The DOI matches its Crossref record; 65 of the 67 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Strategies for Theorizing from Process Data
Langley, A. (1999) · The Academy of Management Review · doi:10.5465/amr.1999.2553248 · Cited by 7,008 · cited 1×
“The third stage of analysis involved a deeper production of case narratives (Langley, 1999) and vignettes from our empirical material, while still engaging in the field and collecting data.”
BackgroundUnderstanding dynamic capabilities
Winter, S. G. (2003) · Strategic Management Journal · doi:10.1002/smj.318 · Cited by 5,315 · cited 2×
“While the terms ‘reconfigure' and ‘configurations’ are central to extant definitions of dyna mic capabilities (Teece et al., 1997; Eisenhardt and Martin, 2000; Winter, 2003), the terms are not unpacked in detail, neither generally nor specifically relating to human-machine interplay.”
BackgroundPlans and Situated Actions: The Problem of Human-Machine Communication
Kantowitz, B. H., & Suchman, L. A. (1990) · The American Journal of Psychology · doi:10.2307/1423221 · Cited by 5,307 · cited 2×
“To do this, we build on Lucy Suchman’s seminal study at Xerox PARC (Suchman, 1987) which challenged fundamental ideas about humans’ and machines’ cognition and communication; ideas that had guided the design of the intelligent interface of the advanced copying machines used by the scientists she…”
MethodA Set of Principles for Conducting and Evaluating Interpretive Field Studies in Information Systems1
Klein, H. K., & Myers, M. D. (1999) · MIS Quarterly · doi:10.2307/249410 · Cited by 5,263 · cited 1×
“For example, from one of the meetings we produced and circulated a summary of our understanding of the strategic conditions and problem context, which served as a means for confirmation and contextualization (Klein and Myers, 1999).”
BackgroundA model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 1×
“, human) performance to fully automatic performance (Parasuraman et al., 2000).”
BackgroundWhy Are There Still So Many Jobs? The History and Future of Workplace Automation
Autor, D. H. (2015) · Journal of Economic Perspectives · doi:10.1257/jep.29.3.3 · Cited by 3,917 · cited 1×
“In addition, one may see indirect effects where not only does automation substitute labour , it also “complements labor, raises output in ways that lead to higher demand for labor, and interacts with adjustments in labor supply” (Autor, 2015, p. 5).”
BackgroundInterpretive case studies in IS research: nature and method
Walsham, G. (1995) · European Journal of Information Systems · doi:10.1057/ejis.1995.9 · Cited by 3,834 · cited 1×
“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
Raisch, S., & Krakowski, S. (2021) · Academy of Management Review · doi:10.5465/amr.2018.0072 · Cited by 2,755 · cited 4×
“This suggests that the processes of automation and augmentation influence one another in a reciprocal manner (Raisch and Krakowski, 2020), and that their organizing emerges as a core capability.”
BackgroundMethodInfluentialThe structure of ill structured problems
Simon, H. A. (1973) · Artificial Intelligence · doi:10.1016/0004-3702(73)90011-8 · Cited by 2,600 · cited 1×
“…that algorithms are more appropriate for structured problem-solving and where outcomes can be clearly described; and human processing is more apt for ill-structured problem-solving, to which solutions tend to require human interpretation and judgment (e.g., Simon, 1973; Shrestha et al., 2019).”
BackgroundRankings and Reactivity: How Public Measures Recreate Social Worlds
Espeland, W. N., & Sauder, M. (2007) · American Journal of Sociology · doi:10.1086/517897 · Cited by 2,584
Flagged first, then by citations
2 of the 67 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- DalleMule, 2017, What’s your data strategy, Harvard Bus. Rev., 95, 112
- Lebovitz, S., Levina, N., Lifshitz-Assaf, H., 2019. Doubting the Diagnosis: How Artificial Intelligence Increases Ambiguity during Professional Decision Making. Available at SSRN 3480593.
