@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}},
}

@misc{1154,
  author       = {{Geierhos, Michaela}},
  booktitle    = {{Enzyklopädie der Wirtschaftsinformatik}},
  editor       = {{Gronau, Norbert and Becker, Jörg and Sinz, Elmar and Suhl, Leena and Leimeister, Jan M.}},
  keywords     = {{Text Mining}},
  publisher    = {{GITO-Verlag}},
  title        = {{{Text Mining}}},
  year         = {{2016}},
}

@article{4690,
  author       = {{Gorbacheva, Elena and Stein, Armin and Schmiedel, Theresa and Müller, Oliver}},
  issn         = {{18670202}},
  journal      = {{Business and Information Systems Engineering}},
  keywords     = {{BPM workforce, Business process management, Competences, Gender diversity, Latent semantic analysis, Skills, Text mining}},
  number       = {{3}},
  pages        = {{213----231}},
  title        = {{{The Role of Gender in Business Process Management Competence Supply}}},
  doi          = {{10.1007/s12599-016-0428-2}},
  year         = {{2016}},
}

@article{4691,
  abstract     = {{Analysts have estimated that more than 80 percent of today’s data is stored in unstructured form (e.g., text, audio, image, video)—much of it expressed in rich and ambiguous natural language. Traditionally, to analyze natural language, one has used qualitative data-analysis approaches, such as manual coding. Yet, the size of text data sets obtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challenges encountered when applying automated text-mining techniques in information systems research. In particular, we showcase how to use probabilistic topic modeling via Latent Dirichlet allocation, an unsupervised text-mining technique, with a LASSO multinomial logistic regression to explain user satisfaction with an IT artifact by automatically analyzing more than 12,000 online customer reviews. For fellow information systems researchers, this tutorial provides guidance for conducting text-mining studies on their own and for evaluating the quality of others.}},
  author       = {{Debortoli, Stefan and Müller, Oliver and Junglas, Iris and vom Brocke, Jan}},
  isbn         = {{9781615679119}},
  issn         = {{1529-3181}},
  journal      = {{Communications of the Association for Information Systems}},
  keywords     = {{Latent dirichlet allocation, Online customer reviews, Text mining, Topic modeling, User satisfaction}},
  pages        = {{555--582}},
  title        = {{{Text Mining for Information Systems Researchers: An Annotated Tutorial}}},
  doi          = {{10.17705/1CAIS.03907}},
  year         = {{2016}},
}

@article{4695,
  author       = {{Debortoli, Stefan and Müller, Oliver and vom Brocke, Jan}},
  isbn         = {{0910-8327 (Print)$\backslash$n0910-8327 (Linking)}},
  issn         = {{18670202}},
  journal      = {{Business and Information Systems Engineering}},
  keywords     = {{Big data, Business intelligence, Competencies, Latent semantic analysis, Text mining}},
  number       = {{5}},
  pages        = {{289----300}},
  title        = {{{Comparing business intelligence and big data skills: A text mining study using job advertisements}}},
  doi          = {{10.1007/s12599-014-0344-2}},
  year         = {{2014}},
}