Motivation

Algorithmic Control (AC) is increasingly implemented in traditional organizations, where supervisors and AC systems jointly shape how work is directed, evaluated, and disciplined. We name this phenomena algorithmic co-supervision. Yet the resulting configurations vary widely, and research lacks a structured understanding of their recurring patterns. This thesis addresses this gap by analyzing existing literature and real-world cases, classifying them using the ACS taxonomy, and deriving archetypes of algorithmic co-supervision. The goal is to clarify how AC manifests across different organizational contexts.

Description

This thesis investigates AC in traditional organizations through a systematic literature review and an analysis of real cases. By classifying these cases along the ACS taxonomy, the work identifies recurring patterns and develops theoretically grounded archetypes of algorithmic co-supervision.

Tasks

• Conduct a systematic literature review on AC/ACS in traditional organizations
• Identify and document real-world AC systems across industries and contexts
• Classify each case along the dimensions of the ACS taxonomy
• Identify recurring patterns and develop archetypes of algorithmic co-supervision
• Discuss and position the resulting archetypes within existing research streams

Requirements

• Interest in digital transformation and future of work
• Analytical skills for recognizing patterns and developing conceptual structures
• Motivation is the most important one

What we offer

• Supervision and methodological guidance
• Feedback on structure and writing process
• Opportunity to contribute to ongoing research projects

Application

If you are interested in writing your thesis, please send me an email (oezdemir@ise.tu-…) including your transcript of records and CV.