Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-10T14:03:27.636Z Has data issue: false hasContentIssue false

Chapter 23 - Algorithms and Routine Dynamics

from Part III - Themes in Routine Dynamics Research

Published online by Cambridge University Press:  11 December 2021

By
Vern L. Glaser ,
Rodrigo Valadao  and
Timothy R. Hannigan
Edited by
Martha S. Feldman ,
Brian T. Pentland ,
Luciana D'Adderio ,
Katharina Dittrich ,
Claus Rerup  and
David Seidl
Martha S. Feldman
Affiliation:
University of California, Irvine
Brian T. Pentland
Affiliation:
Michigan State University
Luciana D'Adderio
Affiliation:
University of Edinburgh
Katharina Dittrich
Affiliation:
University of Warwick
Claus Rerup
Affiliation:
Frankfurt School of Finance and Management
David Seidl
Affiliation:
University of Zurich
Get access

Summary

Organizations increasingly rely upon algorithms to change their routines—with positive, negative, or messy outcomes. In this chapter, we argue that conceptualizing algorithms as an integral part of an assemblage provides scholars with the ability to generate novel theories about how algorithms influence routine dynamics. First, we review existing research that shows how algorithms operate as an actant making decisions; encode the intentions of designers; are entangled in broader assemblages of theories, artifacts, actors, and practices; and generate performative effects. Second, we elucidate five analytical approaches that can help management scholars to identify new connections between routine assemblages, their elements, and organizational outcomes. Finally, we outline directions for future research to explore how studying algorithms can advance our understanding of routine dynamics and how a routine dynamics perspective can contribute to the understanding of algorithms in strategy and organizational theory more broadly .

