Practice With Less AI Makes Perfect: Partially Automated AI During Training Leads to Better Worker Motivation, Engagement, and Skill Acquisition

References

• Akhlaq, A., & Ahmed, E. (2013). The effect of motivation on trust in the acceptance of internet banking in a low-income country. International Journal of Bank Marketing, 31(2), 115–125. https://doi.org/10.1108/02652321311298690
   Google Scholar
• Armstrong, M., & Taylor, S. (2020). Armstrong’s handbook of human resource management practice. Kogan Page Publishers.
   Google Scholar
• Avril, E. (2022). Providing different levels of accuracy about the reliability of automation to a human operator: Impact on human performance. Ergonomics, 66(2), 217–226. (just-accepted), https://doi.org/10.1080/00140139.2022.2069870
   PubMed Web of Science ®Google Scholar
• Avril, E., Cegarra, J., Wioland, L., & Navarro, J. (2022). Automation type and reliability impact on visual automation monitoring and human performance. International Journal of Human–Computer Interaction, 38(1), 64–77. https://doi.org/10.1080/10447318.2021.1925435
   Web of Science ®Google Scholar
• Bahner, J. E., Hüper, A.-D., & Manzey, D. (2008). Misuse of automated decision aids: Complacency, automation bias and the impact of training experience. International Journal of Human-Computer Studies, 66(9), 688–699. https://doi.org/10.1016/j.ijhcs.2008.06.001
   Web of Science ®Google Scholar
• Bainbridge, L. (1983). Ironies of automation. In Analysis, design and evaluation of man–machine systems. (pp. 129–135). Elsevier.
   Google Scholar
• Bell, B. S., Tannenbaum, S. I., Ford, J. K., Noe, R. A., & Kraiger, K. (2017). 100 years of training and development research: What we know and where we should go. The Journal of Applied Psychology, 102(3), 305–323. https://doi.org/10.1037/apl0000142
   PubMed Web of Science ®Google Scholar
• Betella, A., & Verschure, P. F. (2016). The affective slider: A digital self-assessment scale for the measurement of human emotions. PloS One, 11(2), e0148037. https://doi.org/10.1371/journal.pone.0148037
   PubMed Web of Science ®Google Scholar
• Bindewald, J. M., Miller, M. E., & Peterson, G. L. (2020). Creating effective automation to maintain explicit user engagement. International Journal of Human-Computer Interaction, 36(4), 341–354. https://doi.org/10.1080/10447318.2019.1642618
   Web of Science ®Google Scholar
• Bradley, M. M., & Lang, P. J. (2007). Emotion and motivation. In Handbook of psychophysiology (3rd ed, pp. 581–607). Cambridge University Press. https://doi.org/10.1017/CBO9780511546396.025
   Google Scholar
• 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
• Büth, L., Blume, S., Posselt, G., & Herrmann, C. (2018). Training concept for and with digitalization in learning factories: An energy efficiency training case. Procedia Manufacturing, 23, 171–176. https://doi.org/10.1016/j.promfg.2018.04.012
   Google Scholar
• Carretero, M. d P., García, S., Moreno, A., Alcain, N., & Elorza, I. (2021). Methodology to create virtual reality assisted training courses within the Industry 4.0 vision. Multimedia Tools and Applications, 80(19), 29699–29717. https://doi.org/10.1007/s11042-021-11195-2
   Web of Science ®Google Scholar
• Casillo, M., Colace, F., Fabbri, L., Lombardi, M., Romano, A., & Santaniello, D. (2020). Chatbot in industry 4.0: An approach for training new employees [Paper presentation]. 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), https://doi.org/10.1109/TALE48869.2020.9368339
   Google Scholar
• Cazeri, G. T., de Santa-Eulália, L. A., Serafim, M. P., & Anholon, R. (2022). Training for Industry 4.0: A systematic literature review and directions for future research. Brazilian Journal of Operations & Production Management, 19(3), 1–19. https://doi.org/10.14488/BJOPM.2022.007
   Web of Science ®Google Scholar
• Cherif, N., Mezghani, N., Gaudreault, N., Ouakrim, Y., Mouzoune, I., & Boulay, P. (2018). Physiological data validation of the hexoskin smart textile. In Proceedings of the 11th international joint conference on biomedical engineering systems and technologies (BIOSTEC 2018) - BIODEVICES; ISBN 978-989-758-277-6; ISSN 2184-4305 (pp. 150–156). SciTePress.
