The Vicious Circles of Skill Erosion: A Case Study of Cognitive Automation
Open accessAbstract
Cognitive automation powered by advanced intelligent technologies is increasingly enabling organizations to automate more of their knowledge work tasks. Although this often offers higher efficiency and lower costs, cognitive automation exacerbates the erosion of human skill and expertise in automated tasks. Accepting the erosion of obsolete skills is necessary to reap the benefits of technology—however, the erosion of essential human expertise is problematic if workers remain accountable for tasks for which they lack sufficient understanding, rendering them incapable of responding if the automation fails. Though the phenomenon is widely acknowledged, the dynamics behind such undesired skill erosion are poorly understood. Thus, taking the perspective of sociotechnical systems, we conducted a case study of an accounting firm that had experienced skill erosion over a number of years due to reliance on their software’s automated functions. We synthesized our findings using causal loop modeling based on system dynamics. The resulting dynamic model explains skill erosion via an interplay between humans’ automation reliance, complacency, and mindful conduction. It shows how increasing reliance on automation fosters complacency at both individual and organizational levels, weakening workers’ mindfulness across three work task facets (activity awareness, competence maintenance, and output assessment), resulting in skill erosion. Such skill erosion may remain obscure, acknowledged by neither workers nor managers. We conclude by discussing the implications for theory and practice and identifying directions for future research.
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An organizational case study documented self-reinforcing skill erosion cycles in an accounting firm.
“Rinta-Kahila et al.'s (2023) organizational case study documented similar self-reinforcing skill erosion cycles in an accounting firm.”
From The Tragedy of the Cognitive Commons: How AI Could Disrupt the Regeneration of Professional Expertise (2026) · cites this paper as Rinta-Kahila et al.'s (2023)
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Cites
Artificial Intelligence and Management: The Automation–Augmentation Paradox
Raisch, S., & Krakowski, S. (2021) · Academy of Management Review · doi:10.5465/amr.2018.0072 · Cited by 2,755 · see citing works
Google Effects on Memory: Cognitive Consequences of Having Information at Our Fingertips
Sparrow, B., Liu, J., & Wegner, D. M. (2011) · Science · doi:10.1126/science.1207745 · Cited by 1,762 · cited 1× · see citing works
“Relinquishing mindful maintenance of domain competence is similar to the kind of transactive memory process in IT use that Sparrow et al. (2011) identified, where human memory adapts to computing and communication technology: people learn what the computer “knows” and let this “knowing of knowing”…”
BackgroundComplacency and Bias in Human Use of Automation: An Attentional Integration
Parasuraman, R., & Manzey, D. H. (2010) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720810376055 · Cited by 1,710 · cited 6× · see citing works
“…strong that it manifests as complacency : the assumption that “all is well” without understanding the actual state of the matter (Merritt et al., 2019; Parasuraman & Manzey, 2010) or “a feeling of calm satisfaction with your own abilities or situation that prevents you from trying harder”…”
BackgroundInfluentialAutomation bias: a systematic review of frequency, effect mediators, and mitigators
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000089 · Cited by 1,162 · see citing works
Working and organizing in the age of the learning algorithm
Faraj, S., Pachidi, S., & Sayegh, K. (2018) · Information and Organization · doi:10.1016/j.infoandorg.2018.02.005 · Cited by 929 · see citing works
Lebovitz, S., Lifshitz-Assaf, H., & Levina, N. (2022) · Organization Science · doi:10.1287/orsc.2021.1549 · Cited by 632 · cited 5× · see citing works
“Lebovitz et al. (2022) term such lack of AI use as unengaged “augmentation” : no actual augmentation was achieved in cases where humans just overrode AI’s input due to the lack of explanations.”
BackgroundInfluentialAugmenting the algorithm: Emerging human-in-the-loop work configurations
Grønsund, T., & Aanestad, M. (2020) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2020.101614 · Cited by 364 · cited 1× · see citing works
“…a balance between using cognitive technologies to substitute for humans (full automation, taking humans out of the loop) and complementing humans (augmenting humans, keeping them in the loop) (Asatiani et al., 2019; Grønsund & Aanestad, 2020; Krakowski et al., 2022; Raisch & Krakowski, 2021).”
BackgroundAccountability and automation bias
SKITKA, L. J., MOSIER, K., & BURDICK, M. D. (2000) · International Journal of Human-Computer Studies · doi:10.1006/ijhc.1999.0349 · Cited by 324 · cited 1× · see citing works
“Further, scholars have observed “automation bias” in various contexts—a tendency to favor the system’s suggestions when faced with contradictory evidence (Goddard et al., 2012, 2014; Jussupow et al., 2021; Parasuraman & Manzey, 2010; Skitka et al., 2000).”
