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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The deployment of algorithmic management in organizational work introduces novel "human-machine configurations" that could transform relationships between managers and workers and their respective roles.
“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
The Abstract and Conclusion describe new human–algorithm configurations, emerging roles, and the redistribution of work after algorithmic support was introduced. The paper studies algorithmic support for data analysis, however, rather than specifically examining algorithmic management or changes in manager–worker relationships. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI algorithms can be considered performative: they assist in decision-making, can form organizational processes, or take autonomous decisions, leading to new organizational capabilities.
“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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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× · see citing works
“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× · see citing works
“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 5,306 · cited 2× · see citing works
“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× · see citing works
“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× · see citing works
“, 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× · see citing works
“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× · see citing works
“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. M. (2020) · Academy of Management Review · doi:10.5465/2018.0072 · Cited by 3,508 · cited 4× · see citing works
“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× · see citing works
“…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 2,450 · see citing works
(2006) · Cited by 2,256 · see citing works
Walsham, G. (2006) · European Journal of Information Systems · doi:10.1057/palgrave.ejis.3000589 · Cited by 2,217 · cited 2× · see citing works
“Using a sparse form of open coding (Walsham, 2006) of our field notes, we sought out patterns and selected empir ically informed themes such as “secrecy” and “obscuring”, all relating to episodes of questioning the validity of external data.”
BackgroundMethodLicklider, J. C. R. (1960) · IRE Transactions on Human Factors in Electronics · doi:10.1109/thfe2.1960.4503259 · Cited by 1,938 · cited 1× · see citing works
“Thirdly, across this continuum and these functions, automation may augment human performance of a particular task through increasing the capability of humans to approach a problem situation, gain comprehension, and derive solutions (Engelbart, 1962, p. 1; Licklider, 1960).”
BackgroundLeonardi, P. M. (2011) · MIS Quarterly · doi:10.2307/23043493 · Cited by 1,889 · cited 1× · see citing works
“This aligns with, e.g., Leonardi (2011) taking as his unit of analysis the process by which human and technology are intertwined.”
BackgroundPlans and Situated Actions: The Problem of Human Machine Communication.
Woolgar, S., & Suchman, L. A. (1989) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2073874 · Cited by 1,600 · cited 2× · see citing works
“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…”
MethodWhat can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131 · cited 2× · see citing works
“These are still relatively rarer (Brynjolfsson and Mitchell, 2017), nevertheless, popular debates tend to profess a “ replace ment” trope, where human jobs are taken over by intelligent machines (McAfee and Brynjolfsson, 2017).”
BackgroundOrganizational Decision-Making Structures in the Age of Artificial Intelligence
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019) · California Management Review · doi:10.1177/0008125619862257 · Cited by 1,108 · cited 1× · see citing works
“It has long been suggested 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…”
BackgroundAUGMENTING HUMAN INTELLECT: A CONCEPTUAL FRAMEWORK
Engelbart, D. C. (1962) · doi:10.21236/ad0289565 · Cited by 1,091 · cited 1× · see citing works
“Thirdly, across this continuum and these functions, automation may augment human performance of a particular task through increasing the capability of humans to approach a problem situation, gain comprehension, and derive solutions (Engelbart, 1962, p. 1; Licklider, 1960).”
BackgroundAutomation, skills use and training
Nedelkoska, L., & Quintini, G. (2018) · OECD social employment and migration working papers · doi:10.1787/2e2f4eea-en · Cited by 990 · cited 1× · see citing works
“For instance, by automating routine manual and cognitive tasks, human capacity is freed to take on non-routine tasks and capacity and productivity can increase, which again drives an increase in demand for non-routine problem-solving (Nedelkoska and Quintini, 2018).”
BackgroundThe Alignment of Technology and Structure through Roles and Networks
Barley, S. R. (1990) · Administrative Science Quarterly · doi:10.2307/2393551 · Cited by 941 · 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 · cited 2× · see citing works
“, 2017; Markus, 2017) and intelligent technologies (Bailey and Barley, 2019; Faraj et al., 2018; von Krogh, 2018; Rai et al., 2019), including the implications for organizational strategy (Constantiou and Kallinikos, 2015; Woerner and Wixom, 2015; Bhimani, 2015) and work in settings ranging from education (Marjanovic…”
BackgroundRamaprasad, A. (1983) · Behavioral Science · doi:10.1002/bs.3830280103 · Cited by 915 · see citing works
Against Reflexivity as an Academic Virtue and Source of Privileged Knowledge
Lynch, M. (2000) · Theory Culture & Society · doi:10.1177/02632760022051202 · Cited by 903 · see citing works
Debating big data: a literature review on realizing value from big data
Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2017.07.003 · Cited by 880 · cited 2× · see citing works
“Information Systems (IS) researchers have raised fundamental questions as to the ramifications of both datafication processes (Galliers et al., 2017, Günther et al., 2017; Markus, 2017) and intelligent technologies (Bailey and Barley, 2019; Faraj et al., 2018; von Krogh, 2018; Rai et al., 2019),…”
BackgroundThe Rapid Adoption of Data-Driven Decision-Making
Brynjolfsson, E., & McElheran, K. (2016) · American Economic Review · doi:10.1257/aer.p20161016 · Cited by 843 · cited 1× · see citing works
“The capabilities of utilizing data has in itself become a significant strategic asset and an object of strategy formulation (DalleMule and Davenport, 2017; Iansiti and Lakhani, 2020), and data-driven decision-making is increasingly implemented (Brynjolfsson and McElheran, 2016).”
BackgroundMachine, platform, crowd: Harnessing our digital future
Peregrina, Á. F. A. (2018) · Economía industrial · Cited by 681 · see citing works
Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing
von Krogh, G. (2018) · Academy of Management Discoveries · doi:10.5465/amd.2018.0084 · Cited by 641 · cited 7× · see citing works
“More nuanced accounts instead emphasize interrelation and augmentation (Bailey and Barley, 2019; von Krogh, 2018; Markus, 2017; Davenport and Kirby, 2016), where humans and algorithms interact to perform a task.”
