@misc{21714, author = {{Wittmann, Daniel}}, title = {{{Interdependente Geschäftsmodelle: Eine systematische Analyse von Relationen in Geschäftsmodell-Modellierungssprachen}}}, year = {{2021}}, } @phdthesis{28683, abstract = {{In den letzten Jahren haben sich Software-Ökosysteme als neue, erfolgreiche Geschäftsform etabliert. Unternehmen agieren hierbei als Anbieter von Software-Plattformen, auf denen Drittanbieter Softwarelösungen für den Markt anbieten können. Etablierte Beispiele sind hierbei sogenannte App-Stores, die z.B. von Google oder Apple angeboten werden. Beim Aufbau von Software-Ökosystemen müssen vom Plattformanbieter viele architektonische Entwurfsentscheidungen getroffen werden. Bisher gibt es keine Architekturrichtlinien und -werkzeuge, die den Entwurf einer Ökosystemarchitektur unterstützen. Dadurch fehlt hier systematisches, wiederverwendbares Wissen. Plattformanbieter müssen auf ad-hoc Entscheidungen zurückgreifen. Dies kann dann zu Problemen im Betrieb der Software-Plattformen führen, zu erhöhten Ausfallrisiken und Mehrkosten. Der Mangel an Architekturwissen manifestiert sich konkret in zwei Gruppen von Herausforderungen: Erstens fehlt eine Wissensbasis zu Architekturalternativen und zweitens fehlt es an methodischem Wissen zu Entwicklung und Betrieb von Software-Ökosystemen. Eine Architekturwissensbasis würde Orientierungshilfen zu den Bestandteilen von Software-Ökosystemen und deren Abhängigkeiten geben, während methodisches Wissen die Erstellung dieser Systeme erleichtern würde. In der Dissertation werden diese Herausforderungen durch die Entwicklung des Frameworks SecoArc für die Modellierung von Software-Ökosystemen angegangen. Der Beitrag der Dissertation ist zweifach: 1. Das SecoArc-Framework umfasst eine Architekturwissensbasis, die wiederverwendbare Architekturentwurfsentscheidungen von Software-Ökosystemen enthält. Die Wissensbasis wurde entwickelt, indem das Architekturwissen bestehender Ökosysteme sowie aus existierender Fachliteratur ermittelt wurde und in einer Produktlinie für Software-Ökosysteme konsolidiert wurde. Die Produktlinie umfasst architektonische Gemeinsamkeiten und Variabilitäten von Software-Ökosystemen. 2. Das SecoArc-Framework liefert methodisches Wissen, um die Ökosystemarchitektur in Modellen zu entwerfen und zu analysieren. Dieses Wissen wurde entwickelt, indem drei Architekturmuster identifiziert wurden. Jedes Muster erfasst unterschiedliche Beziehungen zwischen architektonischen Entwurfsentscheidungen zu den Qualitätsmerkmalen einer Ökosystemgesundheit und der Erreichung von Geschäftszielen. Die Architekturmuster und die Produktlinie wurden dazu genutzt, ein Modellierungsframework zu entwickeln und in Form eines Prototypen umzusetzen, welches einen Entwurfsprozess, eine Modellierungssprache und eine Architekturanalysetechnik umfasst. Es erleichtert das Modellieren, Analysieren und Vergleichen von Ökosystemarchitekturen. Die Ergebnisse der Dissertation wurden im Rahmen von zwei Studien evaluiert. In der ersten Validierungsstudie wurden das Framework sowie der Prototyp verwendet, um zwei alternative Ökosystemarchitekturen zu entwerfen und zu analysieren. In der zweiten Studie wurde eine Analyse von existierenden Ökosystemen basierend auf den architektonischen Variabilitäten des Frameworks durchgeführt.}}, author = {{Schwichtenberg, Bahar}}, keywords = {{Enterprise Architecture, Architectural Design Decisions, Open Platforms}}, title = {{{Modeling and Analyzing Software Ecosystems}}}, doi = {{10.17619/UNIPB/1-1270 }}, year = {{2021}}, } @article{17860, abstract = {{Purpose The purpose of this paper is to identify strategic options and challenges that arise when an industrial firm moves from providing smart service toward providing a platform. Design/methodology/approach This conceptual study takes on a multidisciplinary research perspective that integrates concepts, theories and insights from service management and marketing, information systems and platform economics. Findings The paper outlines three platform types – smart data platform, smart product platform and matching platform – as strategic options for firms that wish to evolve from smart service providers to platform providers. Research limitations/implications Investigating smart service platforms calls for launching interdisciplinary research initiatives. Promising research avenues are outlined to span boundaries that separate different research disciplines today. Practical implications Managing a successful transition from providing smart service toward providing a platform requires making significant investments in IT, platform-related capabilities and skills, as well as implement new approaches toward relationship management and brand-building. Originality/value The findings described in this paper are valuable to researchers in multiple disciplines seeking to develop and to justify theory related to platforms in industrial scenarios.}}, author = {{Beverungen, Daniel and Kundisch, Dennis and Wünderlich, Nancy}}, issn = {{507-532}}, journal = {{Journal of Service Management}}, keywords = {{Smart service, Platform, Interdisciplinary research, Manufacturing company, Smart service provider, Platform economics, Information systems, Multi-sided markets, Business-to-business (B2B) markets}}, number = {{4}}, pages = {{507--532}}, publisher = {{Emerald Insight}}, title = {{{Transforming into a Platform Provider: Strategic Options for Industrial Smart Service Providers}}}, doi = {{10.1108/JOSM-03-2020-0066}}, volume = {{32}}, year = {{2021}}, } @misc{45236, author = {{N., N.