This dissertation investigates creativity and collaboration across different levels of virtuality, including remote, hybrid, and in-person environments.
It applies a user-centered Design Science Research approach, incorporating qualitative techniques such as diary studies, expert interviews, and prototype assessments to analyze performance satisfaction, preferences, and team dynamics.
The academic contribution features a conceptual framework that identifies and prioritizes creativity drivers, while practical design principles are refined and tested through remote and hybrid design thinking sessions. The results indicate that supporting creative processes involves adapting environments to match task needs, based on divergent and convergent thinking. Social and environmental affordances must balance social presence, anonymity, and accountability to maintain creative momentum within virtual teams. Additionally, a functional environment combined with effective technology management is key to fostering a flow state for creativity. As a practical outcome, recommendations for improving idea sharing and development in remote and hybrid teams assist organizations in navigating digital transformation and sustainably enhancing innovation processes.
Photovoltaics are a key technology in the transition toward renewable energy. Among emerging materials, poly(3-hexylthiophene) (P3HT) offers an attractive alternative to silicon owing to its solution processability, mechanical flexibility, and tunable optoelectronic properties. This thesis investigates how electric fields, mechanical deformation, and processing conditions influence the structural and photophysical properties of P3HT and the flexible substrate poly(ethylene terephthalate) (PET) using complementary time and frequency domain spectroscopic techniques. Femtosecond transient absorption pump-probe spectroscopy demonstrates that below-band-gap excitation directly generates polaron pairs (PPs) in P3HT. The transient response comprises fast PP and slower delocalized polaron pair (DPP) contributions. External electric fields modify the competition between DPP recombination and bimolecular annihilation, while uniaxial stretching up to 7% redistributes PP and DPP populations through polymer chain alignment. Above this strain, the transient response becomes increasingly complex owing to efficient strain transfer and microcrack formation. Raman and ultraviolet–visible spectroscopy correlate mechanical deformation with structural and optical changes. P3HT and PEDOT:PSS/P3HT films retain an essentially constant optical band gap up to approximately 7% strain, followed by a slight widening at 10%, indicating the onset of electronically significant deformation. Analysis of the Raman C=C and C–C stretching modes shows that variations in band position and full width at half maximum distinguish temperature-induced morphological changes from mechanically induced chain alignment. PET exhibits increased optical absorption and irreversible Raman spectral changes above approximately 5% strain, indicating molecular reorganization.
This thesis investigates how uniaxial tensile strain affects the optical and molecular properties of polymer thin films used in flexible organic electronics. It focuses on poly(ethylene terephthalate) (PET) as a flexible substrate and poly(3-hexylthiophene-2,5-diyl) (P3HT) as a semiconducting polymer. The materials are examined as single layers and in PET/P3HT and PET/PEDOT:PSS/P3HT stacks at room temperature.
Strain is applied reproducibly using a custom-built stretching setup. Ultraviolet–visible spectroscopy is used to track changes in optical response and band gap, while Raman spectroscopy reveals chain reorientation, conformational changes, and defect formation during and after deformation.
For PET, tensile strain causes progressive optical degradation, including increased absorption across the ultraviolet and visible regions. Above ≈5% strain, the changes become irreversible and are accompanied by evidence of permanent structural modification. This identifies two regimes: elastic behavior at low strain and irreversible deformation at higher strain.
For P3HT, the optical band gap remains stable up to 7% strain, regardless of annealing temperature or stack configuration. At 10% strain, a reproducible increase of about 4–5 meV appears, indicating onset of measurable electronic perturbation.
Raman results show that P3HT strain response depends strongly on thermal history. Unannealed films mainly deform through reversible chain alignment. Films annealed at 50 °C show a mixed response with greater microstructural heterogeneity, while films annealed at 75 °C exhibit more constrained deformation and stronger defect accumulation. A PEDOT:PSS interlayer improves strain transfer, reduces strain localization, and enhances mechanical tolerance, especially in highly ordered films.
