Sub-theme 33: Digital Biographies: How Algorithms Travel across Time and Space

Luciana D’Adderio
University of Edinburgh, United Kingdom
Vern L. Glaser
Alberta School of Business, Canada
Marleen Huysman
Vrije Universiteit Amsterdam, The Netherlands

Call for Papers

In this sub-theme we invite scholars to consider how digital artifacts (including – but not exclusively – artificial intelligence and data-driven technologies) emerge and evolve as they travel across sites and over time with fundamental consequences for practitioners, practices and organisations. Algorithms and other digital technologies are becoming widespread features of contemporary organizing, bearing profound consequences that we do not yet fully understand. This has led scholars to call for the urgent development of theory, methods and case studies that enable a better understanding of how algorithms can “alter work and organizational realities” (Faraj et al., 2018: 67).
Organizational research on the effects of digital technologies has so far highlighted the positive potential of algorithmic tools to provide organizations with affordances that facilitate value creation by automating structured and repetitive work (Davenport, 2018) and reshaping organizational culture (Fountaine et al., 2019; Leonardi & Neeley, 2022; Schildt, 2020). Other scholars have focused on the dark side of these technologies, including how they enable management to control workers (Kellogg et al., 2020), establish formal and inflexible rules that strip away values-based means of working through social challenges (Lindebaum et al., 2019, 2022), and provide corporations with the ability to manipulate individuals (Cameron, 2021; Cameron & Rahman, 2022) in ways that perpetuate power asymmetries (Curchod et al., 2020; Zuboff, 2022).
Despite this recent progress, however, we are still lacking the ability to capture empirically and theorize the generative and diverse possibilities digital technologies afford organizations (Raisch & Krakowski, 2021; von Krogh, 2018) while exploring their complex and often invisible (albeit often powerful) influence on organizations and organizing such as the provision of services (Aristidou & Barrett, 2018), collaboration between actors such as users and designers, professionals or across teams (Bailey & Barley, 2019; Karunakaran, 2022; Sergeeva et al., 2017; Waardenburg & Huysman, 2022), testing (Marres & Stark, 2020), the production and consumption of knowledge (Monteiro, 2022; Steele, 2016), and the development of ethical AI systems (Floridi et al., 2018; Martin, 2019).
In this sub-theme we invite scholars to engage with one or more aspects of the biographical framework (as illustrated in Glaser et al., 2021, and discussed in Monteiro et al., 2022), to develop a more nuanced and powerful understandings of how algorithms, and more broadly digital technologies, are reshaping organizational life while enabling deeper explanations for the nature of algorithms and their effects. Specifically, we welcome rich, qualitative studies as well as theoretical, conceptual contributions capturing the influence of digital/algorithmic technologies on a range of organizational topics, including: processes of organizational decision-making and generativity; the spread of theories and technologies and their logics; the dynamics of organizational practices, processes and routines, and many more.
Key questions are as follows:

  • How do digital technologies/algorithms emerge and evolve as they travel across space and over time? With what consequences for people, practices and organizations?

  • How do the biographies of digital technologies shape the production and consumption of knowledge?

  • How do digital technologies/algorithms move across organizations and sectors (e.g., finance, health)? What is the role of context in shaping digital technologies and algorithms (and vice versa)?

  • How are values, principles, goals and assumptions inscribed into digital technologies/algorithms at the design stage or incorporated over time? How do they affect practitioners, practices and organizations?

  • How do digital technologies/algorithms shape organizational processes, practices and routines?

  • How do they reconfigure decision making processes and affect power dynamics?

  • How do they affect creative and generative organizational processes?

  • How do digital technologies/algorithms relate to institutions and institutional change?

  • How do they transform occupational knowledge, tasks, skills, cultures and expertise?

  • How do AI agents and generative algorithms – e.g., large language models or LLMs – evolve and adapt their behavior in response to different organizational contexts and tasks? Can we discern a biography?

  • Who is/should be responsible/accountable for algorithm-related failures?

  • How do digital technologies/algorithms reshape the boundaries between actors such as users and designers, organizational groups and teams or between and across institutions?

  • Who is responsible/accountable for digital tech/algorithm’s behaviour? How/can AI systems be made ethical?



