Business Research: Problem-Solving in Organizations (Spring Semester 2026)

General Information

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Lecturer 

Dr. Simone Schweiger
Assistants(s)

Amelie Gschwinder
Contact

amelie.gschwinder@unibe.ch
ECTS 6
 Room

Hörraum A -126, UniS / Seminarraum S 201, UniS
Language

The course will be held in German. The majority of documents will be available in English only. 
Credit Aknowledgement

Students specializing in management: Core subject (Kernbereich) Others: Elective subjects (Wahlbereich)

Registration

Students are required to register for this course on KSL. Registration is open from January 12th until February 16th.

Preconditions

No preconditions for this course exist.

Course Description & Learning Outcoms

In today's dynamic work environment, effective problem solving, an analytical mindset and creative and critical thinking are critical skills. This course guides students through a systematic process of defining, decomposing and solving a problem. It focuses on data collection and analysis, hypothesis-based approaches, divergent and convergent thinking, and inductive and deductive reasoning.

By the end of this lecture, students will acquire the following skills

  • apply both convergent and divergent thinking in a targeted manner.
  • select and apply suitable data collection methods.
  • select suitable data analysis methods.
  • assess the credibility of information and in particular research results.
  • interpret basic key figures of quantitative research results.
  • frame a problem (framing) and reformulate the frame (reframing).
  • systematically break down the causes of a problem in breadth and depth.
  • systematically break down solutions to a problem in breadth and depth.
  • differentiate and apply inductive and deductive reasoning.
  • approach problems based on hypotheses.
  • critically and constructively question approaches to problem solving.
  • recognize potential cognitive biases in the problem-solving process.
  • apply the problem-solving process.

Teaching Approach and Performance Evaluation

The course follows a “flipped classroom” approach and consists of compulsory and optional components. The compulsory components serve to achieve the learning objectives by requiring theoretical input, reflection, transfer and practical application of the content. The optional components serve to repeat, deepen and apply the content.

It is possible to achieve the full 100 points and thus the top grade of 6.0 by completing only the compulsory components. Additional points can be collected with the optional components in order to improve skills in this way and to compensate for any points not achieved in the compulsory components. Students need 50 points to achieve a satisfactory performance (grade 4.0).

Any changes to the course will be communicated via email.

Course Material 

General course materials will be available on ILIAS. 

Tentative Course Overview (subject to change)

The course begins with the kick-off event on 18.02.26.
The modules (introduction and modules 1-4) vary in scope. For an even distribution of the workload during the semester, there is a recommended completion date for each module. Binding deadlines for completing self-study modules (Partial Performance A), submitting individual projects (Partial Performance B), and attending the final meeting apply only toward the end of the semester.

When completing optional components, students can choose whether, how many and which dates they wish to attend (2 optional dates for exercise A and exercise B as well as choice of peer feedback round 1 and / or 2).

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Module

Time  

 Lecture

 Topic

Feb 18

Feb 18 - Feb 25

Feb 18, 16:15 - 18:00 (Hörraum A -126, UniS)

Kick-off 

Introduction
1

Feb 25




Mar 11

 

Step 1: Frame the problem

Framing

Reframing

Interviews

Analysis of qualitative data

Mar 11 - Mar 18 Peer feedback: Round 1 (optional)

Mar 25 or Apr 01

Mar 25 / Apr 01, 16:15 - 18:00 (Seminarraum S 201, UniS)

 Exercise A (optional)
2

Mar 11




Apr 01

 

Step 2: Diagnose the problem

Find potential causes

Identify actual causes

Hypothesis-based approach

Focus groups
3

Apr 01




Apr 29

 

Step 3: Find solutions

Find potential solutions

Choose actual solutions

Survey

Analysis of quantitative data

Apr 29 - May 06 Peer feedback: Round 2 (optional)

Apr 29 or May 06

Apr 29 / May 06, 16:15 - 18:00 (Seminarraum S 201, UniS)

 Exercise B (optional)

Apr 29

Submission of individual project (compulsory)

4

Apr 29


 


May 07

 

Step 4: Implement solutions

Define actions 

Experiment

Analysis quantitative data

Wrap-up
May 13
May 13

May 13, 16:15 - 18:00 (Hörraum A -126, UniS)

Final meeting (compulsory)

Completion of self-study modules (compulsory)