@article{54186,
  author       = {{Santos-Arteaga, Francisco J. and Di Caprio, Debora and Tavana, Madjid}},
  issn         = {{1868-7865}},
  journal      = {{Journal of the Knowledge Economy}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Information and Communication Technologies and Labor Productivity: A Dynamic Slacks-Based Data Envelopment Analysis}}},
  doi          = {{10.1007/s13132-023-01634-w}},
  year         = {{2023}},
}

@article{34197,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Comprehensive data understanding is a key success driver for data analytics projects. Knowing the characteristics of the data helps a lot in selecting the appropriate data analysis techniques. Especially in data-driven product planning, knowledge about the data is a necessary prerequisite because data of the use phase is very heterogeneous. However, companies often do not have the necessary know-how or time to build up solid data understanding in connection with data analysis. In this paper, we develop a methodology to organize and categorize and thus understand use phase data in a way that makes it accessible to general data analytics workflows, following a design science research approach. We first present a knowledge base that lists typical use phase data from a product planning view. Second, we develop a taxonomy based on standard literature and real data objects, which covers the diversity of the data considered. The taxonomy provides 8 dimensions that support classification of use phase data and allows to capture data characteristics from a data analytics view. Finally, we combine both views by clustering the objects of the knowledge base according to the taxonomy. Each of the resulting clusters covers a typical combination of analytics relevant characteristics occurring in practice. By abstracting from the diversity of use phase data into artifacts with manageable complexity, our approach provides guidance to choose appropriate data analysis and AI techniques.</jats:p>}},
  author       = {{Panzner, Melina and von Enzberg, Sebastian and Meyer, Maurice and Dumitrescu, Roman}},
  issn         = {{1868-7865}},
  journal      = {{Journal of the Knowledge Economy}},
  keywords     = {{Economics and Econometrics}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Characterization of Usage Data with the Help of Data Classifications}}},
  doi          = {{10.1007/s13132-022-01081-z}},
  year         = {{2022}},
}

@article{33714,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Industry 4.0 promises many potentials in production. Examples are a data-driven optimization of production processes of individual machines, driverless transport systems, and assistance systems. Nevertheless, companies are still hesitant to invest in Industry 4.0 applications. Studies show that one of the main reasons for that is the unclear economic benefit. In this work, we present a systematic approach for the evaluation of Industry 4.0 applications in production. The main goal of the systematic is to create transparency over the evaluation process of an investment in an Industry 4.0 application in production. The evaluation of a concrete technical solution in an existing production system is supported. As a theoretical foundation, a characterization of investments in Industry 4.0 applications is given. From that, a procedure model is derived. It puts the activities to be carried out, the tools to be used and results in a temporal context. The application of the systematic is shown on the basis of an application example.</jats:p>}},
  author       = {{Joppen, Robert and Kühn, Arno and Förster, Magdalena and Dumitrescu, Roman}},
  issn         = {{1868-7865}},
  journal      = {{Journal of the Knowledge Economy}},
  keywords     = {{Economics and Econometrics}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Evaluation of Industry 4.0 Applications in Production}}},
  doi          = {{10.1007/s13132-022-00959-2}},
  year         = {{2022}},
}

@article{33953,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Comprehensive data understanding is a key success driver for data analytics projects. Knowing the characteristics of the data helps a lot in selecting the appropriate data analysis techniques. Especially in data-driven product planning, knowledge about the data is a necessary prerequisite because data of the use phase is very heterogeneous. However, companies often do not have the necessary know-how or time to build up solid data understanding in connection with data analysis. In this paper, we develop a methodology to organize and categorize and thus understand use phase data in a way that makes it accessible to general data analytics workflows, following a design science research approach. We first present a knowledge base that lists typical use phase data from a product planning view. Second, we develop a taxonomy based on standard literature and real data objects, which covers the diversity of the data considered. The taxonomy provides 8 dimensions that support classification of use phase data and allows to capture data characteristics from a data analytics view. Finally, we combine both views by clustering the objects of the knowledge base according to the taxonomy. Each of the resulting clusters covers a typical combination of analytics relevant characteristics occurring in practice. By abstracting from the diversity of use phase data into artifacts with manageable complexity, our approach provides guidance to choose appropriate data analysis and AI techniques.</jats:p>}},
  author       = {{Panzner, Melina and von Enzberg, Sebastian and Meyer, Maurice and Dumitrescu, Roman}},
  issn         = {{1868-7865}},
  journal      = {{Journal of the Knowledge Economy}},
  keywords     = {{Economics and Econometrics}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Characterization of Usage Data with the Help of Data Classifications}}},
  doi          = {{10.1007/s13132-022-01081-z}},
  year         = {{2022}},
}