IDEAS home Printed from https://ideas.repec.org/a/nat/nathum/v7y2023i11d10.1038_s41562-023-01705-7.html
   My bibliography  Save this article

The positive–negative–competence (PNC) model of psychological responses to representations of robots

Author

Listed:
  • Dario Krpan

    (London School of Economics and Political Science)

  • Jonathan E. Booth

    (London School of Economics and Political Science)

  • Andreea Damien

    (London School of Economics and Political Science)

Abstract

Robots are becoming an increasingly prominent part of society. Despite their growing importance, there exists no overarching model that synthesizes people’s psychological reactions to robots and identifies what factors shape them. To address this, we created a taxonomy of affective, cognitive and behavioural processes in response to a comprehensive stimulus sample depicting robots from 28 domains of human activity (for example, education, hospitality and industry) and examined its individual difference predictors. Across seven studies that tested 9,274 UK and US participants recruited via online panels, we used a data-driven approach combining qualitative and quantitative techniques to develop the positive–negative–competence model, which categorizes all psychological processes in response to the stimulus sample into three dimensions: positive, negative and competence-related. We also established the main individual difference predictors of these dimensions and examined the mechanisms for each predictor. Overall, this research provides an in-depth understanding of psychological functioning regarding representations of robots.

Suggested Citation

  • Dario Krpan & Jonathan E. Booth & Andreea Damien, 2023. "The positive–negative–competence (PNC) model of psychological responses to representations of robots," Nature Human Behaviour, Nature, vol. 7(11), pages 1933-1954, November.
    • Handle: RePEc:nat:nathum:v:7:y:2023:i:11:d:10.1038_s41562-023-01705-7
    DOI: 10.1038/s41562-023-01705-7
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41562-023-01705-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/s41562-023-01705-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Struyf, Anja & Hubert, Mia & Rousseeuw, Peter J., 1997. "Integrating robust clustering techniques in S-PLUS," Computational Statistics & Data Analysis, Elsevier, vol. 26(1), pages 17-37, November.
    2. Savela, Nina & Oksanen, Atte & Pellert, Max & Garcia, David, 2021. "Emotional reactions to robot colleagues in a role-playing experiment," International Journal of Information Management, Elsevier, vol. 60(C).
    3. Armin Granulo & Christoph Fuchs & Stefano Puntoni, 2019. "Psychological reactions to human versus robotic job replacement," Nature Human Behaviour, Nature, vol. 3(10), pages 1062-1069, October.
    4. Mojtaba Vaismoradi & Hannele Turunen & Terese Bondas, 2013. "Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study," Nursing & Health Sciences, John Wiley & Sons, vol. 15(3), pages 398-405, September.
    5. Zdeněk Šulc & Hana Řezanková, 2019. "Comparison of Similarity Measures for Categorical Data in Hierarchical Clustering," Journal of Classification, Springer;The Classification Society, vol. 36(1), pages 58-72, April.
    6. John Horn, 1965. "A rationale and test for the number of factors in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 30(2), pages 179-185, June.
    7. Schweinsberg, Martin & Feldman, Michael & Staub, Nicola & van den Akker, Olmo R. & van Aert, Robbie C.M. & van Assen, Marcel A.L.M. & Liu, Yang & Althoff, Tim & Heer, Jeffrey & Kale, Alex & Mohamed, Z, 2021. "Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 165(C), pages 228-249.
    8. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    9. Hennink, Monique & Kaiser, Bonnie N., 2022. "Sample sizes for saturation in qualitative research: A systematic review of empirical tests," Social Science & Medicine, Elsevier, vol. 292(C).
    10. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    11. Lang, Frieder R. & John, Dennis & Lüdtke, Oliver & Schupp, Jürgen & Wagner, Gert G., 2011. "Short Assessment of the Big Five: Robust Across Survey Methods Except Telephone Interviewing," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 43(2), pages 548-567.
    12. Markus Blut & Cheng Wang & Nancy V. Wünderlich & Christian Brock, 2021. "Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 632-658, July.
    13. Joseph Henrich & Steven J. Heine & Ara Norenzayan, 2010. "Most people are not WEIRD," Nature, Nature, vol. 466(7302), pages 29-29, July.
    14. Henry Kaiser, 1958. "The varimax criterion for analytic rotation in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 23(3), pages 187-200, September.
    15. Brondi, Sonia & Pivetti, Monica & Di Battista, Silvia & Sarrica, Mauro, 2021. "What do we expect from robots? Social representations, attitudes and evaluations of robots in daily life," Technology in Society, Elsevier, vol. 66(C).
    16. Dorothy Bishop, 2019. "Rein in the four horsemen of irreproducibility," Nature, Nature, vol. 568(7753), pages 435-435, April.
    17. Greg Guest & Emily Namey & Mario Chen, 2020. "A simple method to assess and report thematic saturation in qualitative research," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-17, May.
    18. Rotem Botvinik-Nezer & Felix Holzmeister & Colin F. Camerer & Anna Dreber & Juergen Huber & Magnus Johannesson & Michael Kirchler & Roni Iwanir & Jeanette A. Mumford & R. Alison Adcock & Paolo Avesani, 2020. "Variability in the analysis of a single neuroimaging dataset by many teams," Nature, Nature, vol. 582(7810), pages 84-88, June.