Cited by
Sorted by citations
Items 201–300 of 364
A Conceptual Model for Labeling in Reinforcement Learning Systems: A Value Co-creation Perspective
Reinhard, P., Li, M. M., Dickhaut, E., Reh, C., Peters, C., & Leimeister, J. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-32808-4_8 · Cited by 3
Recognize Facial Emotion Using Landmark Technique in Deep Learning
Salah, A., & Hussein, N. J. (2023) · International Conference on Engineering, Science and Advanced Technology (ICESAT) · doi:10.1109/icesat58213.2023.10347313 · Cited by 3
Schierhorst, N. J., Johnen, L., Fimmers, C., Lohrmann, V., Monnet, J., Zhang, H., Bergs, T., Brecher, C., Mertens, A., & Nitsch, V. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.02.106 · Cited by 6
Design Principles for Data-Driven Decision Evaluation
Elgendy, N., Päivärinta, T., Elragal, A. A., et al. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.06.208 · Cited by 3
“Furthermore, DP5 adds to recent research which accentuates the use of a feedback loop for enhancing organizational learning and the learning and utilization of the ML tool (e.g. [2, 32]).”
BackgroundSilverman, G., Te’eni, D., Schwartz, D. G., Mann, Y., Cohen, D., & Lewinsky, D. (2025) · Quality & Quantity · doi:10.1007/s11135-025-02108-8 · Cited by 3
“Concerning the above challenges, growing interest has focused on human-in-the-loop approaches, in which algorithms interact with human intervention to optimize learning (Grønsund and Aanestad 2020; Holzinger 2015).”
Material and Cost estimation of a Customized Product based on the Customer’s description
Baboolal, K., & Hosein, P. A. (2021) · International Conference on Intelligent Data Science Technologies and Applications (IDSTA) · doi:10.1109/idsta53674.2021.9660821 · Cited by 3
“[7] discusses the implementation of machine learning algorithms in businesses and presents a similar implementation to ours.”
On the strategic value of equifinal choice
Arrieta, J. P., & Shrestha, Y. R. (2022) · Journal of Organization Design · doi:10.1007/s41469-022-00112-y · Cited by 3
Vella, D. D., Senarath, Y., Hashmi, E., Abomhara, M., Shaikh, S., Igland, T., Dralega, C. A., Yayilgan, S. Y., Purohit, H., & Akerkar, R. A. (2025) · Digital Society · doi:10.1007/s44206-025-00212-8 · Cited by 2
“Furthermore, we implement a Human-in-the-Loop (HITL) system to facilitate fair decision-making (Dellermann et al., 2021 ; Grønsund & Aanestad, 2020; Jakubik et al., 2023 ).”
Dong, K., Yang, Y., Sheng, T., Liu, L., & Song, K. (2025) · Environment Development and Sustainability · doi:10.1007/s10668-025-06618-1 · Cited by 2
“An increasing number of studies indicate that AI is not only a technological revolution, but also a powerful instrument for addressing various environmental issues and playing a momentous role in ameliorating sustainable development (Flores & Villalobos, 2020; 2020; Grønsund & Aanestad, 2020 ).”
The Enterprising and Elusive Prospects of Human-AI Collaboration
Gadiraju, U., & Balayn, A. (2025) · Springer eBooks · doi:10.1007/978-3-032-01940-0_7 · Cited by 5
Fading Memories: The Role of Machine Learning in Organizational Knowledge Depreciation
Gerlach, J. P., & Lange, D. A. (2026) · Academy of Management Review · doi:10.5465/amr.2024.0408 · Cited by 3
Trzebiatowski, T. M., Suh, E. Y., & Hollie, L. (2026) · Human Resource Management · doi:10.1002/hrm.70071 · Cited by 2
Knowledge Co‐Creation by Human–Machine Social Interaction: Based on Decision Field Theory
Ren, Z., Shen, X., & Xia, S. (2026) · Systems Research and Behavioral Science · doi:10.1002/sres.70103 · Cited by 2
Ex-Post Evaluation of Data-Driven Decisions: Conceptualizing Design Objectives
Elgendy, N., Elragal, A. A., Ohenoja, M., et al. (2022) · Lecture notes in business information processing · doi:10.1007/978-3-031-16947-2_2 · Cited by 2
Is the Human-in-the-Loop Concept Applied in Educational Recommender Systems?
de Ambrosis, M. V., Lopes, D. D. Q., & Motz, R. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-5414-8_61 · Cited by 2
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0015 · Cited by 2
Mucha, T., Seppälä, J., & Puraskivi, H. (2023) · Hawaii International Conference on System Sciences · doi:10.24251/hicss.2023.383 · Cited by 3
“Second, it is necessary to study the performance of human-in-the-loop systems across the full range of changes and not just stability (Fügener et al., 2021), drift (Sturm, Koppe, et al., 2021), or jump (Grønsund & Aanestad, 2020) in isolation.”
