Background

The second edition of the VERTUOSE workshop builds upon the success of our 2023 gathering, titled “Collectively Improve the Quality of Life at Work: How and Which Data to Collect and Analyze?” In that 1st workshop, 11 researchers convened to explore data collection and processing methods aimed at enhancing work quality by addressing information overload, cognitive strain, and communication challenges (Fiore-Gartland, et al., 2015). We delved into various work contexts, including healthcare, shop floors, learning environments, and office settings. Beyond the immediate focus on work quality, we also engaged in broader discussions about data within the workspace.

For this year’s workshop, we retain our initial inquiry but expand its scope. In addition to improving working conditions, we now emphasize the efficiency of workers’ actions, all while respecting the collective agreements established by the relevant stakeholders. By efficiency, we refer to the optimal execution of tasks under favorable conditions.

Every work activity generates digital traces that can be recorded live (Burnett, J. R., et al., 2021) or collected retrospectively. The aim of this workshop is to question the methods by which this data is collected, analyzed and used, whether by those involved in the workplace or by researchers (Flyverbom, et al. 2018). These questions will be raised from a particular angle. In practice, working with data is often associated with the issue of hierarchical control of work (Holten Møller, et al., 2021; Flügge et al., 2021; Levy, 2002). The aim here is to approach data processing for other purposes. Particular attention will be paid to studies that present other uses for the data collected, for example, in terms of improving working conditions (Mark, G. 2023) or developing democracy in the workplace (Kristiansen et al., 2018). We will be looking at the opportunities associated with computerized data collection, especially for organizational actors (managers, workers, trade unions) (Khovanskaya et al., 2020; Pedersen & Bossen, 2024). It seems to us that thinking of alternative uses for control or surveillance is crucial for the design of labor data collection and analysis tools that promote organizational  efficiency (Faraj, S., et al., 2018).

This topic (data at the workplace) therefore raises many questions:

• How can we effectively collect data from work activities? What are the best practices for recording digital traces, whether in real-time or retrospectively?

• What methods can we use to analyze the collected data? How can AI and other analytical tools provide meaningful insights?

• How do we move beyond hierarchical control when working with data? Can data processing serve purposes beyond oversight, such as improving working conditions or promoting workplace democracy?

• What innovative applications exist for the data we collect? How can it contribute to worker well-being and decision-making?

• What advantages does automated data collection offer? How can organizational players (managers, employees, trade unions) leverage data for informed decision-making?

• How can organizations ensure responsible data collection and handling? What protocols should be in place to protect privacy and confidentiality?

• How do we address biases in data collection and analysis? What steps can we take to ensure fairness, especially when making decisions based on data insights?

• What processes should be followed to obtain informed consent from employees regarding data collection? How can transparency build trust?

• Who owns the data generated in the workplace? How can we balance organizational needs with individual rights?

• What are the potential long-term consequences of data utilization? How can we mitigate negative effects?

• How do different stakeholders (employees, management, customers) perceive data usage? How can we align their interests ethically?

References

• Burnett, J. R., & Lisk, T. C. (2021). The future of employee engagement: Real-time monitoring and digital tools for engaging a workforce. In International Perspectives on Employee Engagement (pp. 117-128). Routledge.

• Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28 (1), 62–70.

• Fiore-Gartland, B., & Neff, G. (2015). Communication, mediation, and the expectations of data: Data valences across health and wellness communities. International Journal of Communication, 9, p. 19.

• Flyverbom, M., & Murray, J. (2018). Datastructuring—Organizing and curating digital traces into action. Big Data & Society, 5(2), 2053951718799114.

• Flügge, A. A,, Hildebrandt, T., & Møller, N. H. (2021). Street-level algorithms and AI in bureaucratic decision-making: A caseworker perspective. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1-23.

• Holten Møller, N., Neff, G., Simonsen, J. G., Villumsen, J. C., & Bjørn, P. (2021). Can workplace tracking ever empower? Collective sensemaking for the responsible use of sensor data at work. Proceedings of the ACM on human-computer interaction, 5(GROUP), 1-21.

• Khovanskaya, V., Sengers, P., & Dombrowski, L. (2020). Bottom-Up Organizing with Tools from On High: Understanding the Data Practices of Labor Organizers. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376185

• Kristiansen, K. H., Valeur-Meller, M. A., Dombrowski, L., & Holten Moller, N. L. (2018, April). Accountability in the blue-collar data-driven workplace. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1-12).

• Levy, K. (2022). Data driven: truckers, technology, and the new workplace surveillance: Princeton University Press.

• Mark, G. (2023). Attention Span. A Groundbreaking Way to Restore Balance, Happiness and Productivity: Harper Collins Publishers.

• Pedersen, A.M., & Bossen, C. (2024). Cultivating Data Practices Across Boundaries: How Organizations Become Data-driven. Comput Supported Coop Work (2024). https://doi.org/10.1007/s10606-024-09489-8.