   Google Scholar
• Cohen, J. (1992). Statistical power analysis. Current Directions in Psychological Science, 1(3), 98–101. https://doi.org/10.1111/1467-8721.ep10768783
   Google Scholar
• Da Silva, L., Soltovski, R., Pontes, J., Treinta, F., Leitão, P., Mosconi, E., De Resende, L., & Yoshino, R. (2022). Human resources management 4.0: Literature review and trends. Computers & Industrial Engineering, 168, 108111. https://doi.org/10.1016/j.cie.2022.108111
   Web of Science ®Google Scholar
• Danjou, C., Rivest, L., & Pellerin, R. (2017). Industrie 4.0: Des pistes pour aborder l’ère du numérique et de la connectivité. CEFRIO, p. 27. https://espace2.etsmtl.ca/id/eprint/14934/1/le-passage-au-num%C3%A9rique.pdf
   Google Scholar
• Dattel, A. R., Babin, A. K., & Wang, H. (2023). Human factors of flight training and simulation. In Human Factors in Aviation and Aerospace. (pp. 217–255). Elsevier.
   Google Scholar
• 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
• Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determination in personality. Journal of Research in Personality, 19(2), 109–134. https://doi.org/10.1016/0092-6566(85)90023-6
   Web of Science ®Google Scholar
• Deci, E. L., & Ryan, R. M. (2008). Self-determination theory: A macrotheory of human motivation, development, and health. Canadian Psychology/Psychologie Canadienne, 49(3), 182–185. https://doi.org/10.1037/a0012801
   Web of Science ®Google Scholar
• Dhalmahapatra, K., Maiti, J., & Krishna, O. (2021). Assessment of virtual reality-based safety training simulator for electric overhead crane operations. Safety Science, 139, 105241. https://doi.org/10.1016/j.ssci.2021.105241
   Web of Science ®Google Scholar
• Enang, E., Bashiri, M., & Jarvis, D. (2023). Exploring the transition from techno centric industry 4.0 towards value centric industry 5.0: A systematic literature review. International Journal of Production Research, 61(22), 7866–7902. https://doi.org/10.1080/00207543.2023.2221344
   Web of Science ®Google Scholar
• Endsley, M. R., & Kiris, E. O. (1995). The out-of-the-loop performance problem and level of control in automation. Human Factors: The Journal of the Human Factors and Ergonomics Society, 37(2), 381–394. https://doi.org/10.1518/001872095779064555
   Web of Science ®Google Scholar
• European Commission, Directorate-General for Research and Innovation, Breque, M., De Nul, L., & Petridis, A. (2021). Industry 5.0 – Towards a sustainable, human-centric and resilient European industry. Publications Office of the European Union. https://op.europa.eu/en/publication-detail/-/publication/468a892a-5097-11eb-b59f-01aa75ed71a1/language-en
   Google Scholar
• Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149
   PubMed Web of Science ®Google Scholar
• Forum, W. E. (2016). The future of jobs: Employment, skills and workforce strategy for the fourth industrial revolution. World Economic Forum Geneva.
