OPUS documents https://opus.constructor.university/index/index/ Fri, 10 Jul 2026 12:00:54 +0200 Fri, 10 Jul 2026 12:00:54 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1373 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. Lukas Schmidt doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1373 Fri, 10 Jul 2026 12:00:54 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1372 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. Sofya Hassany Bajaa doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1372 Fri, 03 Jul 2026 11:05:22 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1371 (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. Jan Folkert Klaassen doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1371 Thu, 04 Jun 2026 11:24:13 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1356 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. Maximilian Helms doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1356 Tue, 17 Feb 2026 12:59:02 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1358 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. Majid Sodachi doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1358 Tue, 17 Feb 2026 10:44:39 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1344 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. Julia Bosbach doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1344 Fri, 06 Feb 2026 10:32:54 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1341 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. Daria Platonova doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1341 Wed, 21 Jan 2026 11:13:41 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1326 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. Atit Bashyal doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1326 Mon, 10 Nov 2025 14:16:01 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1335 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. Askar Saukhimov; Aliya Omarova workingpaper https://opus.constructor.university/frontdoor/index/index/docId/1335 Tue, 21 Oct 2025 09:17:42 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1307 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. Tamas Fekete doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1307 Wed, 02 Jul 2025 16:55:01 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1311 Energy management and sustainability have become critical global priorities in response to growing environmental concerns and the need to optimize resource consumption. As industries expand and technological advancements continue to shape modern societies, energy demands are rapidly rising. This leads to escalating levels of carbon emissions and resource depletion, negatively impacting the environment and the economy. The thesis proposes an innovative approach to addressing energy management and sustainability complexities. The research focuses on developing a framework for smart energy systems that can autonomously improve their performance through knowledge sharing and semantic interoperability. The core idea behind this thesis is to enable smart energy systems to self-develop their knowledge models through decentralized technologies, particularly blockchain while ensuring peer-to-peer semantic interaction and collaboration across different environments and supply chains. A key innovation of this thesis is the use of blockchain technology as the underlying platform for achieving semantic interoperability and knowledge exchange among smart systems. By leveraging blockchain’s decentralized nature, a peer-to-peer semantic interaction framework is established. The research introduces a smart contract mechanism and a token-based economic model to incentivize stakeholders within the blockchain network, ensuring that participants align with the sustainability goals of the network. The thesis presents a novel approach to storing and exchanging knowledge models on the blockchain using the InterPlanetary File System (IPFS). This enables real-time updates to smart systems' knowledge models, allowing them to adapt and respond dynamically to changing environmental conditions and data inputs. Through the proposed blockchain ecosystem, the research provides a comprehensive solution for enhancing the interoperability, autonomy, and sustainability of smart energy systems. Mohammad Yaser Mofatteh doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1311 Fri, 27 Jun 2025 10:52:38 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1305 Environmental, Social, and Governance (ESG) reporting is increasingly critical for corporate transparency and accountability as demands from investors, regulators, and the public grow. Despite its importance, ESG reporting faces persistent challenges, including fragmented standards, inconsistent metrics, misalignment with global goals like the UN SDGs, and limited relevance for stakeholders. These issues weaken benchmarking, data reliability, and decision-making, raising the risk of greenwashing. Although previous studies have explored drivers of ESG performance, there is still a clear need for technical solutions that enhance the quality and usefulness of ESG disclosures through integrated approaches. This dissertation addresses these critical gaps by developing and proposing a novel, integrated framework that leverages the strength of semantic technologies, MCDM methods, and ESG maturity models. The research is structured through several interconnected studies, exploring specific industry applications using multi-criteria analysis techniques to identify and prioritize relevant ESG KPIs. A systematic literature review (SLR) provides a foundational understanding of existing ontology-driven solutions and their limitations in addressing reporting challenges and integrating quantitative methods. Based on these insights, the core contribution is the design of an ontology-based framework called ESGOnt. This framework utilizes a modular ESG ontology to standardize terminology, integrate fragmented data sources, and explicitly map ESG metrics to SDG targets. This research contributes by addressing critical challenges