BackgroundStrich, F., University of Bayreuth, G., Mayer, A.-S., University of Passau, G., Fiedler, M., & University of Passau, G. (2021) · Journal of the Association for Information Systems · doi:10.17705/1jais.00663 · Cited by 232 · cited 8× · see citing works
“This phenomenon is commonly referred to as skill erosion or deskilling 2 (Arnold & Sutton, 1998; Strich et al., 2021).”
BackgroundMethodInfluentialLou, B., & Wu, L. (2021) · MIS Quarterly · doi:10.25300/misq/2021/16565 · Cited by 177 · see citing works
Burton-Jones, A., McLean, E. R., & Monod, E. (2015) · European Journal of Information Systems · doi:10.1057/ejis.2014.31 · Cited by 167 · see citing works
Flagged first, then by citations
Research works printed but not linked: 66
- Agnew, A., Forrester, P., Hassard, J., & Procter, S. (1997). Deskilling and reskilling within the labour process: The case of computer integrated manufacturing. International Journal of Production Economics, 52(3), 317-324.
- Allen, R. T., & Choudhury, P. (2022). Algorithm- augmented work and domain experience: The countervailing forces of ability and aversion. Organization Science, 33(1), 149-169.
- Alter, S. (2013). Work system theory: Overview of core concepts, extensions, and challenges for the future. Journal of the Association for Information Systems, 14(2), 72-121.
- Arnold, V., Clark, N., Collier, P. A., Leech, S. A., & Sutton, S. G. (2006). The differential use and effect of knowledge-based system explanations in novice and expert judgment decisions. MIS Quarterly, 30(1), 79-97.
- Arnold, V., & Sutton, S. G. (1998). The theory of technology dominance: Understanding the impact of intelligent decision aids on decision maker's judgments. Advances in Accounting Behavioral Research, 1(3), 175-194.
- Asatiani, A., Malo, P., Nagbøl, P. R., Penttinen, E., Rinta-Kahila, T., & Salovaara, A. (2021). Sociotechnical envelopment of artificial intelligence: an approach to organizational deployment of inscrutable artificial intelligence systems. Journal of the Association for Information Systems, 22(2), 325-352.
- Asatiani, A., Penttinen, E., Rinta-Kahila, T., & Salovaara, A. (2019). Implementation of automation as distributed cognition in knowledge work organizations: Six recommendations for managers. Proceedings of the 40th International Conference on Information Systems.
- Axelsen, M. (2012). Continued use of intelligent decision aids and auditor knowledge: qualitative evidence. Proceedings of the 18 th Americas Conference on Information Systems.
- Axelsen, M. (2014). Technology impeded knowledge acquisition and retention: The effects of long- term use of intelligent decision aids on auditor professional knowledge [Unpublished doctoral thesis]. The University of Queensland.
- Baker, J., & Singh, H. (2019). The roots of misalignment: Insights on strategy implementation from a system dynamics perspective. The Journal of Strategic Information Systems, 28(4), Article 101576.
- Beane, M. (2019). Shadow learning: Building robotic surgical skill when approved means fail. Administrative Science Quarterly, 64(1), 87- 123.
- Von Bertalanffy, L. (1968). General system theory: Foundations, development, applications (Revised ed.). George Braziller.
- Boulding, K. E. (1956). General systems theory: The skeleton of science. Management Science, 2(3), 197-208.
- Braverman, H. (1974). Labor and monopoly capital: The degradation of work in the twentieth century. Monthly Review Press.
- Bravo, E. R. (2015). Automation and substitution of human knowledge: Assessment within the information system context. Proceedings of the Pacific Asia Conference on Information Systems.
- Burton-Jones, A. (2014). What have we learned from the smart machine? Information and Organization 24(2), 71-105.
- Butler, B. S., & Gray, P. H. (2006). Reliability, mindfulness, and information systems. MIS Quarterly, 30(2), 211-224.
- Cambridge Dictionary. (n.d.). Meaning of complacency in English. In Cambridge dictionary. Retrieved in 2023 from https://dictionary.cambridge.org/us/dictionary/ english/complacency.
- Carr, N. (2015). The glass cage: How our computers are changing us (1st ed.). Norton.
- Corbin, J., & Strauss, A. (2015). Basics of qualitative research: Techniques and Procedures for developing a grounded theory (4th ed.). SAGE.
- Davis, C. J., & Hufnagel, E. M. (2007). Through the eyes of experts: A socio-cognitive perspective on the automation of fingerprint work. MIS Quarterly, 31(4), 681-703.
- Dowling, Leech, & Moroney. (2008). Audit support system design and the declarative knowledge of long-term users. Journal of Emerging Technologies in Accounting, (5), 99-108.
- Drummond, H. (2008). The Icarus paradox: An analysis of a totally destructive system. Journal of Information Technology, 23(3), 176-184.
