Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-20T01:54:07.441Z Has data issue: false hasContentIssue false
  • English
  • Français

How Digital Technologies Modify The Work Characteristics: A Preliminary Study

Published online by Cambridge University Press:  22 February 2021

Léa Fréour Open the ORCID record for Léa Fréour [Opens in a new window] ,
Sabine Pohl Open the ORCID record for Sabine Pohl [Opens in a new window]  and
Adalgisa Battistelli Open the ORCID record for Adalgisa Battistelli [Opens in a new window]
Léa Fréour*
Affiliation:
Université de Bordeaux (France) Université Libre de Bruxelles (Belgium)
Sabine Pohl
Affiliation:
Université Libre de Bruxelles (Belgium)
Adalgisa Battistelli
Affiliation:
Université de Bordeaux (France)
*
Correspondence concerning this article should be addressed to Léa Fréour. Université de Bordeaux. LabPsy, EA 4139, Bordeaux (France). Université Libre de Bruxelles. Centre de Recherche en Psychologie du Travail et de la Consommation. Bruxelles (Belgium). E-mail: lea.freour@u-bordeaux.fr
Get access Rights & Permissions [Opens in a new window]

Abstract

New technologies with unprecedented agentic capabilities (i.e., action selection, protocol development) are now introduced in organizations such as Big Data, 3D printing or artificial intelligence. Because they are endowed with novel capabilities that might compete with human agency, they might disrupt the way employees work. Based on the work design model, this study aims to examine their introduction in the daily work activities and the consequent perceptions of the work characteristics. Building on Murray’s et al. (2020) proposal, we offer a classification of the digital technologies to conceptualize their relationship with the work characteristics. To explore the changes induced by two digital technologies (i.e., drones, robotic automation process), we interviewed 3 types of employees (i.e., experts, managers, users) from an organization which has started a digitalization process and we conducted a thematic analysis. Our analysis revealed three main themes that are discussed: A technological theme (arresting, assisting), a work characteristic theme and a theme about the human-technology relationship (agentic, non-agentic). Results showed that employee autonomy has not been reduced when digital technologies executed repetitive and unmotivated tasks and that jobs in the digital work context may be marked by a high level of knowledge characteristics. Moreover, technologies with agentic capabilities may be perceived as a non-human agent. Theoretical contributions for the work design model are then examined.

Keywords

digital workdigitalizationtechnologywork characteristicswork design
Type
Research Article
Information
The Spanish Journal of Psychology , Volume 24 , 2021 , e14
Copyright
© Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Conflicts of Interest: None.

Funding statement: This work was supported by the French Region Nouvelle-Aquitaine 2018-1r40203.

References

Ackerman, P. L., & Kanfer, R. (2020). Work in the 21st century: New directions for aging and adult development. American Psychologist, 75(4), 486–498. https://doi.org/10.1037/amp0000615CrossRefGoogle ScholarPubMed
Ashford, S. J., De Stobbeleir, K., & Nujella, M. (2016). To seek or not to seek: Is that the only question? Recent developments in feedback-seeking literature. Annual Review of Organizational Psychology and Organizational Behavior, 3, 213–239. https://doi.org/10.1146/annurev-orgpsych-041015-062314CrossRefGoogle Scholar
Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122–147. https://doi.org/10.1037/0003-066X.37.2.122CrossRefGoogle Scholar
Bandura, A. (1997). Self-Efficacy: The exercise of control. Freeman.Google Scholar
