@article{37147,
  author       = {{Mirbabaie, Milad and Brendel, Alfred B. and Hofeditz, Lennart}},
  issn         = {{1529-3181}},
  journal      = {{Communications of the Association for Information Systems}},
  keywords     = {{Information Systems}},
  number       = {{1}},
  pages        = {{726--753}},
  publisher    = {{Association for Information Systems}},
  title        = {{{Ethics and AI in Information Systems Research}}},
  doi          = {{10.17705/1cais.05034}},
  volume       = {{50}},
  year         = {{2022}},
}

@article{5633,
  abstract     = {{Literature reviews (LRs) are recognized for their increasing impact in the information systems literature. Methodologists have drawn attention to the question of how we can leverage the value of LRs to preserve and generate knowledge. The panelists who participated in the discussion of ?Standalone Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?? at ICIS 2016 in Dublin acknowledged this significant issue and debated a) what the IS field can learn from other fields and where IS-specific challenges occur, b) how the IS field should move forward to foster the genre of LRs, and c) what best practices are to train doctoral IS students in publishing LRs. This article reports the key takeaways of this panel discussion. Guidance for IS scholars is provided on how to conduct LRs that contribute to the cumulative knowledge development within and across the IS field to best prepare the next generation of IS scholars.}},
  author       = {{Schryen, Guido and Benlian, Alexander and Rowe, Frantz and Shirley, Gregor and Larsen, Kai and Petter, Stacie and Par{\'e}, Guy and Wagner, Gerit and Haag, Steffi and Yasasin, Emrah}},
  issn         = {{1529-3181}},
  journal      = {{Communications of the AIS}},
  keywords     = {{Literature Review, Review Methodology, Research Methodology, Doctoral Training}},
  pages        = {{557 -- 569}},
  publisher    = {{Association for Information Systems (AIS)}},
  title        = {{{Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?}}},
  volume       = {{40}},
  year         = {{2017}},
}

@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{17118,
  author       = {{vom Brocke, Jan and Debortoli, Stefan and Müller, Oliver and Reuter, Nadine}},
  issn         = {{1529-3181}},
  journal      = {{Communications of the Association for Information Systems}},
  number       = {{1}},
  pages        = {{7}},
  title        = {{{How In-memory Technology Can Create Business Value: Insights from the Hilti Case}}},
  doi          = {{10.17705/1cais.03407}},
  volume       = {{34}},
  year         = {{2014}},
}