BackgroundInfluentialThe SAGE Handbook of Process Organization Studies
Langley, A., & Tsoukas, H. (2016) · SAGE Publications Ltd eBooks · doi:10.4135/9781473957954 · Cited by 628 · see citing works
Society-in-the-loop: programming the algorithmic social contract
Rahwan, I. (2018) · Ethics and Information Technology · doi:10.1007/s10676-017-9430-8 · Cited by 553 · cited 1× · see citing works
“6, the loop of algorithm-auditing figures design by human training as congruent with interactive machine learning (Rahwan, 2018).”
BackgroundNew games, new rules: big data and the changing context of strategy
Constantiou, I. D., & Kallinikos, J. (2015) · Journal of Information Technology · doi:10.1057/jit.2014.17 · Cited by 526 · cited 1× · see citing works
“…(Bailey and Barley, 2019; Faraj et al., 2018; von Krogh, 2018; Rai et al., 2019), including the implications for organizational strategy (Constantiou and Kallinikos, 2015; Woerner and Wixom, 2015; Bhimani, 2015) and work in settings ranging from education (Marjanovic and…”
Background(2014) · Digital Commons at University of Maryland Carey Law (University of Maryland Francis King Carey School of Law) · Cited by 524 · see citing works
The ‘problem ’ with automation: inappropriate feedback and interaction, not ‘over-automation’
Norman, D. A. (1990) · Philosophical transactions of the Royal Society of London. Series B, Biological sciences · doi:10.1098/rstb.1990.0101 · Cited by 519 · see citing works
Newell, S., & Marabelli, M. (2015) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2015.02.001 · Cited by 494 · cited 2× · see citing works
“Advances in ‘data-driven intelligence engines’ (Baptista et al., 2017a) based on availability of data and artificial intelligence (AI) have the potential to transform the nature of work and organizations (Newell and Marabelli, 2015).”
Background(2012) · Routledge eBooks · doi:10.4324/9780203854921 · Cited by 438 · see citing works
Open Strategy: Dimensions, Dilemmas, Dynamics
Hautz, J., Seidl, D. N., & Whittington, R. (2016) · Long Range Planning · doi:10.1016/j.lrp.2016.12.001 · Cited by 406 · cited 1× · see citing works
“For instance, mandated data sharing can undermine competitiveness as competitors get hold of strategically sensitive information (Hautz et al., 2017, p. 302), and drive, e.g., ship crews to obfuscate or manipulate data about their true destination.”
BackgroundSuchman, L. A. (1993) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/bf00749015 · Cited by 400 · see citing works
Do Categories Have Politics? The language/action perspective reconsidered
Suchman, L. A. (1993) · Springer eBooks · doi:10.1007/978-94-011-2094-4_1 · Cited by 336 · see citing works
Exploring the impact of artificial Intelligence: Prediction versus judgment
Agrawal, A., Gans, J. S., & Avi Goldfarb, B. (2019) · Information Economics and Policy · doi:10.1016/j.infoecopol.2019.05.001 · Cited by 315 · see citing works
Machine, Platform, Crowd: Harnessing Our Digital Future
Brynjolfsson, E., & McAfee, A. P. (2017) · Cited by 313 · see citing works
Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World
Bhattacharya, D. (2021) · Strategic Analysis · doi:10.1080/09700161.2021.1918951 · Cited by 311 · cited 2× · see citing works
“This approach allows us to pay attention not only to the intertwining of human and machine work, augmentation and automation, but also their underlying feedback mechanisms as a possible source of competitive advantage (Davenport and Kirby, 2016; Iansiti and Lakhani, 2020).”
BackgroundBeyond design and use: How scholars should study intelligent technologies
Bailey, D. E., & Barley, S. R. (2019) · Information and Organization · doi:10.1016/j.infoandorg.2019.100286 · Cited by 215 · see citing works
Social Media and the Emergence of Reflexiveness as a New Capability for Open Strategy
Baptista, J., Wilson, A., Galliers, R. D., et al. (2016) · Long Range Planning · doi:10.1016/j.lrp.2016.07.005 · Cited by 188 · cited 1× · see citing works
“For instance, Baptista et al. (2017b) showed the need for the organization to develop “reflexiveness” to adequately deal with social media data in strategizing.”
BackgroundBig Data: Extending the Business Strategy Toolbox
Woerner, S. L., & Wixom, B. H. (2015) · Journal of Information Technology · doi:10.1057/jit.2014.31 · Cited by 151 · cited 1× · see citing works
“, 2019), including the implications for organizational strategy (Constantiou and Kallinikos, 2015; Woerner and Wixom, 2015; Bhimani, 2015) and work in settings ranging from education (Marjanovic and Cecez-Kecmanovic, 2017), policing (Waardenburg et al.”
BackgroundUsing AI to Enhance Business Operations
Tarafdar, M., Beath, C. M., & Ross, J. W. (2020) · The MIT Press eBooks · doi:10.7551/mitpress/12588.003.0015 · Cited by 146 · cited 1× · see citing works
“Tarafdar et al. (2019), for example, argues that an organization needs five crucial capabilities: data science competence, business domain proficiency, enterprise architecture expertise, operational IT backbone expertise, and digital inquisitiveness.”
