Data Studies and Computational Intensive Theorizing

Location

Room information tbd, LUT University, Lahti campus (Address: Mukkulankatu 19, 15210 Lahti)

Registration

Registration is open until August 31.

Speakers

Assiociate Professor Aleksi Aaltonen, Stevens Institute of Technology, USA.

Associate Professor Dominik Siemon, LUT University, Finland.

Organizer

Associate Professor Dominik Siemon, LUT University, Finland.

Overview

Digital data increasingly shape organizations, platforms, work practices, governance, and everyday life. At the same time, advances in computational methods have created new opportunities for researchers to study digital phenomena and construct theory from large-scale and heterogeneous data sources.

This two-day doctoral seminar examines both digital data as a sociotechnical phenomenon and computational approaches for theorizing in information systems and related disciplines. Participants will discuss how digital data are created, governed, interpreted, and recombined across contexts, as well as how computational methods can support phenomenon-driven research around and based on data.

The seminar combines discussions with interactive exercises and reflections on participants’ own research ideas, datasets, empirical phenomena, and doctoral projects. Throughout the sessions, participants will continuously position and develop their own research in relation to the discussed concepts and approaches. Participants without an existing project/data in the area are equally welcome and can work collaboratively on shared example phenomena and cases.

It should be noted that the aim of this seminar is not to provide technical training in programming, machine learning, or statistics. Instead, the seminar focuses on conceptual foundations, theorizing, and research design. Therefore, participants are expected to have general familiarity with research methods and an interest in digital data and contemporary computational research approaches.

The seminar is designed primarily for doctoral students in information systems and related disciplines (software engineering, business and management etc.).

Prerequisite work

Each participant must send one slide (no more!) describing their research project (or interest, if you don’t yet have a specific project) by Monday, September 7 to Aleksi (aaaltone@stevens.edu) and Dominik (dominik.siemon@lut.fi).

Program

WEDNESDAY, September 9, 2026
10:30 – 10:45	Welcome and logistics (AA, DS)
10:45 – 11:30	Introduction to Data and Data Studies (AA) Digital data as sociotechnical phenomena What is data studies and why we need it?
11:30 – 12:00	Student projects, 2-3 min per project
12:00 – 13:00	LUNCH
13:00 – 14:00	Data Studies (AA) Different types of data: e.g., trace data, personal data, training data The digital data kaleidoscope Emerging research themes
14:00 – 15:00	Computational Theory Construction (DS) Computationally intensive theorizing in IS research Patterns, theorizing, and contribution building
15:00 – 15:30	BREAK
15:30 – 17:00	Examples of Data Studies and Computational Theory Construction Computational theory construction with behavioral trace data: Studying how people use AI-assisted writing tools in a large-scale field experiment (DS) Computational methods in phenomenon-driven research: Studying programmers use of generative AI (AA)
THURSDAY, September 10, 2026
9:00 – 9:30	Positioning computational and digital data research in IS and related disciplines (AA)
9:30 – 11:15	One-to-one sessions with Aleksi and Dominik on individual projects; each student discusses how their project relates to data studies and CTC.
11:15 – 11:30	SHORT BREAK
11:30 – 13:00	Student projects updated, 5 min per project
13:00 – 14:00	LUNCH
13:00 – 15:00	Publishing and reviewing computationally intensive and digital data research; theoretical contributions and rigor (AA)
14:30 – 15:00	Closing reflections

Assessment

The course is assessed based on i) constructive participation in the face-to-face workshop, and ii) essay submitted after the workshop.

Participation (30%)

In two days, we can cover only small amount of topics from data studies and computational theory construction. Therefore, we will try to focus the workshop as we go based on student’s interest and projects. This is only possible if you participate actively, bring up your own viewpoints, and engage what is presented and discussed in the workshop.

Essay (70%)

You must submit about 4,000 words essay (ECIS short paper length) by September 30, 2026. This can be, for instance:

An idea how to use generative AI in your own research mindfully.
A literature review on a topic covered in the course that relates to your own research.
Draft ECIS 2027 short paper submission.

