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Augmenting the algorithm: Emerging human-in-the-loop work configurations
Open accessAbstract
How do configurations of humans and algorithms evolve as firms adopt artificial intelligence (AI) capabilities, and what are the implications for work and organization? We explored these questions through a two-year long case study of an organization in the international maritime trade that introduced automated algorithmic support for data analysis and prediction work. Drawing on a human–machine configuration perspective, we found that humans and the algorithm were configured and reconfigured in multiple ways over time as the organization dealt with the introduction of algorithmic analysis. In contrast to replacing human work, the emergent configurations required new roles and redistribution of extant expertise to augment and improve the accuracy of the algorithm. Our analysis suggests that the new configuration resembled a human-in-the-loop pattern, comprised of both the augmentation work of auditing (i.e. the generation of a ground truth and assessment of the algorithmic output against this) as well as the work of altering the algorithm and the data acquisition architecture. Our research points to the strategic importance of a human-in-the-loop pattern for organizational reflexivity to ensure that the performance of the algorithm meets the organization’s requirements and changes in the environment.
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“In short, the deployment of algorithmic man-agement in organizational work introduces novel “human–machine configurations” that could transform relationships between managers and workers and their respective roles (Grønsund and Aanestad, 2020).”
From Algorithmic management in a work context (2021) · cites this paper as Grønsund and Aanestad, 2020
Experimental — accuracy not yet measuredIn the citing paper
“Simultaneously, the AI algorithms can be considered performative in the sense that they assist in decision-making, the extent to which their use can form organizational processes, or even take autonomous decisions (Faraj et al., 2018; Grønsund & Aanestad, 2020) that leads to new organization capabilities through AI.”
From Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes (2023) · cites this paper as Grønsund & Aanestad, 2020
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BackgroundInterpretive case studies in IS research: nature and method
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“We have conducted an in-depth, qualitative case study based on the interpretive research paradigm (Walsham, 1995).”
MethodArtificial Intelligence and Management: The Automation–Augmentation Paradox
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BackgroundRankings and Reactivity: How Public Measures Recreate Social Worlds
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Items 101–200 of 364
The role of use for the business value of big data analytics
Yeh, Y.-T., Eden, R., Fielt, E., & Syed, R. (2025) · Journal of strategic information systems · doi:10.1016/j.jsis.2025.101888 · Cited by 22
Human in the AI Loop in Production Environments
Emmanouilidis, C., Waschull, S., Bokhorst, J. A. C., & Wortmann, J. C. (2021) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-85910-7_35 · Cited by 23
Horita, F., Baptista, J., & de Albuquerque, J. P. (2020) · Information Systems Frontiers · doi:10.1007/s10796-020-10075-8 · Cited by 19
“These new technologies have therefore the potential to enhance decision making, while at the same time creating tensions and unintended effects that may disrupt established organisational structures and business models (Shrestha et al. 2019; Burton et al. 2019; Grønsund and Aanestad 2020).”
BackgroundHow to build trust in answers given by Generative AI for specific and vague financial questions
Zarifis, A., & Cheng, X. (2024) · Journal of Electronic Business & Digital Economics · doi:10.1108/jebde-11-2023-0028 · Cited by 21
“Firstly, it is necessary to keep expert practitioners in the loop and communicate their role clearly (Grønsund & Aanestad, 2020).”
BackgroundArtificial intelligence machines as relational nonhuman actors in entrepreneurial teams
Murtinu, S., & Massis, A. D. (2025) · Journal of Small Business Management · doi:10.1080/00472778.2025.2461031 · Cited by 18
“As explained by Goldfarb and Tucker (2019) and Greenstein et al. (2013), among others, AI machines reduce the costs to access, compute, and elaborate information, which leads to more effective strategic decision-making (Grønsund & Aanestad, 2020).”