BackgroundJust How Smart Are Smart Machines
Davenport, T. H., & Kirby, J. (2016) · Dialnet (Universidad de la Rioja) · Cited by 135 · see citing works
Exploring Big Data's Strategic Consequences
Bhimani, A. (2015) · Journal of Information Technology · doi:10.1057/jit.2014.29 · Cited by 108 · cited 1× · see citing works
“…Krogh, 2018; Rai et al., 2019), including the implications for organizational strategy (Constantiou and Kallinikos, 2015; Woerner and Wixom, 2015; Bhimani, 2015) and work in settings ranging from education (Marjanovic and Cecez-Kecmanovic, 2017), policing (Waardenburg et al., 2018), recruitment…”
BackgroundJaton, F. (2017) · Social Studies of Science · doi:10.1177/0306312717730428 · Cited by 103 · cited 1× · see citing works
“While trained data scientists may be in short supply, “citizen data scientists” (Watson, 2017), subject matter experts (Scheepers et al., 2018) or human labelers (Jaton, 2017) could play crucial roles.”
BackgroundEditor’s Comments: Next-Generation Digital Platforms: Toward Human–AI Hybrids
Rai, A., Constantinides, P., & Sarker, S. (2019) · Journal of the Association for Information Systems · Cited by 96 · see citing works
Datification, Organizational Strategy, and IS Research: What’s the Score?
Markus, M. L. (2017) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2017.08.003 · Cited by 90 · cited 8× · see citing works
“…researchers have raised fundamental questions as to the ramifications of both datafication processes (Galliers et al., 2017, Günther et al., 2017; Markus, 2017) and intelligent technologies (Bailey and Barley, 2019; Faraj et al., 2018; von Krogh, 2018; Rai et al., 2019), including the…”
BackgroundInfluentialPreparing for the Cognitive Generation of Decision Support
Watson, H. J. (2017) · Journal of the Association for Information Systems · Cited by 84 · see citing works
Human centered automation: oxymoron or common sense?
Sheridan, T. B. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1995.537867 · Cited by 73 · see citing works
Dynamic Reconfiguration in Planetary Exploration: A Sociomaterial Ethnography1
Mazmanian, M., Cohn, M. L., & Dourish, P. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.3.09 · Cited by 72 · cited 1× · see citing works
“Reconfiguration, then, is the process by which new configurations of humans and machines and related objects of work practice emerge (Mazmanian et al., 2014, p. 832; Suchman, 2012).”
BackgroundSeidel, S., Berente, N., Lindberg, A., Lyytinen, K., & Nickerson, J. V. (2018) · Communications of the ACM · doi:10.1145/3210753 · Cited by 72 · see citing works
Galliers, R. D., Newell, S., Shanks, G., & Topi, H. (2017) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2017.08.002 · Cited by 70 · cited 1× · see citing works
“Information Systems (IS) researchers have raised fundamental questions as to the ramifications of both datafication processes (Galliers et al., 2017, Günther et al., 2017; Markus, 2017) and intelligent technologies (Bailey and Barley, 2019; Faraj et al., 2018; von Krogh, 2018; Rai et al., 2019),…”
BackgroundA Trichordal Temporal Approach to Digital Coordination: The Sociomaterial Mangling of the CERN Grid1
Venters, W., Oborn, E., & Barrett, M. (2014) · MIS Quarterly · doi:10.25300/misq/2014/38.3.13 · Cited by 69 · cited 1× · see citing works
“Emergent theoretical concepts such as augmentation and assemblage provided a “ repertoire of lenses ” (Venters et al., 2014) which helped us illuminate and validate observations related to regularities such as ‘ training of the robot ’ (the CDO's terms when referring to the algorithm).”
BackgroundMarjanovic, O., & Ćećez-Kecmanović, D. (2017) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2017.07.001 · Cited by 57 · see citing works
Hiring algorithms : An ethnography of fairness in practice
van den Broek, E., Sergeeva, A., & Huysman, M. (2019) · Data Archiving and Networked Services (DANS) · Cited by 50 · see citing works
Taking a Strong Process Approach to Analyzing Qualitative Process Data
Jarzabkowski, P. A., Jane, L., & Spee, P. (2016) · SAGE Publications Ltd eBooks · doi:10.4135/9781473957954.n15 · Cited by 39 · cited 1× · see citing works
“Here quotes, memos, and early analytical notes were documented to permit a better understanding of what was going on, what the key conditions were, who the key entities were, and what constituted an event of interest (Jarzabkowski et al., 2016).”
BackgroundBrohman, M. K., Addas, S., Dixon, J., & Pinsonneault, A. (2020) · MIS Quarterly · doi:10.25300/misq/2020/15089 · Cited by 32 · see citing works
Internet of Things – Will Humans be Replaced or Augmented?
Pavlou, P. A. (2018) · NIM Marketing Intelligence Review · doi:10.2478/gfkmir-2018-0017 · Cited by 28 · cited 1× · see citing works
“Indeed, a widespread assumption is that “computers and human beings have complementary strengths and problem-solving capabilities […] and human beings generally outperform machines when dealing with ambiguity, vagueness and incomplete information” (Pavlou, 2018, p. 44-45).”
BackgroundCognitive automation as part of Deakin University's digital strategy
Scheepers, R., Lacity, M. C., & Willcocks, L. P. (2018) · London School of Economics and Political Science Research Online (London School of Economics and Political Science) · Cited by 26 · see citing works
Waardenburg, L., Sergeeva, A., & Huysman, M. (2018) · IFIP advances in information and communication technology · doi:10.1007/978-3-030-04091-8_8 · Cited by 23 · cited 2× · see citing works
“, 2019), including the implications for organizational strategy (Constantiou and Kallinikos, 2015; Woerner and Wixom, 2015; Bhimani, 2015) and work in settings ranging from education (Marjanovic and Cecez-Kecmanovic, 2017), policing (Waardenburg et al., 2018), recruitment (Broek et al.”
