Track chairs

Associate Professor Jostein Engesmo, Norwegian University of Science and Technology, Norway
Professor Niki Panteli, Lancaster University, UK
Professor Fay Giæver, Norwegian University of Science and Technology, Norway

Track call

Digitalisation and technological transformation are fundamentally changing how work is organised, experienced and performed. People analytics refers to the application of data-driven and increasingly AI-enhanced analytical methods to describe, predict, and inform decisions about the workforce (McCartney & Fu, 2022; Tursunbayeva et al., 2018; Lee & Lee, 2024). The field of people analytics has traditionally been shaped by two dominant orientations. The first is an instrumental orientation, which views people analytics as a data-driven tool for supporting managerial and HR decision‑making with the aim of enhancing organizational performance (Marler & Boudreau, 2017; McCartney & Fu, 2022; Kim et al., 2025). The second orientation points to the darker implications of datafication, drawing on critiques of surveillance capitalism (Zuboff, 2019) to highlight how employees risk being reduced to quantified representations governed by automated systems (Ajunwa, 2023). This perspective raises concerns about privacy, autonomy, trust, algorithmic bias, and epistemic injustice (Mettler, 2024; Giermindl et al., 2022). Indeed, systematic reviews of workplace surveillance research show that digital monitoring technologies introduce profound dilemmas related to dignity and power asymmetries in the employment relationship (Pawirosumarto & Kurniawan, 2026; Kayas, 2023).

At the same time, emerging AI-based technologies, including AI-enhanced people analytics, are creating new possibilities for employee agency, and a future where workers are empowered by their data rather than governed by it (Ajunwa, 2023), enabling employees to shape their own work environment (Bottesch et al., 2025; Kim et al., 2025). Thus, it is increasingly important to explore the opportunities that emerging AI technologies for organisations and employees alike, but also eamine the impact of  AI-enhanced people analytics on employees and managers.  Technology is not simply something that is implemented and imposed on employees; there is a space of agency in both the use of technology and the shaping of one’s own working conditions. This is particularly evident with emerging technologies such as AI-enhanced people analytics, where established patterns of use do not yet exist and employees must actively explore, interpret, and give direction to tools whose full implications remain unknown (Klein & Watson-Manheim, 2021). In general, engagement with AI blurs the boundary between coexistence and co-creation, as users move from passively working alongside algorithmic systems to actively contributing to their functioning (Waardenburg & Huysman, 2022). Thus, AI systems are performative and continually “in the making,” shaped through the sociomaterial practices of both developers and users (Scott & Orlikowski, 2025). However, the micro-level experiences, practices, and forms of agency that arise when workers interact with AI-enhanced people analytics remain empirically underexplored.

This track therefore takes a practice oriented perspective on AI enhanced analytics and algorithmic systems at work. As workers engage with AI based feedback, monitoring, recommendation, or decision support systems, they may appropriate, adapt, resist, reinterpret, or reconfigure these technologies in ways that influence how work is organised and experienced. We particularly welcome contributions that examine the micro level experiences, practices, and forms of agency that arise when workers interact with AI enhanced analytics, as well as the organizational, institutional, and technological conditions under which such agency is enabled or constrained.

The track invites empirical, conceptual, and critical contributions that advance understanding of how emerging AI technoclogies and AI enhanced analytics can support—or undermine—autonomy, dignity, wellbeing, and meaningful work, and how more responsible, participatory, and human centred digital workplaces might be fostered.

Track areas include but are not limited to:

  • Human–AI interaction and everyday work practices
  • Sociomaterial and practice‑based perspectives on AI and analytics at work
  • Power, surveillance, and resistance in algorithmic and datafied work
  • Trust, transparency, and sensemaking around AI‑enhanced analytics
  • Employee agency, wellbeing, autonomy, and meaningful work in digital contexts
  • Democratic, participatory, and responsible approaches to people analytics and AI at work

References

Ajunwa, I. (2023). The quantified worker: Law and technology in the modern workplace. Cambridge University Press.

Armstrong, M., & Taylor, S. (2020). Armstrong’s handbook of human resource management practice (15th ed.). Kogan Page.

Bottesch, S., Schwenke, C., Förster, M., & Klier, M. (2025). Driving business value through people analytics: Literature review and research agenda from an information systems perspective. Electronic Markets, 35(1), 106. https://doi.org/10.1007/s12525-025-00842-3

Giermindl, L. M., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2022). The dark sides of people analytics: Reviewing the perils for organisations and employees. European Journal of Information Systems, 31(3), 410–435. https://doi.org/10.1080/0960085X.2021.1927213

Kayas, O. G. (2023). Workplace surveillance: A systematic review, integrative framework, and research agenda. Journal of Business Research, 168, 114212. https://doi.org/10.1016/j.jbusres.2023.114212

Kim, S., Khoreva, V., & Vaiman, V. (2025). Strategic human resource management in the era of algorithmic technologies: Key insights and future research agenda. Human Resource Management, 64(2), 447–464. https://doi.org/10.1002/hrm.22268

Klein, S., & Watson-Manheim, M. B. (2021). The (re-)configuration of digital work in the wake of profound technological innovation: Constellations and hidden work. Information and Organization, 31(4), 100377. https://doi.org/10.1016/j.infoandorg.2021.100377

Lee, J. Y., & Lee, Y. (2024). Integrative literature review on people analytics and implications from the perspective of human resource development. Human Resource Development Review, 23(1), 58–87. https://doi.org/10.1177/15344843231217181

Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR analytics. International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699

McCartney, S., & Fu, N. (2022). Promise versus reality: A systematic review of the ongoing debates in people analytics. Journal of Organizational Effectiveness: People and Performance, 9(2), 281–311. https://doi.org/10.1108/JOEPP-01-2021-0013

Mettler, T. (2024). The connected workplace: Characteristics and social consequences of work surveillance in the age of datification, sensorization, and artificial intelligence. Journal of Information Technology, 39(3), 547–567. https://doi.org/10.1177/02683962231202535

Pawirosumarto, S., & Kurniawan, B. (2026). Datafied selves at work: Ethical boundaries of surveillance in people analytics. Journal of Business Ethics. https://doi.org/10.1007/s10551-026-06272-1

Scott, S. V., & Orlikowski, W. J. (2025). Exploring AI-in-the-making: Sociomaterial genealogies of AI performativity. Information and Organization, 35(1), 100558. https://doi.org/10.1016/j.infoandorg.2025.100558

Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics—A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43, 224–247. https://doi.org/10.1016/j.ijinfomgt.2018.08.002

Waardenburg, L., & Huysman, M. (2022). From coexistence to co-creation: Blurring boundaries in the age of AI. Information and Organization, 32(4), 100432. https://doi.org/10.1016/j.infoandorg.2022.100432

Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.