   Google Scholar
• Gagné, M., Parker, S. K., Griffin, M. A., Dunlop, P. D., Knight, C., Klonek, F. E., & Parent-Rocheleau, X. (2022). Understanding and shaping the future of work with self-determination theory. Nature Reviews Psychology, 1(7), 378–392. https://doi.org/10.1038/s44159-022-00056-w
   PubMedGoogle Scholar
• Gao, N., Shao, W., Rahaman, M. S., & Salim, F. D. (2020). n-gage: Predicting in-class emotional, behavioural and cognitive engagement in the wild. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(3), 1–26. https://doi.org/10.1145/3411813
   PubMedGoogle Scholar
• Gehrke, L., Kühn, A. T., Rule, D., Moore, P., Bellmann, C., Siemes, S., Dawood, D., Lakshmi, S., Kulik, J., & Standley, M. (2015). A discussion of qualifications and skills in the factory of the future: A German and American perspective. VDI/ASME Industry, 4(1), 1–28.
   Google Scholar
• Gerhart, B., & Fang, M. (2015). Pay, intrinsic motivation, extrinsic motivation, performance, and creativity in the workplace: Revisiting long-held beliefs. Annual Review of Organizational Psychology and Organizational Behavior, 2(1), 489–521. https://doi.org/10.1146/annurev-orgpsych-032414-111418
   Google Scholar
• Guay, F., Vallerand, R. J., & Blanchard, C. (2000). On the assessment of situational intrinsic and extrinsic motivation: The Situational Motivation Scale (SIMS). Motivation and Emotion, 24(3), 175–213. https://doi.org/10.1023/A:1005614228250
   Web of Science ®Google Scholar
• Hecklau, F., Galeitzke, M., Flachs, S., & Kohl, H. (2016). Holistic approach for human resource management in Industry 4.0. Procedia CIRP, 54, 1–6. https://doi.org/10.1016/j.procir.2016.05.102
   Google Scholar
• Humayun, M. (2021). Industrial revolution 5.0 and the role of cutting edge technologies. International Journal of Advanced Computer Science and Applications, 12(12), 605–615. https://doi.org/10.14569/IJACSA.2021.0121276
   Web of Science ®Google Scholar
• Ivaldi, S., Scaratti, G., & Fregnan, E. (2022). Dwelling within the fourth industrial revolution: Organizational learning for new competences, processes and work cultures. Journal of Workplace Learning, 34(1), 1–26. https://doi.org/10.1108/JWL-07-2020-0127
   Web of Science ®Google Scholar
• Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M., & Kuusk, A. (2022). Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with Applications, 216, 119456. https://doi.org/10.1016/j.eswa.2022.119456
   Web of Science ®Google Scholar
• Jayasekera, S., Hensel, E., & Robinson, R. (2021). Feasibility of using the Hexoskin smart garment for natural environment observation of respiration topography. International Journal of Environmental Research and Public Health, 18(13), 7012. https://doi.org/10.3390/ijerph18137012
   PubMed Web of Science ®Google Scholar
• Kaasinen, E., Liinasuo, M., Schmalfuß, F., Koskinen, H., Aromaa, S., Heikkilä, P., Honka, A., Mach, S., & Malm, T. (2019). A worker-centric design and evaluation framework for operator 4.0 solutions that support work well-being. In Human work interaction design. Designing engaging automation, 5th IFIP WG 13.6 Working Conference, HWID 2018, Espoo, Finland, August 20–21, 2018, Revised Selected Papers 5 (pp. 263–282). Springer International Publishing.
