuable in the context of complex, new (technology-enabled) data disclosure situations.
My dissertation examines literary mythologies of privacy in the authoritarian Russia of 1953–1985. This era was marked by an expansion of “non-state spheres,” or areas of life of which the Communist state increasingly released its control after Joseph Stalin’s death in 1953. Political elites, architects, and designers, as well as ordinary citizens co-constructed and explored the rising spheres through the languages of their respective fields—by producing regulations and laws, designing and erecting new types of buildings, developing new and modernizing already familiar everyday objects as well as devising new ways of integrating these objects into private and public spaces. Alongside these voices, transformations in the cultural sphere in general, and literature in particular, were most vocal. The late Soviet era witnessed the dissolution of the ossified ideology of socialist realism that focused on the glorification of the “new Soviet man” and had held culture in its tight grips since the 1930s. Starting from the 1950s, writers increasingly focused on portraying areas of life that lay beyond one’s public commitments and experimented with new meanings, codes, and forms to give shape to novel spheres of experience of the “late Soviet man” that can be subsumed under the concept of the “private sphere.”
The analytical framework of my dissertation is built around the journey to understand the mechanisms and architecture that powered the imagination of models of distancing oneself from the state and the society at large—scenarios of privacy, as we may call them today. I examine Russian prose and drama of the 1950s–1980s as a laboratory for the ideas of privacy, which was increasingly sought in the society disillusioned by the Communist doctrine and thus progressively alienating from active participation in the public sphere. I analyze the meanings that writers incorporated into new and old forms of domesticity—private flats that became progressively widespread throughout the 1950s–1980s, rooms in communal apartments, individual houses—to determine the spectrum of concepts that nurtured the idea of privacy in the late Soviet literary imagination. I also examine representations of reciprocal paradigms of relations between subjects from which the state and society at large were increasingly excluded. In the examples that I analyze, forms of private withdrawals variate from establishing control over liminal spaces or escaping into the world of feelings and emotions and building a connection to a person or space (significant for the characters for private rather than public reasons) to experimenting with language and pursuing one’s idiolect despite the ubiquitous “officialese,” as well as living in temporalities asynchronous with the public time.
Beyond revealing the visions of different, non-state existences in the late Soviet era, my text also advocates examining the role of official literature as a platform for subversion and change that was no less important in an authoritarian state than dissident literature. I see officially published texts as a cultural subaltern who defies the state of affairs and slowly but firmly turns the “state sphere” into a public one by pushing its own agenda through publications that test and gain ground for bolder visions of Soviet life that are not predicated on the commitment to the public sphere. With individual mechanisms of power assertion employed by the state or literature in the late Soviet era well-researched, the framework is still missing that would capture the shifts of borders between the private and public spheres under the influence of these actors. In devising such framework, I build upon sociological theories of disattendability and civil inattention that Erving Goffman conceived to describe conventions of individual behavior and social interaction in public. Extending these theories toward the studies of literary politics, I argue that by envisioning scenarios of a private retreat and bringing them into officially published editions, literature normalized privacy as a late Soviet imaginary and, therefore, continuously heightened its own disattendability, thereby expanding the borders of the private sphere. On the side of the state, the border was defined by the triggers of disturbance of civil inattention: privacy was conceded in return for disattendability. Under such conditions, literature became a bizarre “private kitchen” that performed private and public functions simultaneously—similar to the kitchens in newly-built individual apartments that were popular loci of socialization in the late Soviet era. It turned into a place where one can escape—it became one of the “niches of privacy” where it was possible to discuss and negotiate the world, in which the society lived or to which it should strive. At the same time, it assumed the role of a surrogate for the public sphere within the “state” sphere by pushing its own agenda through the publication of literary texts that sought to imagine a person rather than a cog in the Communist machine and thus transformed socialist realism into a literary current “with a human face.”
In my research, privacy, literature, and politics are bound together to reveal a vibrant spectacle of the continuous interaction between the state, cultural elites, and the citizens, in which thresholds are erected and crossed incessantly. Fictional private sites were battlefields for the production and contestation of ideologies, and the exposure of these literary wars to the public eye played a fundamental role in shifting the borders between the private and the public spheres in an authoritarian late Soviet Russia. The patterns of relations between the state and culture that I uncover in my dissertation resonate in neo-authoritarian twenty-first-century Russia, making privacy an important lens for our insight into the role of culture in rising authoritarian and failing democratic systems across the globe.
Due to the increasing amount of distributed renewable energy generation and the emerging high demand at consumer connection points, e. g., electric vehicles, the power distribution grid will reach its capacity limit at peak load times if it is not expensively enhanced. Alternatively, smart flexibility management that controls user assets can help to better utilize the existing power grid infrastructure for example by sharing available grid capacity among connected electric vehicles or by disaggregating flexibility requests to hybrid photovoltaic battery energy storage systems in households. Besides maintaining an acceptable state of the power distribution grid, these smart grid applications also need to ensure a certain quality of service and provide fairness between the individual participants, both of which are not extensively discussed in the literature. This thesis investigates two smart grid applications, namely electric vehicle charging-as-a-service and flexibility-provision-as-a-service from distributed energy storage systems in private households.
The electric vehicle charging service allocation is modeled with distributed queuing-based allocation mechanisms which are compared to new probabilistic algorithms. Both integrate user constraints (arrival time, departure time, and energy required) to manage the quality of service and fairness. In the queuing-based allocation mechanisms, electric vehicle charging requests are packetized into logical charging current packets, representing the smallest controllable size of the charging process. These packets are queued at hierarchically distributed schedulers, which allocate the available charging capacity using the time and frequency division multiplexing technique known from the networking domain. This allows multiple electric vehicles to be charged simultaneously with variable charging currents. To achieve high quality of service and fairness among electric vehicle charging processes, dynamic weights are introduced into a weighted fair queuing scheduler that considers electric vehicle departure time and required energy for prioritization. The distributed probabilistic algorithms are inspired by medium access protocols from computer networking, such as binary exponential backoff, and control the quality of service and fairness by adjusting sampling windows and waiting periods based on user requirements.