BackgroundCall for Papers: Strategic Perspectives on Digital Work and Organizational Transformation
Baptista, J. (2017) · The Journal of Strategic Information Systems · doi:10.1016/s0963-8687(17)30391-8 · Cited by 9 · see citing works
Eisenhardt, K. M., & Martin, J. A. (2000) · Strategic Management Journal · doi:10.1002/1097-0266(200010/11)21:10/11
Dynamic capabilities and strategic management
Teece, D. J., Pisano, G., & Shuen, A. (1997) · Strategic Management Journal · doi:10.1002/(sici)1097-0266(199708)18:7
Flagged first, then by citations
2 of the 67 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- DalleMule, L., Davenport, T.H., 2017. What's your data strategy. Harvard Bus. Rev. 95 (3), 112-121.
- Galliers, R.D., Newell, S., Shanks, G., Topi, H., 2017. Datification and its human, organizational and societal effects. J. Strateg. Inf. Syst. 26 (3), 185-190.
- Jaton, F., 2017. We get the algorithms of our ground truths. Soc. Stud. Sci. 47 (6), 811-840.
- Lebovitz, S., Levina, N., Lifshitz-Assaf, H., 2019. Doubting the Diagnosis: How Artificial Intelligence Increases Ambiguity during Professional Decision Making. Available at SSRN 3480593.
- Mazmanian, M., Cohn, M., Dourish, P., 2014. Dynamic reconfiguration in planetary exploration. MIS Quart. 38 (3), 831-848.
Cited by
Sorted by citations
Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102383 · Cited by 783 · see citing works
Budhwar, P., Malik, A., De Silva, M. T. T., & Thevisuthan, P. (2022) · The International Journal of Human Resource Management · doi:10.1080/09585192.2022.2035161 · Cited by 723 · see citing works
“It is also argued that a feedback loop is needed for learning and innovation in organisations (Grønsund & Aanestad, 2020).”
BackgroundInfluentialAlgorithmic management in a work context
Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021) · Big Data & Society · doi:10.1177/20539517211020332 · Cited by 532 · see citing works
“In short, the deployment of algorithmic management in organizational work introduces novel “human–machine configurations” that could transform relationships between managers and workers and their respective roles (Grønsund and Aanestad, 2020).”
BackgroundAlgorithms as work designers: How algorithmic management influences the design of jobs
Parent-Rocheleau, X., & Parker, S. K. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100838 · Cited by 512 · see citing works
Kulkov, I., Kulkova, J., Rohrbeck, R., Menvielle, L., Kaartemo, V., & Makkonen, H. S. (2023) · Sustainable Development · doi:10.1002/sd.2773 · Cited by 502 · see citing works
Artificial intelligence in E-Commerce: a bibliometric study and literature review
Bawack, R. E., Wamba, S. F., Carillo, K., & Akter, S. (2022) · Electronic Markets · doi:10.1007/s12525-022-00537-z · Cited by 453 · see citing works
The rise of artificial intelligence – understanding the AI identity threat at the workplace
Mirbabaie, M., Brünker, F., Möllmann Frick, N. R. J., & Stieglitz, S. (2022) · Electronic Markets · doi:10.1007/s12525-021-00496-x · Cited by 362 · see citing works
“In digital work, AI is applied for various functions, such as (managerial) decision making (Haesevoets et al., 2021), data analysis and prediction work (Grønsund & Aanestad, 2020; Marabelli et al., 2021), or (human-AI) collaboration (Brachten et al., 2020; Mirbabaie et al., 2020, 2021).”
MethodHuman-machine collaboration in managerial decision making
Haesevoets, T., De Cremer, D., Dierckx, K., & Van Hiel, A. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106730 · Cited by 280 · see citing works
Designing Transparency for Effective Human-AI Collaboration
Vössing, M., Kühl, N., Lind, M., & Satzger, G. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10284-3 · Cited by 266 · see citing works
“In recognition of the increasing capabilities of these systems, researchers have started reconsidering whether these systems are simply tools that support human decision-making and have begun studying how humans can collaborate them closely (Dellermann et al., 2019a; Grønsund and Aanestad, 2020).”
BackgroundSpeeding up to keep up: exploring the use of AI in the research process
Chubb, J., Cowling, P., & Reed, D. (2022) · AI & SOCIETY · doi:10.1007/s00146-021-01259-0 · Cited by 218 · see citing works
Quaquebeke, N. V., & Gerpott, F. H. (2023) · Journal of Leadership & Organizational Studies · doi:10.1177/15480518231181731 · Cited by 261 · see citing works
“In this way, we can hopefully maintain a “ human-in-the-loop ” pattern (Grønsund & Aanestad, 2020) whereby human leaders still (co-)generate a ground truth against which to assess algorithmic leadership and potentially adapt the underlying AI.”
BackgroundToward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes
Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023) · Information Systems Frontiers · doi:10.1007/s10796-022-10251-y · Cited by 191 · see citing works
“…AI algorithms can be considered per-formative 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.”
BackgroundThe implementation of artificial intelligence in organizations: A systematic literature review
Lee, M. C. M., Scheepers, H., Lui, A. K. H., & Ngai, E. W. T. (2023) · Information & Management · doi:10.1016/j.im.2023.103816 · Cited by 225 · see citing works
Assessing the impacts of digital transformation on internal auditing: A bibliometric analysis
Pizzi, S., Venturelli, A., Variale, M., & Macario, G. P. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101738 · Cited by 199 · see citing works
Keding, C., & Meissner, P. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120970 · Cited by 168 · see citing works
Hao, X., Demir, E., & Eyers, D. R. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102662 · Cited by 183 · see citing works
Managing with Artificial Intelligence: An Integrative Framework
Hillebrand, L., Raisch, S., & Schad, J. (2025) · Academy of Management Annals · doi:10.5465/annals.2022.0072 · Cited by 193 · see citing works
Ethical management of human-AI interaction: Theory development review
Hammerschmidt, T., Passlack, N., & Posegga, O. (2023) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2023.101772 · Cited by 170 · see citing works
An intelligent knowledge-based chatbot for customer service
Ngai, E. W. T., Lee, M. C. M., Luo, M., Chan, P. S. L., & Liang, T. (2021) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2021.101098 · Cited by 165 · see citing works
Weber, M., Engert, M., Schaffer, N., Weking, J., & Krcmar, H. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10297-y · Cited by 127 · see citing works
“Organizations need to manage AI models throughout their lifecycle as the data and environment evolve continuously, which is likely to involve novel practices such as feedback loops between users and developers (e.g., Grønsund & Aanestad, 2020).”
