Strategic Management of Start ups

Teacher(s)

Roszkowska-Menkes Maria;

Language

English

Prerequisites

Restricted access to INEO / LSM-INGE / LSM-DD / LSM-CEMS students

Main themes

Starting up a wholly new business (opportunity identification, market segmentation/positioning/timing, marketing, managerial/group dynamics implications, resources/funding implications)
Growing a new business (growth trajectories and styles, and their coping mechanisms)
Internationalization of new / young businesses (foreign entry modes, born globals)
Dealing with decline and exit dynamics of new / young businesses (interactions between start-ups and established/incumbent/big business, corporate venturing)
Replacement of founders and succession of leadership at start-ups (growth cycle/stages, investment rounds and management renewal)

Learning outcomes

At the end of this learning unit, the student is able to :

Assess the market readiness / potential of start-up value propositions Assess the strengths and weaknesses of (management) teams behind start-ups Assess the investment readiness of start-ups Understand competitive dynamics (opportunities and threats) around start-ups and the markets they address See through cultural and personal propensity for entrepreneurship according to start-up growth styles

Content

This course explores strategy for start-ups, providing a deep understanding of the key strategic choices innovation-driven entrepreneurs face. It offers a structured framework for selecting and implementing entrepreneurial strategies while addressing the challenges of scaling ventures over time.
A central theme is balancing experimentation and learning with deliberate strategic choices to build a competitive advantage. The course examines how to craft an entrepreneurial strategy, the critical decisions involved, how these choices align to form a cohesive approach, and the playbook for different entrepreneurial strategies.

Teaching methods

• Lectures
• Case sessions
• Exposure to start-up pitches

Evaluation methods

The assessment plan for this course is as follows:
1. Active Participation (individual, 20%)

• Contribution to the class discussion (15%)
  
•  Mind and Market Forum participation report (5%)
  
• Re-sit: There is no opportunity to re-sit this part of the assessment. Failure to actively participate will result in a grade of 0 for the assignment.
  

2. Team Case Analyses  (team-based, 30%)

• Case analyses in the form of in-class presentations and short written reports.
  
• Re-sit: To re-sit this part of the assessment you must submit a new or revised report along with a recorded presentation
  
• Minimum grade of 10 applies for this part of the assessment
  

 3.Final written open book exam (individual, 50%)

• Final case analysis building on the course framework.
  
• Re-sit: To re-sit this part of the assessment you will need to take the exam again in the second session. 
  
• Minimum grade of 10 applies for this part of the assessment
  

Late submission policy:
Late submissions of assignments will result in a grade deduction. Please follow the policy posted on Moodle.

Free-riding:
Free-riding will result in a full grade deduction on the assignment for the student in question. Please adhere to the guidelines for reporting free-riding instances published on Moodle.

Plagiarism:
Plagiarism is a form of free-riding, where you present someone else's intellectual efforts as your own. Reusing parts of a paper written for one course in another course is also considered plagiarism.
By submitting an assignment for evaluation:

• you assert that all your sources that go beyond common knowledge are suitably attributed (please follow the referencing guidelines posted on Moodle);
  
• you assert that you have respected all specific requirements of your assigned work, in particular requirements for transparency and documentation of process, or have explained yourself where this was not possible.
  

If any of these assertions are not true, whether by intent or negligence, you have violated your commitment to academic integrity. This constitutes academic misconduct.

Use of Generative AI Policy:

• The use of generative AI for the course assessments is allowed.
  
• You bear full responsibility for what you present or submit. By submitting your assignment, you affirm that you have verified it accurately reflects the facts.
  
• If you have used AI in preparing your assignment, you must acknowledge it by clearly indicating which AI tools were used and how they assisted you, including specific functionalities or tasks. Be sure to mention the prompts used and how you applied the AI-generated output.
  
• Your submission must represent your own original work. AI tools should only be used to support your work, not to produce it entirely. Ensure that your own thoughts, analysis, and writing are evident in the final submission, so it allows the teacher to evaluate the skills and competencies acquired.
  
• By submitting your assignment you acknowledge that you master the final output and that you are able to explain it and answer questions about it.
  
• Failure to disclose AI use or misuse of AI tools will be considered as fraud, as defined in articles 107-114 of the RGEE.
  

Acknowledgment:

• This course description was reviewed and refined with the assistance of ChatGPT, which helped improve clarity, grammar, and language consistency.
  

Bibliography

To be specified on Moodle

Teaching materials

• Reading material, video material via Moodle

Faculty or entity

> CLSM