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Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems

Author

Listed:
  • Romy Müller

    (Faculty of Psychology, Chair of Engineering Psychology and Applied Cognitive Research, Technische Universität Dresden, 01069 Dresden, Germany)

  • Franziska Kessler

    (Faculty of Psychology, Chair of Learning and Instruction, Technische Universität Dresden, 01069 Dresden, Germany)

  • David W. Humphrey

    (ARC Advisory Group, 80999 Munich, Germany)

  • Julian Rahm

    (Faculty of Electrical and Computer Engineering, Chair of Process Control Systems & Process Systems Engineering Group, Technische Universität Dresden, 01069 Dresden, Germany)

Abstract

In traditional production plants, current technologies do not provide sufficient context to support information integration and interpretation. Digital transformation technologies have the potential to support contextualization, but it is unclear how this can be achieved. The present article presents a selection of the psychological literature in four areas relevant to contextualization: information sampling, information integration, categorization, and causal reasoning. Characteristic biases and limitations of human information processing are discussed. Based on this literature, we derive functional requirements for digital transformation technologies, focusing on the cognitive activities they should support. We then present a selection of technologies that have the potential to foster contextualization. These technologies enable the modelling of system relations, the integration of data from different sources, and the connection of the present situation with historical data. We illustrate how these technologies can support contextual reasoning, and highlight challenges that should be addressed when designing human–machine cooperation in cyber-physical production systems.

Suggested Citation

  • Romy Müller & Franziska Kessler & David W. Humphrey & Julian Rahm, 2021. "Data in Context: How Digital Transformation Can Support Human Reasoning in Cyber-Physical Production Systems," Future Internet, MDPI, vol. 13(6), pages 1-36, June.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:6:p:156-:d:576389
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    References listed on IDEAS

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    1. repec:cup:judgdm:v:5:y:2010:i:5:p:326-338 is not listed on IDEAS
    2. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    3. Luís Miguel Oliveira Machado & Renato Rocha Souza & Maria da Graça Simões, 2019. "Semantic web or web of data? a diachronic study (1999 to 2017) of the publications of tim berners‐lee and the world wide web consortium," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(7), pages 701-714, July.
    4. Schick, Allen G. & Gordon, Lawrence A. & Haka, Susan, 1990. "Information overload: A temporal approach," Accounting, Organizations and Society, Elsevier, vol. 15(3), pages 199-220.
    5. Keller, Kevin Lane & Staelin, Richard, 1987. "Effects of Quality and Quantity of Information on Decision Effectiveness," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(2), pages 200-213, September.
    6. José F. Arocha & Vimla L. Patel & Yogesh C. Patel, 1993. "Hypothesis Generation and the Coordination of Theory and Evidence in Novice Diagnostic Reasoning," Medical Decision Making, , vol. 13(3), pages 198-211, August.
    7. Markus Graube & Johannes Pfeffer & Jens Ziegler & Leon Urbas, 2012. "Linked Data as Integrating Technology for Industrial Data," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 3(3), pages 40-52, July.
    8. Adam N. Glynn & Konstantin Kashin, 2018. "Front-Door Versus Back-Door Adjustment With Unmeasured Confounding: Bias Formulas for Front-Door and Hybrid Adjustments With Application to a Job Training Program," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1040-1049, July.
    9. Chewning, Eugene Jr & Harrell, Adrian M., 1990. "The effect of information load on decision makers' cue utilization levels and decision quality in a financial distress decision task," Accounting, Organizations and Society, Elsevier, vol. 15(6), pages 527-542.
    10. Daniel J. Walters & Philip M. Fernbach & Craig R. Fox & Steven A. Sloman, 2017. "Known Unknowns: A Critical Determinant of Confidence and Calibration," Management Science, INFORMS, vol. 63(12), pages 4298-4307, December.
    11. Andreas Glöckner & Tilmann Betsch, 2008. "Multiple-Reason Decision Making Based on Automatic Processing," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2008_12, Max Planck Institute for Research on Collective Goods.
    12. Jonathan J. Koehler & Molly Mercer, 2009. "Selection Neglect in Mutual Fund Advertisements," Management Science, INFORMS, vol. 55(7), pages 1107-1121, July.
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