Access to online learning: Machine learning analysis from a social justice perspective
McIntyre, N. A. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11280-5 · Cited by 24
“Other than the shift in mindset with regard to analytic models, the role of human expertise is being increasingly emphasised with regard to machine learning model development: hence the term, human-in-the-loop (Cranor, 2008; Dautenhahn, 1998; Grønsund & Aanestad, 2020).”
BackgroundHuman in the AI Loop via xAI and Active Learning for Visual Inspection
Rožanec, J. M., Montini, E., Cutrona, V., Papamartzivanos, D., Klemenčič, T., Fortuna, B., Mladenić, D., Veliou, E., Giannetsos, T., & Emmanouilidis, C. (2024) · Artificial Intelligence in Manufacturing · doi:10.1007/978-3-031-46452-2_22 · Cited by 17
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Beyond effective use: Integrating wise reasoning in machine learning development
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Eftimov, L., & Kitanovikj, B. (2023) · Journal of Human Resource Management · doi:10.46287/ottp6295 · Cited by 23
Herath, S., Bashardoust, A., Bole, Y., & Shrestha, Y. R. (2026) · European Journal of Information Systems · doi:10.1080/0960085x.2026.2616042 · Cited by 16
“Numerous frameworks for human-AI collaboration, such as human-in-the-loop systems (Grønsund & Aanestad, 2020) and human-AI ensembles, have been proposed to effectively integrate the complementary strengths of humans and AI (Choudhary et al., 2023; Shrestha et al., 2019).”
Waschull, S., & Emmanouilidis, C. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.04.246 · Cited by 19
Constantiou, I., Joshi, M. P., & Stelmaszak, M. (2023) · Journal of the Association for Information Systems · doi:10.17705/1jais.00833 · Cited by 21
Strategic Integration of Artificial Intelligence in the C-Suite: The Role of the Chief AI OfficerPreprint
Schmitt, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4812654 · Cited by 17
“Collaborative intelligence (CI) goes beyond human-in-the-loop (Grønsund and Aanestad, 2020; Boyacı et al., 2024; Wang et al., 2023b) and represents a paradigm in which human and AI systems collaborate, combining their distinct strengths to achieve superior outcomes (Schmitt, 2023).”
Deploying AI Governance Practices: A Revelatory Case Study
Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85447-8_19 · Cited by 18
“Simultaneously, the AI algorithms can be considered performative because of the extent to which their use can form organizational processes, and AI algorithms assist in decision-making or even take an autonomous decision [10, 11] that leads to new organization capabilities through AI.”
BackgroundGenerative AI Enhances Team Performance and Reduces Need for Traditional TeamsPreprint
Li, N., Zhou, H., & Mikel-Hong, K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4844976 · Cited by 30
Unpacking AI at work: Data work, knowledge work, and values work
van den Broek, E. (2025) · Information and Organization · doi:10.1016/j.infoandorg.2025.100584 · Cited by 17
Yan, J.-T., Husted, K., & Fath, B. (2026) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2025.102997 · Cited by 16
Micro‐level dynamics in digital transformation: Understanding work‐life role transitions
Mirbabaie, M., & Marx, J. (2024) · Information Systems Journal · doi:10.1111/isj.12514 · Cited by 14
“Changes in work practices are often presented as a secondary or tertiary finding in studies of the broader digital transformation landscape (Grønsund & Aanestad, 2020; Morton et al., 2020; Rahrovani, 2020; Struijk et al., 2023; Wessel et al., 2021).”
Framework for human–XAI symbiosis: extended self from the dual-process theory perspective
Litvinova, Y., Mikalef, P., & Luo, X. R. (2024) · Journal of Business Analytics · doi:10.1080/2573234x.2024.2396366 · Cited by 16
“Also, they are essential for the mutual, in-the-loop, XAI-human and human–XAI learning, or co-evolution (Fabri et al., 2023; Green & Chen, 2019a; Grønsund & Aanestad, 2020; Keding & Meissner, 2021), which we is expected to impact the experience of self-extension (Abdel-Karim et al., 2020).”