InfluentialHolm, B., Jouan, G., Hardarson, E., Sigurdardottir, S. E., Hoelke, K., Murphy, C., Arnardóttir, E. S., Óskarsdóttir, M., & Íslind, A. S. (2024) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2024.1379932 · Cited by 2
Data Science in Military Decision-Making: Foci and Gaps
Meerveld, H., & Lindelauf, R. H. A. (2024) · Global Society · doi:10.1080/13600826.2024.2353657 · Cited by 12
Human-Centered Machine Learning
Schillizzi, C. (2025) · Springer eBooks · doi:10.1007/978-981-95-3658-0_25 · Cited by 3
Work practice view to digitalization – A literature review
Tokkonen, T., Kinnula, A., Kinnula, M., & Kuutti, K. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.01.355 · Cited by 3
The Human-in-the-Loop Data Sensing Architecture Based on Edge Cloud Computing
Gao, C. (2022) · Journal of Sensors · doi:10.1155/2022/9542290 · Cited by 2
“Regarding the introduction of human-in-the-loop systems, according to the autonomy levels classified in the literature [19], some autonomous systems with lower autonomy levels interact passively with humans and are only able to estimate the human state but do not consider the impact of the system state on the human st…”
BackgroundRetractedSmit, D., Eybers, S., & van der Merwe, A. (2023) · EPiC series in computing · doi:10.29007/8tgz · Cited by 2
“Other focus areas that stood out were “knowledge” about AI [37], “governance and compliance“ [15, 53] and “strategy” [40].”
GoPrintBot: An Interactive E-Commerce for Online Printing Services
Ishak, N. I. A., & Ahmad, N. A. (2023) · Journal of Information Systems and Informatics · doi:10.51519/journalisi.v5i2.502 · Cited by 2
“The inclusion of the handover module in the proposed system design holds strategic importance through its human-in-the-loop pattern, which ensures that the algorithm's performance aligns with the organization's requirements and adapts to changes in the environment [14].”
BackgroundSawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0010 · Cited by 2
Defining Creativity Through Assessment
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0003 · Cited by 3
Data-driven Innovation : An exploration of outcomes and processes within federated networks
Rizk, A. (2020) · DiVA (Luleå University of Technology) · Cited by 2
Herath, S., Shrestha, Y. R., & von Krogh, G. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4071519 · Cited by 2
Design Principles Supporting Data-driven Decisions PlatformsPreprint
Elragal, A. A., & Elgendy, N. (2024) · Research Square · doi:10.21203/rs.3.rs-4240652/v1 · Cited by 2
Something Old, Something New, Something Inspired by Deep Blue?
Tegtmeier, P., Terhoeven, J., & Wischniewski, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35741-1_34 · Cited by 1
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0011 · Cited by 1
Sourcing data for data-driven applications: foundational questions
Järvenpää, S. L. (2024) · Edward Elgar Publishing eBooks · doi:10.4337/9781803926216.00009 · Cited by 1
Impact of Artificial Intelligence in Audit. Bibliometric Analysis
Florea, E. C. B., Preda, M., & Olteanu, A. L. (2025) · Proceedings of the ... International Conference on Business Excellence · doi:10.2478/picbe-2025-0010 · Cited by 1
Collaborating with Algorithms: AI for Collaborative Supply Chain Management
Couto, F. A. P., Malta, M. C., & Soares, A. L. (2025) · IFIP advances in information and communication technology · doi:10.1007/978-3-032-05673-3_12 · Cited by 1
The progressive transformation of work with robotic process automation technology
Bunduchi, R., Chiș, D.-M., MIHAILA, A. A., & Crișan, E. L. (2025) · European Journal of Information Systems · doi:10.1080/0960085x.2025.2576231 · Cited by 1
Bejjani, Y., & Vanhaverbeke, W. P. M. (2025) · Journal of Decision System · doi:10.1080/12460125.2025.2581374 · Cited by 1
“Algorithms have achieved a universal state with deep applications across the entire company’s value chain (Grønsund & Aanestad, 2020).”
Human-Centered AI Maturity Model (HCAI-MM): An Organizational Design Perspective
Winby, S., & Xu, W. (2025) · Springer eBooks · doi:10.1007/978-981-97-8440-0_106-1 · Cited by 3
Grimshaw, D., & Miozzo, M. (2026) · Journal of Management Studies · doi:10.1111/joms.70057 · Cited by 2
Hutzschenreuter, T., Kleindienst, I. R., & Perkhofer, F. (2026) · Managerial and Decision Economics · doi:10.1002/mde.70078 · Cited by 4
“Moreover, since the over-time impact of trust in AI on strategists' perceptions and behaviors remains underexplored, longitudinal studies are required (Grønsund and Aanestad 2020), which can help understand AI's long-term implications comprehensively.”
