@phdthesis{Panzer2024,
  author    = {Panzer, Marcel},
  title     = {Design of a hyper-heuristics based control framework for modular production systems},
  doi       = {10.25932/publishup-63300},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-633006},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vi, 334},
  year      = {2024},
  abstract  = {Volatile supply and sales markets, coupled with increasing product individualization and complex production processes, present significant challenges for manufacturing companies. These must navigate and adapt to ever-shifting external and internal factors while ensuring robustness against process variabilities and unforeseen events. This has a pronounced impact on production control, which serves as the operational intersection between production planning and the shop- floor resources, and necessitates the capability to manage intricate process interdependencies effectively. Considering the increasing dynamics and product diversification, alongside the need to maintain constant production performances, the implementation of innovative control strategies becomes crucial. In recent years, the integration of Industry 4.0 technologies and machine learning methods has gained prominence in addressing emerging challenges in production applications. Within this context, this cumulative thesis analyzes deep learning based production systems based on five publications. Particular attention is paid to the applications of deep reinforcement learning, aiming to explore its potential in dynamic control contexts. Analysis reveal that deep reinforcement learning excels in various applications, especially in dynamic production control tasks. Its efficacy can be attributed to its interactive learning and real-time operational model. However, despite its evident utility, there are notable structural, organizational, and algorithmic gaps in the prevailing research. A predominant portion of deep reinforcement learning based approaches is limited to specific job shop scenarios and often overlooks the potential synergies in combined resources. Furthermore, it highlights the rare implementation of multi-agent systems and semi-heterarchical systems in practical settings. A notable gap remains in the integration of deep reinforcement learning into a hyper-heuristic. To bridge these research gaps, this thesis introduces a deep reinforcement learning based hyper- heuristic for the control of modular production systems, developed in accordance with the design science research methodology. Implemented within a semi-heterarchical multi-agent framework, this approach achieves a threefold reduction in control and optimisation complexity while ensuring high scalability, adaptability, and robustness of the system. In comparative benchmarks, this control methodology outperforms rule-based heuristics, reducing throughput times and tardiness, and effectively incorporates customer and order-centric metrics. The control artifact facilitates a rapid scenario generation, motivating for further research efforts and bridging the gap to real-world applications. The overarching goal is to foster a synergy between theoretical insights and practical solutions, thereby enriching scientific discourse and addressing current industrial challenges.},
  language  = {en}
}
@phdthesis{Koehler2024,
  author    = {K{\"o}hler, Wolfgang},
  title     = {Challenges of efficient and compliant data processing},
  doi       = {10.25932/publishup-62784},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-627843},
  school      = {Universit{\"a}t Potsdam},
  pages     = {195},
  year      = {2024},
  abstract  = {Die fortschreitende Digitalisierung ver{\"a}ndert die Gesellschaft und hat weitreichende Auswirkungen auf Menschen und Unternehmen. Grundlegend f{\"u}r diese Ver{\"a}nderungen sind die neuen technologischen M{\"o}glichkeiten, Daten in immer gr{\"o}ßerem Umfang und f{\"u}r vielf{\"a}ltige neue Zwecke zu verarbeiten. Von besonderer Bedeutung ist dabei die Verf{\"u}gbarkeit großer und qualitativ hochwertiger Datens{\"a}tze, insbesondere auf Basis personenbezogener Daten. Sie werden entweder zur Verbesserung der Produktivit{\"a}t, Qualit{\"a}t und Individualit{\"a}t von Produkten und Dienstleistungen oder gar zur Entwicklung neuartiger Dienstleistungen verwendet. Heute wird das Nutzerverhalten, trotz weltweit steigender gesetzlicher Anforderungen an den Schutz personenbezogener Daten, aktiver und umfassender verfolgt als je zuvor. Dies wirft vermehrt ethische, moralische und gesellschaftliche Fragen auf, die nicht zuletzt durch popul{\"a}re F{\"a}lle des Datenmissbrauchs in den Vordergrund der politischen Debatte ger{\"u}ckt sind. Angesichts dieses Diskurses und der gesetzlichen Anforderungen muss heutiges Datenmanagement drei Bedingungen erf{\"u}llen: Erstens die Legalit{\"a}t bzw. Gesetzeskonformit{\"a}t der Nutzung, zweitens die ethische Legitimit{\"a}t. Drittens sollte die Datennutzung aus betriebswirtschaftlicher Sicht wertsch{\"o}pfend sein. Im Rahmen dieser Bedingungen verfolgt die vorliegende kumulative Dissertation vier Forschungsziele mit dem Fokus, ein besseres Verst{\"a}ndnis (1) der Herausforderungen bei der Umsetzung von Gesetzen zum Schutz von Privatsph{\"a}re, (2) der Faktoren, die die Bereitschaft der Kunden zur Weitergabe pers{\"o}nlicher Daten beeinflussen, (3) der Rolle des Datenschutzes f{\"u}r das digitale Unternehmertum und (4) der interdisziplin{\"a}ren wissenschaftlichen Bedeutung, deren Entwicklung und Zusammenh{\"a}nge zu erlangen.},
