Notice: Due to planned maintenance, the website will be unavailable from 3:00 PM to 10:00 PM on Wednesday, February 11, 2026
We apologise in advance for any inconvenience that may be caused and thank you for your understanding.
To content
Department of Business and Economics

Publication on Algorithmic Management in Traditional Organizations in BISE Journal

The image presents a first-person perspective inside a modern, high-tech manufacturing facility. In the sharp foreground, a pair of hands wearing grey, textured protective gloves holds a black tablet computer. The tablet screen displays a detailed analytics dashboard featuring various data visualizations in shades of blue and grey, including pie charts, bar graphs, line charts, and percentage indicators, suggesting real-time monitoring of production metrics.  In the slightly out-of-focus mid-ground, two male workers are collaborating at a stationary machine control panel equipped with a monitor. The worker on the left has his back to the viewer, wears a dark blue work shirt, and is holding a laptop. Facing him in profile is a second worker wearing a high-visibility yellow safety vest over a blue shirt; he is gesturing towards the control screen as if analyzing data. The background is filled with large yellow industrial robotic arms engaged in assembly work on metal frames or car chassis along a production line. © AI generated with Google Gemini, Vincent Heimburg​/​TU Dortmund
Illustration of algorithmic management
Amelie Schmid and Manuel Wiesche have published a new article in the journal "Business & Information Systems Engineering" (BISE). The study investigates the impact of algorithmic management on workers' efficiency and work relationships.

The article "Algorithmic Management in Traditional Organizations - Implications for Workers’ Efficiency and Work Relationships" has recently been published.

Traditional organizations are increasingly utilizing algorithmic management (AM) to manage their permanent workers. While existing research has extensively explored AM within platform organizations, there is a need to understand its application in traditional work environments, given the workers’ close connection to the organization and existing work relationships.

Conducting mixed-methods research within an international automotive supplier, we analyzed an archival dataset comprising 12,743 manufacturing errors, complemented by 15 semi-structured interviews with affected workers and managers.

The results provide objective evidence that AM significantly improves efficiency within traditional organizations. In addition, the interviews reveal that human managers maintain a supportive role in this hybrid organizational setting, while established work relationships among co-workers are heavily influenced by AM. This research offers valuable practical contributions for the successful implementation of AM in traditional organizations.

Link zum Artikel