Een exploratief onderzoek naar de accepteerbaarheid van industriële cobots bij (toekomstige) operatoren | Amsterdam University Press Journals Online
2004
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Volume 34, Issue 1
  • ISSN: 0921-5077
  • E-ISSN: 1875-7235
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Abstract

Samenvatting

Collaboratieve robots (cobots) zullen een prominente plaats innemen op de productievloer van de aankomende vierde industriële revolutie. Het is vandaag echter onbekend hoe operatoren zich positioneren ten aanzien van deze cobot(r)evolutie. Deze exploratieve studie onderzocht met een vragenlijst en een kort semi-gestructureerd interview de ervaren baanbedreiging door en accepteerbaarheid van cobots en cobotfunctionaliteiten bij (toekomstige) operatoren ( = 83). De resultaten wezen op een beperkte aanvankelijk ervaren baanbedreiging. Deze ervaring steeg wel significant op het einde van de vragenlijst – waar de participanten intussen meer hadden geleerd over cobots – naar een nog steeds neutrale score, mogelijk omdat (toekomstige) operatoren twijfelen aan de betrouwbaarheid en capaciteiten van cobots. Daarnaast werd een hogere accepteerbaarheid gevonden voor cobotfunctionaliteiten die fysieke werktaken en kwaliteitscontrole omvatten, terwijl cognitief geavanceerde en adaptieve cobots eerder een neutrale accepteerbaarheid uitlokten. De algemene accepteerbaarheid van cobots was voor de operatoren eerder positief. Opmerkelijk, toekomstige operatoren (studenten) scoorden significant wat lager voor de meeste studievariabelen in vergelijking met actueel tewerkgestelde operatoren. In deze bijdrage worden de beperkingen en implicaties voor de accepteerbaarheid en acceptatie van deze veelbelovende technologie geformuleerd.

© Bram B. Van Acker, Peter D. Conradie, Davy D. Parmentier, Jamil Joundi, Jelle Saldien & Peter Vlerick
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References

  1. Ajoudani, A., Zanchettin, A. M., Ivaldi, S., Albu-Schäffer, A., Kosuge, K., & Khatib, O.(2018). Progress and prospects of the human–robot collaboration. Autonomous Robots, 42(5), 957-975. https://doi.org/10.1007/s10514-017-9677-2
     [Google Scholar]
  2. Ajzen, I.(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.
     [Google Scholar]
  3. Ajzen, I.(2001). Nature and operation of attitudes. Annual Review of Psychology, 52(1), 27-58. https://doi.org/10.1146/annurev.psych.52.1.27
     [Google Scholar]
  4. Akella, P., Peshkin, M., Colgate, E., Wannasuphoprasit, W., Nagesh, N., Wells, J., … Peacock, B.(1999). Cobots for the automobile assembly line. In Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C) (Vol. 1, pp. 728-733). Detroit, MI: IEEE. https://doi.org/10.1109/ROBOT.1999.770061
     [Google Scholar]
  5. Alahmari, A. M., Abidi, M. H., & Ahmad, A.(2016). Development of a virtual manufacturing assembly simulation system. Advances in Mechanical Engineering, 8(3), 1-13. https://doi.org/10.1177/1687814016639824
     [Google Scholar]
  6. Alexandre, B., Reynaud, E., Osiurak, F., & Navarro, J.(2018). Acceptance and acceptability criteria: A literature review. Cognition, Technology and Work, 20(2), 165-177. https://doi.org/10.1007/s10111-018-0459-1
     [Google Scholar]
