@inproceedings{45812,
author = {{Özcan, Leon and Fichtler, Timm and Kasten, Benjamin and Koldewey, Christian and Dumitrescu, Roman}},
keywords = {{Digital Platform, Platform Strategy, Strategic Management, Platform Life Cycle, Interview Study, Business Model, Business-to-Business, Two-sided Market, Multi-sided Market}},
location = {{Ljubljana}},
title = {{{Interview Study on Strategy Options for Platform Operation in B2B Markets}}},
year = {{2023}},
}
@inproceedings{34171,
abstract = {{State estimation when only a partial model of a considered system is available remains a major challenge in many engineering fields. This work proposes a joint, square-root unscented Kalman filter to estimate states and model uncertainties simultaneously by linear combinations of physics-motivated library functions. Using a sparsity promoting approach, a selection of those linear combinations is chosen and thus an interpretable model can be extracted. Results indicate a small estimation error compared to a traditional square-root unscented Kalman filter and exhibit the enhancement of physically meaningful models.}},
author = {{Götte, Ricarda-Samantha and Timmermann, Julia}},
booktitle = {{12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022)}},
keywords = {{joint estimation, unscented transform, Kalman filter, sparsity, data-driven, compressed sensing}},
location = {{Canberra, Australien}},
number = {{1}},
pages = {{85--90}},
title = {{{Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF}}},
doi = {{https://doi.org/10.1016/j.ifacol.2023.02.015}},
volume = {{56}},
year = {{2023}},
}
@inproceedings{29839,
abstract = {{The development of business models is a challenging task that can be supported with software tools. Here, existing approaches and tools do not focus on the company’s situation in which the development takes place (e.g., financial resources, product type). To tackle this challenge, we used design science research to develop a situation-specific business model development approach that contains three stages: First, existing knowledge in terms of tasks to do (e.g., analyze competitive advantage), and decisions to be made (e.g., social media marketing) are stored in repositories. Second, the knowledge is used to compose a development method based on the company’s situation. Third, the development method is enacted to develop a business model. This demonstration paper presents a tool-support called Situational Business Model Developer that supports all stages of our approach. We release the tool under open-source and evaluate it with a case study on developing business models for mobile apps.}},
author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}},
booktitle = {{Proceedings of the 17th International Conference on Wirtschaftsinformatik}},
keywords = {{Business Model Development, Situational Method Engineering, Tool Support}},
location = {{Nuremberg}},
publisher = {{AIS}},
title = {{{Situational Business Model Developer: A Tool-support for Situation-specific Business Model Development}}},
year = {{2022}},
}
@inproceedings{29842,
abstract = {{To build successful software products, developers continuously have to discover what features the users really need. This discovery can be achieved with continuous experimentation, testing different software variants with distinct user groups, and deploying the superior variant for all users. However, existing approaches do not focus on explicit modeling of variants and experiments, which offers advantages such as traceability of decisions and combinability of experiments. Therefore, our vision is the provision of model-driven continuous experimentation, which provides the developer with a framework for structuring the experimentation process. For that, we introduce the overall concept, apply it to the experimentation on component-based software architectures and point out future research questions. In particular, we show the applicability by combining feature models for modeling the software variants, users, and experiments (i.e., model-driven) with MAPE-K for the adaptation (i.e., continuous experimentation) and implementing the concept based on the component-based Angular framework.}},
author = {{Gottschalk, Sebastian and Yigitbas, Enes and Engels, Gregor}},
booktitle = {{Proceedings of the 18th International Conference on Software Architecture Companion }},
keywords = {{continuous experimentation, model-driven, component-based software architectures, self-adaptation}},
location = {{Hawaii}},
publisher = {{IEEE}},
title = {{{Model-driven Continuous Experimentation on Component-based Software Architectures }}},
doi = {{10.1109/ICSA-C54293.2022.00011}},
year = {{2022}},
}
@inproceedings{29220,
abstract = {{Modern services often comprise several components, such as chained virtual network functions, microservices, or
machine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them.
To overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination.
