The DOI matches its Crossref record; 114 of the 116 references it cites were found in a registry or an index.
Humans and Technology: Forms of Conjoined Agency in Organizations
Open accessAbstract
Organizations are increasingly deploying technologies that have the ability to parse through large amounts of data, acquire skills and knowledge, and operate autonomously. These technologies diverge from prior technologies in their capacity to exercise intentionality over protocol development or action selection in the practice of organizational routines, thereby affecting organizations in new and distinct ways. In this article, we categorize four forms of conjoined agency between humans and technologies: (1) conjoined agency with assisting technologies, (2) conjoined agency with arresting technologies, (3) conjoined agency with augmenting technologies, and (4) conjoined agency with automating technologies. We then theorize on the different ways in which these forms of conjoined agency impact a routine’s change at a particular moment in time as well as a routine’s responsiveness to feedback over time. In doing so, we elaborate on how organizations may evolve in varied and diverse ways based on the form(s) of conjoined agency they deploy in their organizational design choices.
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“In sum, AI algorithms, largely powered by machine learning, differ from previous automated decision-making devices in their high level of autonomy (requiring little human input), their self-learning capacities, their potential for interconnectedness, and their ability to handle massive and heterogeneous data in order to perform description, prediction, and prescription tasks (Makarius et al., 2020”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Murray et al., 2020
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Cites
An Evolutionary Theory of Economic Change.
Loasby, B. J., Nelson, R. R., & Winter, S. G. (1983) · The Economic Journal · doi:10.2307/2232409 · Cited by 21,426
(1984) · Cited by 21,166
A BEHAVIORAL MODEL OF RATIONAL CHOICE
Simon, H. A. (1955) · The Quarterly Journal of Economics · doi:10.2307/1884852 · Cited by 15,877
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
(2011) · Cited by 14,317
An Evolutionary Theory of Economic Change
Berkowitz, M. K., Nelson, R. R., & Winter, S. G. (1983) · Southern Economic Journal · doi:10.2307/1058233 · Cited by 10,888
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
Mastering the game of Go without human knowledge
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084
Levinthal, D. A., & March, J. G. (1993) · Strategic Management Journal · doi:10.1002/smj.4250141009 · Cited by 7,646
Emirbayer, M., & Mische, A. (1998) · American Journal of Sociology · doi:10.1086/231294 · Cited by 6,165
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2 of the 116 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- Huq A. Z., 2019, Duke Law Journal, 68, 1004
- Joh E. E., 2016, Harvard Law & Policy Review, 10, 15
- March J. G., 1958, Organizations.
- Thompson J. D., 1967, Organizations in action.
- Tucci C. L., 2017, Creating and capturing value through crowdsourcing.
- Wallin M. W., 2017, Creating and capturing value through crowdsourcing, 127
Cited by
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Items 501–517 of 517
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Zhang, H. (2026) · Journal of Asia Business Studies · doi:10.1108/jabs-01-2026-0072
Jonathan, G. M., Ayeni, F., Witarsyah, D., & Perjons, E. (2026) · Lecture notes in business information processing · doi:10.1007/978-3-032-38498-0_3
신선경, & Park, J. (2026) · Frontiers in Communication · doi:10.3389/fcomm.2026.1966278
The AI CEO: A conceptualisation and outline for future research
Sundermann, J., Fredström, A., & Wincent, J. (2026) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2026.124909
Wei, C. (2026) · Unveiling seven continents yearbook journal · doi:10.65326/u7y10032
Hu, H.-H., Li, C., & Wang, Z.-M. (2026) · SAGE Open · doi:10.1177/21582440261496013
From Artifacts to Actors: A Design Theory for Modeling Algorithmic Actors in Agentic Service Systems
Tugarin, N., & van Husen, C. (2026) · Systems · doi:10.3390/systems14101282
Entrepreneurial theorizing with generative artificial intelligence
Park, S. H., & Murray, A. M. (2026) · Strategic Entrepreneurship Journal · doi:10.1002/sej.70060
Self-citationShoaib, S., Haq, F. U., Maaitah, N., Dhahbi, A., Badawy, M., Sheikh, A. A., & Masood, A. (2026) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2026.103799
Navigating personal control: AI augmentation and its impact on decision-making
Tosetto, D., Mortara, L., Leeb, C. M., & Vinelli, A. (2026) · Technovation · doi:10.1016/j.technovation.2026.103746 · Cited by 1
AI-Enabled Dynamic Capabilities: A Theoretical Extension of the Dynamic Capability FrameworkPreprint
Pal, S., & Bick, M. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7643495
When machines learn with organizations: AI ‐enabled knowledge creation and entrepreneurship
Wu, L., Zahra, S. A., & Hashai, N. (2026) · Strategic Entrepreneurship Journal · doi:10.1002/sej.70062
Zhang, H., Wuebker, R. J., & Barney, J. B. (2026) · Strategic Entrepreneurship Journal · doi:10.1002/sej.70064
Digitally enabled e-commerce across retail supply chains
Sheikh, A. A., Hassan, N. M., Zafar, H., Yu, Z., & Khan, S. A. R. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-30094-3.00007-2
Ojuok, A. (2026) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/26oct568
Salem, M. A. (2026) · Mathematics · doi:10.3390/math14213885
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Metadata
- Authors
- Murray, A., Rhymer, J., & Sirmon, D. G.
- Affiliations
- University of Oregon (US)
- Stanford University (US)
- University of Washington (US)
- Venue
- Academy of Management Review
- Publisher
- Academy of Management
- Published
- 2021-07
- Volume
- 46
- Issue
- 3
- Pages
- 552-571
- ISSN
- 0363-7425 (ISSN-L)
- 1930-3807
- OpenAlex
- W3010543446
- Cited by
- 518
- References
- 116
- Citations by year
- 2026: 176 · 2025: 107 · 2024: 96 · 2023: 61 · 2022: 47 · 2021: 28
- FWCI
- 46.50
- Type
- journal article
- Language
- en
- Open access
- green
- discovery.ucl.ac.uk
- Locations
- Academy of Management Review (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- 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 › Management and Organizational Studies
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Management Theory and Practice
- Keywords
- human-technology interaction, organizational routines, routine change, organizational evolution, organizational design, artificial intelligence in organizations
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