Track description n°5

Algorithmic Management and the Future of Work

Track Chairs

Martin Wiener

TU Dresden (Germany)

Martin Wiener is a Full Professor at TU Dresden, Germany, where he heads the Business Information Systems research group. He conducts research on algorithmic control/management, digital transformation processes, data-driven organizations, and value creation through open data. His research has been published in top IS journals, including ISR, MISQ, and JMIS. Martin currently serves as an Associate Editor for ISR, ISJ, and BISE. Over the past decade, he has regularly co-chaired ECIS tracks and was Program Co-Chair for ECIS 2022 in Timișoara, Romania.

Ulrich Remus

University of Innsbruck (Austria)

ulrich.remus@uibk.ac.at

Ulrich Remus is a Full Professor and Head of the Department of Information Systems, Production and Logistics Management at the University of Innsbruck, Austria. His research focuses on IS project management and control, algorithmic management, the future of work, and negative impacts of IS on individuals and society. His work has appeared or is forthcoming in leading IS journals, such as MISQ, ISR, JMIS, ISJ, EJIS, JIT, and others. He regularly serves as Track Chair and Associate Editor for major IS conferences such as ECIS and ICIS.

Monideepa Tarafdar

University of Massachusetts Amherst (USA)

mtarafdar@isenberg.umass.edu

Monideepa Tarafdar is the Charles J. Dockendorff Endowed Professor of Information Systems at the Isenberg School of Management, University of Massachusetts Amherst, USA. She has been a Visiting Scholar at MIT Sloan CISR and the London School of Economics and Political Science, a Visiting Professor at the Indian Institute of Management Calcutta, and a Senior Research Fellow at the Weizenbaum Internet Institute in Berlin. She has published in top-tier journals in both IS and Operations Management, and has been included in Stanford University’s list of the World’s Top 2% Scientists. In recent years, Monideepa has served in various capacities/roles in leading IS conferences, including ECIS, and journals. She is currently the Editor-in-Chief of the JAIS.

Emerging digital technologies continue to rapidly transform the way work is managed and performed. In this context, organizations are increasingly relying on intelligent algorithms to make autonomous, or at least semi-autonomous, decisions (Recker et al. 2021). Such algorithms not only assist human managers or collaborate with their human counterparts (Tarafdar et al. 2023), but also act as supervisors, coordinating and controlling the actions of workers (Möhlmann et al. 2021; Kellogg et al. 2020). This so-called algorithmic management provides significant benefits to organizations, enabling efficient scaling of business models and operations by automating managerial decisions and engaging in direct interactions with workers (Benlian et al. 2022). For example, in platform-based companies such as Uber, intelligent algorithms take on the role of human managers by selecting and replacing workers as needed, assigning tasks to workers, and providing detailed feedback on their daily work behavior (Möhlmann et al. 2021). More recently, algorithmic management practices have also found their way into more traditional organizations, such as Amazon’s fulfillment centers (Vallas et al. 2022).

A global OECD survey of 6,047 firms suggests that the adoption of algorithmic management systems/tools is progressing rapidly, with 74% of participating firms already using at least one such tool to instruct, monitor, or evaluate their employees (Milanez et al. 2025). Similarly, a growing number of traditional organizations are adopting people analytics tools and employee experience platforms (EXPs), such as Microsoft Viva, that coach employees with personalized recommendations and nudges and influence how they make sense of their work lives (Lamers et al. 2024; Nyman et al. 2024). These and related developments are likely to have a significant impact on organizations by reshaping their existing power and social structures, as well as on human managers coexisting with algorithmic managers (Jarrahi et al. 2021).

Moreover, despite its many benefits, being managed by algorithms has been found to have serious drawbacks for workers (Möhlmann & Henfridsson 2019) who may suffer from low well-being as a result of role conflict (Tarafdar et al. 2023), lack of autonomy (Wiener et al. 2023), and technostress (Cram et al. 2022). In response, workers have begun to engage in what has been termed algoactivism; that is, individual and collective practices of influencing, gaming, or resisting algorithms (Kellogg et al. 2020; Jiang et al. 2021). More broadly, this means challenging the current design of algorithmic management systems and rethinking how they can be redesigned and/or regulated in the future to ensure fair working condition sand meet ethical standards (Gal et al. 2020; Spiekermann et al. 2022), while allowing organizations to reap the benefits of intelligent algorithms for improved performance.

Considering algorithmic management as a key element of the future of work (Wiener et al. 2023), this track addresses the multi-level implications of algorithmic management, with a particular focus on the bright and dark sides of algorithmic work for organizations, managers, and workers (Benlian et al. 2022). In this regard, we invite submissions from all theoretical and methodological perspectives within IS, management, and related disciplines.

Track topics

Topics and questions relevant to the track include, but are not limited to:

Conceptual nature of algorithmic management (and related systems/tools)

What are novel and unique features and associated affordances of algorithmic management (systems)?
How can key dimensions of algorithmic management (i.e., algorithmic coordination and algorithmic control) be conceptualized in different work contexts?
What are the different types of algorithmic management systems? And how do they relate (conceptually) to people analytics tools and employee experience platforms (e.g., Microsoft Viva)?

Design of algorithmic management systems

What design aspects make an algorithmic management system truly intelligent?
How do organizations design, train, deploy, and adapt the algorithms embedded in algorithmic management systems? How can workers be involved in these processes?
What ethical principles and values should guide the design, development, and use of algorithmic management systems?
How does the design of an algorithmic management system evolve over time (within an organization), and what drives this evolution?
How can governmental bodies, as well as top-level executives, effectively regulate the design and use of algorithmic management systems?