Instead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios.}},
author = {{Werner, Stefan and Schneider, Stefan Balthasar and Karl, Holger}},
booktitle = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}},
keywords = {{network management, service management, AI, Monte Carlo Tree Search, model-based, QoS}},
location = {{Budapest}},
publisher = {{IEEE}},
title = {{{Use What You Know: Network and Service Coordination Beyond Certainty}}},
year = {{2022}},
}
@inbook{33500,
abstract = {{This article is dedicated to piezoelectric ultrasonic power transducers that differ to well known medical ultrasonic diagnostic apparatus or non destructive testing devices by the level of power in use; typically several tens of up to more than thousand watts are used in a multitude of different applications. After a short introduction including historical development, the first focus is on theoretical background of the operating principle, design and mechanical modeling. As piezoelectric elements transform electrical to mechanical energy and vice versa, equivalent circuit modeling is also described. After that, sample applications are delineated by the matter wherein ultrasound generates unique effects: incredible high pressure level as well in air as in water, micro-bubbles generating temperature peaks for very short time instances in fluids, acoustoplastic effect, enhancement of diffusion and recrystallization in solids, friction manipulation, incremental deformation and micro-cracking of surfaces, or even generation of macroscopic movements in motors. At the end, some future directions ranging from novel modeling approaches to advanced control and new materials are addressed.}},
author = {{Hemsel, Tobias and Twiefel, Jens}},
booktitle = {{Reference Module in Materials Science and Materials Engineering}},
isbn = {{978-0-12-803581-8}},
keywords = {{Equivalent circuit model, Langevin transducer, Lumped parameter model, Piezoelectric transducer, Ultrasonic processes, Ultrasound}},
publisher = {{Elsevier}},
title = {{{Piezoelectric Ultrasonic Power Transducers}}},
doi = {{10.1016/b978-0-12-819728-8.00047-4}},
year = {{2022}},
}
@inproceedings{36462,
abstract = {{The conduction of structure-borne sound through joints causes energy dissipation. The sound reduction index describes this energy loss as a level decrease in the particle velocity across series-connected damping elements for which the superposition principle applies. This simple model can help to develop a testing method for joints based on this characteristic energy loss. In this paper, this model is experimentally evaluated for multiple in-series clinched aluminium sheets. Samples connected by several clinch points arranged in parallel are investigated experimentally, and the results are discussed.}},
author = {{Stephan, Richard and Brosius, Alexander}},
booktitle = {{The 28th Saxon Conference on Forming Technology SFU and the 7th International Conference on Accuracy in Forming Technology ICAFT}},
keywords = {{clinching, mechanical joining, damping, model, evaluation, dynamics}},
publisher = {{MDPI}},
title = {{{Experimental Measurement Method and Evaluation of an Analytical Approach for Sound Conduction through Multiple Clinched Sheets}}},
doi = {{10.3390/engproc2022026025}},
year = {{2022}},
}
@article{33839,
abstract = {{Modeling is an integral part of many computing-related disciplines and thus also represents a curricular core component in computing education in tertiary education. Competence models in which modeling is integrated at least to some extent already exist in some of these disciplines. However, for the core component of graphical modeling, a competence model that illuminates the relevant competences in detail is still lacking. Therefore, we develop a competence model for graphical modeling with the aim to make teaching and especially assessments in the field more competence-oriented. This paper reports on the first two studies conducted to develop and validate the competence model for graphical modeling. In the first study, the structure of the competence model was developed based on theories and approaches of educational science. Competences relevant for graphical modeling were deductively derived from literature and existing university course descriptions using techniques of qualitative content analysis. The result of the first study is a preliminary competence model. In the second study, the preliminary competence model was reviewed by means of an expert rating in the modeling community. The competence model was revised and refined based on these findings and subsequent expert discussions. The main result of the investigation represents the competence model for graphical modeling (CMGM), which includes a total of 74 competence facets at different cognitive process levels in the five content areas of ”model understanding and interpreting”, ”model building and modifying”, ”values, attitudes, and beliefs”, ”metacognitive knowledge and skills”, and ”social-communicative skills”.}},
author = {{Soyka, Chantal and Schaper, Niclas and Bender, Elena and Striewe, Michael and Ullrich, Meike}},
journal = {{ACM Transactions on Computing Education}},
keywords = {{graphical modeling, conceptual modeling, computer science, competence model, higher education}},
number = {{1}},
publisher = {{Association for Computing Machinery}},
title = {{{Toward a Competence Model for Graphical Modeling}}},
doi = {{10.1145/3567598}},
volume = {{23}},
year = {{2022}},
}
@article{32174,
abstract = {{AbstractIncreasing system complexity can be controlled by using systems engineering processes. INCOSE defines processes with inputs and outputs (artifacts) for this purpose. Specific SE roles are used to organize the tasks of the processes within the company. In this work, the responsibilities for artifacts are evaluated by means of the RACI scheme and examined by a cluster analysis and discussed for a SE transformation project with a German automotive OEM. As a result of the study, the optimal composition for systems engineering teams is identified and the systems engineering roles are prioritized.}},
author = {{Gräßler, Iris and Thiele, Henrik and Grewe, Benedikt and Hieb, Michael}},
issn = {{2732-527X}},
journal = {{Proceedings of the Design Society}},
keywords = {{systems engineering (SE), project management, model-based systems engineering (MBSE)}},
location = {{Dubrovnik}},
pages = {{1875--1884}},
publisher = {{Cambridge University Press (CUP)}},
title = {{{Responsibility Assignment in Systems Engineering}}},
doi = {{10.1017/pds.2022.190}},
volume = {{2}},
year = {{2022}},
}
@article{32307,
abstract = {{The development of new business models is essential for startups to become successful, as well as for established companies to explore new business opportunities. However, developing such business models is a continuous challenging activity where different tasks need to be performed, and business decisions need to be made. Both have to fit the constantly changeable situation in which the business model is developed to reduce the risk of developing ineffective business models with low market penetration. Therefore, a method for developing situation-specific business models is needed. As a solution, we refine the concept of situational method engineering (SME) to business model development. SME, in turn, provides means to construct situation-specific development methods out of fragments from a method repository.