Keywords

Routinesroutine dynamicsalgorithmsartifactsstrategyperformativityinformation technology
Type
Chapter
Information
Cambridge Handbook of Routine Dynamics , pp. 315 - 328
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Amabile, T. M., Barsade, S. G., Mueller, J. S. and Staw, B. M. (2005). Affect and creativity at work. Administrative Science Quarterly, 50(3), 367–403.CrossRefGoogle Scholar
Ames, M. G. (2018). Deconstructing the algorithmic sublime. Big Data & Society, 5(1), https://doi.org/10.1177/2053951718779194.CrossRefGoogle Scholar
Berente, N., Lyytinen, K., Yoo, Y. and King, J. L. (2016). Routines as shock absorbers during organizational transformation: Integration, control, and NASA’s Enterprise Information System. Organization Science, 27(3), 551–572.CrossRefGoogle Scholar
Brayne, S. (2017). Big data surveillance: The case of policing. American Sociological Review, 82(5), 977–1008.CrossRefGoogle Scholar
Buhmann, A., Paßmann, J. and Fieseler, C. (2019). managing algorithmic accountability: Balancing reputational concerns, engagement strategies, and the potential of rational discourse. Journal of Business Ethics. https://doi.org/10.1007/s10551-019-04226-4.CrossRefGoogle Scholar
Burke, A. (2019). Occluded algorithms. Big Data & Society, 6(2), 1–15.CrossRefGoogle Scholar
Cabantous, L. and Gond, J.-P. (2011). Rational decision making as performative praxis: Explaining rationality’s éternel retour. Organization Science, 22(3), 573–586.CrossRefGoogle Scholar
Cabantous, L., Gond, J.-P. and Johnson-Cramer, M. (2010). Decision theory as practice: Crafting rationality in organizations. Organization Studies, 31(11), 1531–1566.Google Scholar
Cacciatori, E. (2012). Resolving conflict in problem-solving: Systems of artefacts in the development of new routines. Journal of Management Studies, 49(8), 1559–1585.Google Scholar
Callon, M. (1980). Struggles and negotiations to define what is problematic and what is not. In Knorr, K. D., Krohn, R. and Whitley, R., eds., The Social Process of Scientific Investigation: Sociology of the Sciences, a Yearbook. Dordrecht: Springer Netherlands, pp. 197–219. https://doi.org/10.1007/978-94-009-9109-5_8Google Scholar
Callon, M. (1986). The sociology of an actor-network: The case of the electric vehicle. In Callon, M., Law, J. and Rip, A., eds., Mapping the Dynamics of Science and Technology: Sociology of Science in the Real World. London: Palgrave Macmillan UK, pp. 19–34.Google Scholar
Callon, M. (2017). Markets, marketization and innovation. In Bathelt, H., Cohendet, P., Henn, S. and Simon, L., eds., The Elgar Companion to Innovation and Knowledge Creation. Northampton, MA: Edward Elgar Publishing. www.elgaronline.com/view/edcoll/9781782548515/9781782548515.00048.xmlGoogle Scholar
Cameron, L. (2020). The Rise of Algorithmic Work: Implications for Organizational Control and Worker Autonomy. Dissertation, University of Michigan.Google Scholar
Christin, A. (2020). Metrics at Work: Journalism and the Contested Meaning of Algorithms. Princeton, NJ: Princeton University Press.Google Scholar
Clegg, S. (1989). Frameworks of Power. London: Sage.Google Scholar
Clegg, S. R., Courpasson, D. and Phillips, N. (2006). Power and Organizations. London: SAGE.Google Scholar
Cohendet, P. S. and Simon, L. O. (2016). Always playable: Recombining routines for creative efficiency at Ubisoft Montreal’s video game studio. Organization Science, 27(3), 614–632.Google Scholar
Curchod, C., Patriotta, G., Cohen, L. and Neysen, N. (2020). Working for an algorithm: Power asymmetries and agency in online work settings. Administrative Science Quarterly, 65(3), 644–676.Google Scholar
D’Adderio, L. (2001). Crafting the virtual prototype: How firms integrate knowledge and capabilities across organisational boundaries. Research Policy, 30(9), 1409–1424.CrossRefGoogle Scholar
D’Adderio, L. (2008). The performativity of routines: Theorising the influence of artefacts and distributed agencies on Routines Dynamics. Research Policy, 37(5), 769–789.CrossRefGoogle Scholar
D’Adderio, L. (2011). Artifacts at the centre of routines: Performing the material turn in routines theory. Journal of Institutional Economics, 7(Special Issue 02), 197–230.CrossRefGoogle Scholar
D’Adderio, L. (2014). The replication dilemma unravelled: How organizations enact multiple goals in routine transfer. Organization Science, 25(5), 1325–1350.CrossRefGoogle Scholar
D’Adderio, L., Glaser, V. and Pollock, N. (2019). Performing theories, transforming organizations: A reply to Marti and Gond. Academy of Management Review, 44(3), 676–679.CrossRefGoogle Scholar
D’Adderio, L. and Pollock, N. (2020). Making routines the same: Crafting similarity and singularity in routines transfer. Research Policy, 49(8), 104029.Google Scholar
D’Adderio, L. and Pollock, N. (2014). Performing modularity: Competing rules, performative struggles and the effect of organizational theories on the organization. Organization Studies, 35(12), 1813–1843.CrossRefGoogle Scholar
Davenport, T. H. (2018). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work, 1st edition. Cambridge, MA: The MIT Press.Google Scholar