Bandura, A. (2008). Reconstrual of “free will” from the agentic perspective of social cognitive theory. In Baer, J., Kaufman, J. C., & Baumeister, R. F. (Eds.), Are we free? Psychology and free will (pp. 1689–1699). Oxford University Press. https://doi.org/10.1017/CBO9781107415324.004Google Scholar
Bareil, C. (2009). Gérer le volet humain du changement [Managing the human side of change]. Les Editions Transcontinental.Google Scholar
Barley, S. R. (1990). The alignment of technology and structure through roles and networks. Administrative Science Quarterly, 35(1), 61–103. https://doi.org/10.2307/2393551CrossRefGoogle ScholarPubMed
Barley, S. R. (2015). Why the internet makes buying a car less loathsome: How technologies change role relations. Academy of Management Discoveries, 1(1), 31–60. https://doi.org/10.5465/amd.2013.0016CrossRefGoogle Scholar
Barrett, M., Oborn, E., Orlikowski, W. J., & Yates, J. (2012). Reconfiguring boundary relations: Robotic innovations in pharmacy work. Organization Science, 23(5), 1213-1522. https://doi.org/10.1287/orsc.1100.0639CrossRefGoogle Scholar
Battistelli, A., & Odoardi, C. (2018). Les défis de la 4ème révolution industrielle: La dynamique entre le changement et l’innovation [The challenges of the 4th industrial revolution: The dynamic between change and innovation]. In Lauzier, M. & Lemieux, N. (Eds.), Améliorer la gestion du changement dans les organisations [Improve change management in organizations]. Presses de l’Université du Québec.Google Scholar
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3, 77–101. http://doi.org/10.1191/1478088706qp063oaCrossRefGoogle Scholar
Brenon, A., Portet, F., & Vacher, M. (2016). Preliminary study of adaptive decision-making system for vocal command in smart home. 12th International Conference on Intelligent Environments (pp. 218–221). IIEE. http://doi.org/10.1109/IE.2016.49Google Scholar
Brettel, M., Friederichsen, N., Keller, M., & Rosenberg, M. (2014). How virtualization, decentralization and network building change the manufacturing landscape: An industry 4.0 perspective. International Journal of Information and Communication Engineering, 8(1), 37–44. https://doi.org/10.5281/zenodo.2658202Google Scholar
Broadbent, E. (2017). Interactions with robots: The truths we reveal about ourselves. Annual Review of Psychology, 68, 627–652. https://doi.org/10.1146/annurev-psych-010416-043958CrossRefGoogle ScholarPubMed
Brockmyer, J. H., Fox, C. M., Curtiss, K. A., McBroom, E., Burkhart, K. M., & Pidruzny, J. N. (2009). The development of the Game Engagement Questionnaire: A measure of engagement in video game-playing. Journal of Experimental Social Psychology, 45(4), 624–634. https://doi.org/10.1016/j.jesp.2009.02.016CrossRefGoogle Scholar
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.Google Scholar
Campion, M. A. (1988). Interdisciplinary approaches to job design: A replication with methodological extensions. Academy of Management Annual Meeting Proceedings, 1987, 249–253. https://doi.org/10.5465/ambpp.1987.17534233Google Scholar
Cascio, W. F., & Montealegre, J. R. (2016). How technology is changing work and organizations. Annual Review of Organizational Psychology and Organizational Behavior, 3, 349–375. https://doi.org/10.1146/annurev-orgpsych-041015-062352CrossRefGoogle Scholar
Davidsson, P., Hajinasab, B., Holmgren, J., Jevinger, Å., & Persson, J. A. (2016). The fourth wave of digitalization and public transport: Opportunities and challenges. Sustainability, 8(12), Article 1248. https://doi.org/10.3390/su8121248CrossRefGoogle Scholar
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenum. http://doi.org/10.1007/978-1-4899-2271-7CrossRefGoogle Scholar
Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86(3), 499–512. https://doi.org/10.1037/0021-9010.86.3.499CrossRefGoogle ScholarPubMed
Deng, X., & Joshi, K. D. (2016). Why individuals participate in micro-task crowdsourcing work environment: Revealing crowd workers’ perceptions. Journal of the Association for Information Systems, 17(10), Article 3. https://doi.org/10.17705/1jais.00441CrossRefGoogle Scholar
