Call for Papers

The fast pace of technological change along with the rapid evolution of socioeconomic arrangements has given rise to new ways of working and by consequence forms of organizing (Autor et al., 2022). Scholarship to date has certainly substantiated our understanding of how work is changing in response to technological change (Bailey et al., 2022; Faraj & Pachidi, 2021; Østerlund et al., 2021), yet we still continue to circle around a core question for our field: how are these radical technological transformations specifically affecting the nature and organization of knowledge work?
 
Recall that knowledge work is defined as work comprising knowledge as the main input into work, main method of achieving work, or the main output of work (Newell et al., 2009). It is differentiated from manufacturing and service work by the necessary application of expert knowledge, which can only be developed in practice. Unlike these other forms of work, knowledge work is less often categorized by routinized problem solving contexts, which further requires workers to rely on creative and sometimes esoteric forms of expertise to address idiosyncratic situations and concomitant tasks (Schultze, 2000). Until recently, it was considered as a highly abstract and, by consequence, elite type of work sufficiently robust to change (Abbott, 1988).
 
As research is beginning to reveal, artificial intelligence (AI), arguably one of the foundational elements of today’s technological landscape, is increasingly becoming as integral a part of knowledge work as it has been in manufacturing in prior decades (Benanav, 2020). AI, broadly writ, is the attempt to make machines exhibit human-like knowing and expertise, including the ability to make situated decisions (i.e., algorithmic and probabilistic prediction) and to learn and adapt (i.e., machine learning, neural nets) (McCarthy et al., 2006). Drawing on ever-growing stores of digital data, these intelligent forms of automation are not only becoming more ubiquitous in a growing array of domains, including healthcare (e.g., Lebovitz et al., 2022), policing (e.g., Waardenburg et al., 2022), human resources (e.g., van den Broek et al., 2021), but they are also becoming ever more invisibly encoded into knowledge work itself via the adoption of AI-based tools and infrastructures (Erickson & Wajcman, 2022).
 
Thus, this moment in the journey to understand the dynamic, evolving interrelations of work and technology could be said to center squarely on questions of knowledge. The encroachment of AI into knowledge work prompts us to evaluate the legitimacy of human vs. machine knowledge, the application of human vs. machine knowledge, the relationality of human vs. machine knowledge, the differences between human vs. machine knowledge, and so on and so forth. As such, we consider it an opportune time to bring together a set of scholars to address conversations about the present and future of work by “bringing the (knowledge) work back in” (Barley & Kunda, 2001). In organizing a sub-theme at the intersection of knowledge work and ‘the future of work’, we hope to encourage interested participants to investigate not only the situated nature of today’s knowledge work and its constitutive praxis, but to push further by linking these insights to the knowledge-centered tensions around expertise, agency, and relationality as articulated above.
 
We welcome submissions from any domain and methodological orientation, especially those that help us better understand questions like these:

• What traditional assumptions about knowledge work are being challenged with new empirical research?

• How can scholars successfully ‘bring (knowledge) work back in’ in the era of AI?

• How, where, and why are work practices shifting to accommodate increased automation and other aspects related to AI?

• What kind of new human-computer divides and/or complementarities may we discern in modern knowledge work?

• How do professional boundaries get reconfigured as digital experts bring a new type of abstract knowledge in knowledge work settings?

• What does human-machine collaboration look like within the modern context of knowledge work? How are these patterns evolving differently within different professions, industries?

• How do knowledge workers develop their expertise when the repetitive mundane tasks that occupational entrants used to perform become automated?

 
As organizational scholars are trying to make sense of what technological advances mean for organization theory, we want to remind our community of the fundamental view that work matters (Barley, 1996). Before theorizing the organizational and field changes that derive from the changes in knowledge work in the digital era, it is more crucial than ever to first reconsider how we conceptualize knowledge work and take a deep look at how knowledge work is currently enacted.
 

References

• Abbott, A. (1988): The System of Professions: An Essay on the Division of Labor. Chicago: University of Chicago Press.
• Autor, D.H., Mindell, D.A., & Reynolds, E. (2022): The Work of the Future: Building Better Jobs in an Age of Intelligent Machines. Cambridge, MA: MIT Press.
• Bailey, D.E., Faraj, S., Hinds, P.J., Leonardi, P.M., & von Krogh, G. (2022): “We Are All Theorists of Technology Now: A Relational Perspective on Emerging Technology and Organizing.” Organization Science, 33 (1), 1–18.
• Barley, S.R. (1996): “Technicians in the Workplace: Ethnographic Evidence for Bringing Work into Organizational Studies.” Administrative Science Quarterly, 41 (3), 404–441.
• Barley, S.R., & Kunda, G. (2001): “Bringing work back in.” Organization Science, 12 (1), 76–95.
• Benanav, A. (2020): Automation and the Future of Work. London: Verso Books.
• Erickson, I., & Wajcman, J. (2022): “Optimizing Temporal Capital: How Big Tech Imagines Time as Auditable.” American Behavioral Scientist, first published online on October 17, 2022; https://doi.org/10.1177/00027642221127243.
• Faraj, S., & Pachidi, S. (2021): “Beyond Uberization: The co-constitution of technology and organizing.” Organization Theory, 2 (1).
• Lebovitz, S., Lifshitz-Assaf, H., & Levina, N. (2022): “To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis.” Organization Science, 33 (1), 126–148.
• McCarthy, J., Minsky, M.L., Rochester, N., & Shannon, C.E. (2006): “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955.” AI Magazine, 27 (4), 12–12.
• Newell, S., Scarbrough, H., & Swan, J. (2009): Managing Knowledge Work and Innovation. London: Red Globe Press.
• Østerlund, C., Jarrahi, M.H., Willis, M., Boyd, K., & Wolf, C. (2021): “Artificial intelligence and the world of work, a co-constitutive relationship.” Journal of the Association for Information Science and Technology, 72 (1), 128–135.
• Schultze, U. (2000): “A Confessional Account of an Ethnography about Knowledge Work.” Management Information Science Quarterly, 24 (1), 3–41.
• van den Broek, E., Sergeeva, A., & Huysman, M. (2021): “When the Machine Meets the Expert: An Ethnography of Developing AI for Hiring.” Management Information Science Quarterly, 45 (3), 1557–1580.
• Waardenburg, L., Huysman, M., & Sergeeva, A.V. (2022): “In the Land of the Blind, the One-Eyed Man Is King: Knowledge Brokerage in the Age of Learning Algorithms.” Organization Science, 33 (1), 59–82.
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