@inproceedings{29147,
  author       = {{Herwix, Alexander and zur Heiden, Philipp}},
  booktitle    = {{Proceedings of the 55th Hawaii International Conference on System Sciences}},
  editor       = {{Bui, Tung X.}},
  isbn         = {{978-0-9981331-5-7}},
  location     = {{Honolulu, HI}},
  title        = {{{Context in Design Science Research: Taxonomy and Framework}}},
  year         = {{2022}},
}

@inproceedings{29148,
  author       = {{zur Heiden, Philipp and Beverungen, Daniel}},
  booktitle    = {{Proceedings of the 55th Hawaii International Conference on System Sciences}},
  editor       = {{Bui, Tung X.}},
  isbn         = {{978-0-9981331-5-7}},
  location     = {{Honolulu, HI}},
  title        = {{{A Renaissance of Context in Design Science Research}}},
  year         = {{2022}},
}

@misc{30737,
  author       = {{Schulz, Michael and Neuhaus, Uwe and Kaufmann, Jens and Kühnel, Stephan and Alekozai, Emal M. and Rohde, Heiko and Hoseini, Sayed and Theuerkauf, René and Badura, Daniel and Kerzel, Ulrich and Lanquillon, Carsten and Daurer, Stephan and Günther, Maik and Huber, Lukas and Thiée, Lukas-Walter and zur Heiden, Philipp and Passlick, Jens and Dieckmann, Jonas and Schwade, Florian and Seyffarth, Tobias and Badewitz, Wolfgang and Rissler, Raphael and Sackmann, Stefan and Gölzer, Philipp and Welter, Felix and Röth, Jochen and Seidelmann, Julian and Haneke, Uwe}},
  publisher    = {{NORDAKADEMIE gAG Hochschule der Wirtschaft}},
  title        = {{{DASC-PM v1.1 - Ein Vorgehensmodell für Data-Science-Projekte}}},
  year         = {{2022}},
}

@inbook{32363,
  author       = {{zur Heiden, Philipp and Priefer, Jennifer and Beverungen, Daniel}},
  booktitle    = {{Forum Dienstleistungsmanagement}},
  editor       = {{Bruhn, Manfred and Hadwich, Karsten}},
  isbn         = {{9783658373436}},
  issn         = {{2662-3382}},
  pages        = {{435--457}},
  publisher    = {{Springer Fachmedien Wiesbaden}},
  title        = {{{Smart Service für die prädiktive Instandhaltung zentraler Komponenten des Mittelspannungs-Netzes}}},
  doi          = {{10.1007/978-3-658-37344-3_14}},
  year         = {{2022}},
}

@article{35732,
  abstract     = {{While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.}},
  author       = {{Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Platform, Text mining, Machine learning, Data communications, Interpretive research, Systems design and implementation}},
  pages        = {{375--396}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Systematizing the lexicon of platforms in information systems: a data-driven study}}},
  doi          = {{10.1007/s12525-022-00530-6}},
  volume       = {{32}},
  year         = {{2022}},
}

@article{30735,
  abstract     = {{While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.}},
  author       = {{Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Management of Technology and Innovation, Marketing, Computer Science Applications, Economics and Econometrics, Business and International Management}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Systematizing the lexicon of platforms in information systems: a data-driven study}}},
  doi          = {{10.1007/s12525-022-00530-6}},
  year         = {{2022}},
}

@inproceedings{24534,
  author       = {{zur Heiden, Philipp and Priefer, Jennifer}},
  booktitle    = {{Pre-Conference 16th International Congress on Wirtschaftsinformatik at Universität Duisburg-Essen}},
  editor       = {{Breitner, Michael H. and Lehnhoff, Sebastian and Nieße, Astrid and Staudt, Philipp and Weinhardt, Christof and Werth, Oliver}},
  location     = {{Universität Duisburg-Essen}},
  publisher    = {{BIS-Verlag der Carl von Ossietzky Universität Oldenburg}},
  title        = {{{Transitioning to Condition-Based Maintenance on the Distribution Grid: Deriving Design Principles from a Qualitative Study}}},
  year         = {{2021}},
}

@inproceedings{21263,
  author       = {{zur Heiden, Philipp and Winter, Daniel}},
  booktitle    = {{Proceedings of the 16th International Conference on Wirtschaftsinformatik}},
  title        = {{{Discovering Geographical Patterns of Retailers' Locations for Successful Retail in City Centers}}},
  year         = {{2021}},
}

@article{17426,
  abstract     = {{<jats:p>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.</jats:p>}},
  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}},
  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}},
  year         = {{2020}},
}

@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}},
}

@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     = {{<jats:p>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.</jats:p>}},
  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}},
}