Dittrich, K. and Seidl, D. (2018). Emerging intentionality in Routine Dynamics: A pragmatist view. Academy of Management Journal, 61(1), 111–138.Google Scholar
Dourish, P. (2016). Algorithms and their others: Algorithmic culture in context. Big Data & Society, 3(2), 1–11.Google Scholar
Elgammal, A. (2018, December 6). AI is blurring the definition of artist. American Scientist. www.americanscientist.org/article/ai-is-blurring-the-definition-of-artist.Google Scholar
Fei, H., Li, Q. and Sun, D. (2017). A survey of recent research on optimization models and algorithms for operations management from the process view. Scientific Programming. https://doi.org/10.1155/2017/7219656.Google Scholar
Feldman, M. S. (2016) Routines as process: past, present, and future. In Rerup, C. and Howard-Grenville, J., eds., Organizational Routines and Process Organization Studies. Oxford: Oxford University Press.Google Scholar
Feldman, M. S. and Orlikowski, W. J. (2011). Theorizing practice and practicing theory. Organization Science, 22(5), 1240–1253.Google Scholar
Fourcade, M. and Healy, K. (2017). Seeing like a market. Socio-Economic Review, 15(1), 9–29.Google Scholar
Garud, R. and Gehman, J. (2019). Performativity: Not a destination but an ongoing journey. Academy of Management Review, 44(3), 679–684.CrossRefGoogle Scholar
Géron, A. (2017). Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 1st edition. Beijing; Boston; Farnham; Sebastopol; Tokyo: O’Reilly Media.Google Scholar
Gibson, J. J. (1979). The Ecological Approach to Visual Perception, 1st edition. New York; London: Routledge.Google Scholar
Gillespie, T. (2014). The Relevance of Algorithms. Cambridge, MA: The MIT Press. https://mitpress.universitypressscholarship.com/view/10.7551/mitpress/9780262525374.001.0001/upso-9780262525374-chapter-9.Google Scholar
Gillespie, T. (2016). Algorithm. In Peters, B., ed., Digital Keywords: A Vocabulary of Information Society and Culture. Princeton, NJ: Princeton University Press, pp. 18–30.Google Scholar
Glaser, V. L. (2014). Enchanted Algorithms: The Quantification of Organizational Decision-Making. Dissertation, Marshall School of Business.Google Scholar
Glaser, V. L. (2017). Design performances: How organizations inscribe artifacts to change routines. Academy of Management Journal, 60(6), 2126–2154.Google Scholar
Glaser, V. L., Pollock, N. and D’Adderio, L. (2021). The biography of an algorithm: Performing algorithmic technologies in organizations. Organization Theory, 2, 1–27.Google Scholar
Grint, K. and Woolgar, S. (1997). The Machine at Work: Technology, Work and Organization. Cambridge, UK; Malden, MA: Blackwell Publishers; Polity Press.Google Scholar
Gusterson, H. (1997). Studying up revisited. Studying Up Revisited, 20(1), 114–119.Google Scholar
Hales, M. and Tidd, J. (2009). The practice of routines and representations in design and development. Industrial and Corporate Change, 18(4), 551–574.Google Scholar
Hargadon, A. (2005). Technology brokering and innovation: Linking strategy, practice, and people. (Knight, D. and Randall, R. M., eds.)Strategy & Leadership, 33(1), 32–36.Google Scholar
Hutchby, I. (2001). Technologies, texts and affordances. Sociology, 35(2), 441–456.Google Scholar
Hyysalo, S., Pollock, N. and Williams, R. A. (2019). Method matters in the social study of technology: Investigating the biographies of artifacts and practices. Science & Technology Studies, 32(3), 2–25.Google Scholar
Iansiti, M. and Lakhani, K. R. (2020, January 1). Competing in the Age of AI. Harvard Business Review, (January–February 2020). https://hbr.org/2020/01/competing-in-the-age-of-ai.Google Scholar
Introna, L. D. (2007). Towards a post-human intra-actional account of socio- technical agency (and morality). Prepared for the Moral Agency and Technical Artifacts Scientific Workshop, NIAS, Hague, 22.Google Scholar
Kellogg, K., Valentine, M. and Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174.CrossRefGoogle Scholar
Kiviat, B. (2019). The moral limits of predictive practices: The case of credit-based insurance scores. American Sociological Review, 84(6), 1134–1158.Google Scholar
Kling, R. (1991). Computerization and social transformations. Science, Technology, & Human Values, 16(3), 342–367.Google Scholar
Kling, R. (1992). Audiences, narratives, and human values in social studies of technology. Science, Technology, & Human Values, 17(3), 349–365.CrossRefGoogle Scholar
Kremser, W. and Schreyögg, G. (2016). The dynamics of interrelated routines: Introducing the cluster level. Organization Science. https://doi.org/10.1287/orsc.2015.1042.Google Scholar
Labatut, J., Aggeri, F. and Girard, N. (2012). Discipline and change: How technologies and organizational routines interact in new practice creation. Organization Studies, 33(1), 39.CrossRefGoogle Scholar
Latour, B. (1986). Visualisation and cognition: Drawing things together. In Kuklick, H., ed., Knowledge and Society Studies in the Sociology of Culture Past and Present. Bingley: JAI Press, 1–40.Google Scholar