The instructors will read and give you feedback on the essays, so use this as an opportunity to get feedback in the way that you think is most useful for you. To this end, note that one of the instructors (Aleksi) is running an ECIS 2027 track “Data Studies in IS Research” so that he has a fairly good idea of what kind of papers tend to make it.

Materials

The following readings have been selected to give you an overview of data studies and computational theory construction. You should read at least two papers from both lists and take a look at the Data Studies Bibliography (https://www.datastudiesbibliography.org) and Computationally-intensive Theory Construction (https://ctcresearch.org) websites and identify papers that could be helpful for your own research (you don’t need to read these at this point, just to know what is out there and available).

Data Studies

Aaltonen, A., Alaimo, C., Parmiggiani, E., Stelmaszak, M., Jarvenpaa, S. L., Kallinikos, J., & Monteiro, E. (2023). What is Missing from Research on Data in Information Systems? Insights from the Inaugural Workshop on Data Research. Communications of the Association for Information Systems, 53(1), 475-490. https://doi.org/10.17705/1CAIS.05320

Stelmaszak, M., Aaltonen, A., & Lyytinen, K. (2026). Looking through the digital data kaleidoscope: Introduction to the Research Handbook on Digital Data. In: Aaltonen, A., Stelmaszak, S. & Lyytinen, K. (eds.). Research Handbook on Digital Data: Interdisciplinary Perspectives, pp. 1–18. Northampton, MA: Edward Elgar. https://doi.org/10.4337/9781035348718.00007

Tuomi, I. (1999). Data Is More than Knowledge: Implications of the Reversed Knowledge Hierarchy for Knowledge Management and Organizational Memory. Journal of Management Information Systems, 16(3), 103-117. https://doi.org/10.1080/07421222.1999.11518258

Xu, D., Stelmaszak, M., & Aaltonen, A. (2025). What is Changing the Game in Data Research? Insights from the “Innovating in Data-based Reality” Professional Development Workshop. Communications of the Association for Information Systems, 56(1), 194-208. https://doi.org/10.17705/1CAIS.05608

Computational Theory Construction

Berente, N., Seidel, S., & Safadi, H. (2019). Research commentary—Data-driven computationally intensive theory development. Information Systems Research, 30(1), 50–64. https://doi.org/10.1287/isre.2018.0774

Miranda, S., Berente, N., Seidel, S., Safadi, H., & Burton-Jones, A. (2022). Editor's comments: Computationally intensive theory construction—A primer for authors and reviewers. MIS Quarterly, 46(2), iii–xviii. https://doi.org/10.25300/MISQ/2022/462E1

Miranda, S. M., Wang, D., & Tian, C. (2022). Discursive fields and the diversity-coherence paradox: An ecological perspective on the blockchain community discourse. MIS Quarterly, 46(3), 1421–1452. https://doi.org/10.25300/MISQ/2022/15736

Lindberg, A. (2020). Developing theory through integrating human and machine pattern recognition. Journal of the Association for Information Systems, 21(1), 90–116. https://doi.org/10.17705/1jais.00593

Lindberg, A., Schecter, A., Berente, N., Hennel, P., & Lyytinen, K. (2024). The entrainment of task allocation and release cycles in open source software development. MIS Quarterly, 48(1), 67–94. https://doi.org/10.25300/MISQ/2023/16789

Siemon, D., Strohmann, T., & Islam, N. (2026). Me and my Replika: Perceived affordances and the formation of psychological ownership of AI companions. European Journal of Information Systems. Advance online publication. https://doi.org/10.1080/0960085X.2026.2673990

Credit points

Doctoral students participating in the seminar can obtain 2 credit points. This requires active participation and completion of the essay submission.

Registration fee

This seminar is free-of-charge for Inforte.fi member organization's staff and their PhD students. For others the participation fee is 400 €. The participation fee includes access to the event and the event materials. Lunch and dinner are not included.