in ESG reporting through a novel, integrated framework. It advances the understanding of how semantic models can be combined with quantitative methods to create robust, transparent, and actionable sustainability reporting systems, strengthening corporate accountability and supporting more effective contributions to global sustainable development. ANNAS VIJAYA doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1305 Thu, 12 Jun 2025 10:04:04 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1302 This dissertation examines Free/Libre Open Source Software (FLOSS) development groups through three interconnected studies, each applying computational social science methods to understand different aspects of online collaboration. The first study analyzes group interactions via pull requests, identifying five organizational structures ranging from hierarchical to collaboratively governed networks. This typology moves beyond the traditional "bazaar-cathedral" dichotomy and reveals how group structure impacts outcomes such as popularity, stability, and productivity. The second study employs an agent-based model informed by Affect Control Theory to explore how cultural dynamics shape roles, status, power distribution, and gender biases within FLOSS communities. Findings illustrate the interplay between cultural norms and social structures, highlighting pathways toward gender equality through cultural norm shifts. The third study investigates macro-level project dynamics, examining how repository fitness, preferential attachment, and aging influence project popularity. It compares the meritocratic nature of scientific research and FLOSS development, employing generative probabilistic models based on stochastic processes to understand the mechanisms driving popularity. Together, these studies demonstrate how platform design, cultural norms, and social structures collectively shape FLOSS projects, advancing our understanding of digital collaborative systems. Nikolas Zöller doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1302 Thu, 24 Apr 2025 11:41:04 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1292 This Ph.D. dissertation investigates the neurobehavioral, psychological, and cognitive factors influencing postural control and its age-related changes through four studies. Postural control is essential for daily activities, and its decline with age increases fall risk, leading to autonomy loss and reduced quality of life. The first study (Imani & Godde, 2021) explores how self-efficacy mediates the relationship between falls and autonomy in older adults. While falls negatively impact autonomy, higher self-efficacy reduces this effect. Cognitive function predicts autonomy but does not moderate the relationship between falls and autonomy. The second study examines the bidirectional link between cognitive and physical function. Results show that cognitive decline affects physical abilities over time and vice versa, highlighting the strong connection between executive function and motor control. This reinforces how cognitive decline, falls, and depressive mood contribute to reduced social participation. The third study investigates the effects of transcranial direct current stimulation (tDCS) combined with balance training in younger adults. Targeting the sensorimotor cortex and dorsolateral prefrontal cortex (DLPFC), the study assesses how anodal tDCS enhances balance. Resting-state EEG is also explored as a predictor of training effects. The fourth study extends this research to older adults, revealing age-related differences in neural activation during balance training. Results suggest individualized stimulation protocols may improve balance outcomes. Overall, these studies emphasize the cognitive-motor link in postural control and propose targeted interventions to enhance balance and autonomy, particularly in older adults at risk of falls. Hadis Imani doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1292 Wed, 23 Apr 2025 12:41:50 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1296 Entrepreneurship is vital in driving innovation, economic growth, and societal transformation. Due to its growing relevance, this dissertation examines the factors that motivate individuals to engage in entrepreneurial endeavors, grounded in the Theory of Planned Behavior (TPB) and enriched by supplementary theoretical perspectives. The research combines qualitative and quantitative methods to explore how attitudes, subjective norms, and perceived behavioral control interact with these diverse aspects. The first study identifies key influences on entrepreneurial intention through qualitative interviews with entrepreneurs, thereby revealing ten critical determinants. The second study expands upon these findings by employing a quantitative approach to validate these parameters through a survey among entrepreneurs in Germany. It demonstrates the central importance of economics-oriented education, materialistic values, and resilience while refining the theoretical assumptions of the TPB in entrepreneurial contexts. Lastly, the third study extends the analysis by comparing the personality traits of entrepreneurs and employees utilizing the OCEAN model. This research delineates substantial differences across all five traits, underscoring the psychological dimensions of entrepreneurial behavior. By amalgamating the three studies and their respective findings, this dissertation contributes to the theoretical understanding of entrepreneurship by enhancing the predictive validity of the TPB for research on entrepreneurship and highlighting differences in personality traits among entrepreneurs and employees. Through the integration of qualitative insights and quantitative validation, this dissertation provides a comprehensive and nuanced perspective on the complex nature of entrepreneurship. Benedikt Jamitzky doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1296 Wed, 23 Apr 2025 12:07:10 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1299 Chronic and non-communicable diseases present ongoing challenges for healthcare systems worldwide. Digital Health