   Google Scholar
• Kaber, D. B., & Endsley, M. R. (2004). The effects of level of automation and adaptive automation on human performance, situation awareness and workload in a dynamic control task. Theoretical Issues in Ergonomics Science, 5(2), 113–153. https://doi.org/10.1080/1463922021000054335
   Google Scholar
• Kadir, B. A., Broberg, O., & Conceição, C. S. d (2019). Current research and future perspectives on human factors and ergonomics in Industry 4.0. Computers & Industrial Engineering, 137, 106004. https://doi.org/10.1016/j.cie.2019.106004
   Web of Science ®Google Scholar
• Kahn, W. A. (1990). Psychological conditions of personal engagement and disengagement at work. Academy of Management Journal, 33(4), 692–724. https://doi.org/10.2307/256287
   Web of Science ®Google Scholar
• Klumpp, M., Hesenius, M., Meyer, O., Ruiner, C., & Gruhn, V. (2019). Production logistics and human-computer interaction-state-of-the-art, challenges and requirements for the future. The International Journal of Advanced Manufacturing Technology, 105(9), 3691–3709. https://doi.org/10.1007/s00170-019-03785-0
   Web of Science ®Google Scholar
• Kolade, O., & Owoseni, A. (2022). Employment 5.0: The work of the future and the future of work. Technology in Society, 71, 102086. 102086. https://doi.org/10.1016/j.techsoc.2022.102086
   Web of Science ®Google Scholar
• Kowal, B., Włodarz, D., Brzychczy, E., & Klepka, A. (2022). Analysis of employees’ competencies in the context of industry 4.0. Energies, 15(19), 7142. https://doi.org/10.3390/en15197142
   Web of Science ®Google Scholar
• Kozlowski, S. W., Toney, R. J., Mullins, M. E., Weissbein, D. A., Brown, K. G., & Bell, B. S. (2001). 2. Developing adaptability: A theory for the design of integrated-embedded training systems. In Advances in human performance and cognitive engineering research (Vol. 1, pp. 59–123). Emerald Group Publishing Limited.
   Google Scholar
• Kraiger, K., Ford, J. K., & Salas, E. (1993). Application of cognitive, skill-based, and affective theories of learning outcomes to new methods of training evaluation. Journal of Applied Psychology, 78(2), 311–328. https://doi.org/10.1037/0021-9010.78.2.311
   Web of Science ®Google Scholar
• Kumar, R., Gupta, P., Singh, S., & Jain, D. (2021). Human empowerment by Industry 5.0 in digital era: Analysis of enablers. Advances in Industrial and Production Engineering: Select Proceedings of FLAME 2020,
   Google Scholar
• Lang, P. J. (1995). The emotion probe: Studies of motivation and attention. The American Psychologist, 50(5), 372–385. https://doi.org/10.1037//0003-066x.50.5.372
   PubMed Web of Science ®Google Scholar
• Lazzara, E. H., Benishek, L. E., Hughes, A. M., Zajac, S., Spencer, J. M., Heyne, K. B., Rogers, J. E., & Salas, E. (2021). Enhancing the organization’s workforce: Guidance for effective training sustainment. Consulting Psychology Journal: Practice and Research, 73(1), 1–26. https://doi.org/10.1037/cpb0000185
   Google Scholar
• Legaspi, R., Xu, W., Konishi, T., Wada, S., Kobayashi, N., Naruse, Y., & Ishikawa, Y. (2024). The sense of agency in human-AI interactions. Knowledge-Based Systems, 286, 111298. https://doi.org/10.1016/j.knosys.2023.111298
   Google Scholar
• Léger, P. M., Cronan, P., Charland, P., Pellerin, R., Babin, G., & Robert, J. (2012). Authentic OM problem solving in an ERP context. International Journal of Operations & Production Management, 32(12), 1375–1394. https://doi.org/10.1108/01443571211284151
   Web of Science ®Google Scholar
• Leng, J., Sha, W., Wang, B., Zheng, P., Zhuang, C., Liu, Q., Wuest, T., Mourtzis, D., & Wang, L. (2022). Industry 5.0: Prospect and retrospect. Journal of Manufacturing Systems, 65, 279–295. https://doi.org/10.1016/j.jmsy.2022.09.017
   Web of Science ®Google Scholar
• Liu, P. (2023). Reflections on automation complacency. International Journal of Human–Computer Interaction, 1–17. https://doi.org/10.1080/10447318.2023.2265240
   Web of Science ®Google Scholar
• Lopez, M. A., Terron, S., Lombardo, J. M., & Gonzalez-Crespo, R. (2021). Towards a solution to create, test and publish mixed reality experiences for occupational safety and health learning: Training-MR.