  language  = {en}
}
@phdthesis{Haase2023,
  author    = {Haase, Jennifer},
  title     = {Creative intensive processes},
  doi       = {10.25932/publishup-59388},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-593886},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiii, 346},
  year      = {2023},
  abstract  = {Creativity - developing something new and useful - is a constant challenge in the working world. Work processes, services, or products must be sensibly adapted to changing times. To be able to analyze and, if necessary, adapt creativity in work processes, a precise understanding of these creative activities is necessary. Process modeling techniques are often used to capture business processes, represent them graphically and analyze them for adaptation possibilities. This has been very limited for creative work. An accurate understanding of creative work is subject to the challenge that, on the one hand, it is usually very complex and iterative. On the other hand, it is at least partially unpredictable as new things emerge. How can the complexity of creative business processes be adequately addressed and simultaneously manageable? This dissertation attempts to answer this question by first developing a precise process understanding of creative work. In an interdisciplinary approach, the literature on the process description of creativity-intensive work is analyzed from the perspective of psychology, organizational studies, and business informatics. In addition, a digital ethnographic study in the context of software development is used to analyze creative work. A model is developed based on which four elementary process components can be analyzed: Intention of the creative activity, Creation to develop the new, Evaluation to assess its meaningfulness, and Planning of the activities arising in the process - in short, the ICEP model. These four process elements are then translated into the Knockledge Modeling Description Language (KMDL), which was developed to capture and represent knowledge-intensive business processes. The modeling extension based on the ICEP model enables creative business processes to be identified and specified without the need for extensive modeling of all process details. The modeling extension proposed here was developed using ethnographic data and then applied to other organizational process contexts. The modeling method was applied to other business contexts and evaluated by external parties as part of two expert studies. The developed ICEP model provides an analytical framework for complex creative work processes. It can be comprehensively integrated into process models by transforming it into a modeling method, thus expanding the understanding of existing creative work in as-is process analyses.},
  language  = {en}
}
@phdthesis{Boeken2022,
  author    = {B{\"o}ken, Bj{\"o}rn},
  title     = {Improving prediction accuracy using dynamic information},
  doi       = {10.25932/publishup-58512},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-585125},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xii, 160},
  year      = {2022},
  abstract  = {Accurately solving classification problems nowadays is likely to be the most relevant machine learning task. Binary classification separating two classes only is algorithmically simpler but has fewer potential applications as many real-world problems are multi-class. On the reverse, separating only a subset of classes simplifies the classification task. Even though existing multi-class machine learning algorithms are very flexible regarding the number of classes, they assume that the target set Y is fixed and cannot be restricted once the training is finished. On the other hand, existing state-of-the-art production environments are becoming increasingly interconnected with the advance of Industry 4.0 and related technologies such that additional information can simplify the respective classification problems. In light of this, the main aim of this thesis is to introduce dynamic classification that generalizes multi-class classification such that the target class set can be restricted arbitrarily to a non-empty class subset M of Y at any time between two consecutive