The second smart grid application under investigation aims to provide flexibility provision-as-a-service that disaggregates power flexibility requests to distributed battery energy storage systems in private households. Commonly, the main purpose of stationary energy storage is to store energy from a local photovoltaic system for later use, e. g., for overnight charging of an electric vehicle. This is optimized locally by a home energy management system, which also allows the scheduling of external flexibility requests defined by the deviation from the optimal power profile at the grid connection point, for example, to perform peak shaving at the transformer. This thesis discusses a linear heuristic and a meta heuristic to disaggregate a flexibility request to the single participating energy management systems that are grouped into a flexibility pool. Thereby, the linear heuristic iteratively assigns portions of the power flexibility to the most appropriate energy management system for one time slot after another, minimizing the total flexibility cost or maximizing the probability of flexibility delivery. In addition, a multi-objective genetic algorithm is proposed that also takes into account power grid aspects, quality of service, and fairness among par-ticipating households. The genetic operators are tailored to the flexibility disaggregation search space, taking into account flexibility and energy management system constraints, and enable power-optimized buffering of fitness values.
Both smart grid applications are validated on a realistic power distribution grid with real driving patterns and energy profiles for photovoltaic generation and household consumption. The results of all proposed algorithms are analyzed with respect to a set of newly defined metrics on quality of service, fairness, efficiency, and utilization of the power distribution grid. One of the main findings is that none of the tested algorithms outperforms the others in all quality of service metrics, however, integration of user expectations improves the service quality compared to simpler approaches. Furthermore, smart grid control that incorporates users and their flexibility allows the integration of high-load applications such as electric vehicle charging and flexibility aggregation from distributed energy storage systems into the existing electricity distribution infrastructure. However, there is a trade-off between power grid aspects, e. g., grid losses and voltage values, and the quality of service provided. Whenever active user interaction is required, means of controlling the quality of service of users’ smart grid applications are necessary to ensure user satisfaction with the services provided.
Code injection attacks like the one used in the high-profile 2017 Equifax breach, have become increasingly common, ranking at the top of OWASP’s list of critical web application vulnerabilities. The injection attacks can also target embedded applications running on processors like ARM and Xtensa by exploiting memory bugs and maliciously altering the program’s behavior or even taking full control over a system. Especially, ARM’s support of low power consumption without sacrificing performance is leading the industry to shift towards ARM processors, which advances the attention of injection attacks as well.
In this thesis, we are considering web applications and embedded applications (running on ARM and Xtensa processors) as the target of injection attacks. To detect injection attacks in web applications, taint analysis is mostly proposed but the precision, scalability, and runtime overhead of the detection depend on the analysis types (e.g., static vs dynamic, sound vs unsound). Moreover, in the existing dynamic taint tracking approach for Java- based applications, even the most performant can impose a slowdown of at least 10–20% and often far more. On the other hand, considering the embedded applications, while some initial research has tried to detect injection attacks (i.e., ROP and JOP) on ARM, they suffer from high performance or storage overhead. Besides, the Xtensa has been neglected though used in most firmware-based embedded WiFi home automation devices.
This thesis aims to provide novel approaches to precisely detect injection attacks on both the web and embedded applications. To that end, we evaluate JavaScript static analysis frameworks to evaluate the security of a hybrid app (JS & native) from an industrial partner, provide RIVULET – a tool that precisely detects injection attacks in Java-based real-world applications, and investigate injection attacks detection on ARM and Xtensa platforms using hardware performance counters (HPCs) and machine learning (ML) techniques.
To evaluate the security of the hybrid application, we initially compare the precision, scalability, and code coverage of two widely-used static analysis frameworks—WALA and SAFE. The result of our comparison shows that SAFE provides higher precision and better code coverage at the cost of somewhat lower scalability. Based on these results, we analyze the data flows of the hybrid app via taint analysis by extending the SAFE’s taint analysis and detected a potential for injection attacks of the hybrid application.
Similarly, to detect injection attacks in Java-based applications, we provide Rivulet which monitors the execution of developer-written functional tests using dynamic taint tracking. Rivulet uses a white-box test generation technique to re-purpose those functional tests to check if any vulnerable flow could be exploited. We compared Rivulet to the state-of-the-art static vulnerability detector Julia on benchmarks and Rivulet outperformed Julia in both false positives and false negatives. We also used Rivulet to detect new vulnerabilities.
Moreover, for applications running on ARM and Xtensa platforms, we investigate ROP1 attack detection by combining HPCs and ML techniques. We collect data exploiting real- world vulnerable applications and small benchmarks to train the ML. For ROP attack detection on ARM, we also implement an online monitor which labels a program’s execution as benign or under attack and stops its execution once the latter is detected. Evaluating our ROP attack detection approach on ARM provides a detection accuracy of 92% for the offline training and 75% for the online monitoring. Similarly, our ROP attack detection on the firmware-only Xtensa processor provides an overall average detection accuracy of 79%.
Last but not least, this thesis shows how relevant taint analysis is to precisely detect injection attacks on web applications and the power of HPC combined with machine learning in the control flow injection attacks detection on ARM and Xtensa platforms.
A Comprehensive Comparison of Fuzzy Extractor Schemes Employing Different Error Correction Codes
(2023)
This thesis deals with fuzzy extractors, security primitives often used in conjunction with Physical Unclonable Functions (PUFs). A fuzzy extractor works in two stages: The generation phase and the reproduction phase. In the generation phase, an Error Correction Code (ECC) is used to compute redundant bits for a given PUF response, which are then stored as helper data, and a key is extracted from the response. Then, in the reproduction phase, another (possibly noisy) PUF response can be used in conjunction with this helper data to extract the original key.
It is clear that the performance of the fuzzy extractor is strongly dependent on the underlying ECC. Therefore, a comparison of ECCs in the context of fuzzy extractors is essential in order to make them as suitable as possible for a given situation. It is important to note that due to the plethora of various PUFs with different characteristics, it is very unrealistic to propose a single metric by which the suitability of a given ECC can be measured.
First, we give a brief introduction to the topic, followed by a detailed description of the background of the ECCs and fuzzy extractors studied. Then, we summarise related work and describe an implementation of the ECCs under consideration. Finally, we carry out the actual comparison of the ECCs and the thesis concludes with a summary of the results and suggestions for future work.
In empirical research, scholars can choose between an exploratory causes-of-effects analysis, a confirmatory effects-ofcauses approach, or a mechanism-of-effects analysis that can be either exploratory or confirmatory. Understanding the choice between the approaches is important for two reasons. First, the added value of each approach depends on how much is known about the phenomenon of interest at the time of the analysis. Second, because of the specializations of methods, there are benefits to a division of labor between researchers who have expertise in the application of a given method. In this preregistered study, we test two hypotheses that follow from these arguments. We theorize that exploratory research is chosen when little is known about a phenomenon and a confirmatory approach is taken when more knowledge is available. A complementary hypothesis is that quantitative researchers opt for confirmatory designs and qualitative researchers for exploration because of their academic socialization. We test the hypotheses with a survey experiment of more than 900 political scientists from the United States and Europe. The results indicate that the state of knowledge has a significant and sizeable effect on the choice of the approach. In contrast, the evidence about the effect of methods expertise is more ambivalent.
