@inproceedings{17087, author = {{Berendes, Carsten Ingo and zur Heiden, Philipp and Niemann, Marco and Hoffmeister, Benedikt and Becker, Jörg}}, booktitle = {{Proceedings of the Twenty-Eighth European Conference on Information Systems (ECIS2020)}}, location = {{Virtual Conference}}, title = {{{Usage of Local Online Platforms in Retail: Insights from retailers' expectations expectations}}}, year = {{2020}}, } @article{17156, author = {{Beverungen, Daniel and Buijs, Joos C. A. M. and Becker, Jörg and Di Ciccio, Claudio and van der Aalst, Wil M. P. and Bartelheimer, Christian and vom Brocke, Jan and Comuzzi, Marco and Kraume, Karsten and Leopold, Henrik and Matzner, Martin and Mendling, Jan and Ogonek, Nadine and Post, Till and Resinas, Manuel and Revoredo, Kate and del-Río-Ortega, Adela and La Rosa, Marcello and Santoro, Flávia Maria and Solti, Andreas and Song, Minseok and Stein, Armin and Stierle, Matthias and Wolf, Verena}}, issn = {{2363-7005}}, journal = {{Business & Information Systems Engineering}}, title = {{{Seven Paradoxes of Business Process Management in a Hyper-Connected World}}}, doi = {{10.1007/s12599-020-00646-z}}, year = {{2020}}, } @inproceedings{16285, abstract = {{To decide in which part of town to open stores, high street retailers consult statistical data on customers and cities, but they cannot analyze their customers’ shopping behavior and geospatial features of a city due to missing data. While previous research has proposed recommendation systems and decision aids that address this type of decision problem – including factory location and assortment planning – there currently is no design knowledge available to prescribe the design of city center area recommendation systems (CCARS). We set out to design a software prototype considering local customers’ shopping interests and geospatial data on their shopping trips for retail site selection. With real data on 500 customers and 1,100 shopping trips, we demonstrate and evaluate our IT artifact. Our results illustrate how retailers and public town center managers can use CCARS for spatial location selection, growing retailers’ profits and a city center’s attractiveness for its citizens.}}, author = {{zur Heiden, Philipp and Berendes, Carsten Ingo and Beverungen, Daniel}}, booktitle = {{Proceedings of the 15th International Conference on Wirtschaftsinformatik}}, keywords = {{Town Center Management, High Street Retail, Recommender Systems, Geospatial Recommendations, Design Science Research}}, location = {{Potsdam}}, title = {{{Designing City Center Area Recommendation Systems }}}, doi = {{doi.org/10.30844/wi_2020_e1-heiden}}, year = {{2020}}, } @article{35723, abstract = {{The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale.}}, author = {{Hoffmann, Martin W. and Wildermuth, Stephan and Gitzel, Ralf and Boyaci, Aydin and Gebhardt, Jörg and Kaul, Holger and Amihai, Ido and Forg, Bodo and Suriyah, Michael and Leibfried, Thomas and Stich, Volker and Hicking, Jan and Bremer, Martin and Kaminski, Lars and Beverungen, Daniel and zur Heiden, Philipp and Tornede, Tanja}}, issn = {{1424-8220}}, journal = {{Sensors}}, keywords = {{Electrical and Electronic Engineering, Biochemistry, Instrumentation, Atomic and Molecular Physics, and Optics, Analytical Chemistry}}, number = {{7}}, publisher = {{MDPI AG}}, title = {{{Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions}}}, doi = {{10.3390/s20072099}}, volume = {{20}}, year = {{2020}}, } @inproceedings{4517, author = {{Wolf, Verena and Bartelheimer, Christian and Beverungen, Daniel}}, booktitle = {{Proceedings of the 52nd Hawaii International Conference on System Sciences (HICSS-52)}}, location = {{Maui, Hawaii}}, title = {{{Digitalization of Work Systems—An Organizational Routines’ Perspective}}}, year = {{2019}}, } @inproceedings{9617, author = {{Betzing, Jan H. and Bartelheimer, Christian and Niemann, Marco and Berendes, Carsten Ingo and Beverungen, Daniel}}, booktitle = {{Proceedings of the 27th European Conference on Information Systems (ECIS)}}, location = {{Stockholm}}, title = {{{Quantifying the Impact of Geospatial Recommendations: A Field Experiment in High Street Retail}}}, year = {{2019}}, } @inproceedings{9708, abstract = {{Increased interconnectedness of multiple actors and digital resources in service eco-systems offer new opportunities for service innovation. In digitally transforming eco-systems, organizations need to explore and exploit innovation simultaneously, which is defined as ambidexterity. However, research on ambidextrous service innovation is scarce. We provide a systematic literature review based on the concepts of ambidexterity, offering two contributions. First, research strands are disconnected, emphasizing either exploration or exploitation of service innovation, despite an organizations’ need to accelerate innovation cycles of exploring and exploiting services. Second, a new framework for ambidextrous service innovation is provided, inspired by the dynamism and generative mechanisms of the ontologically related concept of organizational routines. The framework adopts the perspective of a mutually constitutive relationship between exploring new and exploiting current resources, activities, and knowledge. The findings remedy the scattered literature through a coherent perspective on service innovation that responds to organizations’ needs and guides future research.}}, author = {{Wolf, Verena}}, booktitle = {{Proceedings of the 14th International Conference on Wirtschaftsinformatik}}, keywords = {{Exploration, Exploitation, Service Innovation, Organizational Routines, Ambidexterity}}, location = {{Siegen, Germany}}, title = {{{Ambidexterity in Service Innovation Research: A Systematic Literature Review}}}, year = {{2019}}, } @article{12929, author = {{Bräuer, Sebastian and Plenter, Florian and Klör, Benjamin and Monhof, Markus and Beverungen, Daniel and Becker, Jörg}}, issn = {{2198-3402}}, journal = {{Business Research}}, title = {{{Transactions for trading used electric vehicle batteries: theoretical underpinning and information systems design principles}}}, doi = {{10.1007/s40685-019-0091-9}}, year = {{2019}}, } @article{14023, author = {{Beverungen, Daniel and Breidbach, Christoph F. and Poeppelbuss, Jens and Tuunainen, Virpi Kristiina}}, issn = {{1350-1917}}, journal = {{Information Systems Journal}}, title = {{{Smart service systems: An interdisciplinary perspective}}}, doi = {{10.1111/isj.12275}}, year = {{2019}}, } @inproceedings{2861, abstract = {{The Digital Transformation alters business models in all fields of application, but not all industries transform at the same speed. While recent innovations in smart products, big data, and machine learn-ing have profoundly transformed business models in the high-tech sector, less digitalized industries—like agriculture—have only begun to capitalize on these technologies. Inspired by predictive mainte-nance strategies for industrial equipment, the purpose of this paper is to design, implement, and evaluate a predictive maintenance method for agricultural machines that predicts future defects of a machine’s components, based on a data-driven analysis of service records. An evaluation with 3,407 real-world service records proves that the method predicts damaged parts with a mean accuracy of 86.34%. The artifact is an exaptation of previous design knowledge from high-tech industries to agriculture—a sector in which machines move through rough territory and adverse weather conditions, are utilized exten-sively for short periods, and do not provide sensor data to service providers. Deployed on a platform, the prediction method enables co-creating a predictive maintenance service that helps farmers to avoid resources shortages during harvest seasons, while service providers can plan and conduct maintenance service preemptively and with increased efficiency. }}, author = {{Lüttenberg, Hedda and Bartelheimer, Christian and Beverungen, Daniel}}, location = {{Portsmouth, UK}}, title = {{{Designing Predictive Maintenance for Agricultural Machines}}}, year = {{2018}}, }