Overall, the work defines experimentally grounded strain limits for flexible devices and clarifies how processing history and stack architecture govern mechanical and optoelectronic behavior.
This dissertation examines how digital technologies can support the integration of discipline-specific language in accounting and taxation. Across three essays, it analyzes the acquisition, application, and analysis of specialized terminology. Essay I investigates a wiki-based collaborative glossary in introductory accounting education and shows that it improves students' academic performance and engagement with accounting terminology, while also revealing challenges related to coordination and content reliability. Essay II uses dictionary-based text analysis to create a cross-country dataset on corporate environmental tax legislation and finds that such taxes are shaped more by institutional and political factors than by environmental risks. Essay III develops and applies a domain-specific large language model to qualitative corporate tax disclosures, demonstrating that specialized LLMs outperform general-purpose models and traditional text analysis methods. Overall, the dissertation shows that digital technologies can enhance the acquisition, application, and analysis of specialized language in accounting and taxation. It contributes to accounting education, tax policy research, and methodological debates on the use of natural language processing in discipline-specific contexts.
Children with Autism Spectrum Disorder (ASD) often experience difficulties in sensory processing, motor coordination, behavioral regulation, and adaptive functioning, which can limit participation in daily activities such as eating and social interaction. Selective eating is highly prevalent and may lead to nutritional and developmental challenges.
The Schmetterling Program was developed as an integrative, sequential intervention targeting selective eating, motor skills, and stress regulation in children with ASD. Inspired by the concept of the “butterfly effect,” the program assumes that small, individualized therapeutic changes can produce meaningful developmental improvements. It combines established behavioral strategies such as shaping, imitation chaining, and guided modeling within a sensory-motor framework.
This dissertation evaluates the effectiveness of the Schmetterling Program through three studies involving children aged 2–6 years with ASD. The first study examined the Nutritional Behavior Intervention (NBI) across three families and found improvements in dietary flexibility and adaptive behavior. The second study compared 24 children with a control group and showed reductions in selective eating, improvements in autism symptom severity, and enhanced autonomic regulation measured by heart rate variability (HRV). The third study assessed the Motor Treatment (MT) component using a multiple-baseline design and demonstrated improvements in motor coordination, sensorimotor integration, and social communication.
Overall, the findings suggest that the Schmetterling Program may improve eating behavior, motor functioning, and psychophysiological regulation in children with ASD. The results support the effectiveness of integrative, individualized, and ecologically grounded interventions. Further research is required to confirm long-term outcomes and underlying neurophysiological mechanisms.
(In)security in Africa influences African societies, geopolitics, and geoeconomics. Yet African perspectives on (in)security are rarely sought, and how African states shape their security policies remains unclear: conventional internal/external, domestic/foreign, and secure/insecure distinctions are outdated, while Western/Northern-centric social sciences and African Studies/IR literatures remain trapped in essentialisms, stereotypes, and mono-causal explanations. This thesis argues for new analytical devices to study African security policies in less essentialist and more context-sensitive ways by understanding the practice turn and appropriating Pierre Bourdieu's reflexive, practice-theoretical, and field-analytical sociology through an in-depth sociospatial reading. This helps interpret his concepts as global, interrelated thinking tools for African social formations, against a superficial Bourdieusian International Studies literature. Taking Nigeria as exploratory case study, the thesis traces how field and habitus intersect, develops a Field of Nigerian Security Policy (FNSP) as object and tool, and treats habitus and capital primarily as outcomes of the analysis. It asks how Nigeria shapes its security policy towards Boko Haram as a contemporary space of (in)security, drawing on almost four months of self-reflexive, ethnographic fieldwork. It combines sociological discourse analysis of textual sources with interviews with leading Nigerian experts and practitioners. It reveals how a FNSP is constituted by diverse agents, relations of (informal) cooperation, competition, domination, and transversality, a dual habitus of problems/scarcities and aspirations/necessities, and multiple (including negative) kinds of capital—and how agents' classificatory practices ultimately produce Nigeria's security policy towards Boko Haram. The thesis thereby advances African International Relations theoretically and offers deep practical insights into Nigeria's political space.
The study of pits, skylights, and sinkholes on terrestrial bodies is vital for mapping subsurface voids, geological evolution, and potentially habitable zones. Feature identification remains constrained by sensor resolution and the complexities of radar analysis. This research explores deep learning computer vision to automate landform mapping and evaluates detecting subsurface voids using orbital radar.
To address mapping challenges, this work introduces DeepLandforms, a toolkit developed to automate detection using You Only Look Once (YOLO), Detectron 2, and the Segment Anything Model (SAM). Built on Docker, it includes modules for data preparation, training, and inference, generating outputs compatible with GIS software. Validation against a dataset from the Mars Global Cave Candidate (MGC³) catalog demonstrates capacity for consistent, large-scale surveys.
This research also introduces EchoTerraeTrace, an all-in-one toolkit providing workflows for SHARAD (Mars Reconnaissance Orbiter) and MARSIS (Mars Express) data. During validation North-West of Ascraeus Mons, three unmapped volcanic vents were identified, and the regional paleotopography was refined.
Finally, this research assesses planetary software against FAIR principles. A web-based service built on a dockerized JupyterHub was developed as an all-in-one environment for standardized data processing. This service provides scalability without requiring local high-end resources, supporting reproducible research by moving the code to the data rather than the data to the code.
In intelligent and autonomous systems, users must often share private data with external platforms to enable advanced functionalities. A key challenge in this process arises when the data to be protected is not a single, precise value, but is instead inherently uncertain or represents a range of possible values. This thesis addresses the privacy of such uncertain data, which is formally represented and processed using set-based methods. We review existing privacy-preserving techniques and introduce new mechanisms to safeguard this data during processing on untrusted platforms, exploring both cryptographic and non-cryptographic approaches.
Specifically, the thesis presents novel privacy-preserving mechanisms for set-based data processing, categorized by the data type within the sets.
For sets of real-valued data, we introduce a differential privacy mechanism for set-based estimation in linear and non-linear dynamical systems. This approach protects the sensitive information contained within sets, such as zonotopes that model system uncertainties, while minimizing the loss of utility for the estimation process.
For sets of discrete, binary data, we propose a mechanism that uses Fast Fully Homomorphic Encryption to ensure privacy. This method allows for the secure processing of sets represented as logical zonotopes in untrusted environments, maintaining computational practicality.
The evaluations demonstrate the effectiveness and practicality of the proposed mechanisms across various applications in autonomous and intelligent systems.
Understanding how collective patterns emerge on graphs is a fundamental challenge across disciplines, from biological and ecological networks to computational and physical systems. This thesis explores the interplay between network topology and emergent dynamics using minimal models and spectral graph techniques.
A first investigation focuses on network inference, showing that Turing patterns encode structural information about the underlying graph, which we use to infer missing links. The second study investigates multistability in reaction-diffusion networks, showing how local spectral gaps influence the attractor landscape of Turing patterns using a heuristic binary classification algorithm. Finally, the third study applies the sandpile model to soil erosion processes, bridging concepts from self-organised criticality and connectivity-based geomorphology to investigate the role of minimal models in empirical research.
This thesis combines theoretical analysis, computational modelling and empirical validation to highlight how structure shapes dynamics across different contexts and illustrate the potential of minimal models as predictive, explanatory and exploratory tools for complex systems.
Rare earth elements (REE, or REY including yttrium) are widely used in modern technologies and are increasingly released into aquatic environments. Their environmental behaviour and bioaccumulation in aquatic ecosystems remain poorly understood. This thesis investigates the bioavailability, bioaccumulation, and trophic transfer of both geogenic and anthropogenic REY using aquatic organisms and environmental samples from European freshwater and marine systems.