Pieper, M., & Gleasure, R. (2025) · Information Systems Journal · doi:10.1111/isj.12585 · Cited by 16
“While the latter was still human-driven in many respects, as it was directed and supervised by the development team as ‘humans- in- the-loop’ (cf. Grønsund and Aanestad 2020), the AI/developer self-organisation was nonetheless subject to an independent internal logic; a logic which inverted the…”
Hopf, K., Weigert, A., & Staake, T. (2022) · Journal of Decision System · doi:10.1080/12460125.2022.2059172 · Cited by 14
“Although research acknowledges that data value creation is a complex, cyclical and interactive (Grønsund & Aanestad, 2020; Van den Broek et al., 2022) process that is not understood very well (Sharma et al., 2014), our findings indicate that it is feasible for firms to master this process with only…”
BackgroundMethodInfluentialWastensteiner, J., Nora Tobias, M., Haag, F., & Hopf, K. (2021) · doi:10.20378/irb-49912 · Cited by 17
“Therefore, XAI supports the use of AI to augment work (Grønsund and Aanestad, 2020; Raisch and Krakowski, 2020) instead of replacing humans (Frey and Osborne, 2017).”
MethodThe group mind of hybrid teams with humans and intelligent agents in knowledge-intense work
Hopf, K., Nahr, N., Staake, T., & Lehner, F. (2024) · Journal of Information Technology · doi:10.1177/02683962241296883 · Cited by 15
“…of analysis: The fi rst stream focuses on the interaction of humans and IA, on the level of individual users or developers (e.g., Endsley, 2017; Fügener et al., 2021; Grønsund and Aanestad, 2020; Kane et al., 2021; Lebovitz et al., 2021; Schuetz and Venkatesh, 2020; van den Broek et al., 2021).”
L’intelligence artificielle peut-elle être une innovation responsable ?
Vuarin, L., Lopes, P. G., & Massé, D. (2023) · Innovations · doi:10.3917/inno.pr2.0153 · Cited by 16
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0001 · Cited by 16
Makhortykh, M. (2024) · Memory Mind & Media · doi:10.1017/mem.2024.2 · Cited by 14
“However, in terms of supervisory oversight (e.g. Citron and Pasquale 2014; Grønsund and Aanestad 2020), the presence of humans in the robot-to-robot communication loop is substantially less pronounced due to robotic agents being able to initiate memory communication and humans not directly…”
BackgroundFreisinger, E., & Schneider, S. (2024) · European Management Journal · doi:10.1016/j.emj.2024.10.004 · Cited by 16
Piller, F. T., Nitsch, V., & van der Aalst, W. (2022) · Contributions to Management Science · doi:10.1007/978-3-031-07734-0_10 · Cited by 13
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Innovation tactics for implementing an ML application in healthcare: A long and winding road
Gyldenkærne, C. H., Hansen, J. U., Hertzum, M., & Mønsted, T. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103162 · Cited by 13
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“Instead, new hires are often expected to fill the knowledge gaps of incum-bent workers (Grønsund and Aanestad 2020; Oyaro Gekara and Thanh Nguyen 2018), which can be problematic in the future and contribute to a skills shortage in the workforce.”
Enabling people-centric climate action using human-in-the-loop artificial intelligence: a review
Debnath, R., Tkachenko, N., & Bhattacharyya, M. (2025) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2025.101482 · Cited by 13
Evaluating collaborative rationality-based decisions: a literature review
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Knowledge Management in the Era of Artificial Intelligence : Developing an Integrative Framework
Fteimi, N., & Hopf, K. (2021) · doi:10.20378/irb-49911 · Cited by 13
“Second, the role of AI in the organizational context needs to be better understood, especially how people can train AI to support them in their work (Grønsund and Aanestad, 2020).”