  7. Anandan, T. M.(2013). The end of separation: Man and robot as collaborative coworkers on the factory floor. Retrieved from https://www.robotics.org/content-detail.cfm/Industrial-Robotics-Industry-Insights/The-End-of-Separation-Man-andRobot-as-Collaborative-Coworkers-on-the-Factory-Floor/content_id/4140
     [Google Scholar]
  8. Arntz, M., Gregory, T., & Zierahn, U.(2016). The risk of automation for jobs in OECD countries: A comparative analysis. In Working paper (Vol. 189, p. 34). OECD Publishing. https://doi.org/10.1787/5jlz9h56dvq7-en
     [Google Scholar]
  9. Bauer, A., Ollherr, D., & Buss, M.(2008). Human-robot collaboration: A survey. International Journal of Humanoid Robotics, 5(1), 47-66. https://doi.org/10.1142/S0219843608001303
     [Google Scholar]
  10. Biassoni, F., Ruscio, D., & Ciceri, R.(2016). Limitations and automation: The role of information about device-specific features in ADAS acceptability. Safety Science, 85, 179-186. https://doi.org/10.1016/j.ssci.2016.01.017
     [Google Scholar]
  11. Bläsing, D., Hinrichsen, S., & Bornewasser, M.(2020). Reduction of cognitive load in complex assembly systems. In T.Ahram, R.Taiar, V.Gremeaux-Bader, & K.Aminian (Eds.), Humaninteraction, emerging technologies and future applications II. IHIET 2020: Advances in intelligent systems and computing (pp. 495-500). Cham: Springer. https://doi.org/10.1007/978-3-030-44267-5_75
     [Google Scholar]
  12. Bogue, R.(2016). Europe continues to lead the way in the collaborative robot business. Industrial Robot, 43(1), 6-11. https://doi.org/10.1108/IR-10-2015-0195
     [Google Scholar]
  13. Booker, J. D., Swift, K. G., & Brown, N. J.(2005). Designing for assembly quality: Strategies, guidelines and techniques. Journal of Engineering Design, 16(3), 279-295. https://doi.org/10.1080/09544820500126672
     [Google Scholar]
  14. Bröhl, C., Nelles, J., Brandl, C., Mertens, A., & Schlick, C. M.(2016). TAM reloaded: A technology acceptance model for human-robot cooperation in production systems. In C.Stephanidis (Ed.), HCII 2016 Posters, Part I, CCIS (Vol. 617, pp. 97-103). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-40548-3
     [Google Scholar]
  15. Brolin, A., Thorvald, P., & Case, K.(2017). Experimental study of cognitive aspects affecting human performance in manual assembly. Production and Manufacturing Research, 5(1), 141-163. https://doi.org/10.1080/21693277.2017.1374893
     [Google Scholar]
  16. Brougham, D., & Haar, J.(2018). Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239-257. https://doi.org/10.1017/jmo.2016.55
     [Google Scholar]
  17. Brynjolfsson, E., Mitchell, T., & Rock, D.(2018). What can machines learn, and what does it mean for occupations and the economy?AEA Papers and Proceedings, 108, 43-47. https://doi.org/10.1257/pandp.20181019
     [Google Scholar]
  18. Davis, F. D.(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
     [Google Scholar]
  19. Deci, E. L., Olafsen, A. H., & Ryan, R. M.(2017). Self-determination theory in work organizations: The state of a science. Annual Review of Organizational Psychology and Organizational Behavior, 4(1), 19-43. https://doi.org/10.1146/annurev-orgpsych-032516-113108
     [Google Scholar]
  20. Elprama, S. A., El Makrini, I., Vanderborght, B., & Jacobs, A.(2016). Acceptance of collaborative robots by factory workers: A pilot study on the importance of social cues of anthropomorphic robots. In 25th IEEE International Symposium on Robot and Human Interactive Communication (ROMAN), 2016 (pp. 919-929). New York.