}}, title = {{{Design and Implementation of a Crowd-based Prototype Validation Platform}}}, year = {{2021}}, } @misc{45239, author = {{N., N.}}, title = {{{Lightweight Process Engine for Situation-specific Development of Business Models for Digital Platforms}}}, year = {{2021}}, } @misc{45240, author = {{N., N.}}, title = {{{Development and Evaluation of a Multi Platform Approach for Augmented Reality Product Configuration}}}, year = {{2021}}, } @misc{45238, author = {{N., N.}}, title = {{{Model-based Feature Backlog Synchronization for Dual-Track Development Methods}}}, year = {{2021}}, } @phdthesis{24884, author = {{Szopinski, Daniel}}, title = {{{Essays on Modeling Languages and Software Tools for Business Model Innovation: Theory and Empirical Evidence}}}, doi = {{10.17619/UNIPB/1-1647 }}, year = {{2021}}, } @inproceedings{19606, abstract = {{Mobile shopping apps have been using Augmented Reality (AR) in the last years to place their products in the environment of the customer. While this is possible with atomic 3D objects, there is is still a lack in the runtime configuration of 3D object compositions based on user needs and environmental constraints. For this, we previously developed an approach for model-based AR-assisted product configuration based on the concept of Dynamic Software Product Lines. In this demonstration paper, we present the corresponding tool support ProConAR in the form of a Product Modeler and a Product Configurator. While the Product Modeler is an Angular web app that splits products (e.g. table) up into atomic parts (e.g. tabletop, table legs, funnier) and saves it within a configuration model, the Product Configurator is an Android client that uses the configuration model to place different product configurations within the environment of the customer. We show technical details of our ready to use tool-chain ProConAR by describing its implementation and usage as well as pointing out future research directions.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Schmidt, Eugen and Engels, Gregor}}, booktitle = {{Human-Centered Software Engineering. HCSE 2020}}, editor = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}}, keywords = {{Product Configuration, Augmented Reality, Model-based, Tool Support}}, location = {{Eindhoven}}, publisher = {{Springer}}, title = {{{ProConAR: A Tool Support for Model-based AR Product Configuration}}}, doi = {{10.1007/978-3-030-64266-2_14}}, volume = {{12481}}, year = {{2020}}, } @inproceedings{19739, author = {{Szopinski, Daniel}}, booktitle = {{Proceedings of the 15th International Conference on Design Science Research in Information Systems and Technology (DESRIST)}}, location = {{Virtual Conference/Workshop}}, title = {{{Active Business Model Development Tools: Design Requirements}}}, year = {{2020}}, } @inproceedings{19741, author = {{Szopinski, Daniel}}, booktitle = {{Proceedings of the 41st International Conference on Information Systems (ICIS)}}, location = {{Virtual Conference/Workshop}}, title = {{{Exploring design principles for stimuli in business model development tools}}}, year = {{2020}}, } @inproceedings{19782, author = {{Müller, Michelle and Neumann, Jürgen and Gutt, Dominik and Kundisch, Dennis}}, booktitle = {{Proceedings of the 41th International Conference on Information Systems (ICIS)}}, location = {{Virtual Conference/Workshop}}, title = {{{Toss a Coin to your Host - How Guests End up Paying for the Cost of Regulatory Policies}}}, year = {{2020}}, } @inproceedings{17368, author = {{Vorbohle, Christian and Szopinski, Daniel and Kundisch, Dennis}}, editor = {{Shishkov, B.}}, isbn = {{978-3-030-52305-3}}, location = {{Potsdam, Germany}}, publisher = {{Springer}}, title = {{{Business Model Dependencies: Towards conceptualizing dependencies for extending modeling languages for business models}}}, volume = {{391}}, year = {{2020}}, } @inproceedings{18249, abstract = {{Augmented Reality (AR) has recently found high attention in mobile shopping apps such as in domains like furniture or decoration. Here, the developers of the apps focus on the positioning of atomic 3D objects in the physical environment. With this focus, they neglect the configuration of multi-faceted 3D object composition according to the user needs and environmental constraints. To tackle these challenges, we present a model-based approach to support AR-assisted product con-figuration based on the concept of Dynamic Software Product Lines. Our approach splits products (e.g. table) into parts (e.g. tabletop, ta-ble legs, funnier) with their 3D objects and additional information (e.g. name, price). The possible products, which can be configured out of these parts, are stored in a feature model. At runtime, this feature model can be used to configure 3D object compositions out of the product parts and adapt to user needs and environmental constraints. The benefits of this approach are demonstrated by a case study of configuring modular kitchens with the help of a prototypical mobile-based implementation.