Teachers

Prof. Tuure Tuunanen, University of Jyväskylä

Dr. Juuli Lumivalo, University of Jyväskylä

Prof. Alexander Mädche, Karlsruhe Institute of Technology (KIT)

Mr. Leon Hanschmann, Karlsruhe Institute of Technology (KIT)

Course Description

This course focuses on laddering interview and analysis as a qualitive research methodology. Students will start by reviewing key laddering interview and analysis literature to understand the research approach. This is followed by a critical examination and evaluation of the traditional laddering interview and analysis techniques and their application for research projects in information systems and related areas. The course focuses on advanced analysis methods, interpretive structural modeling for analyzing the data and how to apply the generative AI-based conversational agent LadderChat for performing laddering interviews. The course uses a flipped classroom approach to support learning-by-doing as students study and apply the laddering interview and analysis methodology in a workshop setting.

Goals of the Course

This course is designed to introduce doctoral students and post-doctoral researchers to laddering interview and analysis as a research approach. The course gives the students a broad understanding of designing and using laddering interview and analysis for their own research projects. In addition, students will learn about  the nature of laddering interviews and analysisladdering interviews and analysis as a research approach and current research issues and themes.

Learning Outcomes

1. The student will have a good knowledge and understanding of how to apply (hands-on) laddering interview and analysis laddering interview and analysis laddering interview and analysisladdering interviews and analysis for their research projects within the field of information systems (or related fields).
2. The student will gain competence in critically evaluate published research articles using laddering interviews and analysis in leading academic journals and conference proceedings. evaluate published research articles using laddering interviews and analysis in leading academic journals and conference proceedings. evaluate published research articles using laddering interviews and analysis in leading academic journals and conference proceedings.
3. The student will have a good knowledge and understanding of research design issues related to laddering interviews and analysis methodology and can apply these to their Ph.D. research.
4. The student will collect experiences in performing laddering interviews using a generative AI-based conversational agent
5. The student will gain competence in critical thinking and synthesizing academic sources.

Program

DAY 1 Introduction to the Course, Laddering Interviews 10th March

11:00-12:00 Lunch

12:00-14:00 Introduction to the course, and the laddering interviews and analysis methodology.

• Tuure Tuunanen, Juuli Lumivalo

14:00-14:30 Coffee / Bio Break

15:00-17:00 Practice laddering interviews: Being the interviewer and the interviewee

• Students

18:30 Drinks at DeLorean (cash bar)

DAY 2 Data collection, coding and analysis 11^th March

09:00-09:30 Introducing the Course Case

• Alexander Mädche

09:30-10:00 Introducing LadderChat - generative AI-based conversational agent for laddering

• Leon HanschmannLeon Hanschmann

10:00-12:00 Performing laddering interviews with LadderChat

• Students use the LadderChat to collect interview data in the context of the course case

12:00-13:00 Lunch

13:00-14:00 Traditional Laddering data analysis overview

• Tuure Tuunanen

14:00-17:00 Coding laddering interview data workshop

• Juuli Lumivalo

19:00-22:00 Dinner at Restaurant Cielo

DAY 3 Advanced Laddering Analysis 12^th March

09:00-12:00 Interpretive structural modeling workshop

• Tuure Tuunanen, Juuli Lumivalo

12:00-13:00 Lunch

13:00-14:00 LadderChat in Action

• Leon Hanschmann Leon Hanschmann and Juuli Lumivalo

14:00-14:30 Coffee / Bio Break

14:30-15:30 Interpretive Structural Modeling meets Fuzzy Logic

• Tuure Tuunanen

15:30-16:00 Closing, Instructions for the post-course project

Learning Resources

This course has no textbook since most readings are from academic journals and conference proceedings. The reading package is provided on the Moodle site for the students (cf. Appendix). However, students are expected to read more widely, including additional articles from any recognized journal in IS. Useful literature can also be obtained from the AIS Digital Library, the ACM Digital Library, and other bibliographic databases such as ABI/Inform, Science Direct, or the Emerald Library. Many of these libraries and databases are available online, e.g., from the University of Jyväskylä Library at https://kirjasto.jyu.fi/ (usually, you must log in from outside the university network). Additional resources can be found in the ISWorld Section on Research and Scholarship at http://www.isworld.org/.