- Fang, Y., Lim, K. H., Qian, Y., & Feng, B. (2018). System dynamics modeling for information systems research: Theory development and practical application. MIS Quarterly, 42(4), 1303-1329.
- Fiol, C.-M., & O'Connor, E. J. (2003). Waking up! Mindfulness in the face of bandwagons. Academy of Management Review, 28(1), 54-70.
- Forrester, J. W. (1961). Industrial dynamics. MIT Press.
- Galletta, D. F., Durcikova, A., Everard, A., & Jones, B. M. (2005). Does spell-checking software need a warning label? Communications of the ACM, 48(7), 82-86.
- Garud, R., & Kumaraswamy, A. (2005). Vicious and virtuous circles in the management of knowledge: The case of Infosys Technologies. MIS Quarterly, 29(1), 9-33.
- Goddard, K., Roudsari, A., & Wyatt, J. C. (2014). Automation bias: Empirical results assessing influencing factors. International Journal of Medical Informatics, 83(5), 368-375.
- Hampton, C. (2005). Determinants of reliance: An empirical test of the theory of technology dominance. International Journal of Accounting Information Systems, 6(4), 217- 240.
- Hansen, S. W., Gogan, J. L., Baxter, R. J., & Garfield, M. J. (2019). Informed collaboration in health care: An embedded-cases study in geriatric telepsychiatry. Information Systems Journal, 29(2), 514-547.
- Jussupow, E., Spohrer, K., Heinzl, A., & Gawlitza, J. (2021). Augmenting medical diagnosis decisions? An investigation into physicians' decision-making process with artificial intelligence. Information Systems Research, 32(3), 713-735.
- Kozlowski, S. W. J., & Klein, K. J. (2000). A Multilevel approach to theory and research in organizations: Contextual, temporal, and emergent processes. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research and methods in organizations: Foundations, extensions, and new directions (pp. 3-90).
- Krakowski, S., Luger, J., & Raisch, S. (2022). Artificial intelligence and the changing sources of competitive advantage. Strategic Management Journal, 44(6), 1425-1452.
- Lyytinen, K., & Newman, M. (2008). Explaining information systems change: A punctuated socio-technical change model. European Journal of Information Systems, 17(6), 589- 613.
- Van Maanen, J. (1979). The fact of fiction in organizational ethnography. Administrative Science Quarterly, 24(4), 539-550.
- Martinez-Moyano, I. J., McCaffrey, D. P., & Oliva, R. (2014). Drift and adjustment in organizational rule compliance: Explaining the "regulatory pendulum" in financial markets. Organization Science, 25(2), 321-338.
- Maruyama, M. (1963). The second cybernetics: Deviation-amplifying mutual causal processes. American Scientist, 51(2), 164-179.
- Mascha, M. F., & Smedley, G. (2007). Can computerized decision aids do "damage"? A case for tailoring feedback and task complexity based on task experience. International Journal of Accounting Information Systems, 8(2), 73- 91.
- Masuch, M. (1985). Vicious circles in organizations. Administrative Science Quarterly, 30(1), 14-33.
- Mayer, A., Strich, F., & Fiedler, M. (2020). Unintended consequences of introducing AI systems for decision making. MIS Quarterly Executive, 19(4), 239-257.
- McCall, H., Arnold, V., & Sutton, S. G. (2008). Use of knowledge management systems and the impact on the acquisition of explicit knowledge. Journal of Information Systems, 22(2), 77-101.
- Mees-Buss, J., Welch, C., & Piekkari, R. (2020). From templates to heuristics: How and why to move beyond the Gioia methodology. Organizational Research Methods, 26(2). https://doi.org/ 10.1177/1094428120967716 · DOI
- Moray, N. (2003). Monitoring, complacency, scepticism and eutactic behaviour. International Journal of Industrial Ergonomics, 31(3), 175-178.
- Newell, S., & Marabelli, M. (2015). Strategic opportunities (and challenges) of algorithmic decision-making: A call for action on the long- term societal effects of "datification." The Journal of Strategic Information Systems, 24(1), 3-14.
- Oliver, N., Calvard, T., & Potočnik, K. (2017). Cognition, technology, and organizational limits: Lessons from the Air France 447 disaster. Organization Science, 28(4), 729-743.
- Pentland, B. T. (1999). Building process theory with narrative: From description to explaination. Academy of Management Review, 24(4), 711- 724.
- Rinta-Kahila, T., Penttinen, E., & Lyytinen, K. (2021). Organizational transformation with intelligent automation: Case Nokia Software. Journal of Information Technology Teaching Cases, 11(2), 101-109.
- Rinta-Kahila, T., Penttinen, E., & Lyytinen, K. (2023). Getting trapped in technical debt: Sociotechnical analysis of a legacy system's replacement. MIS Quarterly, 47(1), 1-32.
