Open topics

In educational research, there is growing interest in learning analytics – the evaluation and use of learning platform data to better understand and optimize learning processes (Siemens & Gašević, 2012). However, accessing such data is often challenging. Data donations offer a solution: the GDPR enables individuals to voluntarily provide their data to researchers. But under what conditions are users of learning platforms willing to donate their data to research?

Aim of the Study

In an online experiment, various scenarios (vignettes) will be described, illustrating how such data donations might take place. These vignettes will vary aspects of the data donation process, such as the time required, the complexity of the process, or the intended use of the data. Study participants will be asked how willing they would be to donate their data in each of the described scenarios. Preliminary findings suggest that required effort and privacy concerns are significant influencing factors. This thesis will examine how these and other factors affect willingness to donate, with room for students to set their own areas of focus. Participant recruitment via the free SoSci Panel is an option. Alternatively, it is also possible to write a conceptual thesis, which would involve no data collection but instead focus on describing the design and expected outcomes of the study in detail.

Prerequisite: Familiarity with experimental methods, e.g., through our course “Experimental Behavioral Research in Data Science.”

Reference: Siemens, G., & Gašević, D. (2012). Special issue on learning and knowledge analytics. Educational Technology & Society, 15(3), 1–163.

Applications are closed for winter semester 2024/2025.

In recent years, the use of loyalty card data in research has increased. This data, which is collected by retailers’ loyalty programs, offers valuable insights into consumer behavior and can be used for customer segmentation and personalized marketing, for example, but also for research purposes in the public interest. For example, there is great potential to enrich research into consumer decisions (Katzeff et al., 2020), diets (Fuchs, 2020; Wu et al., 2022) or health trajectories and behavior (Lee et al., 2021; Nevalainen et al., 2018) with such a detailed data source.

The aim of this thesis is to conduct a systematic literature review to identify and analyze studies that use loyalty card data for research purposes in the public interest.

This thesis will be supervised in the context of the research project DataDonations4SustainableChange.

References:

• Fuchs, K. L. (2020). Empowering Diet-Related Health Behavior Change Interventions via Digital Receipts and Food Composition Databases. https://doi.org/10.3929/ethz-b-000450599
  Katzeff, C., Milestad, R., Zapico, J. L., & Bohné, U. (2020). Encouraging Organic Food Consumption through Visualization of Personal Shopping Data. Sustainability, 12(9), Article 9. https://doi.org/10.3390/su12093599
  Lee, C. L., Aveyard, P. N., Jebb, S. A., & Piernas, C. (2021). Using supermarket loyalty card data to provide personalised advice to help reduce saturated fat intake among patients with hypercholesterolemia: A qualitative study of participants’ experiences. Nutrients, 13(4). https://doi.org/10.3390/nu13041146
  Nevalainen, J., Erkkola, M., Saarijärvi, H., Näppilä, T., & Fogelholm, M. (2018). Large-scale loyalty card data in health research. Digital Health, 4, 205520761881689. https://doi.org/10.1177/2055207618816898
  Wu, J., Fuchs, K., Lian, J., Haldimann, M. L., Schneider, T., Mayer, S., Byun, J., Gassmann, R., Brombach, C., & Fleisch, E. (2022). Estimating dietary intake from grocery shopping data—A comparative validation of relevant indicators in Switzerland. Nutrients, 14(1), 1–26. https://doi.org/10.3390/nu14010159

Motivation
Customer Relationship Management (CRM) systems are an extremely important tool for supporting, organizing and steering large sales teams. However, the implementation of CRM systems is challenging, as system adoption is often low, resulting in low usage rates, poor data quality, and thus imitation value creation. Suitable indicators for good system utilization of CRM systems could help to steer and support their adoption.

Methodology:
Literature and internet research, possibly interviews

Task

• Find indicators for the intensity and quality of CRM system usage in the academic literature and in practitioner reports
• Evaluate the quality of the indicators and identify success factors
• If possible, identify different types of system users for different industries and maturity levels.
• Focus the analysis of CRM systems on applications in sales of explanatory industrial goods

Special prerequisites

Reading, interviews, and structuring

In educational research, there is growing interest in learning analytics – the evaluation and use of learning platform data to better understand and optimize learning processes (Siemens & Gašević, 2012). However, accessing such data is often challenging. Data donations offer a solution: the GDPR enables individuals to voluntarily provide their data to researchers. But under what conditions are users of learning platforms willing to donate their data to research?

Aim of the Study

In an online experiment, various scenarios (vignettes) will be described, illustrating how such data donations might take place. These vignettes will vary aspects of the data donation process, such as the time required, the complexity of the process, or the intended use of the data. Study participants will be asked how willing they would be to donate their data in each of the described scenarios. Preliminary findings suggest that required effort and privacy concerns are significant influencing factors. This thesis will examine how these and other factors affect willingness to donate, with room for students to set their own areas of focus. Participant recruitment via the free SoSci Panel is an option. Alternatively, it is also possible to write a conceptual thesis, which would involve no data collection but instead focus on describing the design and expected outcomes of the study in detail.

Prerequisite: Familiarity with experimental methods, e.g., through our course “Experimental Behavioral Research in Data Science.”

Reference: Siemens, G., & Gašević, D. (2012). Special issue on learning and knowledge analytics. Educational Technology & Society, 15(3), 1–163.

Applications are closed.

To address issues of health and sustainability, the scientific community seeks to understand food shopping behaviour. To do this, many researchers rely on retrospective self-reports from consumers. This approach can suffer from biases such as respondents’ memory lapses or social desirability (“I only eat organic meat from happy animals!”).Thanks to digital transformation, more and more supermarket chains have loyalty card programs. By leveraging their data infrastructure, these offer a way to analyze actual purchase data, instead of having to rely on self-reports. There is great potential in making use of this data for the greater good (see e.g., Nevalainen et al., 2018).

Tasks

• Establish a solid motivation, based on literature, why purchase data is worth looking at.
• Collect test data from shopping trips of a convenience sample and self-trial.
• Plan and conceptualize a system that enables the processing of loyalty card data donations with the following functionalities:
  • Receive and safely store data donations (existing applications can be used).
  • Extract the relevant data from the donated files.
  • Enrich the data by
    • linking identifiers of products to an external database (e.g., OpenFoodFacts)
    • conceptualizing a workflow that allows item categorization.
    • Document the process, as well as lessons learned and challenges encountered.

Students should be …
… interested in a thesis that can pave the way for future research in this area.
… interested in a thesis that goes beyond the usual quantitative and qualitative methods.
… confident in their Python skills.

Reference: