School of Business, Social and Decision Sciences
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- Doctoral Thesis (31)
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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 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.
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.
In this thesis, I examine the relationship between internal governance and university performance within the context of Russian higher education from 2012 to 2020, a period marked by the prominent application of New Public Management (NPM) instruments. This study investigates several dimensions of internal governance and its connection to university performance. First, how do internal governance characteristics - such as centralization, stakeholder involvement, external communication, and strategic orientation - relate to university performance? Second, is there a relationship between institutional strategy adoption and university performance from the perspective of university department heads? Additionally, I explore the institutional structures and governance arrangements in Russian higher education, with particular attention to two interrelated developments: the adoption of NPM instruments and system stratification. The study draws on data from a national-level survey of university leaders and administrators, complemented by statistical information. Depending on the data structure and variable characteristics, various quantitative methods - from simple difference tests to conditional efficiency estimations - will be applied to address the research questions.
Industrial Demand Response (IDR) systems have emerged as a key enabler for enhancing grid flexibility, particularly as industries face increasing pressure to optimize energy consumption and integrate with renewable energy sources. However, despite their potential, the adoption and scalability of IDR solutions are limited by a range of technical, infrastructural, and organizational challenges. This dissertation investigates how emerging digital technologies—namely, Artificial Intelligence/Machine Learning (AI/ML) and the computing continuum (edge, fog, and cloud computing)—can be leveraged to overcome these limitations and enable scalable, intelligent, and interoperable IDR architectures.
The study addresses three core research questions. First, it develops a taxonomy of barriers to IDR adoption, distinguishing between technological and non-technological constraints. Second, it explores how distributed computing paradigms can mitigate these challenges by enabling real-time, privacy-aware, and latency-sensitive decision-making across industrial sites. Third, it examines the synergistic integration of AI/ML within the computing continuum, emphasizing methods such as federated learning, transfer learning, and multi-agent reinforcement learning to overcome issues related to data sparsity, system complexity, and semantic heterogeneity.
A reference architecture for IDR aggregators is proposed, combining layered intelligence, semantic interoperability, and orchestration mechanisms. This architecture is mapped to real-world cloud and open-source platforms to demonstrate its practical applicability. The findings confirm that the integration of AI/ML and distributed computing is not only feasible but essential for advancing the resilience, autonomy, and responsiveness of future industrial energy systems.
Digitalization and Lean Management as Tools for Increasing Efficiency in the Transport Industry
(2025)
The essay explores the integration of digitalization and Lean management in the transport industry, emphasizing their combined role in enhancing efficiency, flexibility, and sustainability. It outlines the origins of Lean management in the Toyota Production System and its adaptation to logistics and transport through practices such as 5S, Kaizen, and Just-in-Time. The paper highlights successful examples from global companies like DHL, UPS, Delta Airlines, and DB Schenker, demonstrating measurable improvements in productivity and cost reduction.
Digital technologies—including the Internet of Things, Artificial Intelligence, Big Data, and digital twins—strengthen Lean principles by enabling real-time data analysis, automation, and predictive decision-making. The essay also examines Germany’s leadership in transport digitalization and describes practical observations from the Mercedes-Benz plant in Bremen.
Finally, it discusses challenges such as cybersecurity, integration complexity, and ethical concerns, concluding that the synergy between Lean and digitalization forms the foundation for the future of transport—making it smarter, greener, and more resilient in the global economy.
The transition toward sustainable urban mobility and industrial efficiency requires decision-making tools that go beyond correlation-based analysis to uncover true cause-and-effect relationships. Traditional machine learning models, while effective for prediction, often act as "black boxes," lacking interpretability and failing to reveal the mechanisms underlying complex systems. To address these limitations, this dissertation introduces a modular Causal AI framework for smart decision-making, integrating causal discovery and inference with structured domain knowledge to enhance sustainability outcomes.
The framework is validated across three key domains: (1) urban CO2 emissions, (2) shared mobility demand, and (3) SME energy use. The first case study analyzes over 500,000 vehicles to uncover how engine performance and maintenance drive urban emissions. The second study examines shared bike systems, identifying causal impacts of weather patterns, station topology, and temporal demand fluctuations, supporting more adaptive fleet operations. The third applies the framework in a manufacturing SME, identifying the root causes of energy inefficiency and enabling targeted interventions to improve operational performance without compromising productivity.
This research advances the interpretability and actionability of AI in sustainability contexts by replacing opaque predictive models with transparent, evidence-based causal reasoning. Algorithms such as PC, FCI, GES, and DirectLiNGAM are employed alongside domain ontologies to uncover valid causal relationships and support decision-making. A hybrid approach also addresses feature selection, dimensionality reduction, and model explainability, making the methodology broadly applicable across diverse sustainability challenges.
While the framework demonstrates strong applicability, future work may focus on enhancing real-time scalability, adaptive ontology integration, and broader validation across domains such as electric mobility and smart energy systems. Overall, this thesis contributes a generalizable, interpretable Causal AI framework that enhances systemic understanding and supports sustainable transformation in policy, planning, and industrial decision-making.