Latour, B. (1992). Where are the missing masses? The sociology of a few mundane artifacts. In Shaping Technology/Building Society: Studies in Sociotechnical Change. Cambridge, MA: MIT Press, pp. 225–258.Google Scholar
Latour, B. (1996). On actor-network theory: A few clarifications. Soziale Welt, 47(4), 369–381.Google Scholar
Latour, B. (2005). Reassembling the Social. Oxford: Oxford University Press.CrossRefGoogle Scholar
Latour, B. and Woolgar, S. (1979). Laboratory Life. Princeton, NJ: Princeton University Press.Google Scholar
Leonardi, P. M., Bailey, D. E. and Pierce, C. S. (2019). The coevolution of objects and boundaries over time: Materiality, affordances, and boundary salience. Information Systems Research, 30(2), 665–686.CrossRefGoogle Scholar
Lindebaum, D., Vesa, M. and den Hond, F. (2019). Insights from the machine stops to better understand rational assumptions in algorithmic decision-making and its implications for organizations. Academy of Management Review. https://doi.org/10.5465/amr.2018.0181.CrossRefGoogle Scholar
MacCormick, J. (2012). Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today’s Computers (2nd edition). Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Marti, E. and Gond, J.-P. (2018). When do theories become self-fulfilling? Exploring the boundary conditions of performativity. Academy of Management Review, 43(3), 487–508.Google Scholar
Martin, K. (2019). Ethical implications and accountability of algorithms. Journal of Business Ethics, 160(4), 835–850.Google Scholar
Mayer-Schonberger, V. and Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think, 1st ed. London: Eamon Dolan/Houghton Mifflin Harcourt.Google Scholar
Mazmanian, M., Cohn, M. and Dourish, P. (2014). Dynamic reconfiguration in planetary exploration: A sociomaterial ethnography. Management Information Systems Quarterly, 38(3), 831–848.Google Scholar
Murray, A., Rhymer, J. and Sirmon, D. forthcoming. Humans and Technology: Forms of Conjoined Agency in Organizational Routines. Academy of Management Review.Google Scholar
Nelson, R. R. and Winter, S. G. (1982). An Evolutionary Theory of Economic Change. Cambridge, MA: Belknap Press of Harvard University Press.Google Scholar
Pasquale, F. (2015). The Black Box Society (Reprint edition). Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Pentland, B. T. and Feldman, M. S. (2008). Designing routines: On the folly of designing artifacts, while hoping for patterns of action. Information and Organization, 18(4), 235–250.CrossRefGoogle Scholar
Pentland, B. T. and Hærem, T. (2015). Organizational routines as patterns of action: Implications for organizational behavior. Annual Review of Organizational Psychology and Organizational Behavior, 2, 465–487.CrossRefGoogle Scholar
Pollock, N. and Williams, R. (2009). Software and Organisations: The Biography of the Enterprise-Wide System or How SAP Conquered the World. Abingdon: Routledge.Google Scholar
Pollock, N. and Williams, R. (2016). How Industry Analysts Shape the Digital Future. Oxford: Oxford University Press.Google Scholar
Rerup, C. and Feldman, M. S. (2011). Routines as a source of change in organizational schemata: The role of trial-and-error learning. Academy of Management Journal, 54(3), 577–610.CrossRefGoogle Scholar
Rieder, B. (2017). Scrutinizing an algorithmic technique: The Bayes classifier as interested reading of reality. Information, Communication & Society, 20(1), 100–117.CrossRefGoogle Scholar
Schildt, H. (2020). The Data Imperative: How Digitalization Is Reshaping Management, Organizing, and Work. Oxford: Oxford University Press.Google Scholar
Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 1–12.Google Scholar
Sele, K. and Grand, S. (2016). Unpacking the dynamics of ecologies of routines: Mediators and their generative effects in routine interactions. Organization Science, 27(3), 722–738.Google Scholar
Simon, H. A. (1970). The Sciences of the Artificial, 1st edition. Cambridge, MA: The MIT Press.Google Scholar
Simon, H. A. (1996). The Sciences of the Artificial, 3rd edition. Cambridge, MA: The MIT Press.Google Scholar
Sonenshein, S. (2016). Routines and creativity: From dualism to duality. Organization Science, 27(3), 739–758.Google Scholar
Suchman, L. (2007). Human-Machine Reconfigurations: Plans and Situated Actions, 2nd edition. Cambridge; New York: Cambridge University Press.Google Scholar
Wenzel, M., Krämer, H., Koch, J. and Reckwitz, A. (2020). Future and Organization Studies: On the rediscovery of a problematic temporal category in organizations. Organization Studies.CrossRefGoogle Scholar
Winter, S. G. and Szulanski, G. (2001). Replication as strategy. Organization Science, 12(6), 730–743.Google Scholar
Winter, S. G., Szulanski, G., Ringov, D. and Jensen, R. J. (2011). Reproducing knowledge: Inaccurate replication and failure in franchise organizations. Organization Science, 23(3), 672–685.CrossRefGoogle Scholar
Zbaracki, M. J. and Bergen, M. (2010). When truces collapse: A longitudinal study of price-adjustment routines. Organization Science, 21(5), 955–972.CrossRefGoogle Scholar
Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, 1st edition. New York: PublicAffairs.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×