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Theories in Business and Information Systems Engineering

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Notes

  1. In this context, it is worth mentioning that although data analytics may be able to predict what will happen in a specific context, similar to a theory, it is still theory-free, because it is generally not able to explain why it happens. Without theory, however, it must remain unknown whether these predictions can be generalized and to what extent they are robust to other application scenarios. Therefore, data analytics differs from the traditional paradigm of empirical analysis, which centers around the falsification or validation of hypotheses, which again requires a theory (although not necessarily in the same sense as proposed here – see, e.g., Diesing (2008) for a more elaborate discussion of the relationship between empirical and formal theory) from which these hypotheses are derived in the first place.

  2. An instrument is among others (1) a means whereby something is achieved, performed, or furthered; (2) one used by another as a means or aid or tool (Safra et al. 2003).

  3. The title of the book (Chadarevian and Hopwood 2004) has inspired this observation.

  4. White (1994) distinguishes two different meanings of the word ‘concept’: (1) Concepts are general categories and thing of interest that are used for classification. Concepts thus have fuzzy boundaries. Additionally, classification depends on the context and deployment. (2) Concepts are all the knowledge that the person has, and associates with, the concept’s name. They are reasonable complete in terms of the business. Murphy (2001) and Thalheim (2007) define concepts in a more sophisticated form. According to White (1994), conceptions are systems of explanation.

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Authors and Affiliations

  1. Department of Informatics, Technical University of Munich, Boltzmannstr. 3, 85748, Garching/Munich, Germany

    Martin Bichler

  2. Chair of Information Systems and Enterprise Modelling, Institute for Computer Science and Business Information Systems, University of Duisburg-Essen, Universitaetsstr. 9, 45141, Essen, Germany

    Ulrich Frank

  3. Department of Information Systems, Decision Sciences and Statistics, ESSEC Business School, Avenue Bernard Hirsch, B.P. 50105, 95021, Cergy Pontoise Cedex, France

    David Avison &  Julien Malaurent

  4. German Research Center for Artificial Intelligence (DFKI GmbH) and Saarland University, Campus D3 2, Stuhlsatzenhausweg 3, 66123, Saarbrücken, Germany

    Peter Fettke

  5. Business Information Systems, The University of Sydney Business School, The University of Sydney, Sydney, NSW, 2006, Australia

    Dirk Hovorka

  6. Chair of Internet and Telecommunications Business, Faculty of Business Administration and Economics, University of Passau, Dr.-Hans-Kapfinger-Str. 12, 94032, Passau, Germany

    Jan Krämer & Daniel Schnurr

  7. Innovation Management and Strategy, Faculty of Economics and Business, University of Groningen, Nettelbosje 2, 9747 AE, Groningen, The Netherlands

    Benjamin Müller

  8. Decision Support and Operations Research Lab, Department Business Information Systems, Universität Paderborn, Warburger Str. 100, 33098, Paderborn, Germany

    Leena Suhl

  9. Information Systems Engineering (ISE), Institut für Informatik, Christian-Albrechts-Universität zu Kiel, Olshausenstr. 40, 24098, Kiel, Germany

    Bernhard Thalheim

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  11. Bernhard Thalheim
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Correspondence to Martin Bichler.

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Bichler, M., Frank, U., Avison, D. et al. Theories in Business and Information Systems Engineering. Bus Inf Syst Eng 58, 291–319 (2016). https://doi.org/10.1007/s12599-016-0439-z

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