     [Google Scholar]
  21. Elprama, S. A., Jewell, C. I. C., Jacobs, A., El Makrini, I., & Vanderborght, B.(2017). Attitudes of factory workers towards industrial and collaborative robots. ACM/IEEE International Conference on Human-Robot Interaction, 113-114. https://doi.org/10.1145/3029798.3038309
     [Google Scholar]
  22. Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W.(2016). Tangible industry 4.0: A scenariobased approach to learning for the future of production. Procedia CIRP, 54, 13-18. https://doi.org/10.1016/j.procir.2016.03.162
     [Google Scholar]
  23. Festinger, L.(1954). A theory of social comparison processes. Human Relations, 7(2), 117-140. https://doi.org/10.1177/001872675400700202
     [Google Scholar]
  24. Fletcher, S. R., Johnson, T., & Larreina, J.(2019). Putting people and robots together in manufacturing: Are we ready? In M.Aldinhas Ferreira, J.Silva Sequeira, G.Singh Virk, M.Tokhi, & E. E.Kadar (Eds.), Robotics and well-being. Intelligent systems, control and automation: Science and engineering (pp. 135-147). Cham: Springer. https://doi.org/10.1007/978-3-030-12524-0_12
     [Google Scholar]
  25. Gessl, A. S., Schlögl, S., & Mevenkamp, N.(2019). On the perceptions and acceptance of artificially intelligent robotics and the psychology of the future elderly. Behaviour and Information Technology, 38(11), 1068-1087. https://doi.org/10.1080/0144929X.2019.1566499
     [Google Scholar]
  26. Gombolay, M. C., Gutierrez, R. A., Clarke, S. G., Sturla, G. F., & Shah, J. A.(2015). Decision-making authority, team efficiency and human worker satisfaction in mixed human-robot teams. Autonomous Robots, 39(3), 293-312. https://doi.org/10.1007/s10514-015-9457-9
     [Google Scholar]
  27. Gould, J. D., & Lewis, C.(1985). Designing for usability: Key principles and what designers think. Communications of the ACM, 28(3), 300-311. https://doi.org/10.1145/3166.3170
     [Google Scholar]
  28. Granulo, A., Fuchs, C., & Puntoni, S.(2019). Psychological reactions to human versus robotic job replacement. Nature Human Behaviour, 3(10), 1062-1069. https://doi.org/10.1038/s41562-019-0670-y
     [Google Scholar]
  29. Hoecherl, J., Schmargendorf, M., Wrede, B., & Schlegl, T.(2018). User-centered design of multimodal robot feedback for cobots of human-robot working cells in industrial production contexts. In SR 2018; 50th International Symposium on Robotics (pp. 1-8). Munich, Germany.
     [Google Scholar]
  30. Hold, P., Ranz, F., Sihn, W., & Hummel, V.(2016). Planning operator support in cyber-physical assembly systems. IFAC-PapersOnLine, 49(32), 60-65. https://doi.org/10.1016/j.ifacol.2016.12.190
     [Google Scholar]
  31. Jacobs, A., Tytgat, L., Maus, M., Meeusen, R., & Vanderborght, B.(2019). Homo Roboticus. VUB Press.
     [Google Scholar]
  32. Klein, K. J., & Sorra, J. S.(1996). The challenge of innovation implementation. The Academy of Management Review, 21(4), 1055.
    [Google Scholar]
  33. Landi, C. T., Villani, V., Ferraguti, F., Sabattini, L., Secchi, C., & Fantuzzi, C.(2018). Relieving operators’ workload: Towards affective robotics in industrial scenarios. Mechatronics, 54(August), 144-154. https://doi.org/10.1016/j.mechatronics.2018.07.012
     [Google Scholar]
  34. Longo, F., Nicoletti, L., & Padovano, A.(2017). Smart operators in Industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context. Computers and Industrial Engineering, 113, 144-159. https://doi.org/10.1016/j.cie.2017.09.016
     [Google Scholar]
  35. Makrini, I. El, Merckaert, K., Lefeber, D., & Vanderborght, B.(2017). Design of a collaborative architecture for human-robot assembly tasks. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1624-1629). Vancouver, BC.