BackgroundEffective Generative AI: The Human-Algorithm CentaurPreprint
Saghafian, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4594780 · Cited by 26
Investigating the Impact of Seafarer Training in the Autonomous Shipping Era
Chan, J. P., Pazouki, K., Norman, R. A., et al. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13040818 · Cited by 11
Humanising algorithmic management systems
Jianu, B., Tussyadiah, I. P., & Miller, G. A. (2025) · Annals of Tourism Research · doi:10.1016/j.annals.2025.104021 · Cited by 11
Bankins, S., Weaver, A., Marrone, M., Restubog, S. L. D., & Woo, S. E. (2025) · Information & Management · doi:10.1016/j.im.2025.104272 · Cited by 17
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Hanelt, A., Wiesche, M., Benlian, A., & Hinz, O. F. (2025) · MIS Quarterly · doi:10.25300/misq/2025/18041 · Cited by 16
Brünker, F., Marx, J., Mirbabaie, M., et al. (2023) · Journal of Information Technology · doi:10.1177/02683962231219516 · Cited by 11
“With advancements in information technology (IT), this change is characterised by increasing automation (Rossi et al., 2020), algorithmic control (Grønsund and Aanestad, 2020), but also fl exible work arrangements (Ajzen and Taskin, 2021).”
A vision of human–AI collaboration for enhanced biological collection curation and research
Stenhouse, A., Fisher, N., Lepschi, B. J., Schmidt‐Lebuhn, A. N., Rodríguez, J., Turco, F., Reeson, A. F., Paris, C. L., & Thrall, P. H. (2025) · BioScience · doi:10.1093/biosci/biaf021 · Cited by 12
“…make mistakes, potentially compounding errors in collaborative systems (Guo et al. 2024 ), research has shown that human performance improves when working alongside AI (Grønsund and Aanestad 2020 , Hemmer et al. 2024 ) and that imperfect AI system outputs can still be useful (Weisz et al. 2022 ).”
Klotz, S., Kratzer, S., Westner, M. K., et al. (2021) · Information Systems Management · doi:10.1080/10580530.2021.1996661 · Cited by 10
“Grønsund and Aanestad (2020) and Pousti et al. (2020) use no explicit structure in their vignettes, while Huber et al. (2020) use sub-headings to structure their vignettes (explicit structure).”
BackgroundInfluentialAugmented Intelligence, Augmented Responsibility?
Lüthi, N., Matt, C., Myrach, T., & Junglas, I. A. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00789-9 · Cited by 10
“Research on how algorithms support human-in-the-loop configurations (Grønsund and Aanestad 2020) and the tri-ple-loop design approach (Seidel et al. 2018) highlight the important role of design for the ethical ramifications of IAS.”
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Murphy, L. (2024) · International journal of organizational analysis · doi:10.1108/ijoa-04-2024-4430 · Cited by 11
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“Human-in-the-loop (HiTL) strategy can integrate human decision-making into the controller design, prioritizing human correct decisions over the autonomous permission of the controller [12], [13], [14], [15].”
Hindhede, A. L., & Andersen, V. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07771-x · Cited by 15
“By utilizing data-driven algorithms, personalized medicine seeks to automate cognitive, discretionary tasks traditionally performed by humans [6].”
Human-AI Collaboration – Coordinating Automation and Augmentation Tasks in a Digital Service Company
Schröder, A., Constantiou, I., Tuunainen, V. K., & Austin, R. D. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.025 · Cited by 15
“Grønsund & Aanestad underline the importance of humans in the loop for auditing and altering practices when working with AI [15].”
BackgroundDesigning a Human-in-the-Loop System for Object Detection in Floor Plans
Jakubik, J., Hemmer, P., Vössing, M., Blumenstiel, B., Bartos, A., & Mohr, K. (2022) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i11.21522 · Cited by 9
“In machine learning research, human-in-the-loop systems have emerged as a viable means to acquire human knowledge in situations when the model indicates an increased uncertainty for the prediction of a specific instance (e.g., Amershi et al. 2014, Grønsund and Aanestad 2020).”