Understanding of financial data has always been a point of interest for market participants to make better informed decisions. Recently, different cutting edge technologies have been addressed in the Financial Technology (FinTech) domain, including numeracy understanding, opinion mining and financial ocument processing.
In this thesis, we are interested in analyzing the arguments of financial experts with the goal of supporting investment decisions. Although various business studies confirm the crucial role of argumentation in financial communications, no work has addressed this problem as a computational argumentation task. In other words, the automatic analysis of arguments. In this regard, this thesis presents contributions in the three essential axes of theory, data, and evaluation to fill the gap between argument mining and financial text.
First, we propose a method for determining the structure of the arguments stated by company representatives during the public announcement of their quarterly results and future estimations through earnings conference calls. The proposed scheme is derived from argumentation theory at the micro-structure level of discourse. We further conducted the corresponding annotation study and published the first financial dataset annotated with arguments: FinArg.
Moreover, we investigate the question of evaluating the quality of arguments in this financial genre of text. To tackle this challenge, we suggest using two levels of quality metrics, considering both the Natural Language Processing (NLP) literature of argument quality assessment and the financial era peculiarities.
Hence, we have also enriched the FinArg data with our quality dimensions to produce the FinArgQuality dataset.
In terms of evaluation, we validate the principle of ensemble learning on the argument identification and argument unit classification tasks. We show that combining a traditional machine learning model along with a deep learning one, via an integration model (stacking), improves the overall performance, especially in small dataset settings.
In addition, despite the fact that argument mining is mainly a domain dependent task, to this date, the number of studies that tackle the generalization of argument mining models is still relatively small. Therefore, using our stacking approach and in comparison to the transfer learning model of DistilBert, we address and analyze three real-world scenarios concerning the model robustness over completely unseen domains and unseen topics.
Furthermore, with the aim of the automatic assessment of argument strength, we have investigated and compared different (refined) versions of Bert-based models that incorporate external knowledge in the decision layer. Consequently, our method outperforms the baseline model by 13 ± 2% in terms of F1-score through integrating Bert with encoded categorical features.
Beyond our theoretical and methodological proposals, our model of argument quality assessment, annotated corpora, and evaluation approaches are publicly available, and can serve as strong baselines for future work in both FinNLP and computational argumentation domains.
Hence, directly exploiting this thesis, we proposed to the community, a new task/challenge related to the analysis of financial arguments: FinArg-1, within the framework of the NTCIR-17 conference.
We also used our proposals to react to the Touché challenge at the CLEF 2021 conference. Our contribution was selected among the «Best of Labs».
To answer the research question, all SPIEGEL covers from 1965 to 2021 were examined for a reference to history topics. The report documents the assignments of the 533 covers recorded to the categories of history narrative, politics of memory and politics of the past.
Main article: https://doi.org/10.3167/jemms.2023.150107
Organic agriculture in Java, Indonesia, has been historically intertwined with social movements that struggled for more economically, ecologically, culturally, and socially sustainable agriculture. While these grassroots movements emerged under an authoritarian government that showed little interest in organic agriculture, the turn of the 21st century saw the rapid involvement of the Indonesian government in supporting, regulating and, arguably, commodifying organic agriculture. Institutionalization triggered diverse responses from competing organic actors, reflecting their different standpoints and knowledges. In this context, a transdisciplinary approach is deemed suitable to provide context-specific insights into organic agriculture.
This dissertation draws on anthropology and Science and Technology Studies (STS) to explore the politics of knowledge of organic agriculture in Yogyakarta, Indonesia, as a contribution to a critique of transdisciplinarity. My interest on the hierarchization of different knowledges is inspired by the work of anthropologists of knowledge that asks how the communities they study construct knowledge and how they themselves construct knowledge about these communities. Since transdisciplinary knowledge is co-produced by science and society and reflects their embedded power relations, transdisciplinary research needs to be open to different interpretations, and reflexive towards the unequal distribution of resources, accountability, and responsibility. By linking these two lines of thought, I examine the making of knowledges through reflexive transdisciplinary work. I reflect on how “epistemic living space” (Felt 2009) and “co-presence” (Chua 2015) affect research and shape the politics of knowledge of organic agriculture in Yogyakarta, Indonesia. I argue that the hierarchization of different knowledges of organic agriculture was intertwined with my shifting positionalities, as a field researcher in Indonesia and PhD student at Passau University, as I moved between these two different “field sites”.
This cumulative dissertation is divided into two parts. In Part I, “Knowledge in the making”, I present my contributions towards transdisciplinary knowledge production and politics of knowledge of organic agriculture. Part II, “Publications”, comprises the three stand-alone papers. The first contribution is my formulation of the notion of knowledge in the making. The second is my exploration of the ways that reflexive transdisciplinary work, and living and intersubjective experience shape knowledge in the making. The third is my demonstration of how an understanding of knowledge in the making sheds lights on the politics of knowledge of organic agriculture. This approach serves to examine the politics involved in synthesizing the conceptualizations of organic agriculture employed by different actors into one overarching narrative, such as sustainable agriculture or alternative agriculture. My final contribution is the notion of transdisciplinary moments, a conceptualization of transdisciplinary research practice that accounts for the politics of knowledge in which both scientific and extra-scientific actors are embedded. As a conclusion, I share the lessons learned from pursuing a PhD as a cumulative dissertation in an unstructured setting within a German–Indonesian research project on Indonesian organic agriculture. Finally, I identify bodies of literature and strands of thinking for future engagement within transdisciplinary research and discuss their potential to contribute to radical change in the institutional and value structures of contemporary academia.
In the ongoing 21st century, low- and middle-income countries will face two health challenges that are thoroughly different from what these countries have been dealing with in preceding centuries. First, they are confronted with surging rates of non-communicable diseases (NCDs), and second, climate change will take its toll and is predicted to cause catastrophic health impairments and exacerbate chronic health conditions further. Both will pose a disproportionate health and economic burden on low- and middle-income countries, which are also the countries least able to cope with them. By threatening individual health and socioeconomic improvements, and by putting an immense burden on already constrained health care systems, they impede the progress in poverty reduction and widen health inequities between the rich and the poor.