BackgroundComplementarity in human-AI collaboration: concept, sources, and evidence
Hemmer, P., Schemmer, M., Kühl, N., Vössing, M., & Satzger, G. (2025) · European Journal of Information Systems · doi:10.1080/0960085x.2025.2475962 · Cited by 145 · see citing works
“To alleviate possible detrimental effects, configurations have been suggested that keep humans in the decision-making loop (Grønsund & Aanestad, 2020; Herath Pathirannehelage et al., 2024; Mikalef et al., 2022).”
Andersson, C., Hallin, A., & Ivory, C. J. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2021.101662 · Cited by 165 · see citing works
Reviewing the Need for Explainable Artificial Intelligence (xAI)
Gerlings, J., Shollo, A., & Constantiou, I. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.156 · Cited by 136 · see citing works
“researchers for interdisciplinary research into AI phenomena [11], [84].”
BackgroundFrick, N. R. J., Mirbabaie, M., Stieglitz, S., & Salomon, J. (2021) · Journal of Decision Systems · doi:10.1080/12460125.2020.1870065 · Cited by 138 · see citing works
A framework for understanding artificial intelligence research: insights from practice
Bawack, R. E., Wamba, S. F., & Carillo, K. (2021) · Journal of Enterprise Information Management · doi:10.1108/jeim-07-2020-0284 · Cited by 126 · see citing works
Menzies, J. L., Sabert, B., Hassan, R., & Mensah, P. K. (2024) · Thunderbird International Business Review · doi:10.1002/tie.22370 · Cited by 145 · see citing works
Bringing artificial intelligence to business management
Feuerriegel, S., Shrestha, Y. R., von Krogh, G., & Zhang, C. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00512-5 · Cited by 111 · see citing works
Algorithmic decision-making and system destructiveness: A case of automatic debt recovery
Rinta-Kahila, T., Someh, I., Gillespie, N. A., Indulska, M., & Gregor, S. D. (2021) · European Journal of Information Systems · doi:10.1080/0960085x.2021.1960905 · Cited by 119 · see citing works
“Indeed, the understanding of how decision-making tasks can be delegated from humans to algorithms responsibly is only starting to emerge (Baird & Maruping, 2021; Grønsund & Aanestad, 2020).”
BackgroundJohnson, M., Albizri, A., Harfouche, A., & Wamba, S. F. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2022.102479 · Cited by 110 · see citing works
The lifecycle of algorithmic decision-making systems: Organizational choices and ethical challenges
Marabelli, M., Newell, S., & Handunge, V. R. (2021) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2021.101683 · Cited by 119 · see citing works
“More recently, however, the opposite problem has started to emerge – that of the human too often taken out of the loop (Grønsund and Aanestad 2020).”
Sawyer, R. K., & Henriksen, D. (2023) · doi:10.1093/oso/9780197747537.001.0001 · Cited by 101 · see citing works
Shifting ML value creation mechanisms: A process model of ML value creation
Shollo, A., Hopf, K., Thiess, T., & Müller, O. (2022) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2022.101734 · Cited by 96 · see citing works
The Pathway of Urban Planning AI: From Planning Support to Plan-Making
Peng, Z., Lu, K.-F., Liu, Y., & Zhai, W. (2023) · Journal of Planning Education and Research · doi:10.1177/0739456x231180568 · Cited by 90 · see citing works
“HITL is a model or simulation that necessitates human interaction, commonly observed in flight or driving simulators (Grønsund and Aanestad 2020).”
Kim, S., Khoreva, V., & Vaiman, V. (2024) · Human Resource Management · doi:10.1002/hrm.22268 · Cited by 119 · see citing works
Izadi, S., & Forouzanfar, M. (2024) · AI · doi:10.3390/ai5020041 · Cited by 102 · see citing works
“This aspect is particularly vital in sensitive areas such as healthcare, finance, or legal advice, where inaccurate information or inappropriate language can have serious consequences [146].”
BackgroundThe Vicious Circles of Skill Erosion: A Case Study of Cognitive Automation
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 (2023) · Journal of the Association for Information Systems · doi:10.17705/1jais.00829 · Cited by 86 · 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).”
BackgroundEffective human–AI work design for collaborative decision-making
Jain, R., Garg, N., & Khera, S. N. (2023) · Kybernetes · doi:10.1108/k-04-2022-0548 · Cited by 85 · see citing works
Who needs explanation and when? Juggling explainable AI and user epistemic uncertainty
Jiang, J., Kahai, S. S., & Yang, M. H. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102839 · Cited by 82 · see citing works
Herath, S., Shrestha, Y. R., & von Krogh, G. (2024) · European Journal of Information Systems · doi:10.1080/0960085x.2024.2330402 · Cited by 97 · see citing works
“As an antidote, the human-in-the-loop literature endorses the presence of humans in ML workflows to identify instances where systems might fail, assess associated risks, and develop contingency plans to mitigate risks (Grønsund & Aanestad, 2020, Xin et al., 2018).”
InfluentialKeegan, A. E., & Meijerink, J. (2022) · Human Resource Management · doi:10.1002/hrm.22120 · Cited by 78 · see citing works
“The gigs performed by crowdworkers vary from repetitive, atomistic activities such as photo-tagging and other so-called “human-in-the-loop” microtasks related to AI and machine learning (Gray & Suri, 2019; Grønsund & Aanestad, 2020) to broader assignments such as developing code and designing logos.”