We develop a concept for the continuous situation-specific development of business models based on design science. The approach uses the roles of a domain expert, a method engineer, and a business developer together with a repository with method fragments for developing business models and a repository with modeling artifacts for supporting the development. Both repositories are filled by utilizing the experience of domain experts. Out of these repositories, situation-specific development methods for developing business models can be continuously composed based on the changeable situation by the method engineer and enacted by the business developer. We implement it as an open-source tool and evaluate its applicability in an industrial case study of developing a business model for a local event platform. Our results show that situation awareness supports the continuous development of business models.}},
author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}},
journal = {{International Journal on Software and Systems Modeling (SoSyM) }},
keywords = {{Business Model Development, Situational Method Engineering, Situation-specific, Business Model Canvas, Continuous Development}},
title = {{{Continuous Situation-specific Development of Business Models: Knowledge Provision, Method Composition, Method Enactment}}},
year = {{2022}},
}
@article{43433,
abstract = {{Ziel dieser Studie ist es den digitalen moodlegestützten asynchronen Sprachkurs Fachspezifisches Chinesisch für das „Maschinenbau in China Programm“ (mb-cn) der Fakultät für Maschinenbau der Universität Paderborn zu evaluieren, um Handlungsempfehlungen für zukünftig ähnlich aufgebaute Projekte zu entwickeln. Dazu wurden im Sommersemester 2021 sechs leitfadengestützte Interviews geführt. Die Interviews wurden anschließend mithilfe von deduktiv ermittelten Kategorien, die sich aus dem Technology Acceptance Model 2 (TAM2) nach Venkatesh und Davis (2000) ergaben, nach Mayring (2015) analysiert, um abschließend die Forschungsfrage zu beantworten: „Wie bewerten mb-cn Ingenieurstudierende die wahrgenommene Nützlichkeit der digitalen Sprachlernangebote des Kurses Fachspezifisches Chinesisch?“.}},
author = {{Hambach, Dennis}},
journal = {{ die hochschullehre. Interdisziplinäre Zeitschrift für Hochschule und Lehre}},
keywords = {{Technology Acceptance Model, Fachspezifische Chinesischsprachkurse, digitale Lehre, Moodle, Evaluation}},
number = {{8}},
pages = {{1--15}},
publisher = {{wbv Publikation}},
title = {{{Evaluation eines digitalen Fachspezifischen Chinesischsprachkurses für Studierende des Ingenieurwesens}}},
doi = {{10.3278/HSL2249W}},
year = {{2022}},
}
@inproceedings{51343,
abstract = {{This paper presents preliminary work on the formalization of three prominent cognitive biases in the diagnostic reasoning process over epileptic seizures, psychogenic seizures and syncopes. Diagnostic reasoning is understood as iterative exploration of medical evidence. This exploration is represented as a partially observable Markov decision process where the state (i.e., the correct diagnosis) is uncertain. Observation likelihoods and belief updates are computed using a Bayesian network which defines the interrelation between medical risk factors, diagnoses and potential findings. The decision problem is solved via partially observable upper confidence bounds for trees in Monte-Carlo planning. We compute a biased diagnostic exploration policy by altering the generated state transition, observation and reward during look ahead simulations. The resulting diagnostic policies reproduce reasoning errors which have only been described informally in the medical literature. We plan to use this formal representation in the future to inversely detect and classify biased reasoning in actual diagnostic trajectories obtained from physicians.}},
author = {{Battefeld, Dominik and Kopp, Stefan}},
booktitle = {{Proceedings of the 8th Workshop on Formal and Cognitive Reasoning}},
keywords = {{Diagnostic reasoning, Cognitive bias, Cognitive model, POMDP, Bayesian network, Epilepsy, CDSS}},
location = {{Trier}},
title = {{{Formalizing cognitive biases in medical diagnostic reasoning}}},
year = {{2022}},
}
@inproceedings{26539,
abstract = {{In control design most control strategies are model-based and require accurate models to be applied successfully. Due to simplifications and the model-reality-gap physics-derived models frequently exhibit deviations from real-world-systems. Likewise, purely data-driven methods often do not generalise well enough and may violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired loss functions separately have shown promising results to conquer these drawbacks. In this contribution we extend existing methods towards the identification of non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN and a physics-inspired loss term (-L) to successfully identify the system's dynamics, while maintaining the consistency with physical laws. The proposed method is demonstrated on two real-world nonlinear systems and outperforms existing techniques regarding complexity and reliability.}},