     [Google Scholar]
  36. Maurtua, I., Ibarguren, A., Kildal, J., Susperregi, L., & Sierra, B.(2017). Humanrobot collaboration in industrial applications: Safety, interaction and trust. International Journal of Advanced Robotic Systems, 14(4), 1-10. https://doi.org/10.1177/1729881417716010
     [Google Scholar]
  37. Maus, M., Vanderborght, B., Meeusen, R., & Jacobs, A.(2018). Zullen we op de “De Dag van de Arbeid” vanaf nu “De Dag van de Robot” vieren? Retrieved from https://www.vrt.be/vrtnws/nl/2018/04/30/opinie-michel-maus-en-co-daag-van-de-robot/
     [Google Scholar]
  38. McGaughey, E.(2018). Will robots automate your job away? Full employment, basic income, and economic democracy. Centre for Business Research, University of Cambridge. Working Paper No. 496, 1-34. https://doi.org/10.2139/ssrn.3044448
     [Google Scholar]
  39. Morgeson, F. P., & Humphrey, S. E.(2006). The Work Design Questionnaire (WDQ):Developing and validating a comprehensive measure for assessing job design and the nature of work. Journal of Applied Psychology, 91(6), 1321-1339. https://doi.org/10.1037/0021-9010.91.6.1321
     [Google Scholar]
  40. Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J.(2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16, 1-13. https://doi.org/10.1177/1609406917733847
     [Google Scholar]
  41. Oztemel, E., & Gursev, S.(2020). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31(1), 127-182. https://doi.org/10.1007/s10845-018-1433-8
     [Google Scholar]
  42. Parasuraman, A.(2000). Technology Readiness Index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320. https://doi.org/10.1177/109467050024001
     [Google Scholar]
  43. Patterson, M., Warr, P., & West, M.(2004). Organizational climate and company productivity: The role of employee affect and employee level. Journal of Occupational and Organizational Psychology, 77, 193-216. Retrieved from https://doi.org/10.1348/096317904774202144
     [Google Scholar]
  44. Peternel, L., Tsagarakis, N., Caldwell, D., & Ajoudani, A.(2016). Adaptation of robot physical behaviour to human fatigue in human-robot co-manipulation. IEEE-RAS International Conference on Humanoid Robots, 489-494. https://doi.org/10.1109/HUMANOIDS.2016.7803320
     [Google Scholar]
  45. QSR.(2018). NVivo qualitative data analysis software. QSR International Pty Ltd. Version 12.
     [Google Scholar]
  46. Rogers, E. M.(1995). Diffusion of innovations (4th ed.). New York, NY: Free Press.
     [Google Scholar]
  47. Russell, S. J., & Norvig, P.(2009). Artificial intelligence: A modern approach (3rd ed.). Upper Saddle River, NJ: Prentice Hall.
     [Google Scholar]
  48. Sauppé, A., & Mutlu, B.(2015). The social impact of a robot co-worker in industrial settings. CHI ‘15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, April 2015, 3613-3622. https://doi.org/10.1145/2702123.2702181
     [Google Scholar]
  49. Shalin, V. L., Prabhu, G. V., & Helander, M. G.(1996). A cognitive perspective on manual assembly. Ergonomics, 39(1), 108-127. https://doi.org/10.1080/00140139608964438
     [Google Scholar]
  50. Tabachnick, B. G., & Fidell, L. S.(2013). Using multivariate statistics (6th ed.). Boston, MA: Pearson Education.
     [Google Scholar]
  51. Takayama, L., Ju, W., & Nass, C.(2008). Beyond dirty, dangerous and dull: What everyday people think robots should do. 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI), Amsterdam, pp. 25-32. doi: 10.1145/1349822.1349827
     [Google Scholar]
  52. Tesser, A.(1988). Toward a self-evaluation maintenance model of social behavior. Advances in Experimental Social Psychology, 21, 181-227. https://doi.org/10.1016/S0065-2601(08)60227-0
     [Google Scholar]
  53. TNS Opinion & Social. (2015). Special Eurobarometer 427 / Wave EB82.4 – Autonomous systems. European Commission, Directorate-General for Networks, Content and Technology (DG CONNECT) & Directorate-General for Communication (DG COMM). Retrieved from http://ec.europa.eu/public_opinion/index_en.htm
     [Google Scholar]
  54. Um, J., Lyons, A., Lam, H. K. S., Cheng, T. C. E., & Dominguez-Pery, C.(2017). Product variety management and supply chain performance: A capability perspective on their relationships and competitiveness implications. International Journal of Production Economics, 187, 15-26. https://doi.org/10.1016/j.ijpe.2017.02.005
     [Google Scholar]
  55. Van Acker, B. B., Conradie, P. D., Vlerick, P., & Saldien, J.(2020). Employee acceptability of wearable mental workload monitoring: exploring effects of framing the goal and context in corporate communication. Cognition, Technology & Work. https://doi.org/10.1007/s10111-020-00633-0
     [Google Scholar]
  56. Venkatesh, V., & Davis, F. D.(2000). A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
     [Google Scholar]