   Google Scholar
• Macey, W. H., & Schneider, B. (2008). The meaning of employee engagement. Industrial and Organizational Psychology, 1(1), 3–30. https://doi.org/10.1111/j.1754-9434.2007.0002.x
   Web of Science ®Google Scholar
• Magnani, F. (2021). Digitization of operational processes: Use case of standardization in an Assembly Learning Factory. Advances on Mechanics, Design Engineering and Manufacturing III: Proceedings of the International Joint Conference on Mechanics, Design Engineering & Advanced Manufacturing, JCM 2020, June 2–4, 2020,
   Google Scholar
• Mann, A., & Harter, J. (2016). The worldwide employee engagement crisis. Gallup Business Journal, 7(1), 1–5.
   Google Scholar
• Martínez-Olvera, C., & Mora-Vargas, J. (2019). A comprehensive framework for the analysis of Industry 4.0 value domains. Sustainability, 11(10), 2960. https://doi.org/10.3390/su11102960
   Web of Science ®Google Scholar
• Mellal, M. A. (2020). Obsolescence–A review of the literature. Technology in Society, 63, 101347. https://doi.org/10.1016/j.techsoc.2020.101347
   Web of Science ®Google Scholar
• Meyer, J. P., & Gagnè, M. (2008). Employee engagement from a self-determination theory perspective. Industrial and Organizational Psychology, 1(1), 60–62. https://doi.org/10.1111/j.1754-9434.2007.00010.x
   Web of Science ®Google Scholar
• Meyer, J. P., Gagné, M., & Parfyonova, N. M. (2010). Toward an evidence-based model of engagement: What we can learn from motivation and commitment research. In Handbook of employee engagement. Edward Elgar Publishing.
   Google Scholar
• Molino, M., Cortese, C. G., & Ghislieri, C. (2020). The promotion of technology acceptance and work engagement in industry 4.0: From personal resources to information and training. International Journal of Environmental Research and Public Health, 17(7), 2438. https://doi.org/10.3390/ijerph17072438
   PubMed Web of Science ®Google Scholar
• Morgan, J. (2014). The future of work: Attract new talent, build better leaders, and create a competitive organization. John Wiley & Sons.
   Google Scholar
• Nazareno, L., & Schiff, D. S. (2021). The impact of automation and artificial intelligence on worker well-being. Technology in Society, 67, 101679. https://doi.org/10.1016/j.techsoc.2021.101679
   Web of Science ®Google Scholar
• Neumann, W. P., Winkelhaus, S., Grosse, E. H., & Glock, C. H. (2021). Industry 4.0 and the human factor–A systems framework and analysis methodology for successful development. International Journal of Production Economics, 233, 107992. https://doi.org/10.1016/j.ijpe.2020.107992
   Web of Science ®Google Scholar
• Neumann, A., Hajji, A., Rekik, M., & Pellerin, R. (2022). A model for advanced planning systems dedicated to the Engineer-To-Order context. International Journal of Production Economics, 252, 108557. https://doi.org/10.1016/j.ijpe.2022.108557
   Web of Science ®Google Scholar
• Oberländer, M., Beinicke, A., & Bipp, T. (2020). Digital competencies: A review of the literature and applications in the workplace. Computers & Education, 146, 103752. https://doi.org/10.1016/j.compedu.2019.103752
   Web of Science ®Google Scholar
• Onnasch, L. (2015). Crossing the boundaries of automation—Function allocation and reliability. International Journal of Human-Computer Studies, 76(3), 12–21. https://doi.org/10.1016/j.ijhcs.2014.12.004
   Web of Science ®Google Scholar