author = {{Götte, Ricarda-Samantha and Timmermann, Julia}},
booktitle = {{2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)}},
keywords = {{data-driven, physics-based, physics-informed, neural networks, system identification, hybrid modelling}},
location = {{Cairo, Egypt}},
pages = {{67--76}},
title = {{{Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering}}},
doi = {{10.1109/AIRC56195.2022.9836982}},
year = {{2022}},
}
@inproceedings{31066,
abstract = {{While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. }},
author = {{Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}},
booktitle = {{14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}},
keywords = {{neural networks, physics-guided, data-driven, multi-objective optimization, system identification, machine learning, dynamical systems}},
location = {{Casablanca, Morocco}},
number = {{12}},
pages = {{19--24}},
title = {{{Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}}},
doi = {{https://doi.org/10.1016/j.ifacol.2022.07.282}},
volume = {{55}},
year = {{2022}},
}
@inproceedings{29803,
abstract = {{Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro and
power electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation in
the contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capture
this process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for the
bonding process even without detailed model knowledge. We propose the use of batch constrained Bayesian
optimization for the control design. Hence, Bayesian optimization is precisely adapted to the application of
bonding: the constraint is used to check one quality feature of the process and the use of batches leads to
more efficient experiments. Our approach is suitable to determine a feed-forward control for the bonding
process that provides very high quality bonds without using a physical model. We also show that the quality
of the Bayesian optimization based control outperforms random search as well as manual search by a user.
Using a simple prior knowledge model derived from data further improves the quality of the connection.
The Bayesian optimization approach offers the possibility to perform a sensitivity analysis of the control
parameters, which allows to evaluate the influence of each control parameter on the bond quality. In summary,
Bayesian optimization applied to the bonding process provides an excellent opportunity to develop a feedforward
control without full modeling of the underlying physical processes.}},
author = {{Hesse, Michael and Hunstig, Matthias and Timmermann, Julia and Trächtler, Ansgar}},
booktitle = {{Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)}},
isbn = {{978-989-758-549-4}},
keywords = {{Bayesian optimization, Wire bonding, Feed-forward control, model-free design}},
location = {{Online}},
pages = {{383--394}},
title = {{{Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design}}},
year = {{2022}},
}
@inproceedings{56993,
author = {{Schaffer, Michael and Lea, Budde and Schulte, Carsten and Buhl, Heike M.}},
booktitle = {{52nd DGPs Congress - Abstracts}},
editor = {{Bermeitinger, Christina and Greve, Werner}},
keywords = {{Cognition, Motivation, Technical Model, Mental Model, Explainer, Explainee, Qualitative Content Analysis}},
location = {{Hildesheim}},
title = {{{Die Anpassungen von Erklärungen an das Verständnis des Erklärgegenstandes der Gesprächspartner}}},
year = {{2022}},
}
@inproceedings{33914,
abstract = {{Workshops on business model generation lead to collaborative work phases and discussions on business models. Therefore, tools such as the Business Model Canvas are used, typically filled with sticky notes. Generated content needs to be digitized in a time-consuming manual follow-up as part of the documentation and basis for a further use of the results in the company. In addition, there are challenges, such as decentralized work and digital workshop formats. Augmented Reality offers a way to reduce the digitization effort and enables decentralized work. In this research, the potentials of the use of AR technology in workshops on business model generation is investigated. Therefore, functions are implemented and evaluated in a demonstrator that reduces digitization effort and enable distributed work.}},
author = {{Gräßler, Iris and Grewe, Benedikt and Kramer, Hendrik and Pottebaum, Jens}},
booktitle = {{LUT Scientific and Expertise Publications}},
keywords = {{business model generation, augmented reality, workshop, collaborative work, digitization, AR-supported workshop concept, immersive technologies, decentralized work, business model canvas}},
location = {{Copenhagen}},
title = {{{Supporting Business Model Generation with Augmented Reality}}},
year = {{2022}},
}
@misc{25126,