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Schmidt, Eugen and Engels, Gregor}}, booktitle = {{Human-Centered Software Engineering. HCSE 2020}}, editor = {{Bernhaupt, Regina and Ardito, Carmelo and Sauer, Stefan}}, keywords = {{Product Configuration, Augmented Reality, Runtime Adaptation, Dynamic Software Product Lines}}, location = {{Eindhoven}}, publisher = {{Springer}}, title = {{{Model-based Product Configuration in Augmented Reality Applications}}}, doi = {{10.1007/978-3-030-64266-2_5}}, volume = {{12481}}, year = {{2020}}, } @techreport{22161, author = {{Kundisch, Dennis}}, pages = {{1}}, title = {{{Digitale mehrseitige Plattformen – Besser verstehen, wie digitale Plattformen funktionieren}}}, volume = {{3}}, year = {{2020}}, } @article{6202, author = {{Szopinski, Daniel and Schoormann, T. and John, Thomas and Knackstedt, R. and Kundisch, Dennis}}, journal = {{Electronic Markets}}, number = {{3}}, pages = {{469--494}}, title = {{{Software tools for business model innovation: Current state and future challenges}}}, volume = {{30}}, year = {{2020}}, } @inproceedings{16933, abstract = {{The continuous innovation of its business models is an important task for a company to stay competitive. During this process, the company has to validate various hypotheses about its business models by adapting to uncertain and changing customer needs effectively and efficiently. This adaptation, in turn, can be supported by the concept of Software Product Lines (SPLs). SPLs reduce the time to market by deriving products for customers with changing requirements using a common set of features, structured as a feature model. Analogously, we support the process of business model adaptation by applying the engineering process of SPLs to the structure of the Business Model Canvas (BMC). We call this concept a Business Model Decision Line (BMDL). The BMDL matches business domain knowledge in the form of a feature model with customer needs to derive hypotheses about the business model together with experiments for validation. Our approach is effective by providing a comprehensive overview of possible business model adaptations and efficient by reusing experiments for different hypotheses. We implement our approach in a tool and illustrate the usefulness with an example of developing business models for a mobile application.}}, author = {{Gottschalk, Sebastian and Rittmeier, Florian and Engels, Gregor}}, booktitle = {{Proceedings of the 22nd IEEE International Conference on Business Informatics}}, keywords = {{Business Model Decision Line, Business Model Adaptation, Hypothesis-driven Adaptation, Software Product Line, Feature Model}}, location = {{Antwerp}}, publisher = {{IEEE}}, title = {{{Hypothesis-driven Adaptation of Business Models based on Product Line Engineering}}}, doi = {{10.1109/CBI49978.2020.00022}}, year = {{2020}}, } @inproceedings{16934, abstract = {{To build successful products, the developers have to adapt their product features and business models to uncertain customer needs. This adaptation is part of the research discipline of Hypotheses Engineering (HE) where customer needs can be seen as hypotheses that need to be tested iteratively by conducting experiments together with the customer. So far, modeling support and associated traceability of this iterative process are missing. Both, in turn, are important to document the adaptation to the customer needs and identify experiments that provide most evidence to the customer needs. To target this issue, we introduce a model-based HE approach with a twofold contribution: First, we develop a modeling language that models hypotheses and experiments as interrelated hierarchies together with a mapping between them. While the hypotheses are labeled with a score level of their current evidence, the experiments are labeled with a score level of maximum evidence that can be achieved during conduction. Second, we provide an iterative process to determine experiments that offer the most evidence improvement to the modeled hypotheses. We illustrate the usefulness of the approach with an example of testing the business model of a mobile application.}}, author = {{Gottschalk, Sebastian and Yigitbas, Enes and Engels, Gregor}}, booktitle = {{Business Modeling and Software Design}}, editor = {{Shishkov, Boris}}, keywords = {{Hypothesis Engineering, Model-based, Customer Need Adaptation, Business Model, Product Features}}, location = {{Potsdam}}, pages = {{276--286}}, publisher = {{Springer International Publishing}}, title = {{{Model-based Hypothesis Engineering for Supporting Adaptation to Uncertain Customer Needs}}}, doi = {{10.1007/978-3-030-52306-0_18}}, volume = {{391}}, year = {{2020}}, } @inproceedings{15211, author = {{Szopinski, Daniel and Schoormann, Thorsten and Kundisch, Dennis}}, booktitle = {{Tagungsband der 15. Internationalen Tagung Wirtschaftsinformatik 2020 (WI)}}, location = {{Potsdam, Germany}}, title = {{{Visualize different: Towards researching the fit between taxonomy visualizations and taxonomy tasks}}}, year = {{2020}}, } @inproceedings{13584, author = {{Szopinski, Daniel and Schoormann, Thorsten and Kundisch, Dennis}}, booktitle = {{Proceedings of the 53rd Hawaii International Conference on System Sciences (HICSS)}}, location = {{Maui, Hawaii}}, title = {{{Criteria as a prelude for guiding taxonomy evaluation}}}, year = {{2020}}, }