BackgroundReflections on the human-algorithm complex duality perspectives in the auditing process
Tiron‐Tudor, A., & Deliu, D. (2021) · Qualitative Research in Accounting & Management · doi:10.1108/qram-04-2021-0059 · Cited by 77 · see citing works
Challenging AI for Sustainability: what ought it mean?
Falk, S., & van Wynsberghe, A. (2023) · AI and Ethics · doi:10.1007/s43681-023-00323-3 · Cited by 77 · see citing works
Mettler, T. (2023) · Journal of Information Technology · doi:10.1177/02683962231202535 · Cited by 90 · see citing works
“…could experiment with distinct approaches to the effective governance of algorithmic decision-making, for instance, whether today ’ s work environments would not be more productive with human-in-the-loop work con fi gurations than with fully automated decision tools (Grønsund and Aanestad, 2020).”
Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities
Durlik, I., Miller, T. I., Kostecka, E., Łobodzińska, A., & Kostecki, T. (2024) · Applied Sciences · doi:10.3390/app14145994 · Cited by 86 · see citing works
How do citizens perceive the use of Artificial Intelligence in public sector decisions?
Haesevoets, T., Verschuere, B., Van Severen, R., & Roets, A. (2024) · Government Information Quarterly · doi:10.1016/j.giq.2023.101906 · Cited by 74 · see citing works
The assessment list for trustworthy artificial intelligence: A review and recommendations
Radclyffe, C. J. T., Ribeiro, M., & Wortham, R. H. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1020592 · Cited by 71 · see citing works
“…Human Agency and Oversight questions is to test the subject organization for how maturely they have considered the role of a human-in-command, human-in-the-loop, or human-on-the-loop in the context of the AI system (Boulanin and Verbuggen, 2017; Wachter et al., 2017; Grønsund and Aanestad, 2020).”
BackgroundUnderstanding Human-AI Augmentation in the Workplace: A Review and a Future Research Agenda
Trinh, N. T. T., & Elbanna, A. R. (2025) · Information Systems Frontiers · doi:10.1007/s10796-025-10591-5 · Cited by 89 · see citing works
Preventing the digital scars of COVID-19
Marabelli, M., Vaast, E., & Li, J. L. (2021) · European Journal of Information Systems · doi:10.1080/0960085x.2020.1863752 · Cited by 68 · see citing works
“To this end, we urge IS scholars to expand on recent disciplinary conversations on this topic (cf. Grønsund & Aanestad, 2020) to address future AI/ML challenges in healthcare (and beyond).”
BackgroundLet Me Take Over: Variable Autonomy for Meaningful Human Control
Methnani, L., Tubella, A. A., Dignum, V., & Theodorou, A. A. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.737072 · Cited by 73 · see citing works
“Indeed, the human’s optimal position relative to the loop will vary depending on the human’s role as well as the overall context under which the human and system operate (Grønsund and Aanestad, 2020).”
BackgroundIvanov, S. H. (2022) · foresight · doi:10.1108/fs-09-2021-0183 · Cited by 72 · see citing works
Harfouche, A., Quinio, B., Saba, M., & Saba, P. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10352-8 · Cited by 55 · see citing works
“Following this motivation, the recently emerging trend in AI design is based on exploring human-in-the-loop (Grønsund & Aanestad, 2020) approaches and developing contextual explanatory AI applications.”
MethodResultConceptualizing generative AI as style engines: Application archetypes and implications
Riemer, K., & Peter, S. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102824 · Cited by 63 · see citing works
Te’eni, D., Yahav, I., Zagalsky, A., Schwartz, D. G., Silverman, G., Cohen, D., Mann, Y., & Lewinsky, D. (2023) · Management Science · doi:10.1287/mnsc.2022.03518 · Cited by 61 · see citing works
Li, Y., Song, Y., Sun, Y., & Zeng, M. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102518 · Cited by 63 · see citing works
Human and Technological Infrastructures of Fact-checking
Juneja, P., & Mitra, T. (2022) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3555143 · Cited by 67 · see citing works
“Scholars are also studying how to design human and machine configurations to operationalize human-in-the-loop systems [90, 178].”
BackgroundCommunications of the Association for Information SystemsPreprint
Marabelli, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5250701 · Cited by 59 · see citing works
Leavitt, K., Barnes, C. M., & Shapiro, D. L. (2024) · Academy of Management Review · doi:10.5465/amr.2022.0058 · Cited by 58 · see citing works
Disentangling Human-AI Hybrids
Fabri, L., Häckel, B., Oberländer, A., Rieg, M., & Stohr, A. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00810-1 · Cited by 43 · see citing works
“More complex human-AI hybrids are characterized by a flexible form of interworking that allows for a switch between parallel and sequential work (Grønsund and Aanestad 2020).”
BackgroundAutomated decision-making: Hoteliers’ perceptions
Ivanov, S. H., & Webster, C. (2023) · Technology in Society · doi:10.1016/j.techsoc.2023.102430 · Cited by 60 · see citing works
Artificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration
Bezrukova, K., Griffith, T. L., Spell, C., Rice, V., & Yang, H. E. (2023) · Group & Organization Management · doi:10.1177/10596011231160574 · Cited by 51 · see citing works
“Human team members can be in the loop for the design/redesign of AI systems and auditing (Grønsund & Aanestad, 2020).”
Generative artificial intelligence in construction: A Delphi approach, framework, and case study
Taiwo, R., Bello, I. T., Abdulai, S. F., Yussif, A., Salami, B. A., Saka, A. B., Seghier, M. E. A. B., & Zayed, T. M. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.12.079 · Cited by 55 · see citing works
“This human-in-the-loop auditing step provides quality control and ensures the data is aligned with true construction language [110].”
Henriksen, D., Creely, E., & Mehta, R. (2021) · Qualitative Inquiry · doi:10.1177/10778004211065813 · Cited by 56 · see citing works
“…over-claims that surround the capabilities of AI (de Saint Laurent, 2018), and advocate exploring the possibilities of creativity as divested of its human-centered alignments or of new human–AI assemblages where creativity emerges in the interstices between the two (Grønsund & Aanestad, 2020).”