  57. Venkatesh, V., Thong, J. Y. L., & Xu, X.(2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328-376.
     [Google Scholar]
  58. Victorino, L., Karniouchina, E., & Verma, R.(2009). Exploring the use of the abbreviated technology readiness index for hotel customer segmentation. Cornell Hospitality Quarterly, 50(3), 342-359. https://doi.org/10.1177/1938965509336809
     [Google Scholar]
  59. Villani, V., Pini, F., Leali, F., & Secchi, C.(2018). Survey on human-robot collaboration in industrial settings: Safety, intuitive interfaces and applications. Mechatronics, 55(March), 248-266. https://doi.org/10.1016/j.mechatronics.2018.02.009
     [Google Scholar]
  60. Vlassenroot, S., Brookhuis, K., Marchau, V., & Witlox, F.(2010). Towards defining a unified concept for the acceptability of Intelligent Transport Systems (ITS): A conceptual analysis based on the case of Intelligent Speed Adaptation (ISA). Transportation Research Part F: Traffic Psychology and Behaviour, 13(3), 164-178. https://doi.org/10.1016/j.trf.2010.02.001
     [Google Scholar]
  61. Walczuch, R., Lemmink, J., & Streukens, S.(2007). The effect of service employees’ technology readiness on technology acceptance. Information and Management, 44(2), 206-215. https://doi.org/10.1016/j.im.2006.12.005
     [Google Scholar]
  62. Wan, X., & Sanders, N. R.(2017). The negative impact of product variety: Forecast bias, inventory levels, and the role of vertical integration. International Journal of Production Economics, 186, 123-131. https://doi.org/10.1016/j.ijpe.2017.02.002
     [Google Scholar]
  63. Weiss, A., & Huber, A.(2016). User experience of a smart factory robot: Assembly line workers demand adaptive robots. In AISB2016: Proceedings of the 5th International Symposium on New Frontiers in Human-Robot Interaction (pp. 1-3).
     [Google Scholar]
  64. Wollschlaeger, M., Sauter, T., & Jasperneite, J.(2017). The future of industrial communication: Automation networks in the era of the Internet of Things and Industry 4.0. IEEE Industrial Electronics Magazine, 11(1), 17-27. https://doi.org/10.1109/MIE.2017.2649104
     [Google Scholar]
  65. Xu, L. Da, Xu, E. L., & Li, L.(2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 1-22. https://doi.org/10.1080/00207543.2018.1444806
     [Google Scholar]
  66. Yogeeswaran, K., Złotowski, J., Livingstone, M., Bartneck, C., Sumioka, H., & Ishiguro, H.(2016). The interactive effects of robot anthropomorphism and robot ability on perceived threat and support for robotics research. Journal of Human-Robot Interaction, 5(2), 29-47. https://doi.org/10.5898/jhri.5.2.yogeeswaran
     [Google Scholar]
  67. Young, M. S., Brookhuis, K. A., Wickens, C. D., & Hancock, P. A.(2014). State of science: Mental workload in ergonomics. Ergonomics, 58(1), 1-17. https://doi.org/10.1080/00140139.2014.956151
     [Google Scholar]
  68. Złotowski, J., Yogeeswaran, K., & Bartneck, C.(2017). Can we control it? Autonomous robots threaten human identity, uniqueness, safety, and resources. International Journal of Human-Computer Studies, 100, 48-54. https://doi.org/10.1016/j.ijhcs.2016.12.008
     [Google Scholar]
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Een exploratief onderzoek naar de accepteerbaarheid van industriële cobots bij (toekomstige) operatoren
G&O 34, 13 (2021); https://doi.org/10.5117/GO2021.1.003.VANA
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Keyword(s): acceptability; cobot; operator; technology acceptance; use intention

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