Against this background, this thesis investigates the potential of NCD prevention and treatment measures in the context of Southeast Asia, with case studies in Indonesia. Specifically, it seeks to understand what kind of health interventions have the potential to be (cost-)effective considering the cultural background, lifestyle, health literacy and health system capacities in the region. Further, this thesis analyzes the interplay between NCDs and climate change and assesses the financial burden that both might pose in the decades to come. Hence, this thesis contributes to a better understanding of how the two health challenges of the 21st century, NCDs and climate change, can be addressed in the context of Southeast Asia and offers insights into what type of health policies and interventions can play a supportive role.
Teaching Journalism Literacy in Schools: The Role of Media Companies as
Media Educators in Germany
(2023)
German journalism is facing major challenges including declining circulation, funding, trust, and political allegations of spreading disinformation. Increased media literacy in the population is one way to counter these issues and their implications. This especially applies to the sub‐concept of journalism literacy, focusing on the ability to consume news critically and reflectively, thus enabling democratic participation. For media companies, promoting journalism literacy seems logical for economic and altruistic reasons. However, research on German initiatives is scarce. This article presents an explorative qualitative survey of experts from seven media companies offering journalistic media education projects in German schools, focusing on the initiatives’ content, structure, and motivation. Results show that initiatives primarily aim at students and teachers, offering mostly education on journalism (e.g., teaching material) and via journalism (e.g., journalistic co‐production with students). While these projects mainly provide information on the respective medium and journalistic practices, dealing with disinformation is also a central goal. Most initiatives are motivated both extrinsically (e.g., reaching new audiences) and intrinsically (e.g., democratic responsibility). Despite sometimes insufficient resources and reluctant teachers, media companies see many opportunities in their initiatives: Gaining trust and creating resilience against disinformation are just two examples within the larger goal of enabling young people to be informed and opinionated members of a democratic society.
Religion can unite and divide, it can lead to a strengthening or a weakening of identity and legitimacy. Religion can stoke conflicts but it can also pacify them – within societies and in international politics. Religion endures and it can exist independently of states, it can constitute them, and it can provide new forms of states, societies, and empires. Arguably, religion shapes or even constitutes the international society of states, an aspect so far neglected in the field of International Relations. The dissertation provides a new definition of religion for International Relations and the English School in particular. Based upon this understanding of religion, the five publications presented in the dissertation provide new analytical and theoretical concepts and approaches to fill the research gap. Religion is integrated into the theoretical framework of the English School in the form of a “prime institution” and with the help of the “quilt model”. While the former expands the theoretical framework, the latter adds an analytical layer. Based upon this definition religion is also introduced as a concept (“hybrid actorness”) in Foreign Policy Analysis, opening it up to become less state-centrist and more transnational-oriented, thereby boosting its relevance considering the evolving international (global) society. In another step, the Securitization framework of analysis is expanded to include (freedom) of religion. By revisiting the publications, the dissertation is able to identify next steps in terms of avenues of research. Finally, the dissertation reveals areas of study which contribute to increasing the pertinence of IR, particularly of the English School.
Data-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that educa-tion lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper con-cludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education.
In the Internet of Things (IoT), Low-Power Wide-Area Networks (LPWANs) are designed to provide low energy consumption while maintaining a long communications’ range for End Devices (EDs). LoRa is a communication protocol that can cover a wide range with low energy consumption. To evaluate the efficiency of the LoRa Wide-Area Network (LoRaWAN), three criteria can be considered, namely, the Packet Delivery Rate (PDR), Energy Consumption (EC), and coverage area. A set of transmission parameters have to be configured to establish a communication link. These parameters can affect the data rate, noise resistance, receiver sensitivity, and EC. The Adaptive Data Rate (ADR) algorithm is a mechanism to configure the transmission parameters of EDs aiming to improve the PDR. Therefore, we introduce a new algorithm using the Multi-Armed Bandit (MAB) technique, to configure the EDs’ transmission parameters in a centralized manner on the Network Server (NS) side, while improving the EC, too. The performance of the proposed algorithm, the Low-Power Multi-Armed Bandit (LP-MAB), is evaluated through simulation results and is compared with other approaches in different scenarios. The simulation results indicate that the LP-MAB’s EC outperforms other algorithms while maintaining a relatively high PDR in various circumstances.
This article presents a proposal on how the European Union’s regulatory framework on genetically modified (GM) plants should be reformed in light of recent developments in genomic plant breeding techniques. The reform involves a three-tier system reflecting the genetic changes and resulting traits of GM plants. The article is intended to contribute to the ongoing debate over how best to regulate plant gene editing techniques in the EU.
After the enactment of the GDPR in 2018, many companies were forced to rethink their privacy management in order to comply with the new legal framework. These changes mostly affect the Controller to achieve GDPR-compliant privacy policies and management.However, measures to give users a better understanding of privacy, which is essential to generate legitimate interest in the Controller, are often skipped. We recommend addressing this issue by the usage of privacy preference languages, whereas users define rules regarding their preferences for privacy handling. In the literature, preference languages only work with their corresponding privacy language, which limits their applicability. In this paper, we propose the ConTra preference language, which we envision to support users during privacy policy negotiation while meeting current technical and legal requirements. Therefore, ConTra preferences are defined showing its expressiveness, extensibility, and applicability in resource-limited IoT scenarios. In addition, we introduce a generic approach which provides privacy language compatibility for unified preference matching.
Due to their high numbers, refugees’ labour market inclusion has become an important topic for Germany in recent years. Because of a lack of research on meso-level actors’ influences on labour market inclusion and the transcendent role of organizations in modern societies, the article focuses on the German professional chambers’ role in the process of refugee inclusion. The study shows that professional chambers are intermediaries between economic actors, the government and refugees, which all follow their own logics and ideas of labour market inclusion (the state, the market and the community logic). The measures taken by professional chambers mainly reflect a governmental logic (to reduce refugee unemployment) combined with a market logic (to provide human resources to economic actors). A community logic (altruism) only comes into play as a rather unintended consequence of measures addressing the other two logics. The measures of two types of professional chambers are compared. Close similarities between them reveal that the organization type is of theoretical relevance to explain the type of measures organizations opt for.
