The Effect of Task-Technology Fit on Task Performance across Data Literacy Levels – A Design Science Research Study

Research topic/area

Digital Government · Public Administration · Task-Technology Fit · Data Literacy · Design Science

Type of thesis

Bachelor / Master

Start time

-

Application deadline

31.08.2026

Duration of the thesis

-

Description

Public administrations rely on highly specialized IT systems (*Fachverfahren*) that provide a high Task-Technology Fit (TTF) — the degree to which a system matches the requirements of the task at hand. While high TTF is generally assumed to improve performance, its effect may depend critically on user Data Literacy: the capacity to work with data independently and critically.

Users with low Data Literacy may benefit strongly from high-fit systems that compensate for missing skills, while users with high Data Literacy may not need such tailored support — and could even be constrained by it. This thesis designs and evaluates two system prototypes (low TTF vs. high TTF) for a standardized data-based task, and tests whether Data Literacy moderates the effect of system fit on task performance using a 2×2 experimental design.

Potential subtasks
- Literature Review: Synthesize existing research on TTF, Data Literacy, and their interaction in organizational and administrative contexts.
- Artifact Design: Develop two functionally equivalent system variants — one with high TTF (guided, task-specific interface) and one with low TTF (generic data interface) — for a standardized administrative data task.
- Pre-Measurement: Assess participants' Data Literacy using a validated instrument, then classify them into low and high Data Literacy groups.
- Experiment: Randomly assign participants within each group to either the low-TTF or high-TTF system and measure task performance (accuracy and completion time).
- Analysis: Run a 2×2 ANOVA to test main effects and the TTF × Data Literacy interaction.
- Design Principles: Derive actionable design guidelines from the empirical findings.

What you get and disclaimer

The thesis is meant to teach you the scientific approach. Please keep that in mind. We expect you to learn and use scientific methods and to submit a scientifically sound result. Our role is to provide you with everything you need to succeed at that. We expect that you create a piece of work, in which you demonstrate a sound data collection method, an extensive dataset and your analysis of that dataset. A motivated student who works consistently on the thesis throughout the designated period can expect a very good grade. Details on the process and the format that we use to support you in succeeding with your thesis can be found here:

Bachelor: https://bwsyncandshare.kit.edu/s/g7pKCiScRZY5YtH
Master: https://bwsyncandshare.kit.edu/s/MiN8yTkfgeCrnSW

If you are interested, please send a CV and transcript of records by email to fabian.steinert@kit.edu

References
- Kim, J., Hong, L., & Evans, S. (2024). Toward measuring data literacy for higher education: Developing and validating a data literacy self-efficacy scale. Journal of the Association for Information Science and Technology, 75(8), 916-931. https://doi.org/10.1002/asi.24934
- Ongena, G. (2023). Data literacy for improving governmental performance: A competence-based approach and multidimensional operationalization. Digital Business, 3(1). https://doi.org/10.1016/j.digbus.2022.100050
- Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236.
- Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28 (1), 75–105. https://doi.org/10.2307/25148625
- Kuechler, B., & Vaishnavi, V. (2008). On theory development in design science research: anatomy of a research project. European Journal of Information Systems, 17 (5), 489–504. https://doi.org/10.1057/ejis.2008.40
- Ruoff, M., Gnewuch, U., Mädche, A., & Scheibehenne, B. (2023). Designing conversational dashboards for effective use in crisis response. Journal of the Association for Information Systems, 24(6), 1500–1526

Requirement

Requirements for students

• A general interest in digitalization, public administration, data-driven work, and (social) sustainability. If you’re interested in tackling a pressing issue at this intersection using a research-based approach, we look forward to hearing from you.

Faculty departments

• Economic & law sciences
  Information Engineering
  Business management

Supervision

Title, first name, last name

Fabian Steinert

Organizational unit

KIT WIN - ESIS

Email address

fabian.steinert@kit.edu

Link to personal homepage/personal page

Website

Application via email

Application documents

• Curriculum vitae
• Grade transcript

E-Mail Address for application
Senden Sie die oben genannten Bewerbungsunterlagen bitte per Mail an fabian.steinert@kit.edu


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