The hollowing of the manager: character in the age of algorithmic authority
Enstroem, R. (2026) · International Studies of Management and Organization · doi:10.1080/00208825.2026.2642242 · Cited by 1
“While “human-in-the-loop” models are often positioned as safeguards for ethical oversight (Cimini et al. 2020; Grønsund and Aanestad 2020), they risk leaving the human role symbolic rather than substantive if not supported by institutional structures for judgment, reflexivity, and moral reasoning.”
From AI to AGI: a human-in-the-loop framework for responsible IS futures
Cingillioglu, I., & Lun, P. (2026) · Enterprise Information Systems · doi:10.1080/17517575.2026.2653244 · Cited by 3
Human-Centered AI Maturity Model (HCAI-MM): An Organizational Design Perspective
Winby, S., & Xu, W. (2026) · Springer eBooks · doi:10.1007/978-981-95-3658-0_106 · Cited by 2
Privacy Challenges of AI-Enabled Monitoring in the Workplace
Bosua, R., & Evans, N. (2026) · Journal of the Australian Library and Information Association · doi:10.1080/24750158.2026.2694614 · Cited by 1
“…based on their status settings, they felt the presence of managers and leaders as ‘people-in-the-loop’ (PITL) or ‘humans-in-the-loop’ (HITL) augmented and ‘softened’ the harsh realities that algorithmic control may bring into the social fabric of organisations (Grønsund & Aanestad, 2020).”
Work Redesign after Digital Augmentation of Human Expertise and Agency
Larsson, S., Johansson, E., Nilsson, A., & Andersson, L. (2025) · Journal of Applied Organizational Systems and Behavior · doi:10.51847/f9ejhcqn5d · Cited by 1
To Leverage the Innovation Capability by Co-creation of Human-Machine
Chen, J. X. S. (2021) · Forest Chemicals Review · doi:10.17762/jfcr.vi.167 · Cited by 1
“Today, collaborative human-machine teamwork has become a widespread consensus in all walks of life [9-15].”
BackgroundKüstermann, G. C., & Bittner, E. A. C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05544-7_5 · Cited by 1
Coping with the Opportunities and Challenges of Smart Policing: A Research Model
Afzal, M. Y., & Panagiotopoulos, P. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15086-9_30 · Cited by 1
Challenges in the Model Development Process: Discussions with Data Scientists
Gerhart, N., Torres, R., & Giddens, L. (2023) · Communications of the Association for Information Systems · doi:10.17705/1cais.05325 · Cited by 1
AI and Human Relationship in the Workplace: A Literature Review and Future Research Agenda
Trinh, N. T. T., & Elbanna, A. R. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-50192-0_14 · Cited by 1
Proliferations in Algorithmic Control: Review of the Phenomenon and Its Implications
Dutta, S., Pramanik, H. S., Rajan, S. G., Rajan, R. G., & Satapathy, S. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-50188-3_5 · Cited by 1
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0004 · Cited by 1
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0005 · Cited by 1
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0002 · Cited by 3
Tokkonen, T., Kinnula, A., Kuutti, K., & Kinnula, M. (2023) · Proceedings of the International Conference on Information Systems Development · doi:10.62036/isd.2023.34 · Cited by 1
“…drivers induced change in the workplace have been new regulations, forcing a change to happen e.g., [13], radical technology-driven disruption e.g., [26], need to respond for market pressure or competition e.g., [19, 37], or just responding to an opportunity that presented itself e.g., [20], [30].”
InfluentialNasrullah, U. (2024) · Psychology & Psychological Research International Journal · doi:10.23880/pprij-16000385
Delgado, C. F., Ferretti, M. T., & Carnevale, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-13681-8.00012-6 · Cited by 1
Assessing the Economic Benefits of E- Commerce Adoption for Small and Medium Enterprises (SMEs)
(2024) · International Journal of Progressive Research in Engineering Management and Science · doi:10.58257/ijprems35760 · Cited by 2
Barbano, C. A., Berton, L., Renzulli, R., et al. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.11.045 · Cited by 1
Reciprocal Human-AI Collaboration: Designing Configuration and Delegation for Continual Learning
Te’eni, D. (2025) · Progress in IS · doi:10.1007/978-3-031-83512-4_11 · Cited by 1
Hybrid Workforce: Competences for Effective Collaboration Between Employees and Software Robots
Kedziora, D., Juszczyk, M., & Lesniewska, A. (2026) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2026.3663559 · Cited by 1
“Asmoreadvancedtechnologies,suchasAIareintroduced inworkplaces,newconfigurationsofhumansandtechnology emergewitheffectsthatchallengeoldnotionsoforganizations andwork[14].”