• Onnasch, L., Wickens, C. D., Li, H., & Manzey, D. (2014). Human performance consequences of stages and levels of automation: An integrated meta-analysis. Human Factors, 56(3), 476–488. https://doi.org/10.1177/0018720813501549
   PubMed Web of Science ®Google Scholar
• Pacher, C., Woschank, M., & Zunk, B. M. (2023). The role of competence profiles in industry 5.0-related vocational education and training: Exemplary development of a competence profile for industrial logistics engineering education. Applied Sciences, 13(5), 3280. https://doi.org/10.3390/app13053280
   Google Scholar
• Parasuraman, R. (2000). Designing automation for human use: Empirical studies and quantitative models. Ergonomics, 43(7), 931–951. https://doi.org/10.1080/001401300409125
   PubMed Web of Science ®Google Scholar
• Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors: The Journal of the Human Factors and Ergonomics Society, 39(2), 230–253. https://doi.org/10.1518/001872097778543886
   Web of Science ®Google Scholar
• Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans: a Publication of the IEEE Systems, Man, and Cybernetics Society, 30(3), 286–297. https://doi.org/10.1109/3468.844354
   PubMed Web of Science ®Google Scholar
• Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied Psychology, 71(4), 1171–1204. https://doi.org/10.1111/apps.12241
   Web of Science ®Google Scholar
• Passalacqua, M., Léger, P.-M., Nacke, L. E., Fredette, M., Labonté-Lemoyne, É., Lin, X., Caprioli, T., & Sénécal, S. (2020). Playing in the backstore: Interface gamification increases warehousing workforce engagement. Industrial Management & Data Systems, 120(7), 1309–1330. https://doi.org/10.1108/IMDS-08-2019-0458
   Web of Science ®Google Scholar
• Passalacqua, M., Cabour, G., Pellerin, R., Léger, P. M., & Doyon-Poulin, P. (2024). Human-centered AI for industry 5.0 (HUMAI5. 0): Design framework and case studies. In Human-centered AI (pp. 260–274). Chapman and Hall/CRC.
   Google Scholar
• Rauch, E., Linder, C., & Dallasega, P. (2020). Anthropocentric perspective of production before and within industry 4.0. Computers & Industrial Engineering, 139, 105644. https://doi.org/10.1016/j.cie.2019.01.018
   Web of Science ®Google Scholar
• R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
   Google Scholar
• Rosin, F., Forget, P., Lamouri, S., & Pellerin, R. (2020). Impacts of Industry 4.0 technologies on Lean principles. International Journal of Production Research, 58(6), 1644–1661. https://doi.org/10.1080/00207543.2019.1672902
   Web of Science ®Google Scholar
• Rosin, F., Forget, P., Lamouri, S., & Pellerin, R. (2021). Impact of Industry 4.0 on decision-making in an operational context. Advances in Production Engineering & Management, 16(4), 500–514. https://doi.org/10.14743/apem2021.4.416
   Web of Science ®Google Scholar
• Rosin, F., Forget, P., Lamouri, S., & Pellerin, R. (2022). Enhancing the decision-making process through industry 4.0 technologies. Sustainability, 14(1), 461. https://doi.org/10.3390/su14010461
   Web of Science ®Google Scholar
• Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54–67. https://doi.org/10.1006/ceps.1999.1020
   PubMed Web of Science ®Google Scholar
• Ryan, R. M., & Deci, E. L. (2008). Self-determination theory and the role of basic psychological needs in personality and the organization of behavior. In Handbook of personality: Theory and research (3rd ed., pp. 654–678). The Guilford Press.