Organizational implications of algorithmic management

What are the enabling and inhibiting factors for the adoption and use of algorithmic management systems?
How can (traditional) organizations decide on the ‘right’ division of labor between human and algorithmic managers?
What are the downsides and challenges of algorithmic management from an organizational perspective, and how can they be mitigated?
How does the introduction of an algorithmic management system change the power and social structures of an organization?
In what ways do algorithmic systems serve as “organizational memory,” and how does this affect issues of accountability and transparency?
How does algorithmic management play out in different industry sectors?

Managerial implications of algorithmic management

How can top-level managers ensure (over time) that the work instructions provided by intelligent algorithms are well aligned with the overall goals of their organization?
What managerial roles emerge from the coexistence of human and algorithmic managers?
How do operational managers/supervisors interact with coexisting algorithmic management systems?

Worker-level implications of algorithmic management

How do workers judge the legitimacy (e.g., in terms of competence development, fairness, and group identification) of algorithmic management systems in different work contexts?
What are the direct and indirect effects of algorithmic management on workers’ performance and their well-being?
Under what conditions do algorithmic management systems empower workers (e.g., by enabling effective self-management)?

Algoactivism

How are workers organizing to collectively resist the use of algorithms for management purposes?
What algoactivistic practices are individual workers using to influence, game, or circumvent algorithmic oversight?
What is the constructive potential of worker algoactivism, and how can this potential be harnessed by organizations/managers?

Track fit

The track aims to contribute to a balanced view of the bright and dark sides of algorithmic management from the perspectives of different stakeholders, with the overarching goal of deriving actionable insights for shaping the future of work in a positive way. As such, the track fits well with the conference theme (“digital technology for business, management, and society”).

In addition, very positive feedback and a significant number of submissions at both ECIS 2024 in Paphos/Cyprus and ECIS 2025 in Amman/Jordan. In light of this, we would very much welcome the opportunity to build on this success and continue the scholarly debate on algorithmic management and the future of work at ECIS 2026.

References

Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022). Algorithmic management: Bright and dark sides, practical implications, and research opportunities. Business & Information Systems Engineering, 64(6), 825-839.
Cram, W. A., Wiener, M., Tarafdar, M., & Benlian, A. (2022). Examining the impact of algorithmic control on Uber drivers’ technostress. Journal of Management InformationSystems, 39(2), 426-453.
Gal, U., Jensen, T. B., & Stein, M. K. (2020). Breaking the vicious cycle of algorithmic management: A virtue ethics approach to people analytics. Information and Organization, 30(2), 100301.
Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data & Society, 8(2), 1-14.
Jiang, J., Adam, M., & Benlian, A. (2021). Algoactivistic practices in ridesharing – A topic modeling & grounded theory approach. Proceedings of the 29^th European Conference on Information Systems (ECIS), Marrakech.
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.
Lamers, L., Meijerink, J., & Rettagliata, G. (2024). Blinded by “algo economicus”: Reflecting on the assumptions of algorithmic management research to move forward. Human Resource Management, 63(3), 413-426.
Milanez, A., Lemmens, A., & Ruggiu, C. (2025). Algorithmic management in the workplace: New evidence from an OECD employer survey. OECD Artificial Intelligence Papers, No. 31 (February 6). https://doi.org/10.1787/287c13c4-en
Möhlmann, M., & Henfridsson, O. (2019). What people hate about being managed by algorithms, according to a study of Uber drivers. Harvard Business Review (August 30). https://hbr.org/2019/08/what-people-hate-about-being-managed-by-algorithms-accordingto-a-study-of-uber-drivers
Möhlmann, M., Zalmanson, L., Henfridsson, O., & Gregory, R. W. (2021). Algorithmicmanagement of work in online labor platforms: When matching meets control. MIS Quarterly, 45(4), 1999-2022.
Nyman, S., Bødker, M., & Blegind Jensen, T. (2024). Reforming work patterns or negotiating workloads? Exploring alternative pathways for digital productivity assistants through a problematization lens. Journal of Information Technology, 39(3), 503-520.
Recker, J., Lukyanenko, R., Jabbari, M., Samuel, B. M., & Castellanos, A. (2021). From representation to mediation: A new agenda for conceptual modeling research in a digital world. MIS Quarterly, 45(1), 269-300.
Spiekermann, S., Krasnova, H., Hinz, O., Baumann, A., Benlian, A., Gimpel, H., Heimbach, I., Köster, A., Maedche, A., Niehaves, B., Risius, M., & Trenz, M. (2022). Values and ethics in information systems: A state-of-the-art analysis and avenues for future research. Business & Information Systems Engineering, 64(2), 247-264.
Tarafdar, M., Page, X., Marabelli, M. (2023) Algorithms as co-workers: Human algorithm role interactions in algorithmic work. Information Systems Journal, 33(2), 232-267.
Vallas, S. P., Johnston, H., & Mommadova, Y. (2022). Prime suspect: Mechanisms of labor control at Amazon’s warehouses. Work and Occupations, 49(4), 421-456.
Wiener, M., Cram, W. A., & Benlian, A. (2023). Algorithmic control and gig workers: A legitimacy perspective of Uber drivers. European Journal of Information Systems, 32(3),485-507.