My study examines how the configuration of the capitalist frontier through extractivism shapes ethnicity, gender, and intersectionality in the areas surrounding a nickel mine in Sorowako (East Luwu District, South Sulawesi), logging and coal mining along the Lalang River (Murung Raya District, Central Kalimantan) Indonesia. The colonial frontier intersects with the capitalist frontier and provides the circumstances for its formation. The colonial restrictions, religions, commodities, the imposition of labor discipline, and political changes have molded ethnic identities and relationships with nature. Furthermore, using autoethnography and Feminist Political Ecology, I combine my experiences as a woman academic-activist with the experiences of the people in my research area. I identify how communities and individuals interact with the multiple-frontier in everyday life by defining the configuration of the frontier from above and below. Thus, my dissertation contributes to understanding how I, the community, and the capitalist frontier landscape are contained and can potentially transform into multidimensional resistance. I develop a link between the body as the interior frontier and the extractive landscape to be transformed into a perspective of “Tubuh-tanah air” as a future arena of engagement and resistance to extractivism.
This dissertation examines the overarching research question of how the suppliers’ brand management in the form of brand identity, brand culture, and brand essence influences buyer-seller relationships in three independent essays.
In Essay 1, I address the structure, capabilities, and outcomes of brand identity from a supplier perspective. Through qualitative interviews with suppliers, I examine how widespread the concept of brand identity is in practice and what exactly practitioners understand by it. Going further, I look at what capabilities and conditions are necessary for brand identity to be successful and what outcomes suppliers hope to achieve. Using an Information-Display-Matrix (IDM) test and a sample of Master of Business Administration (MBA) students, I examine the relevance of brand functions in more detail.
In Essay 2, I use a dyadic dataset with matched buyer-seller dyads to examine the causes and effects of perceptual congruence and incongruence of brand culture strength on the buyer-seller relationship, while considering relationship-specific investments and interaction mechanisms as moderating effects. I show that congruence and incongruence have different effects on customer loyalty and price sensitivity and that these are strongly context-dependent.
In Essay 3, I deal with brand essence strength interactions and their effects on the buyer-seller relationship. I use a dyadic dataset with matched buyer-seller dyads to show how brand essence strength influences customer loyalty and customer profitability, and how it interacts with key customer attitudes and other important buyer-seller relationship closeness indicators.
This dissertation makes a significant contribution to the literature on brand identity, brand culture, and brand essence in buyer-seller relationships. Furthermore, my dissertation offers practical implications for managers at B2B suppliers who (re)shape their brand management with a focus on the inner parts of the brand.
1. IT-Exposure and Firm Value: We analyze the joint influence of a firm’s information technology (IT)-Exposure and investment behavior on firm value. Estimating a firm’s (partial) IT-Exposure allows for distinguishing between firms with a business model that is challenged by IT above and below market average. Hence, we estimate the annual IT-Exposure of a firm using a 3-factor Fama-French model extended by an IT-proxy. Subsequently, we analyze the relationship with Tobin’s Q in a panel data context, accounting for the relationship between IT-Exposure and investments proxied by R&D as well as CapEx. We use more than 48,000 firm-year observations for firms in the Russell 3000 Index covering the period 1990 to 2018. Although IT-Exposure has a negative impact on firm value, this discount can be overcompensated by up to 2.1 times by sufficient investments through R&D and CapEx, giving a firm with an average Tobin’s Q a premium of 14.8% to 19.2%, while controlling for endogeneity.
2. Corporate Social Responsibility, Risk, and Firm Value: An Unconditional Quantile Regression Approach: This paper examines the impact of corporate social responsibility (CSR) on firm risk, comprising total risk, idiosyncratic risk, and systematic risk, as well as firm value. We focus on analyzing the interrelationships along the entire distribution of the dependent variables, thus estimating an unconditional quantile regression (UQR). The analysis is based on CSR scores from Refinitiv and MSCI, using up to 12,013 firm-year observations over the period 2002 to 2019 for all U.S. companies listed on NYSE, NASDAQ, and AMEX. UQR reveals strongly heterogeneous effects along the unconditional quantiles of the dependent variables, which are reflected in sign changes, magnitude and significance variations. For CSR we find a risk-reducing as well as value-enhancing effect. When applying fixed effects OLS, we can just partly confirm the risk-reducing and value-enhancing effect of CSR shown in the literature.
3. Heterogenous Effects of Religiosity on Firm Risk and Firm Value: An Unconditional Quantile Regression Approach: This paper examines the impact of religiosity on firm risk, comprising total risk, idiosyncratic risk, and systematic risk, as well as firm value. We focus on analyzing the interrelationships along the entire distribution of the dependent variables, thus estimating an unconditional quantile regression (UQR). The analysis is based on all U.S. companies listed on NYSE, NASDAQ, and AMEX for the period from 1980 through 2020. UQR reveals strongly heterogeneous effects along the unconditional quantiles of the dependent variables, which are reflected in sign changes, magnitude and significance variations. Overall, the risk-reducing effect of religiosity is more pronounced in the higher quantiles of the distribution. We further observe a value-reducing as well as value-enhancing religiosity effect. When applying fixed effects OLS, we can confirm the risk-reducing and non-existing value effect of religiosity shown in the literature. The robustness of our results is underpinned by a battery of additional tests.
Abstract 1: This paper investigates whether market quality, uncertainty, investor sentiment and attention, and macroeconomic news affect bitcoin price discovery in spot and futures markets. Over the period December 2017 – March 2019, we find significant time variation in the contribution to price discovery of the two markets. Increases in price discovery are mainly driven by relative trading costs and volume, and by uncertainty to a lesser extent. Additionally, medium-sized trades contain most information in terms of price discovery. Finally, higher news-based bitcoin sentiment increases the informational role of the futures market, while attention and macroeconomic news have no impact on price discovery.
Abstract 2: We investigate whether local religious norms affect stock liquidity for U.S. listed companies. Over the period 1997–2020, we find that firms located in more religious areas have higher liquidity, as reflected by lower bid-ask spreads. This result persists after the inclusion of additional controls, such as governance metrics, and further sensitivity and endogeneity analyses. Subsample tests indicate that the impact of religiosity on stock liquidity is particularly evident for firms operating in a poor information environment. We further show that firms located in more religious areas have lower price impact of trades and smaller probability of information-based trading. Overall, our findings are consistent with the notion that religiosity, with its antimanipulative ethos, probably fosters trust in corporate actions and information flows, especially when little is known about the firm. Finally, we conjecture an indirect firm value implication of religiosity through the channel of stock liquidity.
Abstract 3: This study shows that higher physical distance to institutional shareholders is associated with higher stock price crash risk. Since monitoring costs increase with distance, the results are consistent with the monitoring theory of local institutional investors. Cross-sectional analyses show that the effect of proximity on crash risk is more pronounced for firms with weak internal governance structures. The significant relation between distance and crash risk still holds under the implementation of the Sarbanes-Oxley Act, however, to a lower extent. Also, the existence of the channel of bad news hoarding is confirmed. Finally, I show that there is heterogeneity in distance-induced monitoring activities of different types of institutions.