  • Aristidou, A., & Barrett, M. (2018): “Coordinating Service Provision in Dynamic Service Settings: A Position-practice Relations Perspective.“ Academy of Management Journal, 61 (2), 685–714.
  • Bailey, D.E., & Barley, S.R. (2020): “Beyond design and use: How scholars should study intelligent technologies.“ Information and Organization, 30 (2),
  • Cameron, L.D. (2022): “Making out” while driving: Relational and efficiency games in the gig economy.“ Organization Science, 33 (1), 231–252.
  • Cameron, L.D., & Rahman, H. (2021): “Expanding the Locus of Resistance: Understanding the Co-constitution of Control and Resistance in the Gig Economy.“ Organization Science, 33 (1), 38–58.
  • Curchod, C., Patriotta, G., Cohen, L., & Neysen, N. (2020): “Working for an algorithm: Power asymmetries and agency in online work settings.“ Administrative Science Quarterly, 65 (3), 644–676.
  • Davenport, T.H. (2018): The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. Cambridge, MA: The MIT Press.
  • Faraj, S., Pachidi, S., & Sayegh, K. (2018): “Working and organizing in the age of the learning algorithm.“ Information and Organization, 28 (1), 62–70.
  • Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018): “AI4People – An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations.“ Minds and Machines, 28 (4), 689–707.
  • Fountaine, T., McCarthy, B., & Saleh, T. (2019): “Building the AI-Powered Organization.“ Harvard Business Review, Magazine (July–August 2019), 62–73.
  • Glaser, V.L., Pollock, N., & D’Adderio, L. (2021): “The Biography of an Algorithm: Performing algorithmic technologies in organizations.“ Organization Theory, 2 (2), 1–27.
  • Karunakaran, A. (2022): “Status–Authority Asymmetry between Professions: The Case of 911 Dispatchers and Police Officers.“ Administrative Science Quarterly, 67 (2), 423–468.
  • Kellogg, K.C., Valentine, M. A., & Christin, A. (2020): “Algorithms at Work: The New Contested Terrain of Control.“ Academy of Management Annals, 14 (1), 366–410.
  • Leonardi, P., & Neeley, T. (2022): The Digital Mindset: What It Really Takes to Thrive in the Age of Data, Algorithms, and AI. Boston, MA: Harvard Business Review Press.
  • Lindebaum, D., Moser, C., Ashraf, M., & Glaser, V.L. (2023): “Reading The Technological Society to Understand the Mechanization of Values and Its Ontological Consequences.“ Academy of Management Review, 48 (3), 575–592.
  • Lindebaum, D., Vesa, M., & den Hond, F. (2020): “Insights From “The Machine Stops“ to Better Understand Rational Assumptions in Algorithmic Decision Making and Its Implications for Organizations.“ Academy of Management Review, 48 (1), 247–263.
  • Marres, N., & Stark, D. (2020): “Put to the test: For a new sociology of testing.“ The British Journal of Sociology, 71 (3), 423–443.
  • Martin, K. (2019): “Ethical Implications and Accountability of Algorithms.“ Journal of Business Ethics, 160 (4), 835–850.
  • Monteiro, E. (2022): Digital Oil: Machineries of Knowing. Cambridge, MA: The MIT Press.
  • Monteiro, E., Constantinides, P., Scott, S., Shaikh, M., & Burton-Jones, A. (2022): “Qualitative research methods in information systems: a call for phenomenon-focused problematization.“ MIS Quarterly, 46 (4), iii–xix.
  • Raisch, S., & Krakowski, S. (2021): “Artificial Intelligence and Management: The Automation–Augmentation Paradox.“ Academy of Management Review, 46 (1), 192–210.
  • Schildt, H. (2020): The Data Imperative: How Digitalization is Reshaping Management, Organizing, and Work. Oxford, UK: Oxford University Press.
  • Sergeeva, A., Huysman, M., Soekijad, M., & Hooff, B. (2017): “Through the Eyes of Others: How Onlookers Shape the Use of Technology at Work.“ MIS Quarterly, 41 (4), 1153–1178.
  • Steele, C.W.J. (2016): “Analytics in Action: The Production of Data, Insight, and Validation.“ Northwestern University Dissertation.
  • von Krogh, G. (2018): “Artificial Intelligence in Organizations: New Opportunities for Phenomenon-Based Theorizing.“ Academy of Management Discoveries, 4 (4), 404–409.
  • Waardenburg, L., & Huysman, M. (2022): “From coexistence to co-creation: Blurring boundaries in the age of AI.“ Information and Organization, 32 (4), 1–11.
  • Zuboff, S. (2022): “Surveillance Capitalism or Democracy? The Death Match of Institutional Orders and the Politics of Knowledge in Our Information Civilization.“ Organization Theory, 3 (3),
Luciana D’Adderio is a Professorial Chancellor’s Fellow in Data Driven Innovation at the Usher Institute, University of Edinburgh, United Kingdom, and a Turing Fellow with The Alan Turing Institute of Data Science and Artificial Intelligence. Her latest research investigates the use of Artificial Intelligence in healthcare. Luciana has published in leading Innovation, Organizational and Information Systems journals and is currently a member of the Editorial Board of ‘Organization Science’, ‘Organization Studies’, and ‘Information and Organizations’.
Vern L. Glaser is an Associate Professor of Entrepreneurship and Family Enterprise and the Eric Geddes Professor of Business in the Department of Strategy, Entrepreneurship and Management at the Alberta School of Business, Canada. He is the Academic Director for the University of Alberta’s Centre for Entrepreneurship and Family Enterprise and the Alberta Business Family Institute.
Marleen Huysman is Professor of Knowledge and Organization at the School of Business and Economics, Vrije Universiteit Amsterdam, The Netherlands, where she leads the KIN research group and the KIN Center for Digital Innovation. She teaches and publishes on topics related to the practices of developing and using digital technologies – in particular artificial intelligence –and new ways of working. Marleen’s research has been published in various leading journals in the field of information systems and organization science.