BackgroundBeyond Algorithm Aversion in Human-Machine Decision-Making
Burton, J. W., Stein, M., & Jensen, T. B. (2023) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-031-30085-1_1 · Cited by 11
Algorithmic control in app‐work platforms: Exploring its curvilinear impact on work well‐being
Huo, W., Wang, Y., Liang, B., Song, M., & Xie, J. (2025) · European Management Review · doi:10.1111/emre.70005 · Cited by 10
Knowledge workers’ trust and reception of generative AI’s advice in complex tasks
Amrollahi, A., Yang, J., Fazal‐e‐Hasan, S. M., & Badreddine, B. (2026) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2026.103031 · Cited by 8
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From code to context: the impact of organizational problem framing on machine learning success
Toscani, G. (2025) · Journal of Decision System · doi:10.1080/12460125.2025.2468033 · Cited by 8
Human-driven and human-centred cybersecurity: policy-making implications
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Aydıner, A. S., Ortaköy, S., & Özsürünç, Z. (2023) · International journal of information systems and project management · doi:10.12821/ijispm110102 · Cited by 12
“It is possible having new relations restructure existing roles into more accurate implementation of the technology [22].”
BackgroundKirkby, A., Baumgarth, C., & Henseler, J. (2025) · Journal of Brand Management · doi:10.1057/s41262-025-00387-y · Cited by 16
Talking about machines: discursive sensemaking and workplace learning in the age of AI
Viktorelius, M., & Larsson, S. (2025) · Studies in Continuing Education · doi:10.1080/0158037x.2025.2570144 · Cited by 8
“Research shows, for instance, how expertise and work practices are reconfigured by practitioners’ active enactment of algorithmic technology (Grønsund and Aanestad 2020; Willems and Hafermalz 2021); how opacity, complexity, and uncertainty are handled (Lebovitz, Lifshitz-Assaf, and Levina 2022);…”
Human-machine interaction for maritime transport using a data-driven approach
Qiao, R., & Fan, S. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111956 · Cited by 7
Procurement of artificial intelligence for radiology practice
Silsand, L., Severinsen, G.-H., Linstad, L. H., & Ellingsen, G. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.427 · Cited by 7
Role of emerging technologies for empowering resilience and transparency in supply chains
Kazançoğlu, Y., Mangla, S. K., Luthra, S., & Agarwal, M. R. (2025) · Annals of Operations Research · doi:10.1007/s10479-025-06741-2 · Cited by 6
“Firms can utilize AI to examine possible key facilitators of supply and demand shifts (Grønsund & Aanestad, 2020 ), find correlations and patterns, and develop a new organizational control structure (Faraj et al., 2018 ; Sullivan & Wamba, 2022).”
Augmented Learning for Joint Creativity in Human-GenAI Co-Creation
Luan, Y. L., Kim, Y. J., & Zhou, J. (2025) · Information Systems Research · doi:10.1287/isre.2024.0984 · Cited by 12
Control of active lower limb prosthesis using human-in-the-loop scheme
Hernández, I., & Yu, W. (2022) · Cogent Engineering · doi:10.1080/23311916.2022.2067026 · Cited by 6
“Grønsund and Aanestad (2020) consider humans as supervisors who are responsible for managing performance.”
BackgroundMethodDesigning Expert-Augmented Clinical Decision Support Systems to Predict Mortality Risk in ICUs
Chen, J., Lowin, M., Kellner, D., Hinz, O. F., Adam, E., Ippolito, A., & Wenger-Alakmeh, K. (2023) · Künstliche Intell. · doi:10.1007/s13218-023-00808-7 · Cited by 7
“This “human in the loop” (HITL) setting enables human experts to constantly audit and alter ML systems [17].”
Speeding up Explorative BPM with Lightweight IT: the Case of Machine Learning
Bojer, C. S., Bygstad, B., & Øvrelid, E. (2024) · Information Systems Frontiers · doi:10.1007/s10796-024-10474-1 · Cited by 3
“However, task-level changes can enable redesigning the process (Enholm et al., 2021; May et al., 2020) or require either addition of new tasks or changes in task composition and structure (Grønsund & Aanestad, 2020; Kunz et al., 2022).”