InfluentialJunior, A. G. L., & Frizera, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-32-385952-3.00010-x · Cited by 1
Makridis, C. A., & Hickman, L. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4365176 · Cited by 1
“While organizations must carefully manage risk and mitigate bias (Cowgill 2020), AI has the ability to augment human capabilities in the attraction and cultivation of talent in organizations (Choi et al. 2022, Gronsund and Aenestad 2020).”
BackgroundSawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0014 · Cited by 1
Human-AI Interaction In Regulating Productivity And Wellbeing
Ekandjo, T. A. T. (2024) · doi:10.26686/wgtn.25377481 · Cited by 2
Process Innovation with Analytics in Manufacturing: Exploration through Demonstrator Development
Bojer, C. S. (2024) · VBN Forskningsportal (Aalborg Universitet) · doi:10.54337/aau679672279 · Cited by 1
Herath, S., Johannsson, J. G., Shrestha, Y. R., & von Krogh, G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5153658 · Cited by 1
Grønsund, T. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.687
“Recent research has described how organizations bring humans into the loop of the algorithm to augment and improve the accuracy of analysis [27].”
BackgroundInfluentialSelf-citationWertbasiertes Design von Entscheidungsunterstützungssystemen
Lüthi, N., Matt, C., & Myrach, T. (2022) · HMD Praxis der Wirtschaftsinformatik · doi:10.1365/s40702-022-00845-0
Três experiências de inovação social em Santa Catarina
Lins, H. N. (2022) · doi:10.5151/vi-enei-865
Reply to ‘Mere algorithms can be demotivating’
Gagné, M., Parent‐Rocheleau, X., Dunlop, P. D., et al. (2022) · Nature Reviews Psychology · doi:10.1038/s44159-022-00113-4
Dimensions of Influence in Trucking: Beyond Work Community
Tokkonen, T., Tokkonen, H., Kinnula, A., Kinnula, M., & Kuutti, K. (2023) · International Conference on Communities and Technologies (C&T) · doi:10.1145/3593743.3593769 · Cited by 1
“On the one hand they can take care of menial tasks, freeing the worker to focus on more meaningful and value-adding aspects of the work [20].”
BackgroundThe societal-semantic dimension of mental disease: A computational-qualitative approachPreprint
Gladwin, T. E. (2023) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/zbyrk
Social Issues or the Social as an Issue—Rethinking Sociality in a Post-Digital Era
Hallin, A., & Ivory, C. J. (2023) · Europäische Kulturen in der Wirtschaftskommunikation · doi:10.1007/978-3-658-40705-6_5
Xue, C., Zhang, H., & Zhao, Q. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4503140
The Noise of the News: Spectral Analysis of Early Swedish Television News 1958 - 1978.
Malmstedt, J. (2023) · VIEW Journal of European Television History and Culture · doi:10.18146/view.291
Annunziata, F. (2023) · Edward Elgar Publishing eBooks · doi:10.4337/9781035310722.00010
de Oliveira, H. P. S., & Britto, J. (2023) · Economia e Sociedade · doi:10.1590/1982-3533.2023v32n3art06
Human-Machine Task Allocation in Learning Reciprocally to Solve Problems
Te’eni, D. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-49215-0_9
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0006
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0021
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0020 · Cited by 2
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.002.0003
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0009 · Cited by 1
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0016 · Cited by 1
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0017 · Cited by 1
Third Edition Literature Review Methodology
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.005.0001
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.002.0005
Journals That Publish Creativity Research
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.005.0005
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0012
Influential Creativity Researchers, 1800–1990
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.005.0008
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0019
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0018
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0008
A Timeline of Creativity Research
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.005.0002
Influential Books in Creativity Research
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.005.0006
Colleges and Universities with a Degree or Certificate in Creativity
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.005.0007
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.003.0013
Sawyer, R. K., & Henriksen, D. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780197747537.005.0003
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