Domínguez, A., Saenz-De-Navarrete, J., de-Marcos, L., Fernández-Sanz, L., Pagés, C., & Martínez-Herráiz, J.-J. (2013). Gamifying learning experiences: Practical implications and outcomes. Computers & Education, 63, 380–392. https://doi.org/10.1016/j.compedu.2012.12.020CrossRefGoogle Scholar
Eriksson-Zetterquist, U., Lindberg, K., & Styhre, A. (2009). When the good times are over: Professionals encountering new technology. Human Relations, 62(8), 1145–1170. https://doi.org/10.1177/0018726709334879CrossRefGoogle Scholar
Eurofound. (2018). New tasks in old jobs: Drivers of change and implications for job quality. Publications Office of the European Union. http://eurofound.link/fomeef18004Google Scholar
Feldman, M. S. (2000). Organizational routines as a source of continuous change. Organization Science, 11(6), 611–629. https://doi.org/10.1287/orsc.11.6.611.12529CrossRefGoogle Scholar
Fiore, S. M., Wiltshire, T. J., Lobato, E. J. C., Jentsch, F. G., Huang, W. H., & Axelrod, B. (2013). Toward understanding social cues and signals in human–robot interaction: Effects of robot gaze and proxemic behavior. Frontiers in Psychology, 4, Article 859. https://doi.org/10.3389/fpsyg.2013.00859CrossRefGoogle ScholarPubMed
Flin, R., Mearns, K., O'Connor, P., & Bryden, R. (2000). Measuring safety climate: Identifying the common features. Safety Science, 34(1–3), 177–192. https://doi.org/10.1016/S0925-7535(00)00012-6CrossRefGoogle Scholar
Frey, C. B., & Osborne, M. A. (2013). The future of employment: How susceptible are jobs to computerization. University of OxfordGoogle Scholar
Fried, Y. (1991). Meta-Analytic comparison of the Job Diagnostic Survey and Job Characteristics Inventory as correlates of work satisfaction and performance. Journal of Applied Psychology, 76(5), 690–697. https://doi.org/10.1037/0021-9010.76.5.690CrossRefGoogle Scholar
Gibson, C. B., Gibbs, J. L., Stanko, T. L., Tesluk, P., & Cohen, S. G. (2011). Including the “I” in virtuality and modern job design: Extending the job characteristics model to include the moderating effect of individual experiences of electronic dependence and copresence. Organization Science, 22(6), 1369–1683. https://doi.org/10.1287/orsc.1100.0586CrossRefGoogle Scholar
Grant, A. M., Fried, Y., Parker, S. K., & Frese, M. (2010). Putting job design in context: Introduction to the special issue. Journal of Organizational Behavior, 31, 145–157. https://doi.org/10.1002/job.679CrossRefGoogle Scholar
Hackman, J. R., & Oldham, G. R. (1975). Development of the Job Diagnostic Survey. Journal of Applied Psychology, 60(2), 159–170. https://doi.org/10.1037/h0076546CrossRefGoogle Scholar
Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for industry 4.0 scenarios. System Sciences (HICSS), 49th Hawaii International Conference (pp. 3928–3937). http://doi.org/10.1109/HICSS.2016.488CrossRefGoogle Scholar
Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23–34. https://doi.org/10.1016/j.compind.2017.04.002CrossRefGoogle Scholar
Howard, A. (1995). The changing nature of work . Jossey-Bass.Google Scholar
Howitt, D. (2016). Introduction to qualitative research methods in psychology: Putting theory into practice (3rd Ed.). Pearson.Google Scholar
Humphrey, S. E., Nahrgang, J. D., & Morgeson, F. P. (2007). Integrating motivational, social, and contextual work design features: A meta-analytic summary and theoretical extension of the work design literature. Journal of Applied Psychology, 92(5), 1332–1356. https://doi.org/10.1037/0021-9010.92.5.1332CrossRefGoogle ScholarPubMed
Inkermann, D., Schneider, D., Martin, N. L., Lembeck, H., Zhang, J., & Thiede, S. (2019). A framework to classify Industry 4.0 technologies across production and product development. Procedia CIRP, 84, 973–978. https://doi.org/10.1016/j.procir.2019.04.218CrossRefGoogle Scholar