   Google Scholar
• Salas, E., Tannenbaum, S. I., Kraiger, K., & Smith-Jentsch, K. A. (2012). The science of training and development in organizations: What matters in practice. Psychological Science in the Public Interest: a Journal of the American Psychological Society, 13(2), 74–101. https://doi.org/10.1177/1529100612436661
   PubMed Web of Science ®Google Scholar
• Saniuk, S., Caganova, D., & Saniuk, A. (2021). Knowledge and skills of industrial employees and managerial staff for the industry 4.0 implementation. Mobile Networks and Applications, 28(1), 220–230. https://doi.org/10.1007/s11036-021-01788-4
   Web of Science ®Google Scholar
• Sauer, J., Chavaillaz, A., & Wastell, D. (2016). Experience of automation failures in training: Effects on trust, automation bias, complacency and performance. Ergonomics, 59(6), 767–780. https://doi.org/10.1080/00140139.2015.1094577
   PubMed Web of Science ®Google Scholar
• Schaufeli, W. B., Bakker, A. B., & Salanova, M. (2003). Utrecht work engagement scale-9. Educational and Psychological Measurement.
   Google Scholar
• Schaufeli, W. B., Salanova, M., González-Romá, V., & Bakker, A. B. (2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies, 3(1), 71–92. https://doi.org/10.1023/A:1015630930326
   Google Scholar
• Schmid, Y., & Dowling, M. (2020). New work: New motivation? A comprehensive literature review on the impact of workplace technologies. Management Review Quarterly, 72(1), 59–86. https://doi.org/10.1007/s11301-020-00204-7
   Google Scholar
• Shaffer, F., McCraty, R., & Zerr, C. L. (2014). A healthy heart is not a metronome: An integrative review of the heart’s anatomy and heart rate variability. Frontiers in Psychology, 5, 1040. https://doi.org/10.3389/fpsyg.2014.01040
   PubMed Web of Science ®Google Scholar
• Simões, B., Amicis, R. d., Segura, A., Martín, M., & Ipiña, I. (2021). A cross reality wire assembly training system for workers with disabilities. International Journal on Interactive Design and Manufacturing (IJIDeM), 15(4), 429–440. https://doi.org/10.1007/s12008-021-00772-2
   Google Scholar
• Smith, C. M., Chillrud, S. N., Jack, D. W., Kinney, P., Yang, Q., & Layton, A. M. (2019). Laboratory validation of hexoskin biometric shirt at rest, submaximal exercise, and maximal exercise while riding a stationary bicycle. Journal of Occupational and Environmental Medicine, 61(4), e104–e111. https://doi.org/10.1097/JOM.0000000000001537
   PubMed Web of Science ®Google Scholar
• Soo, K. K. Y., Mavin, T. J., & Kikkawa, Y. (2021). Mastering automation: New airline pilots’ perspective. International Journal of Human–Computer Interaction, 37(7), 717–727. https://doi.org/10.1080/10447318.2021.1890487
   Web of Science ®Google Scholar
• Spreitzer, G. M. (1995). Psychological empowerment in the workplace: Dimensions, measurement, and validation. Academy of Management Journal, 38(5), 1442–1465. https://doi.org/10.2307/256865
   Web of Science ®Google Scholar
• Strenge, B., & Schack, T. (2021). Empirical relationships between algorithmic SDA-M-based memory assessments and human errors in manual assembly tasks. Scientific Reports, 11(1), 9473. https://doi.org/10.1038/s41598-021-88921-1
   PubMed Web of Science ®Google Scholar
• Szalma, J. L. (2014). On the application of motivation theory to human factors/ergonomics: Motivational design principles for human–technology interaction. Human Factors, 56(8), 1453–1471. https://doi.org/10.1177/0018720814553471
   PubMed Web of Science ®Google Scholar
• Szalma, J. L. (2020). Basic needs, goals and motivation. Journal: The Cambridge Handbook of Personality Psychology, 330–338. https://doi.org/10.1017/9781108264822.030
   Google Scholar