Interventions (DHIs) provide a promising solution by empowering patients to engage in health-related decisions and manage their behaviours. At present, many DHIs suffer from low user adoption or fail to progress beyond research. This thesis analyses the design, build, and evaluation of such tools to guide the effective co-creation of DHIs. Across five studies, this thesis examines the design, build, and evaluation of DHIs. The first two studies explore how to effectively plan the co-creation of DHIs, identifying both facilitators and challenges. The second study applies co-creation methods to incorporate end users in developing design specifications for a DHI. The third study evaluates the impact of including end users in the build phase, whilst the final two studies assess evaluation strategies, demonstrating how synthetic data and machine learning can be used to manage missing data and predict intervention outcomes. Findings highlight the importance of adaptive, inclusive design processes, careful planning of co-creator involvement, and the application of behavioural science across all phases. The thesis offers practical guidance for future researchers, emphasising the value of empowering patients, addressing attrition, and applying predictive analytics to maximise the real-world impact of DHIs. Vinayak Anand Kumar doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1299 Wed, 23 Apr 2025 10:31:51 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1293 Small business lending is significantly influenced by the interplay between the information institution types, the information models, and the due diligence process undertaken by financial institutions. While previous research has examined the impact of hard and soft information on credit availability, limited attention has been given to how different combinations of these information types and various financial institution types influence the quality of loan applications and the loan application success rates. Additionally, the role of due diligence in assessing small business loan applications and the specific signals lenders rely on for decision-making remain underexplored. This study integrates insights from three research streams to provide a unique analysis of small business lending dynamics. First, based on primary data collected from 242 small firms in Ghana and considering different financial institutions, including non-banks, we examine the effect of three distinct combinations of hard and soft information on loan application success rates. Our findings challenge conventional wisdom, indicating that an increased emphasis on soft information does not necessarily enhance transparency or improve loan application success rates. Moreover, while small-sized banks positively influence credit availability, this effect does not extend to small-sized non-banks. Second, leveraging signal theory, we analyze the due diligence process undertaken by financial institutions through qualitative insights from 24 loan officer interviews. We identify 11 key hard and soft signals that influence credit risk assessments, including key person risk, change in leadership risk, articulation of company value, adaptation to change, data accuracy, and completeness. These insights highlight the multifaceted nature of credit decision-making. Oghenekome Umuerri doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1293 Thu, 03 Apr 2025 11:22:26 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1290 The existing Industry 4.0 maturity models (I4.0 MM) have mostly been built and tested in developed nations, making them less effective in developing countries with unique issues. Additionally, flexible updated models are needed to support the smooth integration of I4.0 adoption in rapid technological advancements and organizational matters. The research addresses the challenges by developing an adaptable I4.0 MM using approaches starting with structured literature reviews (SLRs), investigating the causal relationship and prioritization of I4.0 MMs key driving factors, aligning it with reputable reference architecture model (RAMs), and developing an ontology, named Ontomat 4.0, to facilitate interoperability of I4.0 MMs. The research's general findings highlight the core gaps in existing I4.0 MMs, the need to prioritize and the interdependence of the key driving factor in I4.0 transformation, the importance of strategically enhancing I4.0 adoption by aligning the key factor of I4.0 MMs with RAMs, and the necessity of a framework with an approach that can bridge the theoretical foundation of I4.0 MMs with practical application. The research concludes by describing contributions to the issues and challenges in the findings. However, while the research acknowledges the significant progress in its accomplishment, there are limitations to the study that need to be addressed in future research directions, including integrating sustainability metrics and increasingly essential factors, such as Customers, the potential integration of artificial intelligence (AI) within Ontomat 4.0, future exploration equipped with longitudinal studies, and the expansion of Ontomat 4.0 into a collaborative ecosystem where knowledge sharing and best practices can grow. Linda Salma Angreani doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1290 Fri, 21 Mar 2025 11:07:48 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1264 Local fishers have historically been subjected to the detrimental effects of overfishing, a consequence of open access practices that are likely to persist in the coming decades. The open-access nature of many fisheries is a significant driver of overfishing, which poses a substantial threat to marine ecosystems and their livelihoods that depend on them. The concept of common property theory provides a theoretical framework that can be used to explain the phenomenon of overfishing due to open access practices. Fishery resources are regarded as examples of a common property, implying that these resources belong to all fishers. This assumption gives rise to intense competition among fishers to exploit the fishery resources. One