predictions. This task is solved by a combination of two algorithmic approaches. First, classifier calibration, which transforms predictions into posterior probability estimates that are intended to be well calibrated. The analysis provided focuses on monotonic calibration and in particular corrects wrong statements that appeared in the literature. It also reveals that bin-based evaluation metrics, which became popular in recent years, are unjustified and should not be used at all. Next, the validity of Platt scaling, which is the most relevant parametric calibration approach, is analyzed in depth. In particular, its optimality for classifier predictions distributed according to four different families of probability distributions as well its equivalence with Beta calibration up to a sigmoidal preprocessing are proven. For non-monotonic calibration, extended variants on kernel density estimation and the ensemble method EKDE are introduced. Finally, the calibration techniques are evaluated using a simulation study with complete information as well as on a selection of 46 real-world data sets. Building on this, classifier calibration is applied as part of decomposition-based classification that aims to reduce multi-class problems to simpler (usually binary) prediction tasks. For the involved fusing step performed at prediction time, a new approach based on evidence theory is presented that uses classifier calibration to model mass functions. This allows the analysis of decomposition-based classification against a strictly formal background and to prove closed-form equations for the overall combinations. Furthermore, the same formalism leads to a consistent integration of dynamic class information, yielding a theoretically justified and computationally tractable dynamic classification model. The insights gained from this modeling are combined with pairwise coupling, which is one of the most relevant reduction-based classification approaches, such that all individual predictions are combined with a weight. This not only generalizes existing works on pairwise coupling but also enables the integration of dynamic class information. Lastly, a thorough empirical study is performed that compares all newly introduced approaches to existing state-of-the-art techniques. For this, evaluation metrics for dynamic classification are introduced that depend on corresponding sampling strategies. Thereafter, these are applied during a three-part evaluation. First, support vector machines and random forests are applied on 26 data sets from the UCI Machine Learning Repository. Second, two state-of-the-art deep neural networks are evaluated on five benchmark data sets from a relatively recent reference work. Here, computationally feasible strategies to apply the presented algorithms in combination with large-scale models are particularly relevant because a naive application is computationally intractable. Finally, reference data from a real-world process allowing the inclusion of dynamic class information are collected and evaluated. The results show that in combination with support vector machines and random forests, pairwise coupling approaches yield the best results, while in combination with deep neural networks, differences between the different approaches are mostly small to negligible. Most importantly, all results empirically confirm that dynamic classification succeeds in improving the respective prediction accuracies. Therefore, it is crucial to pass dynamic class information in respective applications, which requires an appropriate digital infrastructure.},
  language  = {en}
}
@phdthesis{Dannenmann2023,
  author    = {Dannenmann, Barbara},
  title     = {K{\"o}nnen technologiegest{\"u}tzte Verhandlungstrainings unter Einsatz von K{\"u}nstlicher Intelligenz und Virtueller Realit{\"a}t das Vertriebstraining verbessern?},
  doi       = {10.25932/publishup-57737},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-577378},
  school      = {Universit{\"a}t Potsdam},
  pages     = {245},
  year      = {2023},
  abstract  = {Digitale und gesellschaftliche Entwicklungen fordern kontinuierliche Weiterbildung f{\"u}r Mitarbeiter im Vertrieb. Es halten sich in dieser Berufssparte aber immer noch einige Mythen zum Training von Vertriebsmitarbeitern. Unter anderem deshalb wurde in der Vergangenheit der Trainingsbedarf im Vertrieb stark vernachl{\"a}ssigt. Die Arbeit befasst sich deshalb zun{\"a}chst mit der Frage, wie der Vertrieb in Deutschland aktuell geschult wird (unter Einbezug der Corona-Pandemie) und ob sich aus den Trainingsgewohnheiten erste Hinweise zur Erlangung eines strategischen Wettbewerbsvorteils ergeben k{\"o}nnten. Dabei greift die Arbeit auf, dass Investitionen in das Training von Vertriebsmitarbeitern eine Anlage in die Wettbewerbsf{\"a}higkeit des Unternehmens sein k{\"o}nnten. Automatisierte Trainings, beispielsweise