Moving beyond human-centric organizational designs
Mortimore, D. (2024) · Journal of Organization Design · doi:10.1007/s41469-024-00167-z · Cited by 6
“Essential to attaining a tight fit is a systematic approach to architecting how organizations use AIS capabilities, as well as mitigating their limitations (Grønsund and Aanestad 2020; Jain et al. 2022; Makarius et al. 2020; Parry et al. 2016; Puranam et al. 2012).”
Effective Generative AI: The Human-Algorithm CentaurPreprint
Saghafian, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4587250 · Cited by 7
Kunz, P. C., Spohrer, K., & Heinzl, A. (2025) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2025.101902 · Cited by 7
Developing a machine learning-based evaluation system for the recruitment of maritime professionals
Pekdas, I. G., Uflaz, E., Tornacı, F., Arslan, Ö., & Turan, O. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.119406 · Cited by 7
Beyond aversion – principles of appropriate algorithmic decision-making in human resource management
Strohmeier, S., Becker, M., & Scheer-Weller, E. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128954 · Cited by 9
Human-in-the-loop: Explainable or accurate artificial intelligence by exploiting human bias?
Valtonen, L., & Mäkinen, S. (2022) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice/itmc-iamot55089.2022.10033225 · Cited by 8
A Era dos Algoritmos de Inteligência Artificial no Controle Gerencial
Ferreira, C. D. O., & Avelar, E. A. (2023) · Revista Mineira de Contabilidade · doi:10.51320/rmc.v24i2.1543 · Cited by 5
The multi-dimensional space of the futures of work
Carmel, E., & Sawyer, S. (2022) · Information Technology and People · doi:10.1108/itp-12-2020-0857 · Cited by 6
Liability Rules For AI-Facilitated Wrongs: An Ecosystem Approach To Manage Risk And UncertaintyPreprint
Panezi, A. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3768779 · Cited by 5
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.005.0004 · Cited by 5
Panizzon, M., Janissek-Muniz, R., Borges, N. M., & Cainelli, A. (2025) · BAR - Brazilian Administration Review · doi:10.1590/1807-7692bar2025240196 · Cited by 7
Lai, X., & Rau, P. P. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2025.2545310 · Cited by 6
“In summary, algorithmic management in the work-place has transformed the relationships and roles of two key stakeholders: managers and employees (Grønsund and Aanestad 2020).”
A typology of AI-based tasks for the HR function
Registre, J. F. R., & Saba, T. (2024) · Strategic HR Review · doi:10.1108/shr-04-2024-0026 · Cited by 8
Li, M. M., Löfflad, D., Reh, C., & Oeste-Reiß, S. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.041 · Cited by 4
“Thus, we present our resulting 6 design features (df1 – df6) (Prat et al., 2022) to indicate how support agents would interact with our HiL nexus point (Grønsund & Aanestad, 2020 implement an intuitive and user-friendly system to avoid unnecessary workload increase (Kaber et al., 2001).”