potential solution to this problem is to establish a territorial use rights system (TURF) that would prevent open-access practices. This thesis argues that all relevant stakeholders in small-scale fisheries management should prioritize environmental stewardship, regardless of the system used to address the overfishing problem caused by open-access practices, as this constitutes a principal factor in determining sustainability. This perspective is particularly relevant in the context of TURF implementation as numerous studies have demonstrated that TURF is an effective means of fostering stewardship. This thesis presents a collection of three studies that address the two primary topics of TURF and stewardship. While this thesis is primarily based on a case study of TURF implementation and stewardship actions (fishing logbook) in Kepulauan Seribu, Indonesia, I hope that the resulting publications will serve as additional references and contribute to the global discussion on TURF and stewardship. Rifki Furqan doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1264 Thu, 16 Jan 2025 10:23:17 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1256 Addressing environmental governance challenges necessitates collaboration among diverse societal actors to collectively develop and modify rules, norms, and social structures. A significant obstacle in environmental governance lies in the problems of institutional fit, where the existing governance arrangements may be mismatched with the specific social-ecological conditions at the local level. This misalignment poses a hurdle to effective and sustainable environmental management. Collective action’s theoretical lens is used in this study to navigate the varied interests, goals, and perspectives involved, aiming to comprehend the different factors influencing collaboration in the management of shared resources. Recognizing that collective action is inherently difficult, this dissertation focuses on the importance of deliberation to facilitate discussions on the risks, benefits, values, and capacities of different actors. Adiska Octa Paramita doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1256 Tue, 10 Dec 2024 11:14:56 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1257 Steel manufacturing is characterized by its high energy consumption and the production of high-value-added products. In real-world steel production, unforeseen events frequently disrupt schedules, emphasizing the critical need for effective and adaptive planning to ensure continuous operations. This study makes significant contributions to the steel industry by offering new approaches to improve efficiency, streamline operations, and optimize production processes, ultimately driving advancements in steel manufacturing performance. To address the challenges inherent to EAF-based steelmaking, a seamless pipeline of algorithms has been developed. This pipeline works together to enhance the manufacturing planning process by providing an accurate chemical condensation of molten steel and classifying this outcome according to the most feasible steel grade, which can be obtained with a minimum of purification. Finally, the pipeline reschedules the planning process with the objective of maximizing machine utilization. To achieve these goals, the steelmaking stage key performance indicators (KPIs) that have the most impact on the quality of the final product were first identified. In the next step, a novel prediction algorithm utilizing a multilayer feedforward neural network was developed to estimate key quality parameters. Finally, to enhance the pipeline's resilience to disruptions, a Genetic Algorithm (GA) is employed to mitigate the impact of scenarios where processing times for jobs vary and do not align with the established schedule. This is a prevalent disruption event that renders the base schedule infeasible. The proposed algorithm aims to minimize total completion and waiting times, thereby enhancing operational efficiency and minimizing manufacturing costs. Mohammad Niyayesh doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1257 Thu, 05 Dec 2024 10:56:58 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1253 Over the last few decades, environmental governance research has embraced a complex adaptive systems (CAS) framing: solving sustainability challenges requires an understanding of the social-ecological systems (SES) they are embedded in, consisting of interlinked components and relationships which form dynamic and emergent patterns. Many frameworks have been developed to help conceptually understand SES, however less focus has been given to advancing methods for SES research. I identify a particular need to advance quantitative SES methods, as despite a growing range of available approaches, much quantitative SES research heavily relies on classic statistical methods which by design ignore interactive effects and focus on reducing systems to individual variables. This creates tensions when applied to systems shaped by highly interactive and context-sensitive processes. Further, despite an emphasis on standardizability, quantitative research has not led to widespread synthesis of SES knowledge. There is a need to advance quantitative SES methods in ways which 1) incorporate complex system properties into case studies and 2) synthesize generalizable findings across cases without overly abstracting case complexity. In this thesis I explore these methodological challenges within the literature on Elinor Ostrom’s social-ecological systems framework (SESF). I then apply recent advances in methods for complexity through the case study of small-scale aquaculture governance in Indonesia: a participatory modeling method called fuzzy cognitive mapping to analyze “mental models” of aquaculture complexity, and archetypes analysis to synthesize generalizable patterns in complexity across a large set of heterogeneous aquaculture cases. I conclude that advancing SES methods to inform sustainable outcomes requires more critical engagement with “complex systems thinking” in not only conceptualizing environmental governance problems