basierend auf Virtual Reality (VR) und K{\"u}nstlicher Intelligenz (KI), k{\"o}nnten in der Aus- und Weiterbildung des Vertriebs einen effizienten Beitrag in der Sicherstellung eines strategischen Wettbewerbsvorteils leisten. Durch weitere Forschungsfragen befasst sich die Arbeit anschließend damit, wie ein automatisiertes Vertriebstraining mit KI- und VR-Inhalten unter Einbeziehung der Nutzer gestaltet werden muss, um Vertriebsmitarbeiter in einem daf{\"u}r ausgew{\"a}hlten Verhandlungskontext zu trainieren. Dazu wird eine Anwendung mit Hilfe von Virtual Reality und K{\"u}nstlicher Intelligenz in einem Verhandlungsdialog entwickelt, getestet und evaluiert. Die vorliegende Arbeit liefert eine Basis f{\"u}r die Automatisierung von Vertriebstrainings und im erweiterten Sinne f{\"u}r Trainings im Allgemeinen.},
  language  = {de}
}
@phdthesis{Gandhi2022,
  author    = {Gandhi, Nilima},
  title     = {Visionary leadership and job satisfaction},
  doi       = {10.25932/publishup-57269},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-572691},
  school      = {Universit{\"a}t Potsdam},
  pages     = {154},
  year      = {2022},
  abstract  = {Current business organizations want to be more efficient and constantly evolving to find ways to retain talent. It is well established that visionary leadership plays a vital role in organizational success and contributes to a better working environment. This study aims to determine the effect of visionary leadership on employees' perceived job satisfaction. Specifically, it investigates whether the mediators meaningfulness at work and commitment to the leader impact the relationship. I take support from job demand resource theory to explain the overarching model used in this study and broaden-and-build theory to leverage the use of mediators. To test the hypotheses, evidence was collected in a multi-source, time-lagged design field study of 95 leader-follower dyads. The data was collected in a three-wave study, each survey appearing after one month. Data on employee perception of visionary leadership was collected in T1, data for both mediators were collected in T2, and employee perception of job satisfaction was collected in T3. The findings display that meaningfulness at work and commitment to the leader play positive intervening roles (in the form of a chain) in the indirect influence of visionary leadership on employee perceptions regarding job satisfaction. This research offers contributions to literature and theory by first broadening the existing knowledge on the effects of visionary leadership on employees. Second, it contributes to the literature on constructs meaningfulness at work, commitment to the leader, and job satisfaction. Third, it sheds light on the mediation mechanism dealing with study variables in line with the proposed model. Fourth, it integrates two theories, job demand resource theory and broaden-and-build theory providing further evidence. Additionally, the study provides practical implications for business leaders and HR practitioners. Overall, my study discusses the potential of visionary leadership behavior to elevate employee outcomes. The study aligns with previous research and answers several calls for further research on visionary leadership, job satisfaction, and mediation mechanism with meaningfulness at work and commitment to the leader.},
  language  = {en}
}
@phdthesis{Brinkmann2022,
  author    = {Brinkmann, Maik},
  title     = {Towards a joint public service delivery? The effects of blockchain on the relationship of public administrations with external stakeholders},
  doi       = {10.25932/publishup-56449},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-564499},
  school      = {Universit{\"a}t Potsdam},
  pages     = {X, 126, CCLXVIII},
  year      = {2022},
  abstract  = {Public administrations confront fundamental challenges, including globalization, digitalization, and an eroding level of trust from society. By developing joint public service delivery with other stakeholders, public administrations can respond to these challenges. This increases the importance of inter-organizational governance—a development often referred to as New Public Governance, which to date has not been realized because public administrations focus on intra-organizational practices and follow the traditional "governmental chain." E-government initiatives, which can lead to high levels of interconnected public services, are currently perceived as insufficient to meet this goal. They are not designed holistically and merely affect the interactions of public and non-public stakeholders. A fundamental shift toward a joint public service delivery would require scrutiny of established processes, roles, and interactions between stakeholders. Various scientists and practitioners within the public sector assume that the use of blockchain institutional