BackgroundMethodInfluentialThe Facets and Consequences of Uncertainty in Human–AI Interaction
Magni, F., Yang, H. H. J., & Gong, Y. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197501061.013.22 · Cited by 4
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
Jakubik, J., Weber, D., Hemmer, P., Vössing, M., & Satzger, G. (2025) · Lecture notes in information systems and organisation · doi:10.1007/978-3-031-80122-8_9 · Cited by 8
Configurable Intelligent Design Based on Hierarchical Imitation Models
Yavich, R., Malev, S., Volinsky, I., & Rotkin, V. (2023) · Applied Sciences · doi:10.3390/app13137602 · Cited by 4
Mucha, T., Ma, S.-J., & Abhari, K. (2023) · Internet Research · doi:10.1108/intr-10-2022-0769 · Cited by 4
Collaboration between individuals and AI: fusing mental effort and AI for work meaningfulness
Vodiškar, M., & Ruiner, C. (2025) · AI & Society · doi:10.1007/s00146-025-02772-2 · Cited by 5
Persson, D. R., & Andersen, T. O. (2026) · Digital Health · doi:10.1177/20552076261425134 · Cited by 5
Borasi, R., Vaughan-Brogan, P., Miller, D., Mason, S., Yu Jung, H. A. N., & DeAngelis, K. J. (2026) · Discover Education · doi:10.1007/s44217-026-01209-2 · Cited by 3
Digital transformation of work: Theorizing the process from an identity theory perspective
Zimmer, M. P., & Gierlich-Joas, M. (2026) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2026.101966 · Cited by 5
On Intelligence Augmentation and Visual Analytics to Enhance Clinical Decision Support Systems
Heart, T., Padman, R., Ben‐Assuli, O., Gefen, D., & Klempfner, R. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.452 · Cited by 3
McCartney, J. A., & Le, J. A. (2024) · The International Journal of Human Resource Management · doi:10.1080/09585192.2024.2361710 · Cited by 4
“They also have no coworkers, because tasks are so small and uncomplex that they can be individually completed (Gray & Suri, 2019; Grønsund & Aanestad, 2020).”
Jala, J. V. M., Nacalaban, E. A., Sandinao, N. R. A., Gaid, J. T., & Ventayen, R. J. M. (2024) · International Conference on Data Intelligence and Cognitive Informatics (ICDICI) · doi:10.1109/icdici62993.2024.10810883 · Cited by 3
Liu, X., Zhou, Y., Tang, Y., Qian, J., & Zhou, Y. (2025) · Science of Computer Programming · doi:10.1016/j.scico.2025.103296 · Cited by 5
Järvenpää, S. L., & Välikangas, L. (2025) · Strategic Organization · doi:10.1177/14761270251350678 · Cited by 7
“Others conclude that humans ought to remain “in the loop” to provide organizational experience (Anthony et al., 2023; Baird and Maruping, 2021; Grønsund and Aanestad, 2020; Raisch and Fomina, 2025; Sturm et al., 2021; Weber et al., 2024).”
Heinrich, K., Janiesch, C., Krancher, O., Stahmann, P., Wanner, J., & Zschech, P. (2025) · PLoS ONE · doi:10.1371/journal.pone.0328411 · Cited by 4
“Sturm et al. [41] and Grønsund and Aanestad [42] complement that there are several learning strategies for human-AI interaction.”
Leveraging AI in peace processes: A framework for digital dialogues
Waehlisch, M., & Kufus, F. (2025) · Data & Policy · doi:10.1017/dap.2025.10031 · Cited by 7
“Similarly, Grønsund and Aanestad (2020) emphasize the significance of “ human-in-the-loop ” frameworks, ensuring that AI-driven insights are critically examined rather than blindly adopted.”
Metadata
- Authors
- Grønsund, T., & Aanestad, M.
- Affiliations
- University of Oslo (NO)
- Corresponding author
- Grønsund, Tor
- ORCID
- Aanestad, Margunn
- Venue
- The Journal of Strategic Information Systems
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 29
- Issue
- 2
- Pages
- 101614
- ISSN
- 0963-8687 (ISSN-L)
- OpenAlex
- W3037276488
- Cited by
- 364
- References
- 67
- Citations by year
- 2026: 55 · 2025: 70 · 2024: 56 · 2023: 102 · 2022: 42 · 2021: 37 · 2020: 2
- FWCI
- 61.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The Journal of Strategic Information Systems (publishedVersion, cc-by-nc-nd)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc-nd)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Funders
- Norges Forskningsråd
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Keywords
- human-in-the-loop, artificial intelligence adoption, algorithmic auditing, maritime trade, algorithmic prediction, data acquisition architecture, human-algorithm collaboration
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