Feature binding has been proven to be a common and general mechanism underlying human information processing and action control. There is strong evidence showing that when humans perform a task, stimuli (e.g., the target, the distractor) and responses are bound together into an episodic representation, called an event file or a stimulus-response (S-R) episode, which can be retrieved upon feature repetition. As compared with the target and the distractor, the context (i.e., an additional stimulus presented together with the target and the distractor, but not associated with any response keys throughout the whole course of the task), which is considered as task-irrelevant, did not receive that much attention in previous studies. The current thesis was aimed to provide insights into the different roles the context plays in S-R binding and retrieval. Specifically, in Study One and Two, the role of context as an element that can be integrated into an S-R episode was investigated, with a focus on the saliency and the inter-trial variability of the contextual stimulus. Both properties were found to influence how the context is integrated into an S-R episode. More specifically, results show that both saliency and inter-trial variability determine whether the context is directly bound in a binary fashion with the response, or it enters in to a configural binding together with another stimulus and the response. In Study Three, intrigued by the role of context as an event segmentation factor in the event perception literature, whether the context can demarcate the integration window of an S-R episode was tested. Results provide consistent evidence that sharing a common context leads to a stronger binding between a stimulus and the response, as compared with the condition when these elements are separated by different contexts, thereby suggesting a binding principle of common context. Taken together, the current thesis specifies the role of context in S-R binding and retrieval, and sheds some light on how contextual information influences human behavior.
This dissertation uses four studies to examine the context-contingent strategic factors that are critical to the success of digital transformation strategies from the perspectives of capital markets, incumbents, and start-ups. It focuses on a better understanding of (1) digital innovations and their quantitative evaluation, (2) power disruptions in digitally servitized supply chains, (3) strategic measures and dynamics in digital B2B platform markets, and (4) strategizing by data-driven start-ups in digitalized business networks.
Digital platforms consist of technical elements such as software and hardware and associated social elements such as organizational processes and standards. When such social or technical elements seem logical individually but inconsistent when juxtaposed they form tensions. Prior research on platforms often focused on individual elements of digital platforms but neglected possible related and conflicting elements which offers limited insight about underlying tensions. While some studies on platforms considered tensions, they largely assumed that centralized platform owners being responsible for responding to tensions, neglecting collective response mechanisms in blockchain-based decentralized autonomous organizations (DAOs) where decentralized participants typically respond to tensions. The examination of tensions in the context of centralized platforms and decentralized autonomous organizations offers an opportunity to surface conflicting elements that form novel types of socio-technical tensions which require collective and technology-enabled response mechanisms. This thesis explored what tensions exist in centralized and decentralized digital platform contexts and how platform participants can respond to selected tensions. For this purpose, this thesis comprises five essays that employ multiple different research methods including interviews analyzed by using techniques of grounded theory, qualitative meta-analysis of published case studies, and systematic literature reviews. The findings derived from all five essays contribute to a better understanding of tensions in digital platforms. In particular, this thesis (1) offers a lens for analyzing platforms as collective organizations in which tensions arise at the collective meta-organizational level requiring collective responses, (2) identifies new tensions and response mechanisms related to generativity and collectivity, and (3) points to a novel category of socio-technical tensions that are especially salient in digital platforms.
This thesis investigates the quality of randomly collected data by employing a framework built on information-based complexity, a field related to the numerical analysis of abstract problems. The quality or power of gathered information is measured by its radius which is the uniform error obtainable by the best possible algorithm using it. The main aim is to present progress towards understanding the power of random information for approximation and integration problems.
In the first problem considered, information given by linear functionals is used to recover vectors, in particular from generalized ellipsoids. This is related to the approximation of diagonal operators which are important objects of study in the theory of function spaces. We obtain upper bounds on the radius of random information both in a convex and a quasi-normed setting, which extend and, in some cases, improve existing results. We conjecture and partially establish that the power of random information is subject to a dichotomy determined by the decay of the length of the semiaxes of the generalized ellipsoid.
Second, we study multivariate approximation and integration using information given by function values at sampling point sets. We obtain an asymptotic characterization of the radius of information in terms of a geometric measure of equidistribution, the distortion, which is well known in the theory of quantization of measures. This holds for isotropic Sobolev as well as Hölder and Triebel-Lizorkin spaces on bounded convex domains. We obtain that for these spaces, depending on the parameters involved, typical point sets are either asymptotically optimal or worse by a logarithmic factor, again extending and improving existing results.
Further, we study isotropic discrepancy which is related to numerical integration using linear algorithms with equal weights. In particular, we analyze the quality of lattice point sets with respect to this criterion and obtain that they are suboptimal compared to uniform random points. This is in contrast to the approximation of Sobolev functions and resolves an open question raised in the context of a possible low discrepancy construction on the two-dimensional sphere.
Blockchain technology enables the automated recording of information and execution of contract content – utilizing so-called Smart Contracts – without relying on trusted intermediaries (Beck et al., 2016). A blockchain is best described as a decentralized digital ledger (Atzori, 2015). The decentralized data storage on the blockchain makes the recorded information tamper-proof and creates transparency along the value chain. Therefore blockchain changes fundamentally the way data and information are processed (Al-Jaroodi & Mohamed, 2019; Avital et al., 2016). This has given rise to numerous use cases for blockchain in a wide range of industries.
Fundamentally, blockchain technology can be used at any time when information needs to be stored in an automated and tamper-proof manner (Crosby et al., 2016). In the financial industry, blockchain helps to automate peer-to-peer transactions. This makes middlemen obsolete, which can reduce transaction costs (Cai, 2018). In the public health sector, blockchain is primarily used for decentralized storage of patient records. By using blockchain technology, these patient records are secured against manipulation and unauthorized access by third parties (Mettler, 2016). Another interesting use case can be seen in the electricity market. Blockchain technology makes it possible to integrate micro producers of electricity, such as private households, into the power grid in a cost-efficient way (Cheng et al., 2017). However, blockchain technology also offers several applications in the creative industries to support the daily work of professionals.