Note: You are provided copies of copyrighted materials made for educational purposes. These include extracts of copyright works copied under copyright licenses. You may not make these materials available to other persons nor make a further copy for any other purpose. Failure to comply with the terms of this warning may expose you to legal action by a rights owner and/or disciplinary action by the University.

Course Assignment

Students read the pre-course reading package (see Appendix A) and develop a laddering interview and analysis methodology-based project idea based on their doctoral dissertation project or some other interest. Note that you can discuss the idea with the lecturer before the course starts. The post-course assignment consists of a revised submission of the original assignment submission, and it should incorporate how you have advanced your initial idea. Details of the course assignment and assessment criteria are included in the online learning environment of the course.

The pre-course assignment submission deadline is 5^th March 2026.

The post-course assignment submission deadline is 1^st April 2026.

Assessment

Class Participation                50%

Individual Assignment           50%

Note: If the student is late submitting the assignment(s), the student will fail that course assessment.

Assessment Detail

Class Participation will be marked pass/failed. The course assignment’s project report aims to familiarize the students with a broader range of topics laddering interviews and analysis methodology, which has been covered in class. The reading package is provided in the Appendix of this course outline. The project report is a ‘take home’ exam, and it will assess how well the students have understood the given material and how they can apply the concepts to develop a research project that applies laddering interviews and analysis as a research approach.

For passing the course, the students need to have:

• At least 50% of marks for both assessments
• At least 50% of the total marks.

Course Work Time Requirement (3 ECTS = 81h)

Class Participation & Presentations                                                                               :   24 hours

Class Preparation                 (incl. pre-course assignment)                                           :   36 hours

Research Project Report Final Write-Up                                                   :   21 hours

TOTAL           :   81 hours

Course Advice

Prerequisites: Students should have completed a qualitative research methods courses at a master’s or doctoral level.

The course will use an in-class workshop format. It is organized as a series of workshops that involve active student participation.

Please also refer to the additional handouts about the University's policy on plagiarism. This course will use turnitin.com, a software application, to assess your written work. The outcome of this assessment will be considered for your final grade in the course.

Generative artificial intelligence (such as ChatGPT, etc.) tool use should follow the University of Jyväskylä policy[1]. The use is generally permitted, but the student should generate the final text and insights offered in the course assessment. For details, see the university policy document.

Appendix A: Reading Package for Pre-Course Assignment

Hanschmann, L., Mokelke, M., & Maedche, A. (2024). LadderChat An LLM-Based Conversational Agent for Laddering Interviews. In: Følstad, A., et al. Chatbots and Human-Centered AI. CONVERSATIONS 2024. Lecture Notes in Computer Science, vol 15545. Springer, Cham. https://doi.org/10.1007/978-3-031-88045-2_4

Peffers, K., & Tuunanen, T. (2005). Planning for IS applications: a practical, information theoretical method and case study in mobile financial services. Information & Management, 42(3), 483-501.

Peffers, K., Gengler, C. E., & Tuunanen, T. (2003). Extending critical success factors methodology to facilitate broadly participative information systems planning. Journal of management information systems, 20(1), 51-85.

Rietz, T., & Maedche, A. (2023). Ladderbot—A conversational agent for human-like online laddering interviews, International Journal of Human-Computer Studies (IJHCS), Volume 171, 2023, https://doi.org/10.1016/j.ijhcs.2022.102969.

Tuunanen, T., & Kuo, I. T. (2015). The effect of culture on requirements: a value-based view of prioritization. European Journal of Information Systems, 24(3), 295-313.

Tuunanen, T., & Peffers, K. (2018). Population targeted requirements acquisition. European Journal of Information Systems, 27(6), 686-711.

Tuunanen, T., Lumivalo, J., Vartiainen, T., Zhang, Y., & Myers, M. D. (2024). Micro-level mechanisms to support value co-creation for design of digital services. Journal of Service Research, 27(3), 381-396.

Tuunanen, T., Salo, M., & Li, F. (2023). Modular service design of information technology-enabled services. Journal of Service Research, 26(2), 270-282