Data-Driven Behavioral Research
Digital technologies, including smart meters, smartphones, and wearables, are ubiquitous in our daily lives. Our research group therefore uses these technologies and quantitative empirical methods to gain insights into human behavior in the real world and to develop context-specific tools for behavior change.
- Sustainability
- Health
- Mobility
- Energy
- Human-AI Interaction
- Conception, design, and evaluation of digital interventions (e.g., feedback systems, nudges, persuasive information systems)
- Study of the acceptance, use, and impact of digital technologies on individual behavior
- Quantitative analysis of real-world behavioral data and high-resolution modeling (e.g., corporate data, energy usage data)
Interdisciplinary integration of business informatics, behavioral economics, and decision psychology
Since spring 2024, approximately 70 rooms in Building 16 of the Nuremberg Energy Campus (EnCN) have been monitored using measurement technology. The monitoring covers temperature, humidity, CO₂ concentration, as well as the status of window and door openings and thermostat settings. The goal of the project was to gain a detailed picture of the indoor climate and user behavior in order to identify potential savings in building operations. Initial energy-saving measures have already been successfully implemented based on the findings.
The internal project analyses have been completed; however, the monitoring hardware continues to generate data continuously, which is stored on FAU’s own servers. Steps for data cleaning as well as approaches for further analyses are documented in the associated GitHub repository.
Both historical and continuously collected data are available for research projects across a wide range of disciplines and institutions—from energy efficiency and building physics to behavioral and comfort research. For non-anonymized use, prior approval under data protection law must be obtained. Researchers without an affiliation with FAU or TH Ohm can access anonymized data or enter into a corresponding usage agreement. Interested parties may contact Leonie Manzke (leonie.manzke@fau.de) or Sebastian Hummel (sebastian.hummel@th-nuernberg.de). We welcome collaboration inquiries!
People involved: Leonie Manzke, Prof. Verena Tiefenbeck, Prof. Richard T. Watson, Sebastian Hummel
External funding provided by: Bavarian State Ministry of Science and the Arts (enabled the purchase of hardware), Bavarian Institute for Digital Transformation (bidt, Leonie Manzke), Senior Fellowship at the Dr. Theo and Friedl Schöller Foundation (Prof. Watson)
The operation of non-residential buildings accounts for a significant portion of energy consumption, and much of the potential for energy efficiency remains untapped. While current practice focuses largely on structural renovation measures, non-structural measures receive less attention despite their comparatively low investment requirements and short payback periods. This ongoing study examines the drivers and barriers that influence decision-making processes regarding non-structural measures, as well as potential ways to implement them more effectively.
The goal is to develop practical recommendations for building managers, owners, and consultants in Germany. The goal is to demonstrate to these stakeholders how energy efficiency and economic benefits can be combined. Methodologically, the study follows the CLAROS framework and combines Q-methodology with validation interviews to identify and characterize patterns of thought and argumentation.
Participants: Angela Smandzik, Leonie Manzke, Prof. Verena Tiefenbeck, Prof. Richard T. Watson
External funding provided by: Bavarian Institute for Digital Transformation (bidt, Leonie Manzke), Senior Fellowship at the Dr. Theo and Friedl Schöller Foundation (Prof. Watson)
Image Credits: AdobeStock_407968987