BackgroundDigital workplace transformation: Subtraction logic as deinstitutionalising the taken-for-granted
Zimmer, M. P., Baiyere, A., & Salmela, H. (2023) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2023.101757 · Cited by 50 · see citing works
Sachan, S., Almaghrabi, F., Yang, J., & Xu, D. (2024) · International Review of Financial Analysis · doi:10.1016/j.irfa.2024.103149 · Cited by 48 · see citing works
Wijnhoven, F. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10206-9 · Cited by 48 · see citing works
“In intelligence amplification, AI has an indirect effect on actual human decisions as a consequence of keeping the human in the loop of AI outcome quality checking, interpretation, and maintenance of these systems (Grønsund & Aanestad, 2020).”
BackgroundWhen Algorithms Delegate to Humans: Exploring Human-Algorithm Interaction at Uber
Stelmaszak, M., Möhlmann, M., & Sørensen, C. (2024) · MIS Quarterly · doi:10.25300/misq/2024/17911 · Cited by 48 · see citing works
“Similarly, much of IS research generally tends to zoom into the theorization of the interaction between “the human” (or “many humans”) and “the algorithm” (Grønsund & Aanestad, 2020; Murray et al., 2021; Rai et al., 2019).”
InfluentialCreating AI business value through BPM capabilities
Zebec, A., & Stemberger, M. I. (2024) · Business Process Management Journal · doi:10.1108/bpmj-07-2023-0566 · Cited by 42 · see citing works
Towards Unbiased and Accurate Deferral to Multiple Experts
Keswani, V., Lease, M., & Kenthapadi, K. (2021) · Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3461702.3462516 · Cited by 45 · see citing works
“Most machine-human pipelines employed in real-world applications have multiple human experts available to share the load and to cover different kinds of input samples [12, 29].”
BackgroundHuman-Centric AI to Mitigate AI Biases
Harfouche, A., Quinio, B., & Bugiotti, F. (2023) · Journal of Global Information Management · doi:10.4018/jgim.331755 · Cited by 42 · see citing works
“Following this motivation, the recently emerging trend in AI design is based on the exploration of human-in-the-loop (Grønsund & Aanestad, 2020; Luo et al., 2022) approaches and the development of contextual explanatory AI applications.”
BackgroundSturm, T., Pumplun, L., Gerlach, J. P., Kowalczyk, M., & Buxmann, P. (2023) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2023.101790 · Cited by 45 · see citing works
Can enterprise digital transformation reduce the reliance on bank credit? Evidence from China
Chen, X., Yan, Y., & Ji, Q. (2023) · Economic Modelling · doi:10.1016/j.econmod.2023.106632 · Cited by 40 · see citing works
Human-AI collaboration in software engineering: lessons learned from a hands-on workshop
Hamza, M., Siemon, D., Akbar, M. A., & Rahman, T. (2024) · Proceedings of the 7th ACM/IEEE International Workshop on Software-intensive Business · doi:10.1145/3643690.3648236 · Cited by 38 · see citing works
“The findings also point to the need for a careful adoption and learning process when integrating AI into software engineering practices in line with theories such as human-in-the-loop Gronsund and Aanestad [29] or hybrid intelligence.”
Toward a Theory of Identity Performance in Unsettled Digital Work: The Becoming of ‘Digital Nomads’
Prester, J., Ćećez-Kecmanović, D., & Schlagwein, D. (2023) · Journal of Information Technology · doi:10.1177/02683962231196310 · Cited by 40 · see citing works
“…for organisations and individual workers, such as increasing work complexity, transformation of structures and processes, spatial and temporal independence, and fl attening organisational hierarchies (Davison and Ou, 2017; Forman et al., 2014; Grønsund and Aanestad, 2020; Morton et al., 2020).”
BackgroundZeng, F., Chen, A., Xu, S., Chan, H. K., & Li, Y. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13040797 · Cited by 58 · see citing works
The design of reciprocal learning between human and artificial intelligence
Zagalsky, A., Te'eni, D., Yahav, I., Schwartz, D. G., Silverman, G., Cohen, D., Mann, Y., & Lewinsky, D. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479587 · Cited by 35 · see citing works
“One possible approach for improving the detection performance is to “keep the human in the loop” [31, 55] in order to boost the artificial intelligence with human intelligence, going beyond the current practice of involving the human primarily during the initial training of machine learning (ML)…”
BackgroundInfluentialThe rise of people analytics and the future of organizational research
Polzer, J. T. (2022) · Research in Organizational Behavior · doi:10.1016/j.riob.2023.100181 · Cited by 34 · see citing works
Fusing domain knowledge with machine learning: A public sector perspective
Sundberg, L., & Holmström, J. (2024) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2024.101848 · Cited by 32 · see citing works
Human–Agent Team Dynamics: A Review and Future Research Opportunities
Iftikhar, R., Chiu, Y.-T., Khan, M. S., & Caudwell, C. B. (2023) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2023.3331369 · Cited by 31 · see citing works
“Besides individual learning from humans and AIs, drawing on the “human-in-the-loop” perspective, building incremental and iterative feedback loops at the team level has the potential to ensure team adaptation at HAT [151].”
BackgroundLetting losses be lessons: Human-machine cooperation in maritime transport
Fan, S., Shi, K., Weng, J., et al. (2024) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2024.110547 · Cited by 31 · see citing works
Artificial Intelligence, Firm Resilience to Supply Chain Disruptions, and Firm Performance
Sullivan, Y. W., & Wamba, S. F. (2022) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2022.719 · Cited by 35 · see citing works
“For example, firms may use AI to assess potential key enablers of changes in supply and demand [26], identify patterns and correlations, and create a new form of organizational control (e.g., evaluations by users) [27].”
