IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v13y2021i6p156-d576389.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/13/6/156/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/13/6/156/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. repec:cup:judgdm:v:5:y:2010:i:5:p:326-338 is not listed on IDEAS
    3. 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.
    4. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Jonathan J. Koehler & Molly Mercer, 2009. "Selection Neglect in Mutual Fund Advertisements," Management Science, INFORMS, vol. 55(7), pages 1107-1121, July.
    10. 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.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Peter Gordon Roetzel, 2019. "Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework developmen," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 479-522, December.
    2. Peng Cheng & Zhe Ouyang & Yang Liu, 0. "The effect of information overload on the intention of consumers to adopt electric vehicles," Transportation, Springer, vol. 0, pages 1-20.
    3. Jascha-Alexander Koch & Michael Siering, 2019. "The recipe of successful crowdfunding campaigns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(4), pages 661-679, December.
    4. Massimiliano Celli & Simona Arduini & Tommaso Beck, 2024. "Corporate Sustainability Reporting Directive (CSRD) and His Future Application Scenario for Italian SMEs," International Journal of Business and Management, Canadian Center of Science and Education, vol. 19(4), pages 1-44, July.
    5. Stocks, Morris H. & Harrell, Adrian, 1995. "The impact of an increase in accounting information level on the judgment quality of individuals and groups," Accounting, Organizations and Society, Elsevier, vol. 20(7-8), pages 685-700.
    6. Tianchang Ni & Runping Zhu & Richard Krever, 2023. "Responses to News Overload in a Non-Partisan Environment: News Avoidance in China," SAGE Open, , vol. 13(3), pages 21582440231, July.
    7. Tomi Rajala, 2019. "Mind the Information Expectation Gap," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(1), pages 104-125, March.
    8. Peng Cheng & Zhe Ouyang & Yang Liu, 2020. "The effect of information overload on the intention of consumers to adopt electric vehicles," Transportation, Springer, vol. 47(5), pages 2067-2086, October.
    9. Jansen, E. Pieter, 2002. "The use of performance information case studies in local social services departments," Research Report 02A19, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    10. Huan-Ming Chuang & Yi-Deng Liao, 2021. "Sustainability of the Benefits of Social Media on Socializing and Learning: An Empirical Case of Facebook," Sustainability, MDPI, vol. 13(12), pages 1-20, June.
    11. repec:dgr:rugsom:02a19 is not listed on IDEAS
    12. Adam Sanjurjo, 2015. "Search, Memory, and Choice Error: An Experiment," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-16, June.
    13. Tuttle, Brad & Burton, F. Greg, 1999. "The effects of a modest incentive on information overload in an investment analysis task," Accounting, Organizations and Society, Elsevier, vol. 24(8), pages 673-687, November.
    14. Maren Hartmann & Barbara E. Weißenberger, 2024. "Information overload research in accounting: a systematic review of the literature," Management Review Quarterly, Springer, vol. 74(3), pages 1619-1667, September.
    15. Kimball L. Chapman & Nayana Reiter & Hal D. White & Christopher D. Williams, 2019. "Information overload and disclosure smoothing," Review of Accounting Studies, Springer, vol. 24(4), pages 1486-1522, December.
    16. Joost Impink & Mari Paananen & Annelies Renders, 2022. "Regulation‐induced Disclosures: Evidence of Information Overload?," Abacus, Accounting Foundation, University of Sydney, vol. 58(3), pages 432-478, September.
    17. Jackson, Thomas W. & Farzaneh, Pourya, 2012. "Theory-based model of factors affecting information overload," International Journal of Information Management, Elsevier, vol. 32(6), pages 523-532.
    18. Bettis-Outland, Harriette, 2012. "Decision-making's impact on organizational learning and information overload," Journal of Business Research, Elsevier, vol. 65(6), pages 814-820.
    19. Rahman, Shaikh Moksadur, 2020. "Relationship between Job Satisfaction and Turnover Intention: Evidence from Bangladesh," Asian Business Review, Asian Business Consortium, vol. 10(2), pages 99-108.
    20. Wang Kai, 2019. "Towards a Taxonomy of Idea Generation Techniques," Foundations of Management, Sciendo, vol. 11(1), pages 65-80, January.
    21. Bridgelall, Raj & Stubbing, Edward, 2021. "Forecasting the effects of autonomous vehicles on land use," Technological Forecasting and Social Change, Elsevier, vol. 163(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:13:y:2021:i:6:p:156-:d:576389. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.