Shells of three invasive freshwater bivalves (Corbicula fluminea, Dreissena polymorpha, and Dreissena bugensis) collected from seven major European rivers show strong REY bioaccumulation, with concentrations up to five orders of magnitude higher than in ambient water. Anthropogenic lanthanum contamination from the Rhine River was recorded in mussel shells, whereas no enrichment of anthropogenic gadolinium from MRI contrast agents was observed, suggesting its stability in freshwater systems.
Further analyses of freshwater (Anodonta anatina) and marine (Mytilus edulis) mussels reveal higher REY concentrations in internal organs than in muscle tissues and shells, while biological processes exert only minor influence on REY fractionation. A trophic-level study along the Rhine River shows a general biodilution trend from primary producers to fish, while shale-normalised REY patterns remain consistent across trophic levels. These results indicate that mussels can serve as effective biomonitors for environmental REY contamination.
The Influence of Digital Transformation on well-being – analysis of life stages and business sectors
(2026)
The accelerating pace of digital transformation (DT) is profoundly reshaping the world of work, placing new demands on employees and affecting their well-being. As employee well-being is closely linked to engagement and performance, this PhD project investigates how organizations can engage employees during DT, with particular consideration of their well-being. The Self-Determination Theory (SDT) serves as the kernel theory in this research for understanding well-being, expanded to include physical health. Furthermore, both different working conditions and various life stages of employees are incorporated in order to capture the dynamic nature of well-being. However, promoting well-being requires a comprehensive understanding of its multifaceted effects, both positive and negative, on employees, a challenge further intensified by the ongoing DT. While many companies recognize the benefits of DT, they often struggle with its implementation and the associated impacts on the workforce. Maturity models are a common tool to provide guidance during DT by serving as frameworks for assessing and developing organizational capabilities. In practice, maturity models are often too strategic, inflexible, and insufficiently user-centered. Furthermore, social aspects such as employee well-being have so far been largely neglected. To close this gap, an adaptable human-centered maturity model focusing on well-being was designed and empirically validated within the framework of this cumulative dissertation consisting of six papers, following the Design Science Research (DSR) approach. The model uniquely integrates basic psychological needs, physical health, and life stage perspectives, dimensions largely absent in existing DT maturity models. Overall, this PhD project advances the human-centered discourse on well-being by providing a practice-oriented maturity model that supports organizations in identifying the effects of DT on well-being and deriving appropriate courses of action.
This thesis investigates the integration of sustainability assessment considering Industry 4.0 technologies and the use of Markov Decision Process capabilities. The manufacturing industry is facing increasing pressure to improve sustainability assessment performance, and Industry 4.0 technologies like Digital Twins, Internet of Things, Big Data Analytics, Cloud Computing, Machine Learning, and Artificial Intelligence have the potential to support these efforts. However, effectively integrating sustainability assessment goals and Industry 4.0 technologies within manufacturing systems can be challenging. The research addresses this challenge by developing a framework for optimizing the flow of operations in a manufacturing system while incorporating sustainability assessment and Industry 4.0 technologies effectively. The framework utilizes the Markov Decision Process to model the decision-making process of the manufacturing system and its decision-makers. From the other side, it includes sustainability assessment goals as constraints or objectives in the Markov Decision Process model. The use of Industry 4.0 technologies is integrated into the framework to gather data and optimize the decision-making process based on that data. The thesis begins by reviewing the literature on sustainability assessment, Industry 4.0 technologies, and their impacts with regard to manufacturing systems. The proposed framework is then presented, and its capabilities are demonstrated through case studies of single and multiple agents on a shop floor. The trend in pioneer manufacturing firms is to implement new technological applications on their shop floor to agile their Manufacturing Execution System. The findings from the case study indicate that the proposed framework can effectively support decision-making at the top-tier level of the enterprise by integrating sustainability assessment and the Industry 4.0 paradigm.