Haesevoets, T., Verschuere, B., & Roets, A. (2025) · Government Information Quarterly · doi:10.1016/j.giq.2025.102029 · Cited by 45 · see citing works
Speculative foresight: A foray beyond digital transformation
Hovorka, D. S., & Mueller, B. (2024) · Information Systems Journal · doi:10.1111/isj.12530 · Cited by 29 · see citing works
“Innovation through the exploitation of digital capabilities and ‘ blurring the boundaries between physical and online structures ’ (Hanelt et al., 2021, p. 1165) along with the strategic utilisation of data itself have become a focus of digital strategy formulation (Grønsund & Aanestad, 2020).”
Odeyemi, O. O., & Alaba, P. A. (2024) · Corrosion Reviews · doi:10.1515/corrrev-2024-0046 · Cited by 31 · see citing works
Nilsson, K. H., Santander, N. M., Lee, M. K., Brulin, E., Bodin, T., & Håkansta, C. (2025) · Safety Science · doi:10.1016/j.ssci.2025.106863 · Cited by 39 · see citing works
Holldack, F., Banh, L., & Strobel, G. (2026) · Electronic Markets · doi:10.1007/s12525-025-00861-0 · Cited by 29 · see citing works
“The most restrictive is the “human-in-the-loop” approach, where a human agent manually approves all decisions or at least those of a critical nature made by the agentic IS artifact prior to execution (Grønsund & Aanestad, 2020; Jussupow et al., 2021).”
Explanatory Interactive Machine Learning
Pfeuffer, N., Baum, L., Stammer, W., Abdel-Karim, B. M., Schramowski, P., Bucher, A. M., Hügel, C., Rohde, G. G. U., Kersting, K., & Hinz, O. F. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00806-x · Cited by 30 · see citing works
“, Grønsund and Aanestad 2020) and especially IML (e.g., Amershi et al. 2015; Abdel-Karim et al. 2020) constitute one possibility to include non-technical users in a data science process. Although human-in-the-loop methods have proven successful in improving various systems (e.g., Amershi et al. 2015) by including diff…”
BackgroundMethodFostering humanistic algorithmic management: A process of enacting human-algorithm complementarity
Cui, T., Tan, B. C. C., & Shi, Y. (2024) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2024.101838 · Cited by 32 · see citing works
Sullivan, R., Veen, A., & Riemer, K. (2024) · Information and Organization · doi:10.1016/j.infoandorg.2024.100528 · Cited by 29 · see citing works
Quality work in the future: New directions via a co-evolving sociotechnical systems perspective
Parker, S. K., Ballard, T., Billinghurst, M., Collins, C. G., Dollard, M. F., Griffin, M. A., Johal, W., Jorritsma, K., Kowalkiewicz, M., Kyndt, E., et al. (2025) · Australian Journal of Management · doi:10.1177/03128962251331813 · Cited by 37 · see citing works
“In a rare study that explicitly focuses on this sort of co-evolution over time, Grønsund and Aanestad (2020) investigated how an organisation introduced algorithmic support for analysing geospatial satellite data in a ship brokering company.”
Laamanen, T. M. M., Weiser, A., von Krogh, G., & Ocasio, W. C. (2025) · Long Range Planning · doi:10.1016/j.lrp.2025.102561 · Cited by 26 · see citing works
Human-in-the-loop for computer vision assurance: A survey
Wilchek, M., Hanley, W., Lim, J., Luther, K., & Batarseh, F. A. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106376 · Cited by 28 · see citing works
Algorithmic Interactions in Open Source Work
Shaikh, M., & Vaast, E. (2022) · Information Systems Research · doi:10.1287/isre.2022.1153 · Cited by 23 · see citing works
Coordinating Human and Machine Learning for Effective Organizational Learning
Sturm, T., Gerlach, J. P., Pumplun, L., Mesbah, N., Peters, F., Tauchert, C., Nan, N., & Buxmann, P. (2021) · MIS Quarterly · doi:10.25300/misq/2021/16543 · Cited by 158 · see citing works
Kronblad, C., Essén, A., & Mähring, M. (2024) · MIS Quarterly · doi:10.25300/misq/2024/18251 · Cited by 24 · see citing works
Responsible AI for Cities: A Case Study of GeoAI in African Informal Settlements
Tonnarelli, F., & Mora, L. (2025) · Journal of Urban Technology · doi:10.1080/10630732.2025.2450755 · Cited by 23 · see citing works
Digital-First Learning and Assessment Systems for the 21st Century
Langenfeld, T. E., Burstein, J. C., & von Davier, A. A. (2022) · Frontiers in Education · doi:10.3389/feduc.2022.857604 · Cited by 23 · see citing works
A Value Co-Creation Perspective on Data Labeling in Hybrid Intelligence Systems: A Design Study
Li, M. M., Reinhard, P., Peters, C., Oeste-Reiß, S., & Leimeister, J. M. (2023) · Information Systems · doi:10.1016/j.is.2023.102311 · Cited by 22 · see citing works
Navigating the organizational AI journey: The AI transformation framework
Holmström, J., & Magnusson, J. (2025) · Business Horizons · doi:10.1016/j.bushor.2025.01.002 · Cited by 26 · see citing works
The arcanum of artificial intelligence in enterprise applications: Toward a unified framework
Herrmann, H. (2022) · Journal of Engineering and Technology Management · doi:10.1016/j.jengtecman.2022.101716 · Cited by 21 · see citing works
Generative AI in the Construction Industry: A State-of-the-art Analysis
Taiwo, R., Bello, I. T., Abdulai, S. F., Yussif, A.-M., Salami, B. A., Saka, A., & Zayed, T. (2024) · arXiv · doi:10.48550/arxiv.2402.09939 · Cited by 21 · see citing works
- 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 › Sociology and Political Science › 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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