The term creative industries encompasses industries and sectors which hold intellectual property at the core of their value creation (Caves, 2000). According to DCMS (1998),creative industries include not only classic art sectors such as fine art, painting or crafting, but also areas such as marketing, game developing, film and video or music. As the main drivers of innovation, the creative industries have a steadily increasing influence on the overall economic impact. Often, ideas, products and services from the creative industries ultimately flow into other areas, such as the automotive sector, and support them in achieving their entrepreneurial goals (Banks, 2010; Jones et al., 2004). In order to continuously maintain the position as an innovation driver, a certain form of organization has prevailed in the creative industries.
For the creative industries to react flexibly to new requirements and a constantly changing environment, work is usually carried out as project-based (DeFillippi, 2015). For this purpose, the teams of the project-based organization are predominantly formed using freelancers who are specialists in the required field. As a result, many recurring organizational activities arise, such as contracting, team finding or onboarding (DeFillippi, 2015; Eikhof & Haunschild, 2006). Therefore, professionals from the creative industries have to spend significant time on activities that do not serve their core task of creating intellectual property. These tasks not only reduce the efficiency of their work, but also hinder their creative flow (Foord, 2009; Hennekam & Bennett, 2016). In this regard, blockchain represents a promising technology to support professionals in the creative industries.
For the creative industries, blockchain is primarily used to automate formal processes and secure intellectual property rights (O’Dair, 2018). Blockchain technology enables artists and creatives more freedom for their own creative activities. By automating repetitive activities, professionals from the creative industries are freed from typical management tasks (Arcos, 2018; Cong & He, 2019). Furthermore, for the first time, intellectual property can be secured in a cost- and time-efficient way by utilizing blockchain technology. This is made possible by the decentralized nature of the blockchain, which makes subsequent manipulation of the contents of the intellectual property impossible (Avital et al., 2016; Beck et al., 2016; Regner et al., 2019). Ultimately, the use of so-called Non-Fungible Tokens (NFTs) create the opportunity for artists and creatives to sell unique digital art (Regner et al., 2019). Thus, blockchain generates entirely new ways for creative industries to organize their projects and opens new markets to sell their work. While several use cases of blockchain technology can be identified in the creative industries, the widespread use of blockchain is still lacking.
So far, no Blockchain service or blockchain application has managed to take a dominant market position in the creative industries. At first sight, this seems surprising since artists and creatives could fundamentally benefit from this technology. At the same time, professionals from the creative industries would not be dependent on middlemen or central entities. This circumstance gave the impulse for the research presented in this thesis. I was able to identify that professionals from the creative industries are still underutilizing blockchain technology for three main reasons: (1) When using blockchain technology, professionals from the creative industries experience strong resistance from their stakeholders, who want to prevent the use of blockchain. (2) The perceived constraints by artists and creatives in using blockchain still deter many from using this technology extensively. (3) Several blockchain applications lack a persuasive design, resulting in many artists and creatives continue to prefer conventional services and products.
The generalization of univariate splines to higher dimensions is not straightforward. There are different approaches, each with its own advantages and drawbacks. A promising approach using Delaunay configurations and simplex splines is due to Neamtu.
After recalling fundamentals of univariate splines, simplex splines, and the wellknown, multivariate DMS-splines, we address Neamtu’s DCB-splines. He defined two variants that we refer to as the nonpooled and the pooled approach, respectively. Regarding these spline spaces, we contribute the following results.
We prove that, under suitable assumptions on the knot set, both variants exhibit the local finiteness property, i.e., these spline spaces are locally finite-dimensional and at each point only a finite number of basis candidate functions have a nonzero value. Additionally, we establish a criterion guaranteeing these properties within a compact region under mitigated assumptions.
Moreover, we show that the knot insertion process known from univariate splines does not work for DCB-splines and reason why this behavior is inherent to these spline spaces. Furthermore, we provide a necessary criterion for the knot insertion property to hold true for a specific inserted knot. This criterion is also sufficient for bivariate, nonpooled DCB-splines of degrees zero and one. Numerical experiments suggest that the sufficiency also holds true for arbitrary spline degrees.
Univariate functions can be approximated in terms of splines using the Schoenberg operator, where the approximation error decreases quadratically as the maximum distance between consecutive knots is reduced. We show that the Schoenberg operator can be defined analogously for both variants of DCB-splines with a similar error bound.
Additionally, we provide a counterexample showing that the basis candidate functions of nonpooled DCB-splines are not necessarily linearly independent, contrary to earlier statements in the literature. In particular, this implies that the corresponding functions are not a basis for the space of nonpooled DCB-splines.
Data is an important resource in our economy and society, substantially improving overall business efficiency, innovativeness and competitiveness, and shaping our everyday lives. Yet, to leverage the data's full potential, its access and availability is vital. Thus, data sharing across organizations is of particular importance. This thesis examines the role of data sharing in the digital economy and contributes to a better understanding why data sharing matters, why it is still underutilized, and how data sharing can be encouraged. Thereby, the thesis contributes to the ongoing academic debate as well as the practical and political efforts on how to promote data sharing.
The thesis is comprised of three studies. Study 1 examines personal data sharing among (competing) online services. Particularly, it investigates the consequences of Article 20 in the General Data Protection Regulation (GDPR, May 2018), ensuring the right to data portability. This relatively new right allows online service users to transfer any personal data from one service provider to another. Focusing on a) the amount of data provided by users and b) the amount of user data disclosed to third party data brokers by service providers, the study investigates the right to data portability's effect on competitiveness and consumer surplus. Study 2 and Study 3 focus on non-personal data sharing among competing firms. Study 2 examines the literature to identify and classify barriers to non-personal, machine-generated data sharing. The study explains firms' reluctance to sharing data and discusses policy and managerial implications for overcoming the data sharing barriers. Study 3 focuses on data sharing via platforms. It investigates the Business-to-Business (B2B) data sharing platform design implications for promoting industrial data sharing. In particular, Study 3 investigates the dimensions control and transparency regarding their effect in eliciting cooperation and encouraging data sharing among firms.
In summary, this thesis examines and reveals how access and availability of data can be increased through creating beneficial data sharing conditions in B2B relationships. Particularly, the thesis contributes to the understanding of a) the implications of data sharing laws, defined in the GDPR for personal data and b) the challenges and measures of the not yet successfully established, non-personal data sharing.
Over the last decades, ongoing advancements in information technology (i.e., Internet and mobile devices) have expanded a firm’s ability to communicate and interact with consumers and hence, create the potential of building sustainable relationships. Tailoring offerings through (1) consumer-initiated customization and (2) firm-initiated personalization is considered a key driver of long-term consumer relationships. As technologies continue to evolve, the opportunities for tailored marketing expand and enable new technology-driven business models that help to leverage customization and personalization and strengthen customer relationships in the era of the digital economy.