technology could fundamentally change the relationship between public and non-public stakeholders. At first glance, inter-organizational, joint public service delivery could benefit from the use of blockchain. This dissertation aims to shed light on this widespread assumption. Hence, the objective of this dissertation is to substantiate the effect of blockchain on the relationship between public administrations and non-public stakeholders. This objective is pursued by defining three major areas of interest. First, this dissertation strives to answer the question of whether or not blockchain is suited to enable New Public Governance and to identify instances where blockchain may not be the proper solution. The second area aims to understand empirically the status quo of existing blockchain implementations in the public sector and whether they comply with the major theoretical conclusions. The third area investigates the changing role of public administrations, as the blockchain ecosystem can significantly increase the number of stakeholders. Corresponding research is conducted to provide insights into these areas, for example, combining theoretical concepts with empirical actualities, conducting interviews with subject matter experts and key stakeholders of leading blockchain implementations, and performing a comprehensive stakeholder analysis, followed by visualization of its results. The results of this dissertation demonstrate that blockchain can support New Public Governance in many ways while having a minor impact on certain aspects (e.g., decentralized control), which account for this public service paradigm. Furthermore, the existing projects indicate changes to relationships between public administrations and non-public stakeholders, although not necessarily the fundamental shift proposed by New Public Governance. Lastly, the results suggest that power relations are shifting, including the decreasing influence of public administrations within the blockchain ecosystem. The results raise questions about the governance models and regulations required to support mature solutions and the further diffusion of blockchain for public service delivery.},
  language  = {en}
}
@phdthesis{Brenner2022,
  author    = {Brenner, Andri Caspar},
  title     = {Sustainable urban growth},
  doi       = {10.25932/publishup-55522},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555223},
  school      = {Universit{\"a}t Potsdam},
  pages     = {231},
  year      = {2022},
  abstract  = {This dissertation explores the determinants for sustainable and socially optimalgrowth in a city. Two general equilibrium models establish the base for this evaluation, each adding its puzzle piece to the urban sustainability discourse and examining the role of non-market-based and market-based policies for balanced growth and welfare improvements in different theory settings. Sustainable urban growth either calls for policy actions or a green energy transition. Further, R\&D market failures can pose severe challenges to the sustainability of urban growth and the social optimality of decentralized allocation decisions. Still, a careful (holistic) combination of policy instruments can achieve sustainable growth and even be first best.},
  language  = {en}
}
@phdthesis{Schumacher2022,
  author    = {Schumacher, Jochen},
  title     = {Entwicklung eines Industrie 4.0 Reifegradindex f{\"u}r produzierende Unternehmen},
  doi       = {10.25932/publishup-55464},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-554642},
  school      = {Universit{\"a}t Potsdam},
  pages     = {VI, 275},
  year      = {2022},
  abstract  = {Das Ziel dieser Arbeit ist die Entwicklung eines Industrie 4.0 Reifegradindex f{\"u}r produzierende Unternehmen (KMU und Mittelstand) mit diskreter Produktion. Die Motivation zu dieser Arbeit entstand aus dem Z{\"o}gern vieler Unternehmen - insbesondere KMU und Mittelstand - bei der Transformation in Richtung Industrie 4.0. Im Rahmen einer Marktstudie konnte belegt werden, dass 86 Prozent der befragten produzierenden Unternehmen kein f{\"u}r ihr Unternehmen geeignetes Industrie 4.0 Reifegradmodell gefunden haben, mit dem sie ihren Status Quo bewerten und Maßnahmen f{\"u}r einen h{\"o}heren Grad der Reife ableiten k{\"o}nnten. Die Bewertung bestehender Reifegradmodelle zeigte Defizite hinsichtlich der Industrie 4.0 Abdeckung, der Betrachtung der sozio-technischen Dimensionen Mensch, Technik und Organisation sowie der Betrachtung von Management und Unternehmenskultur. Basierend auf den aktuellen Industrie 4.0 Technologien und Handlungsbereichen wurde ein neues, modular aufgebautes Industrie 4.0 Reifegradmodell entwickelt, das auf einer ganzheitlichen Betrachtung aller sozio-technischen Dimensionen Mensch, Technik und Organisation sowie deren Schnittstellen basiert. Das Modell ermittelt neben dem Overall Industry 4.0 Maturity Index (OI4MI) vier weitere Indizes zur Bewertung der Industrie 4.0 Reife des Unternehmens. Das Modell wurde bei einem Unternehmen validiert und steht nun als Template f{\"u}r darauf aufbauende Forschungsarbeiten zur Verf{\"u}gung.},