This research paper investigates the advanced Artificial Intelligence (AI) architecture underpinning Germany's multimodal transportation ecosystem, drawing from the author's year-long immersive professional experience. The study shifts the focus from traditional physical logistics to the efficiency of data processing throughput, characterizing the transport network as a dynamic information organism.
The author analyzes the integration of Edge Computing and IoT sensors through the MQTT protocol, facilitating resilient data transmission in unstable environments. Furthermore, the paper details the implementation of Real-time Stream Processing using Apache Kafka and Redis, alongside the application of LSTM (Long Short-Term Memory) neural networks for high-precision delay forecasting. A significant technical analysis is provided on Neural Text-to-Speech (NTTS) synthesis for passenger notifications, emphasizing its role in enhancing user experience (UX) through natural language generation.
Beyond technical frameworks, the author addresses critical infrastructure security via TLS 1.3 and PKI, ensuring compliance with GDPR standards. The paper concludes that the success of modern transport lies in its "algorithmic soul"—a data-driven approach that prioritizes reliability and transparency. Ultimately, the author advocates for the strategic transfer of these AI-integrated architectures to Kazakhstan’s "Smart City" initiatives, suggesting that such digital transformation will serve as a catalyst for the broader technological evolution of the national economy.
Modern online courses often replicate traditional instruction as static artifacts, failing to reveal the cognitive causes of learner errors. This paper proposes a self-improving educational ecosystem integrating interactive modules, diagnostic assessments, and AI-driven analytics in a closed feedback loop.
The model is implemented on a real platform using WordPress as a flexible application framework. Each module combines theory, interactive practice (H5P), and diagnostic assessment. Natural-language queries to an AI assistant serve as diagnostic signals, revealing hidden cognitive barriers. A three-level management model separates operational support (AI tutor), pedagogical quality assurance, and strategic product development. Continuous improvement follows a four-stage cycle: signal collection, pattern analysis, targeted instructional adjustments, and impact verification.
This approach demonstrates that intelligent, evidence-based learning management can transform courses into self-correcting systems, where each cohort improves the experience for the next.
Digital transformation (DT) fundamentally reshapes organizational structures and work processes. Despite its strategic importance, up to 70% of DT initiatives fail, primarily due to insufficient consideration of human factors. This cumulative dissertation addresses this gap by developing and validating a human-centered Change Management Maturity Model that systematically integrates employee needs into digital transformation processes, with particular emphasis on the AI-driven third phase of DT.
Existing DT maturity models predominantly focus on technological, strategic, and organizational aspects while neglecting human-centered dimensions such as employee motivation, psychological well-being, and change readiness. Likewise, established change management frameworks tend to operate either at the organizational level (e.g., McKinsey 7S) or the individual level (e.g., ADKAR), without systematically integrating both perspectives. To address this limitation, this dissertation proposes a comprehensive maturity model comprising nine dimensions across three categories: Motivation & Leadership Behavior, Dealing with Change, and Well-being & Health.
The research follows an echeloned Design Science Research (eDSR) approach and is structured as a cumulative dissertation consisting of six research papers. The model is grounded in multiple kernel theories, including Self-Determination Theory, Herzberg’s Two-Factor Theory, Maslow’s Hierarchy of Needs, the Dynamic Capabilities Framework, and established change management models. Empirical validation was conducted in the skilled trades sector and across industries in the retail sector, demonstrating the model’s applicability across organizational contexts and its practical relevance for managing AI-driven transformation initiatives.