Irizarry, J., Gheisari, M., & Walker, B. N. (2012). Usability assessment of drone technology as safety inspection tools. Journal of Information Technology in Construction (ITcon), 17(12), 194–212.Google Scholar
Isla Díaz, R., & Díaz Cabrera, D. (1997). Safety climate and attitude as evaluation measures of organizational safety. Accident Analysis & Prevention, 29(5), 643–650. https://doi.org/10.1016/S0001-4575(97)00015-8CrossRefGoogle ScholarPubMed
Jetter, J., Eimecke, J., & Rese, A. (2018). Augmented reality tools for industrial applications: What are potential key performance indicators and who benefits? Computers in Human Behavior, 87, 18–33. https://doi.org/10.1016/j.chb.2018.04.054CrossRefGoogle Scholar
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. http://doi.org/10.1136/svn-2017-000101CrossRefGoogle ScholarPubMed
Johns, G. (2006). The essential impact of context on organizational behavior. Academy of Management Review, 31(2), 386–408. https://doi.org/10.5465/amr.2006.20208687CrossRefGoogle Scholar
Johns, G. (2010). Some unintended consequences of job design. Journal of Organizational Behavior, 31, 361–369. https://doi.org/10.1002/job.669CrossRefGoogle Scholar
Karasek, R. A.. (1979). Job demands, job decision latitude, and mental strain: Implications for job redesign. Administrative Science Quarterly, 24, 285–308. http://doi.org/10.2307/2392498CrossRefGoogle Scholar
Kemp, N. J., & Clegg, C. W. (1987). Information technology and job design: A case study on computerized numerically controlled machine tool working. Behaviour and Information Technology, 6(2), 109–124. https://doi.org/10.1080/01449298708901821CrossRefGoogle Scholar
Kipper, L. M., Furstenau, L. B., Hoppe, D., Frozza, R., & Iespen, S. (2020). Scopus scientific mapping production in industry 4.0 (2011–2018): A bibliometric analysis. International Journal of Production Research, 58, 1605–1627. https://doi.org/10.1080/00207543.2019.1671625CrossRefGoogle Scholar
Koceski, S., & Koceska, N. (2016). Evaluation of an assistive telepresence robot for elderly healthcare. Journal of Medical Systems, 40(5), Article 121. https://doi.org/10.1007/s10916-016-0481-xCrossRefGoogle ScholarPubMed
Kohler, D., & Weisz, J.-D. (2016). Industrie 4.0: Comment caractériser cette quatrième révolution industrielle et ses enjeux? [Industry 4.0: How can we characterize this fourth industrial revolution and its challenges?]. Annales des Mines - Réalités industrielles, 4, 51–56. https://doi.org/10.3917/rindu1.164.0051CrossRefGoogle Scholar
Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory. Oxford University PressGoogle Scholar
Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data & Society, 5(1). https://doi.org/10.1177/2053951718756684CrossRefGoogle Scholar
Lughofer, E., & Sayed-Mouchaweh, M. (2019). Prologue: Predictive maintenance in dynamic systems. In Lughofer, E. & Sayed-Mouchaweh, M. (Eds.), Predictive maintenance in dynamic systems (pp.1–23). Springer, Cham. https://doi.org/10.1007/978-3-030-05645-2_1CrossRefGoogle Scholar
Lupushor, S., & Fradera, A. (2017). The future of work. In Hertel, G., Stone, D. L., Johnson, R. D. & Passmore, J. (Eds.), The Wiley Blackwell handbook of the psychology of the internet at work (pp. 481–508). John Wiley & Sons.CrossRefGoogle Scholar
McFarland, L. A., & Ployhart, R. E. (2015). Social media: A contextual framework to guide research and practice. Journal of Applied Psychology, 100(6), 1653–1677. https://doi.org/10.1037/a0039244CrossRefGoogle ScholarPubMed
Mendling, J., Decker, G., Hull, R., Reijers, H. A., & Weber, I. (2018). How do machine learning, robotic process automation, and blockchains affect the human factor in business process management? Communications of the Association for Information Systems, 43, 1–23. http://doi.org/10.17705/1CAIS.04319Google Scholar