  language  = {de}
}
@phdthesis{Gleiss2022,
  author    = {Gleiß, Alexander},
  title     = {Case Studies on Digital Transformation},
  doi       = {10.25932/publishup-54615},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-546159},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xii, 198},
  year      = {2022},
  abstract  = {Digital transformation (DT) has not only been a major challenge in recent years, it is also supposed to continue to enormously impact our society and economy in the forthcoming decade. On the one hand, digital technologies have emerged, diffusing and determining our private and professional lives. On the other hand, digital platforms have leveraged the potentials of digital technologies to provide new business models. These dynamics have a massive effect on individuals, companies, and entire ecosystems. Digital technologies and platforms have changed the way persons consume or interact with each other. Moreover, they offer companies new opportunities to conduct their business in terms of value creation (e.g., business processes), value proposition (e.g., business models), or customer interaction (e.g., communication channels), i.e., the three dimensions of DT. However, they also can become a threat for a company's competitiveness or even survival. Eventually, the emergence, diffusion, and employment of digital technologies and platforms bear the potential to transform entire markets and ecosystems. Against this background, IS research has explored and theorized the phenomena in the context of DT in the past decade, but not to its full extent. This is not surprising, given the complexity and pervasiveness of DT, which still requires far more research to further understand DT with its interdependencies in its entirety and in greater detail, particularly through the IS perspective at the confluence of technology, economy, and society. Consequently, the IS research discipline has determined and emphasized several relevant research gaps for exploring and understanding DT, including empirical data, theories as well as knowledge of the dynamic and transformative capabilities of digital technologies and platforms for both organizations and entire industries. Hence, this thesis aims to address these research gaps on the IS research agenda and consists of two streams. The first stream of this thesis includes four papers that investigate the impact of digital technologies on organizations. In particular, these papers study the effects of new technologies on firms (paper II.1) and their innovative capabilities (II.2), the nature and characteristics of data-driven business models (II.3), and current developments in research and practice regarding on-demand healthcare (II.4). Consequently, the papers provide novel insights on the dynamic capabilities of digital technologies along the three dimensions of DT. Furthermore, they offer companies some opportunities to systematically explore, employ, and evaluate digital technologies to modify or redesign their organizations or business models. The second stream comprises three papers that explore and theorize the impact of digital platforms on traditional companies, markets, and the economy and society at large. At this, paper III.1 examines the implications for the business of traditional insurance companies through the emergence and diffusion of multi-sided platforms, particularly in terms of value creation, value proposition, and customer interaction. Paper III.2 approaches the platform impact more holistically and investigates how the ongoing digital transformation and "platformization" in healthcare lastingly transform value creation in the healthcare market. Paper III.3 moves on from the level of single businesses or markets to the regulatory problems that result from the platform economy for economy and society, and proposes appropriate regulatory approaches for addressing these problems. Hence, these papers bring new insights on the table about the transformative capabilities of digital platforms for incumbent companies in particular and entire ecosystems in general. Altogether, this thesis contributes to the understanding of the impact of DT on organizations and markets through the conduction of multiple-case study analyses that are systematically reflected with the current state of the art in research. On this empirical basis, the thesis also provides conceptual models, taxonomies, and frameworks that help describing, explaining, or predicting the impact of digital technologies and digital platforms on companies, markets and the economy or society at large from an interdisciplinary viewpoint.},
  language  = {en}
}