abstract = {{Motivated by the prospect of computing agents that explore unknown environments and construct convex hulls on the nanoscale, we investigate the capabilities and limitations of a single deterministic finite automaton robot in the three-dimensional hybrid model for programmable matter. In this model, active robots move on a set of passive tiles, called configuration, with the geometric shape of rhombic dodecahedra on the adjacency graph of the face-centered cubic sphere-packing. We show that the exploration problem is equally hard in the hybrid model and in three-dimensional mazes, in which tiles have the shape of cubes and are positioned at the vertices of $\mathbb{Z}^3$. Thereby, a single robot with a constant number of pebbles cannot solve this problem in the hybrid model on arbitrary configurations. We provide algorithms for a robot with two pebbles that solve the exploration problem in the subclass of compact configurations of size $n$ in $\O(n^3)$ rounds. Further, we investigate the robot's capabilities of detection and hull construction in terms of restricted orientation convexity. We show that a robot without any pebble can detect strong $\O$-convexity in $\O(n)$ rounds, but cannot detect weak $\O$-convexity, not even if provided with a single pebble. Assuming that a robot can construct tiles from scratch and deconstruct previously constructed tiles, we show that the strong $\O$-hull of any given configuration of size $n$ can be constructed in $\O(n^4)$ rounds, even if the robot cannot distinguish constructed from native tiles.}},
author = {{Liedtke, David Jan}},
keywords = {{Robot Exploration, Finite Automaton, Hybrid Model for Programmable Matter, Convex Hull}},
title = {{{Exploration and Convex Hull Construction in the Three-Dimensional Hybrid Model}}},
year = {{2021}},
}
@inproceedings{21639,
abstract = {{The development of effective business models is an essential task in highly competitive markets like mobile ecosystems. Existing development methods for these business models do not specifically focus that the development process profoundly depends on the situation (e.g., market size, regulations) of the mobile app developer. Here, a mismatch between method and situation can lead to poor resource management and longer development cycles. In software engineering, situational method engineering is used for software projects to configure a development method out of a method repository based on the project situation. Analogously, we support creating situation-specific business model development methods with a method base and new user roles. Here, the method engineer obtains the knowledge of the domain expert and stores it in the method base as elements, building blocks, and patterns. The expert knowledge is derived from a grey literature review on mobile development processes. After this, the method engineer constructs the development method based on the described situation of the business developer. We provide an open-source tool and evaluate it by constructing a local event platform's business model development method. }},
author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}},
booktitle = {{Enterprise, Business-Process and Information Systems Modeling}},
keywords = {{Business Model Development, Situational Method Engineering, Mobile App, Business Model Development Tools}},
publisher = {{Springer}},
title = {{{Situation-specific Business Model Development Methods for Mobile App Developers}}},
doi = {{10.1007/978-3-030-79186-5_17}},
year = {{2021}},
}
@inbook{25528,
abstract = {{Developing effective business models is a complex process for a company where several tasks (e.g., conduct customer interviews) need to be accomplished, and decisions (e.g., advertisement as a revenue stream) must be made. Here, domain experts can guide the choices of tasks and decisions with their knowledge. Nevertheless, this knowledge needs to match the situation of the company (e.g., financial resources) and the application domain of the product/service (e.g., mobile app) to reduce the risk of developing ineffective business models with low market penetration. This is not covered by one-size-fits-all development methods without tailoring before the enaction.
Therefore, we conduct a design science study to create a situation-specific development approach for business models. Based on situational method engineering and our previous work in storing knowledge of methods and models in distinct repositories, this paper shows the situation-specific composition and enaction of business model development methods. First, the method engineer composes the development method out of both repositories based on the situational context. Second, the business developer enacts the method and develops the business model. We implement the approach in a tool and evaluate it with a industrial case study on mobile apps.}},
author = {{Gottschalk, Sebastian and Yigitbas, Enes and Nowosad, Alexander and Engels, Gregor}},
booktitle = {{Product-focused Software Process Improvement}},
keywords = {{Business Model Development, Situational Method Engineering, Lean Development, Kanban Boards, Canvas Models}},
location = {{Turin}},
publisher = {{Springer}},
title = {{{Situation- and Domain-specific Composition and Enactment of Business Model Development Methods}}},
year = {{2021}},
}