Morgeson, F. P., & Campion, M. A. (2002). Minimizing tradeoffs when redesigning work: Evidence from a longitudinal quasi-experiment. Personnel Psychology, 55, 589–612. https://doi.org/10.1111/j.1744-6570.2002.tb00122.xCrossRefGoogle Scholar
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/10.1037/0021-9010.91.6.1321CrossRefGoogle Scholar
Murray, A., Rhymer, J., & Sirmon, D. (2020). Humans and technology: Forms of conjoined agency in organizations. Academy of Management Review. https://doi.org/10.5465/amr.2019.0186CrossRefGoogle Scholar
Nass, C., Steuer, J., & Tauber, E. R. (1994). Computers are social actors . In Adelson, B., Dumais, S., & Olson, J. (Eds.), CHI’94: Proceedings of the SIGCHI Conference on Human factors in Computing Systems (pp 72–78). Association for Computing Machinery. https://doi.org/10.1145/191666.191703CrossRefGoogle Scholar
O’Kane, P., Smith, A., & Lerman, M. P. (2019). Building transparency and trustworthiness in inductive research through computer-aided qualitative data analysis software. Organizational Research Methods, 24, 104–139. https://doi.org/10.1177/1094428119865016CrossRefGoogle Scholar
Oesterreich, T. D., & Teuteberg, F. (2016). Understanding the implications of digitization and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Computers in Industry, 83, 121–139. https://doi.org/10.1016/j.compind.2016.09.006CrossRefGoogle Scholar
Oh, C., Lee, T., Kim, Y., Park, S., Kwon, S., & Suh, B. (2017). Us vs. them: Understanding artificial intelligence technophobia over the Google DeepMind Challenge Match. Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 2523–2534). Association for Computing Machinery. https://doi.org/10.1145/3025453.3025539CrossRefGoogle Scholar
Oldham, G. R., & Hackman, J. R. (2010). Not what it was and not what it will be: The future of job design research. Journal of Organizational Behavior, 31, 463–479. https://doi.org/10.1002/job.678CrossRefGoogle Scholar
Oztemel, E., & Gursev, S. (2018). Literature review of Industry 4.0 and related technologies. Journal of Intelligent Manufacturing, 31, 127–182. https://doi.org/10.1007/s10845-018-1433-8CrossRefGoogle Scholar
Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors, 52(3), 381–410. https://doi.org/10.1177/0018720810376055CrossRefGoogle ScholarPubMed
Parker, S. K. (2014). Beyond motivation: Job and work design for development, health, ambidexterity, and more. Annual Review of Organizational Psychology and Organizational Behavior, 65, 661–691. https://doi.org/10.1146/annurev-psych-010213-115208Google ScholarPubMed
Parker, S. K., & Grote, G. (2020). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied Psychology. https://doi.org/10.1111/apps.12241CrossRefGoogle Scholar
Parker, S. K., Morgeson, F. P., & Johns, G. (2017). One hundred years of work design research: Looking back and looking forward. Journal of Applied Psychology, 102(3), 403–420. https://doi.org/10.1037/apl0000106CrossRefGoogle ScholarPubMed
Parker, S. K., van den Broeck, A., & Holman, D. J. (2017). Work design influences: A synthesis of multilevel factors that affect the design of jobs. Academy of Management Annals, 11(1), 267–308. https://doi.org/10.5465/annals.2014.0054CrossRefGoogle Scholar
Parker, S. K., & Wall, T. D. (1998). Modern manufacturing and the work design agenda. In Parker, S. K. & Wall, T. D. (Eds.), Job and work design: Organizing work to promote well-being and effectiveness (pp. 57–84). SAGE Publications, Inc.Google Scholar
Petrillo, A., De Felice, F., Cioffi, R., & Zomparelli, F. (2018). Fourth industrial revolution: Current practices, challenges, and opportunities. In Petrillo, A., Cioffi, R., & De Felice, F. (Eds.), Digital Transformation in Smart Manufacturing (pp. 1–20). IntechOpen. https://doi.org/10.5772/intechopen.72304CrossRefGoogle Scholar