Digital tools are increasingly used in inclusive early childhood education and care (ECEC) to support children’s social skills — especially communication, emotion recognition, self-regulation, prosocial behavior, and peer interaction. Across Europe, policy momentum for inclusive digital education is accelerating, while research is expanding from general “screen time” debates toward evidence-based, developmentally appropriate, educator-mediated designs. This paper synthesizes current trends, highlights the European and German context, and proposes a feasible mixed-method study plan to evaluate digital social-skills interventions in inclusive ECEC settings. The review maps tool categories (tablet apps, serious games, social robots, multimodal platforms, and digital assessment/screening), describe equity and accessibility design principles, and identify evidence gaps (long-term outcomes, implementation fidelity, child-led vs. adult-guided interaction, and inclusion of multilingual/migrant families). We propose a pragmatic evaluation framework aligned with European priorities for accessibility, quality, and teacher capacity-building.
This paper examines current mathematical modeling methods used for the development and optimization of composite polymer stabilizers. A review of key challenges related to the stability of polymer materials is provided, along with a discussion of modern computational approaches, including molecular dynamics, finite element analysis, thermodynamic modeling, and machine learning. The necessity of an interdisciplinary approach integrating chemistry, materials science, and computational technologies is justified. Perspectives on the further development of modeling methods to enhance the efficiency and stability of polymer stabilizers are presented.
The research essay examines the opportunities and barriers to implementing artificial intelligence (AI) in the education system of the Republic of Kazakhstan based on a comparative analysis of the experience of Germany. The aim of the work is to identify key factors influencing the successful integration of AI tools into educational practice, including regulatory, methodological, technological and personnel aspects. The paper analyzes international and national strategies for regulating AI, features of the legal framework of Kazakhstan and the European Union, as well as models of teacher training. Special attention is paid to the issues of academic integrity, ethics, personal data protection and overcoming the digital divide. Based on the German experience, we formulate systematic recommendations for Kazakhstan aimed at developing institutional teacher training, reforming the assessment system and forming national AI sovereignty in education. The forecast of AI development in the educational system of Kazakhstan until 2029 is made, emphasizing the need to move from fragmented technology implementation to a sustainable, ethically verified and inclusive model of digital education.
This essay examines the role of artificial intelligence tutors in Kazakhstan's education system, addressing whether AI-based tools replace teachers or serve as supportive instruments that enhance teaching practice. Against the backdrop of ongoing digital transformation in education, the central thesis argues that AI tutors do not replace teachers but function as complementary tools that assist with specific tasks such as assignment evaluation, progress monitoring, and personalized feedback. While AI systems offer significant advantages — including personalized learning pathways, expanded access to educational resources in underserved rural areas, and reduced administrative burden for teachers — they cannot substitute essential pedagogical elements such as human interaction, emotional intelligence, cultural awareness, and moral guidance.
The analysis draws on theoretical frameworks from UNESCO and OECD, examining both the potential and limitations of AI integration within Kazakhstan's educational context, where regional disparities in infrastructure and digital literacy create uneven implementation. Through comparative insights from Germany's education system, the essay demonstrates how AI tutors can be effectively integrated as supportive tools while preserving the teacher's central role in pedagogical decision-making and student development. The findings emphasize that successful AI integration requires maintaining human oversight, addressing the digital divide, and ensuring that teachers retain professional autonomy in shaping educational outcomes.
This paper examines the integration of artificial intelligence (AI) into the education system, focusing on its key opportunities and challenges in the context of the Fourth Industrial Revolution. Artificial intelligence has become an important tool for modernizing education, enhancing learning quality, supporting personalized instruction, and improving educational management processes.
The study is based on a comparative analysis of the experiences of Germany and Kazakhstan in implementing AI in education. The German model emphasizes strategic planning, teacher training, and ethical and legal regulation, while Kazakhstan’s approach focuses on accessibility and rapid implementation through widely used EdTech platforms.
The findings indicate that the effective use of artificial intelligence depends not only on technological infrastructure but also on teacher readiness, data security, and ethical responsibility. The paper highlights the potential of AI to transform education and identifies the key conditions for its sustainable and balanced integration.