but also in empirical research design. Ben Nagel doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1253 Tue, 03 Dec 2024 13:18:32 +0100 https://opus.constructor.university/frontdoor/index/index/docId/1245 The rapid rise of new technologies, such as mobile phones, social networks, and increased internet access, has created new opportunities for retailers to expand through omnichannel strategies, which aim to provide a seamless customer experience across different channels. While omnichannel can offer benefits and competitive advantages, it faces challenges like price inconsistencies and poor information sharing. Despite its growing recognition, its integration with Product Lifecycle Management (PLM) is underexplored. This thesis investigates the overlap and mutual effects of omnichannel and PLM, particularly emphasizing the importance of data and knowledge sharing between them. As research into data analytics in omnichannel evolves, tracking and tracing mechanisms in PLM become increasingly critical. The main objective of this study is to develop a bi-directional framework connecting omnichannel and PLM using a System Dynamics approach. This framework aims to incorporate omnichannel mechanisms into PLM and provide cause-and-effect analysis to better understand the operational role of omnichannel within PLM. Additionally, the research introduces an approach to integrate Business-to-Business (B2B) aspects from PLM with Business-to-Customer (B2C) elements through omnichannel strategies. Leveraging omnichannel's influence on touchpoints like influencers and social media, this approach helps companies align consumer behavior with strategic goals, boosting competitiveness. Ultimately, this thesis aims to bridge the gap between omnichannel and PLM, driving a shift in consumer behavior and strengthening the integration of B2B and B2C in retail. Noushin Mohammadian doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1245 Wed, 23 Oct 2024 13:39:45 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1234 Polarisation is a great concern in current social and political debates. A divergence of opinions or, more generally, a lack of societal agreement, for example on fundamental problems like climate change, presents a barrier to rapid action against a looming crisis. There are many theories on why people polarise on certain topics. However, the drivers of polarisation in social environments are multi-faceted and involve complex feedbacks among social, cognitive, and structural processes. While humans require interactions with each other to form shared views and cooperate effectively on many problems, social influence can produce a variety of opinion patterns, such as consensus, persistent disagreement, or polarisation. In this thesis, I develop mathematical models of opinion formation or perception to uncover the conditions underpinning the emergence of such patterns. I formalise how psychological factors distort the way individuals perceive others into a mathematical language and analyse how these perceptions affect the formation of consensus or the persistence of disagreement in a virtual society. The factors are: (1) noise, (2) bias, or (3) subjective perception. Taken together, the three studies demonstrate that these factors distorting people's perceptions or responses to social influence have a non-negligible and sometimes surprising impact on collective opinion patterns. This thesis highlights the importance to better understand the mechanisms behind social phenomena and their non-trivial consequences on opinion dynamics. While the models and the conclusions presented in the thesis may not be readily used to predict opinion patterns, owing to the complexity and inherent uncertainty of our society, they contribute to the social sciences by demonstrating counter-intuitive consequences of seemingly obvious theoretical assumptions, highlighting gaps and potentially critical ambiguities in social theories, and suggesting future directions for empirical analysis. Peter Steiglechner doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1234 Fri, 23 Aug 2024 12:18:57 +0200 https://opus.constructor.university/frontdoor/index/index/docId/1200 Cluster analysis is a common part of data analysis. Its aim is the identification of unknown structure in data and the determination of a partition with groups of objects as similar as possible (so-called clusters). In contrast to the frequent occurrence of mixed-type data in real-world applications, involving numerical as well as categorical features, research tends to concentrate on data containing exclusively numerical features. There are comparatively few methods for clustering mixed-type data, with the k-prototypes algorithm being presumably the most widely recognized. The purpose of this cumulative dissertation is to expand the scope of this clustering algorithm. It addresses aspects that are not treated in Huang's original publication of the k-prototype algorithm, including the validation of the number of clusters, variable selection of data to be clustered, imputation of incomplete data, algorithm initialization, and the integration of an alternative distance measure in the algorithm routine. These issues are covered as they are prevalent in the application of the k-prototypes algorithm on real-world data. In these clustering tasks, the user lacks knowledge about the optimal number of clusters or the most useful variables to determine the cluster partition. In addition, incomplete data often occur and need to be dealt with. The algorithm’s initialization is analyzed to optimize the iterative routine, which was originally published with a random-based choice of initial prototypes. Additionally, the distance-based partitioning algorithm is extended to ordinal data for distance calculation with the change of the algorithm’s distance measure. To conduct the research, simulation studies on artificially generated data are utilized as well as exemplary analyzes on real-world data. Rabea Aschenbruck doctoralthesis https://opus.constructor.university/frontdoor/index/index/docId/1200 Wed, 12 Jun 2024 16:13:37 +0200