- Rinta-Kahila, T., Someh, I., Gillespie, N., Indulska, M., & Gregor, S. (2022). Algorithmic decision- making and system destructiveness: A case of automatic debt recovery. European Journal of Information Systems, 31(3), 313-338.
- Rosemann, M., & Vessey, I. (2008). Toward improving the relevance of information systems research to practice: Rhe role of applicability checks. MIS Quarterly, 32(1), 7-22.
- Sætre, A. S., & Van de Ven, A. H. (2021). Generating theory by abduction. Academy of Management Review, 46(4), 684-701.
- Salovaara, A., Lyytinen, K., & Penttinen, E. (2019). High reliability in digital organizing: mindlessness, the frame problem, and digital operations. MIS Quarterly, 43(2), 555-578.
- Sarker, S., Chatterjee, S., Xiao, X., & Elbanna, A. (2019). The sociotechnical axis of cohesion for the IS discipline: Its historical legacy and its continued relevance. MIS Quarterly, 43(3), 695-719.
- Scheepers, R., Lacity, M. C., & Willcocks, L. P. (2018). Cognitive automation as part of Deakin University's digital strategy. MIS Quarterly Executive, 17(2), 89-107.
- Schuppan, T. (2014). E-government at work level: Skilling or de-skilling? Proceedings of the 47th Hawaii International Conference on System Sciences (pp. 1927-1934).
- Senge, P. M. (2006). The fifth discipline: The art & practice of the learning organization (2nd ed.). Crown Books.
- Sterman, J. D. (2001). System dynamics modeling: Tools for learning in a complex world. California Management Review, 43(4), 8-25.
- Stone, G. D. (2007). Agricultural deskilling and the spread of genetically modified cotton in Warangal. Current Anthropology, 48(1), 67-87.
- Sutton, S. G., Arnold, V., & Holt, M. (2018). How much automation is too much? Keeping the human relevant in knowledge work. Journal of Emerging Technologies in Accounting, 15(2), 15-25.
- Thatcher, J. B., Wright, R. T., Sun, H., Zagenczyk, T. J., & Klein, R. (2018). Mindfulness in information technology use: Definitions, distinctions, and a new measure. MIS Quarterly, 42(3), 831-847.
- Tomasello, M. (1999). The cultural origins of human cognition. Harvard University Press.
- Trauth, E. M., & Jessup, L. M. (2000). Understanding computer-mediated discussions: positivist and interpretive analyses of group support system use. MIS Quarterly, 24(1), 43-78.
- Triki, A., & Weisner, M. M. (2014). Lessons from the literature on the theory of technology dominance: Possibilities for an extended research framework. Journal of Emerging Technologies in Accounting, 11(1), 41-69.
- Wallace, M., & Kalleberg, A. L. (1982). Industrial transformation and the decline of craft: The decomposition of skill in the printing industry. American Sociological Review, 47(3), 307-324.
- Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE.
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Lovett, N. (2026) · Human Resource Development Review · doi:10.1177/15344843261470602 · Cited by 7 · see citing works
- Authors
- Rinta-Kahila, T., The University of Queensland Business School, Penttinen, E., Aalto University School of Business, Salovaara, A., Aalto University / University of Helsinki, Soliman, W., University of Agder, N. /. U. O. J., Ruissalo, J., & Aalto University School of Business
- Affiliations
- The University of Queensland (AU)
- Aalto University (FI)
- University of Helsinki (FI)
- University of Agder (NO)
- Venue
- Journal of the Association for Information Systems
- Publisher
- Association for Information Systems
- Published
- 2023
- Volume
- 24
- Issue
- 5
- Pages
- 1378-1412
- ISSN
- 1536-9323 (ISSN-L)
- OpenAlex
- W4386554296
- Cited by
- 86
- References
- 11
- Citations by year
- 2026: 55 · 2025: 24 · 2024: 7
- FWCI
- 37.11
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Association for Information Systems (publishedVersion)
- Aaltodoc (Aalto University) (submittedVersion, other-oa)
- Journal of the Association for Information Systems (submittedVersion)
- Työväentutkimus Vuosikirja (submittedVersion)
- Jyväskylä University Digital Archive (University of Jyväskylä) (submittedVersion, public-domain)
- Funders
- University of Auckland
- University of New South Wales
- Academy of Finland (298879)
- Aalto-Yliopisto
- Jenny ja Antti Wihurin Rahasto
- Topics
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Decision Sciences › Management Science and Operations Research › Complex Systems and Decision Making
- Keywords
- skill erosion, cognitive automation, automation reliance, complacency, mindfulness, knowledge work, accounting firms, socio-technical systems, system dynamics, human expertise, competence maintenance, activity awareness
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