Plesner, U., & Raviola, E. (2016). Digital technologies and a changing profession: New management devices, practices and power relations in news work. Journal of Organizational Change Management, 29(7), 1044–1065. https://doi.org/10.1108/JOCM-09-2015-0159CrossRefGoogle Scholar
Powles, J., & Hodson, H. (2017). Google DeepMind and healthcare in an age of algorithms. Health Technology, 7, 351–367. https://doi.org/10.1007/s12553-017-0179-1CrossRefGoogle Scholar
Pozdnyakova, U. A., Golikov, V. V., Peters, I. A., & Morozova, I. A. (2019). Genesis of the revolutionary transition to industry 4.0 in the 21st Century and overview of previous industrial revolutions. In Popkova, E., Ragulina, Y., & Bogoviz, A. (Eds.), Industry 4.0: Industrial revolution of the 21st century (pp. 11–19). Springer, Cham. http://doi.org/10.1007/978-3-319-94310-7_2CrossRefGoogle Scholar
Procci, K., Singer, A. R., Levy, K. R., & Bowers, C. (2012). Measuring the flow experience of gamers: An evaluation of the DFS–2. Computers in Human Behavior, 28(6), 2306–2312. https://doi.org/10.1016/j.chb.2012.06.039CrossRefGoogle Scholar
Richter, A., Heinrich, P., Stocker, A., & Schwabe, G. (2018). Digital work design: The interplay of human and computer in future work practices as an interdisciplinary (grand) challenge. Business and Information Systems Engineering, 60(3), 259–264. https://doi.org/10.1007/s12599-018-0534-4CrossRefGoogle Scholar
Rubin, V. L., Chen, Y., & Thorimbert, L. M. (2010). Artificially intelligent conversational agents in libraries. Library Hi Tech, 28(4), 496–522. https://doi.org/10.1108/07378831011096196CrossRefGoogle Scholar
Salancik, G. R., & Pfeffer, J. (1978). A social information processing approach to job attitudes and task design. Administrative Science Quarterly, 23(2), 224–253. http://doi.org/10.2307/2392563CrossRefGoogle ScholarPubMed
Salkin, C., Oner, M., Ustundag, A., & Cevikcan, E. (2018). A Conceptual framework for industry 4.0. In Industry 4.0: Managing the digital transformation (pp. 3–23). Springer, Cham. https://doi.org/10.1007/978-3-319-57870-5_1CrossRefGoogle Scholar
Saunders, C., Wiener, M., Klett, S., & Sprenger, S. (2017). The impact of mental representations on ICT-related overload in the use of mobile phones. Journal of Management Information Systems, 34(3), 803–825. https://doi.org/10.1080/07421222.2017.1373010CrossRefGoogle Scholar
Schneider, P. (2018). Managerial challenges of Industry 4.0: An empirically backed research agenda for a nascent field. Review of Managerial Science, 12(3), 803–848. https://doi.org/10.1007/s11846-018-0283-2CrossRefGoogle Scholar
Schroeder, A. N., Bricka, T. M., & Whitaker, J. H. (2021). Work design in a digitized gig economy. Human Resource Management Review, 31, Article 100692. https://doi.org/10.1016/j.hrmr.2019.100692CrossRefGoogle Scholar
Schuh, G., Anderl, R., Gausemeier, J., ten Hompel, M., & Wahlster, W. (2017). Industry 4.0 maturity index. Managing the digital transformation of companies. Acatech - National Academy of Science and Engineering. https://www.acatech.de/wp-content/uploads/2018/03/acatech_STUDIE_Maturity_Index_eng_WEB.pdfGoogle Scholar
Schwab, K. (2017). La quatrième révolution industrielle [The fourth industrial revolution]. Dunod.Google Scholar
Schwarzmüller, T., Brosi, P., Duman, D., & Welpe, I. M. (2018). How does the digital transformation affect organizations? Key themes of change in work design and leadership. Management Revue, 29(2), 114–138. https://doi.org/10.5771/0935-9915-2018-2-114CrossRefGoogle Scholar
Seligman, M. E. (1972). Learned helplessness. Annual Review of Medicine, 23(1), 407–412. http://doi.org/10.1146/annurev.me.23.020172.002203CrossRefGoogle ScholarPubMed
Sewell, G., Barker, J. R., & Nyberg, D. (2012). Working under intensive surveillance: When does ‘measuring everything that moves’ become intolerable? Human Relations, 65(2), 189–215. https://doi.org/10.1177/0018726711428958CrossRefGoogle Scholar
