IDEAS home Printed from https://ideas.repec.org/b/zbw/hbsedi/429.html
   My bibliography  Save this book

Ratings als arbeitspolitisches Konfliktfeld: Das Beispiel Zalando

Author

Listed:
  • Staab, Philipp
  • Geschke, Sascha-Christopher

Abstract

Algorithmische Rating- und Scoringverfahren befinden sich in der Arbeitswelt auf dem Vormarsch. Anhand einer Fallstudie eines besonders ambitionierten Systems beleuchtet die Studie die mit umfassenden betrieblichen Rating- und Scoring-Systemen verbundenen arbeitspolitischen Konfliktfelder: Verschärfte Kontrolle und Konkurrenz innerhalb der Belegschaft schaden dem Betriebsklima; technologische Intransparenz wird zur Legitimierung betrieblicher Ungleichheit eingesetzt; Legalitätsfragen insbesondere im Bereich des Datenschutzes bleiben dabei ungeklärt.

Suggested Citation

  • Staab, Philipp & Geschke, Sascha-Christopher, 2020. "Ratings als arbeitspolitisches Konfliktfeld: Das Beispiel Zalando," Study / edition der Hans-Böckler-Stiftung, Hans-Böckler-Stiftung, Düsseldorf, volume 127, number 429, June.
  • Handle: RePEc:zbw:hbsedi:429
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/225048/1/1735457361.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Logg, Jennifer M. & Minson, Julia A. & Moore, Don A., 2019. "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, Elsevier, vol. 151(C), pages 90-103.
    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. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    2. Daniel Woods & Mustafa Abdallah & Saurabh Bagchi & Shreyas Sundaram & Timothy Cason, 2022. "Network defense and behavioral biases: an experimental study," Experimental Economics, Springer;Economic Science Association, vol. 25(1), pages 254-286, February.
    3. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.
    4. Bryce McLaughlin & Jann Spiess, 2022. "Algorithmic Assistance with Recommendation-Dependent Preferences," Papers 2208.07626, arXiv.org, revised Jan 2024.
    5. Markus Jung & Mischa Seiter, 2021. "Towards a better understanding on mitigating algorithm aversion in forecasting: an experimental study," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 32(4), pages 495-516, December.
    6. Gómez de Ágreda, Ángel, 2020. "Ethics of autonomous weapons systems and its applicability to any AI systems," Telecommunications Policy, Elsevier, vol. 44(6).
    7. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl & Joshua Gawlitza, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Information Systems Research, INFORMS, vol. 32(3), pages 713-735, September.
    8. Shiri Melumad & Rhonda Hadi & Christian Hildebrand & Adrian F. Ward, 2020. "Technology-Augmented Choice: How Digital Innovations Are Transforming Consumer Decision Processes," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 7(3), pages 90-101, October.
    9. repec:cup:judgdm:v:15:y:2020:i:3:p:449-451 is not listed on IDEAS
    10. Jean-Pierre Benoît & Juan Dubra & Giorgia Romagnoli, 2022. "Belief Elicitation When More than Money Matters: Controlling for "Control"," American Economic Journal: Microeconomics, American Economic Association, vol. 14(3), pages 837-888, August.
    11. Chiara Longoni & Andrea Bonezzi & Carey K. Morewedge, 2020. "Resistance to medical artificial intelligence is an attribute in a compensatory decision process: response to Pezzo and Becksted (2020)," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 15(3), pages 446-448, May.
    12. Chaohui Xu & Xingtong Chen & Wei Dai, 2022. "Effects of Digital Transformation on Environmental Governance of Mining Enterprises: Evidence from China," IJERPH, MDPI, vol. 19(24), pages 1-20, December.
    13. Chen Yang & Jing Hu, 2022. "When do consumers prefer AI-enabled customer service? The interaction effect of brand personality and service provision type on brand attitudes and purchase intentions," Journal of Brand Management, Palgrave Macmillan, vol. 29(2), pages 167-189, March.
    14. Manav Raj & Justin Berg & Rob Seamans, 2023. "Art-ificial Intelligence: The Effect of AI Disclosure on Evaluations of Creative Content," Papers 2303.06217, arXiv.org.
    15. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    16. Noemi Festic, 2022. "Same, same, but different! Qualitative evidence on how algorithmic selection applications govern different life domains," Regulation & Governance, John Wiley & Sons, vol. 16(1), pages 85-101, January.
    17. Zhang, Lixuan & Yencha, Christopher, 2022. "Examining perceptions towards hiring algorithms," Technology in Society, Elsevier, vol. 68(C).
    18. Hemesath, Sebastian & Tepe, Markus, 2023. "Framing the approval to test self-driving cars on public roads. The effect of safety and competitiveness on citizens' agreement," Technology in Society, Elsevier, vol. 72(C).
    19. Yoan Hermstrüwer & Pascal Langenbach, 2022. "Fair Governance with Humans and Machines," Discussion Paper Series of the Max Planck Institute for Research on Collective Goods 2022_04, Max Planck Institute for Research on Collective Goods, revised 01 Mar 2023.
    20. Sarah D. English & Stephanie Denison & Ori Friedman, 2022. "Expectations of how machines use individuating information and base-rates," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 17(3), pages 628-645, May.
    21. repec:cup:judgdm:v:17:y:2022:i:3:p:628-645 is not listed on IDEAS
    22. Mesbah, Neda & Tauchert, Christoph & Buxmann, Peter, 2021. "Whose Advice Counts More – Man or Machine? An Experimental Investigation of AI-based Advice Utilization," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124796, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).

    More about this item

    Statistics

    Access and download statistics

    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:zbw:hbsedi:429. 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: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/boeckde.html .

    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.