    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. Christoph Huber & Anna Dreber & Jürgen Huber & Magnus Johannesson & Michael Kirchler & Utz Weitzel & Miguel Abellán & Xeniya Adayeva & Fehime Ceren Ay & Kai Barron & Zachariah Berry & Werner Bönte , 2023. "Competition and moral behavior: A meta-analysis of forty-five crowd-sourced experimental designs," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(23), pages 2215572120-, June.
    2. Felix Holzmeister & Magnus Johannesson & Robert Böhm & Anna Dreber & Jürgen Huber & Michael Kirchler, 2023. "Heterogeneity in effect size estimates: Empirical evidence and practical implications," Working Papers 2023-17, Faculty of Economics and Statistics, Universität Innsbruck.
    3. Matkovskyy, Roman & Bouraoui, Taoufik & Hammami, Helmi, 2016. "Analysing the financial strength of Tunisia: An approach to estimate an index of financial safety," Research in International Business and Finance, Elsevier, vol. 38(C), pages 485-493.
    4. Hauck, Jana & Suess-Reyes, Julia & Beck, Susanne & Prügl, Reinhard & Frank, Hermann, 2016. "Measuring socioemotional wealth in family-owned and -managed firms: A validation and short form of the FIBER Scale," Journal of Family Business Strategy, Elsevier, vol. 7(3), pages 133-148.
    5. Sabi, Humphrey M. & Uzoka, Faith-Michael E. & Langmia, Kehbuma & Njeh, Felix N., 2016. "Conceptualizing a model for adoption of cloud computing in education," International Journal of Information Management, Elsevier, vol. 36(2), pages 183-191.
    6. Bernadett-Miriam Dobai & Laszlo Barna Iantovics & Andreea Paiu & Dan Dobreanu, 2022. "Exploratory factor analysis for identifying cieds patients' concerns during the covid-19 pandemic in Europe," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 20(1), pages 50-56.
    7. Uzir, Md Uzir Hossain & Al Halbusi, Hussam & Lim, Rodney & Jerin, Ishraq & Abdul Hamid, Abu Bakar & Ramayah, Thurasamy & Haque, Ahasanul, 2021. "Applied Artificial Intelligence and user satisfaction: Smartwatch usage for healthcare in Bangladesh during COVID-19," Technology in Society, Elsevier, vol. 67(C).
    8. Paul M. Johnson & Corey E. Brady & Craig Philip & Hiba Baroud & Janey V. Camp & Mark Abkowitz, 2020. "A Factor Analysis Approach Toward Reconciling Community Vulnerability and Resilience Indices for Natural Hazards," Risk Analysis, John Wiley & Sons, vol. 40(9), pages 1795-1810, September.
    9. Rachid Laaja & Karen Macours, 2021. "Measuring Skills in Developing Countries," Journal of Human Resources, University of Wisconsin Press, vol. 56(4), pages 1254-1295.
    10. Anne M Verhallen & Remco J Renken & Jan-Bernard C Marsman & Gert J ter Horst, 2019. "Romantic relationship breakup: An experimental model to study effects of stress on depression (-like) symptoms," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-13, May.
    11. Jimmy Semakula & Rene A. Corner-Thomas & Stephen T. Morris & Hugh T. Blair & Paul R. Kenyon, 2021. "Application of Machine Learning Algorithms to Predict Body Condition Score from Liveweight Records of Mature Romney Ewes," Agriculture, MDPI, vol. 11(2), pages 1-20, February.
    12. Pötschke, Ivonne, 2019. "The Ties That Bind: Exploring relationship-oriented values in family firms from employees' perspective," Working Papers 3, Helmut Schmidt University, Research Cluster OPAL.
    13. Nate Breznau & Eike Mark Rinke & Alexander Wuttke & Hung H. V. Nguyen & Muna Adem & Jule Adriaans & Amalia Alvarez-Benjumea & Henrik K. Andersen & Daniel Auer & Flavio Azevedo & Oke Bahnsen & Dave Bal, 2022. "Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(44), pages 2203150119-, November.
    14. Roberta De Vito & Ruggero Bellio & Lorenzo Trippa & Giovanni Parmigiani, 2019. "Multi‐study factor analysis," Biometrics, The International Biometric Society, vol. 75(1), pages 337-346, March.
    15. Ertugrul Uysal & Sascha Alavi & Valéry Bezençon, 2022. "Trojan horse or useful helper? A relationship perspective on artificial intelligence assistants with humanlike features," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1153-1175, November.
    16. Sonia Cruz-Ros, 2009. "Multi-item models for evaluating managerial and organizational resources in service firms," Service Business, Springer;Pan-Pacific Business Association, vol. 3(3), pages 229-257, September.
    17. Christopher Kohl & Marlene Knigge & Galina Baader & Markus Böhm & Helmut Krcmar, 2018. "Anticipating acceptance of emerging technologies using twitter: the case of self-driving cars," Journal of Business Economics, Springer, vol. 88(5), pages 617-642, July.
    18. Artür Manukyan & Erhan Çene & Ahmet Sedef & Ibrahim Demir, 2014. "Dandelion plot: a method for the visualization of R-mode exploratory factor analyses," Computational Statistics, Springer, vol. 29(6), pages 1769-1791, December.
    19. Hilger, Anne & Nordman, Christophe Jalil & Sarr, Leopold, 2018. "Cognitive and Non-Cognitive Skills, Hiring Channels, and Wages in Bangladesh," IZA Discussion Papers 11578, Institute of Labor Economics (IZA).
    20. Petyo Boshnakov, 2016. "A Comparative Study of Tastes and Preferences for Local and Foreign Wines in Bulgaria," International Journal of Management Science and Business Administration, Inovatus Services Ltd., vol. 2(11), pages 33-39, October.

    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:nat:nathum:v:7:y:2023:i:11:d:10.1038_s41562-023-01705-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.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.