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human-Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118CrossRefGoogle Scholar
Skitka, L. J., Mosier, K., & Burdick, M. D. (2000). Accountability and automation bias. International Journal of Human Computer Studies, 52(4), 701–717. https://doi.org/10.1006/ijhc.1999.0349CrossRefGoogle Scholar
Spada, S., Ghibaudo, L., Gilotta, S., Gastaldi, L., & Cavatorta, M. P. (2017). Analysis of exoskeleton introduction in industrial reality: Main issues and EAWS risk assessment. In Goonetilleke, R. & Karwowski, W. (Eds.), Advances in physical ergonomics and human factors. AHFE 2017. Advances in intelligent systems and computing (Vol. 602, pp. 236–244). Springer, Cham. https://doi.org/10.1007/978-3-319-60825-9_26Google Scholar
Suh, A., & Prophet, J. (2018). The state of immersive technology research: A literature analysis. Computers in Human Behavior, 86, 77–90. https://doi.org/10.1016/j.chb.2018.04.019CrossRefGoogle Scholar
Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the Longwall Method of coal-getting: An examination of the psychological situation and defenses of a work group in relation to the social structure and technological content of the work system. Human Relations, 4(1), 3–38. https://doi.org/10.1177/001872675100400101CrossRefGoogle Scholar
Turner, P. (2017). A psychology of user experience. Springer International Publishing. https://doi.org/10.1007/978-3-319-70653-5CrossRefGoogle Scholar
Wall, T. D., Corbett, J. M., Clegg, C. W., Jackson, P. R., & Martin, R. (1990). Advanced manufacturing technology and work design: Towards a theoretical framework. Journal of Organizational Behavior, 11(3), 201–219. https://doi.org/10.1002/job.4030110304CrossRefGoogle Scholar
Wang, B., Liu, Y., & Parker, S. K. (2020). How does the use of information communication technology affects individuals: A work design perspective. Academy of Management Annals, 14, Article 2. https://doi.org/10.5465/annals.2018.0127CrossRefGoogle Scholar
Waschull, S., Bokhorst, J. A. C., Molleman, E., & Wortmann, J. C. (2019). Work design in future industrial production: Transforming towards cyber-physical systems. Computers and Industrial Engineering, 139, Article 105679. https://doi.org/10.1016/j.cie.2019.01.053Google Scholar
Weller, C., Kleer, R., & Piller, F. T. (2015). Economic implications of 3D printing: Market structure models in light of additive manufacturing revisited. International Journal of Production Economics, 164, 43–56. https://doi.org/10.1016/j.ijpe.2015.02.020CrossRefGoogle Scholar
Wilkesmann, M., & Wilkesmann, U. (2018). Industry 4.0 – Organizing routines or innovations ? Journal of Information and Knowledge Management Systems, 48(2), 238–254. https://doi.org/10.1108/VJIKMS-04-2017-0019CrossRefGoogle Scholar
Wood, R. E. (1986). Task complexity: Definition of the construct. Organizational Behavior and Human Decision Processes, 37(1), 60–82. https://doi.org/10.1016/0749-5978(86)90044-0CrossRefGoogle Scholar
Xie, J. L., Elangovan, A. R., Hu, J., & Hrabluik, C. (2018). Charting new terrain in work design: A study of hybrid work characteristics. Applied Psychology, 68(3), 479–512. https://doi.org/10.1111/apps.12169CrossRefGoogle Scholar
Yoo, Y., Lyytinen, K. J., Boland, R. J., Berente, N., Gaskin, J., Schutz, D., & Srinivasan, N. (2010). The next wave of digital innovation: Opportunities and challenges: A report on the research workshop 'Digital challenges in innovation research'. SSRN. https://doi.org/10.2139/ssrn.1622170CrossRefGoogle Scholar
Zammuto, R. F., Griffith, T. L., Majchrzak, A., Dougherty, D. J., & Faraj, S. (2007). Information technology and the changing fabric of organization. Organization Science, 18(5), 749–883. https://doi.org/10.1287/orsc.1070.0307CrossRefGoogle Scholar
Zuboff, S. (1988). In the age of the smart machine: The future of work and power. Heinemann Professional Publishing.Google Scholar