• UK commission for employment and skills. (2014). The future of work: Jobs and skills in 2030 [Evidence report 84]. https://assets.publishing.service.gov.uk/media/5a7e312aed915d74e6224b43/er84-the-future-of-work-evidence-report.pdf
   Google Scholar
• Ulmer, J., Braun, S., Cheng, C.-T., Dowey, S., & Wollert, J. (2020). Gamified virtual reality training environment for the manufacturing industry [Paper presentation]. 2020 19th International Conference on Mechatronics-Mechatronika (ME),
   Google Scholar
• Usuga Cadavid, J. P., Lamouri, S., Grabot, B., Pellerin, R., & Fortin, A. (2020). Machine learning applied in production planning and control: A state-of-the-art in the era of industry 4.0. Journal of Intelligent Manufacturing, 31(6), 1531–1558. https://doi.org/10.1007/s10845-019-01531-7
   Web of Science ®Google Scholar
• Vallerand, R. J., Blais, M. R., Lacouture, Y., & Deci, E. L. (1987). L'Échelle des Orientations Générales à la Causalité: Validation canadienne française du General Causality Orientations Scale. Canadian Journal of Behavioural Science / Revue Canadienne Des Sciences du Comportement, 19(1), 1–15. https://doi.org/10.1037/h0079872
   Web of Science ®Google Scholar
• Van den Broeck, A., Howard, J. L., Van Vaerenbergh, Y., Leroy, H., & Gagné, M. (2021). Beyond intrinsic and extrinsic motivation: A meta-analysis on self-determination theory’s multidimensional conceptualization of work motivation. Organizational Psychology Review, 11(3), 240–273. https://doi.org/10.1177/20413866211006173
   Web of Science ®Google Scholar
• Van der Klink, M. R., & Streumer, J. N. (2002). Effectiveness of on‐the‐job training. Journal of European Industrial Training, 26(2/3/4), 196–199. https://doi.org/10.1108/03090590210422076
   Google Scholar
• Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model. Decision Sciences, 33(2), 297–316. https://doi.org/10.1111/j.1540-5915.2002.tb01646.x
   Web of Science ®Google Scholar
• Vidal-Balea, A., Blanco-Novoa, O., Fraga-Lamas, P., Vilar-Montesinos, M., & Fernández-Caramés, T. M. (2020). A collaborative augmented reality application for training and assistance during shipbuilding assembly processes. Proceedings, 54(1), 4. https://doi.org/10.3390/proceedings2020054004
   PubMedGoogle Scholar
• Wickens, C. (2018). Automation stages & levels, 20 years after. Journal of Cognitive Engineering and Decision Making, 12(1), 35–41. https://doi.org/10.1177/1555343417727438
   Google Scholar
• Wickens, C. D., & Dixon, S. R. (2007). The benefits of imperfect diagnostic automation: A synthesis of the literature. Theoretical Issues in Ergonomics Science, 8(3), 201–212. https://doi.org/10.1080/14639220500370105
   Google Scholar
• Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114–123.
   Web of Science ®Google Scholar
• Zawadzki, P., Zywicki, K., Bun, P., & Gorski, F. (2020). Employee training in an intelligent factory using virtual reality. IEEE Access. 8, 135110–135117. https://doi.org/10.1109/ACCESS.2020.3010439
   Web of Science ®Google Scholar
• Zecca, G., Györkös, C., Becker, J., Massoudi, K., de Bruin, G. P., & Rossier, J. (2015). Validation of the French Utrecht Work Engagement Scale and its relationship with personality traits and impulsivity. European Review of Applied Psychology, 65(1), 19–28. https://doi.org/10.1016/j.erap.2014.10.003
   Web of Science ®Google Scholar
• Zhou, L., Jiang, Z., Geng, N., Niu, Y., Cui, F., Liu, K., & Qi, N. (2022). Production and operations management for intelligent manufacturing: A systematic literature review. International Journal of Production Research, 60(2), 808–846. https://doi.org/10.1080/00207543.2021.2017055
   Web of Science ®Google Scholar
• Zirar, A., Ali, S. I., & Islam, N. (2023). Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda. Technovation, 124, 102747. https://doi.org/10.1016/j.technovation.2023.102747
   Web of Science ®Google Scholar