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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Makarius, E. E., Mukherjee, D., Fox, J. D., & Fox, A. K. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.07.045 · Cited by 780
Artificial intelligence and the changing sources of competitive advantage
Krakowski, S. M., Luger, J., & Raisch, S. (2022) · Strategic Management Journal · doi:10.1002/smj.3387 · Cited by 773
Managing artificial intelligence
Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021) · MIS Quarterly · doi:10.25300/misq/2021/16274 · Cited by 898
Algorithms 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
Thinking responsibly about responsible AI and ‘the dark side’ of AI
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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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