Across three independent essays, the purpose of this dissertation is to answer the overarching research question of how innovative technology-driven business models versus traditional business models in the domains of customization and personalization influence consumer behavior. Thereby, this dissertation contributes to an understanding of challenges and opportunities of innovative customization and personalization business models with the ultimate goal of enabling their successful diffusion in the marketplace.
Specifically, in Essay 1 and Essay 2, I investigate an innovative business model located in the realm of customization, that is, internal product upgrades (i.e., offering fee-based access to originally built-in, but deliberately restricted, optional features). Using a conceptual approach, Essay 1 provides a framework for understanding how internal product upgrades will likely influence consumers’ responses. As such, it outlines evolving challenges and opportunities of internal product upgrades and derives questions for future research. In Essay 2, I use an empirical approach to examine pitfalls of internal product upgrades in the product usage phase. Drawing on research on normative expectations and perceived ownership, this essay reveals that consumers respond less favorably to internal (vs. external) product upgrades and investigates managerially relevant boundary conditions.
Finally, Essay 3 creates novel insights into a business model in the domain of personalization. This essay examines how the increasingly prevalent data disclosure practice of firms engaging in a network with other firms to exchange consumer data, which we denote as Business Network Data Exchange (BNDE), influences consumers’ privacy-related decision-making. In particular, this essay shows that consumers are less likely to disclose personal data in BNDE (vs. traditional dyadic) data exchange settings and that immediate affective reactions are crucial in explaining consumers’ privacy-related decision-making.
Within this dissertation, I make substantial contributions at a more general level to literature on customization and personalization by comparing innovative business models to established ones. At the individual essay level, I extend existing research in the domains of product feature modifications, norm violations, and privacy-related decision making. Moreover, this dissertation provides actionable implications for managers who are facing the decision to transform their established business model into an innovative technology-driven one.
Network communication has become a part of everyday life, and the interconnection among devices and people will increase even more in the future. A new area where this development is on the rise is the field of connected vehicles. It is especially useful for automated vehicles in order to connect the vehicles with other road users or cloud services. In particular for the latter it is beneficial to establish a mobile network connection, as it is already widely used and no additional infrastructure is needed. With the use of network communication, certain requirements come along.
One of them is the reliability of the connection. Certain Quality of Service (QoS) parameters need to be met. In case of degraded QoS, according to the SAE level specification, a downgrade of the automated system can be required, which may lead to a takeover maneuver, in which control is returned back to the driver. Since such a handover takes time, prediction is necessary to forecast the network quality for the next few seconds. Prediction of QoS parameters, especially in terms of Throughput (TP) and Latency (LA), is still a challenging task, as the wireless transmission properties of a moving mobile network connection are undergoing fluctuation. In this thesis, a new approach for prediction Network Quality Parameters (NQPs) on Transmission Control Protocol (TCP) level is presented. It combines the knowledge of the environment with the low level parameters of the mobile network. The aim of this work is to perform a comprehensive study of various models including both Location Smoothing (LS) grid maps and Learning Based (LB) regression ones. Moreover, the possibility of using the location independence of a model as well as suitability for automated driving is evaluated.
A firm's entrepreneurial orientation (EO) is its propensity to act proactively, innovate, take risks, and engage in competitive and autonomous behaviors. Prior research shows that EO is an im-portant factor for new ventures to overcome barriers to survival and fostering growth, measured by annual sales and employment growth rates. In particular, individual-level EO (IEO) is an important driver of a firm’s EO. The firm’s ability to exploit opportunities appearing in the mar-ket and to achieve superior performance depends on the employees’ skills and experiences to act and think entrepreneurially. The main objective of this dissertation is to investigate how and when employees engage in entrepreneurial behaviors at work. Building on three essays, this dissertation takes an interdisciplinary approach to employee entrepreneurial behaviors in new ventures, encompassing both entrepreneurship and gamification research. The first main contri-bution proposed in this field is a more nuanced understanding of how employee entrepreneurial behaviors help young firms cope with growth-related, organization-transforming challenges (i.e., changes in organizational culture that accompany growth, the introduction of hierarchical structures, and the formalization of processes). When new ventures grow, employees’ IEO tends to manifest in introducing technological innovations and business improvements rather than in actions related to risk-taking. Second, this dissertation reveals the relevance of self-efficacy for entrepreneurial behaviors and explores how gamification can enhance employee entrepreneurial behaviors in new ventures. Based on these findings, this dissertation contributes to EO research by highlighting the role of IEO as a building block for EO pervasiveness. This research further develops our knowledge on the use of gamification in new ventures. This cu-mulative dissertation is structured as follows. Part A is an introduction to the study of entrepre-neurial behaviors. Part B contains the three essays.
In three essays, this dissertation examines the past, present and future of branding in an international context, contributing to the research area of global/local brands, while also offering managers valuable insights for their branding strategies.
The first essay provides scholars and practitioners a detailed state of the art of global/local brand research and proposes promising angles for future research, especially considering major
challenges for our societies.
The second essay incorporates the segment of cosmopolitan consumers into perceived brand globalness/localness research. Theoretically grounded in the concepts of social identity theory and complexity, the essay builds on perceived brand globalness/localness to analyze how cosmopolitans arrange both their global and local orientations. Aside offering scholars a new theoretical lens regarding consumer cosmopolitanism, managers can benefit from the gained insights, if cosmopolitans are a particular target group in their business strategy.
The third and final essay meta-analytically investigates how the variables perceived brand globalness and localness materialize on various key outcome variables. At heart of this essay is a comparison of both perceived brand globalness and localness, offering scholars and practitioners valuable empirical insights on similarities and differences between their effects on outcomes such as brand quality.
The identification and estimation of trends in hydroclimatic time series remains an important task in applied climate research. The statistical challenge arises from the inherent nonlinearity, complex dependence structure, heterogeneity and resulting non-standard distributions of the underlying time series. Quantile regressions are considered an important modeling technique for such analyses because of their rich interpretation and their broad insensitivity to extreme distributions. This paper provides an asymptotic justification of quantile trend regression in terms of unknown heterogeneity and dependence structure and the corresponding interpretation. An empirical application sheds light on the relevance of quantile regression modeling for analyzing monthly Central England temperature anomalies and illustrates their various heterogenous trends. Our results suggest the presence of heterogeneities across the considered seasonal cycle and an increase in the relative frequency of observing unusually high temperatures.