Course objectives
The opening up of national economies, the globalization of economies and the new global impact technologies affect all industries. In this context, it is important to conduct an analysis at the industrial level. This involves understanding how industries are structured, how they are evolving, how they are affected by new regulations, and what are the new interactions between industry sectors.
Objectives
The main objective of this course is to raise students’ awareness to the importance of the differences between industries in the contemporary economy. This course provides a critical review of the different approaches to industry analysis. It aims to help students understand industrial developments leading to the formation of new markets, aspects related to business connectivity and the social structure of industries, and how differences between industries affect the competitiveness and behavior of firms, their performance and their strategy of entering foreign markets.
At the methodological level, this course relies on different tools of business intelligence. It is based in particular on a technological platform (Nüance-R), which combines a set of analysis tools based on the R programming language.
Through this course, students will learn in terms of content relating to the industrial sectors and also in terms of method with the tools of business intelligence.
The course examines the following topics:
Differences between industries, market structures, evaluation of industrial performance, value added, competitiveness, degree of rivalry
New interactions between industries, value chains, industrial clusters, aspects of business connectivity (networks) within different industries
Effects of industry on corporate behavior: entry strategy, financing, performance.
Pedagogical Approach
This course is taught using the Harvard Business School case method, supplemented by student readings and presentations. It is built on the materials developed by the Institute of Strategy and Competitiveness at Harvard Business School. It also relies on the business intelligence and business analysis tools developed at CIRANO, as well as a privileged access to www.mondo.international.
This course consists of twelve sessions. Each session is divided into two equal periods. The first period deals with course content and readings (use of case studies), and the second period encourages the mastery of business intelligence tools and sector analysis, particularly through group work.
More specifically, each session is divided into two parts: (1) a case study and (2) learning analytical tools using the nuance-R business intelligence platform, based on the R programming language.
In this course, the teacher uses book chapters, articles, case studies and videos to help students develop an advanced understanding of the industrial aspects. This material will help students familiarize themselves with key frameworks and approaches to industry analysis. This course also includes interactive forms of teaching based on the business intelligence platform of nuance-R, based on the R programming language.
Fluency in computer language, statistics or econometrics is not required. However, through the use of the nuance-R platform, students will develop basic skills in statistics and econometrics using the R programming language. Students need thus to have some openness about acquiring a new set of skills.
In addition, students will develop skills in the use of statistical sources and websites (eg databases: WITS, OECD, United Nations or www.mondo.international). The sessions will require active input from students in the form of discussions and team presentations. The professor therefore expects students to make the necessary readings for each session and to be able to ask questions and make constructive comments.
Assessments
| Component | Grade |
|---|---|
| 1. Participation | 15% |
| 2. Individual essay: analysis of an industry or an industrial cluster | 22.5% |
| 3. Team essay: Analysis of Promising Industry Sectors within a Country | 22.5 |
| 4. Data Visualisation Challenge | 5% |
| 5. Final exam | 35% |
1. Participation (15%)
Students will be evaluated on their active participation in the course at each session, including case studies and readings.
The evaluation of participation is based on three criteria: attendance, preparation and relevance.
- Attendance: the student is present in class, arrives on time, is committed to the dynamics of the class and contributes to the good atmosphere of the group.
- Preparation: the student demonstrates that he has read the materials, prepared his homework, and is ready to answer questions and participate in class discussions.
- Relevance: the interventions made by the student contribute to the learning of others. These interventions include answers to questions asked, questions of clarification or sharing of experience.
Examples:
| Component | Grade |
|---|---|
| I attended all the 6 classes so far, but was not an active member of class conversations | C range |
| I might have missed one class but compensated with some good insights into class conversations | B range |
| I did not miss classes and I am an active member of class conversations | A range |
2. Individual essay: analysis of an industry or an industrial cluster (22.5%)
2.1 Organization and goals
The goal is to develop new skills, while using knowledge and frameworks seen in class.
Students will prepare a report/analysis of an industry focusing on one of the subjects of the course (eg how the chosen industry affects the entry strategies of firms into a new market). You should choose an industry/cluster based on the availability of data; a good starting point is on https://warin.ca/teaching.html#API.
The length of the report should be 4000 words, excluding the bibliography and the appendices (a word count option can be found in the Add-Ins menu of your console, and there is also a Spelling and Grammar correction tool in the Edit menu, see the forum on this website).
NOTE: The basic codes in RMarkdown is provided [Here].
2.2 Evaluation Criteria
The report will be evaluated as follows:
- 15% of the grade will relate to the quality of the sources (databases) and the quality of the knowledge mobilized (academic references),
- 60% of the paper will focus on the relevance of the report, the choice of economic indicators, how knowledge is mobilized and the overall quality of reporting,
- 25% of the note will address the use of R code in data collection, in the creation of relevant R charts, and in the creation of tables in Markdown code. This is the data Collection and Visualization steps.
2.3 Resources
- A list of databases: https://warin.ca/teaching.html#Tutorials
- Mondo International: https://www.mondo.international/
- NOT Google Search
3. Team essay: analysis of a leading industrial cluster (22.5%)
3.1 Organization and goals
The students will be grouped into several teams of 3 people and each team will be responsible to write a report and present their analysis about an interesting industrial cluster of their choice during session 11. The complete report will be written using the nuance-R platform. The report is due at the beginning of Session 12 (one week after class presentation). A list of clusters and examples will be provided in due time.
The length of the report should be 9000 words (see the forum for how to count the words), excluding the bibliography and appendices.
NOTE: The basic codes in RMarkdown is provided [Here].
3.2 Evaluation Criteria
The evaluation of the study will be as follows:
- 15% of the grade will relate to the quality of the sources (databases) and the quality of the knowledge mobilized (academic references),
- 50% of the note will focus on the relevance of the report, the choice of economic indicators chosen, how knowledge is mobilized and the overall quality of the presentation of the report,
- 30% of the note will address the use of R code for data collection, the creation of relevant R code charts, the creation of tables in Markdown code, and the use of the code learned to analyze the data This is the overall data Collection, Visualization and Analysis steps.
- 5% of the score will be on individual performance during the team presentation of the study results.
4. Data Visualisation Challenge (5%)
Starting week 3, you will individually have to work on one visual (see Mondo, WEF, etc.) about an industry or a cluster. The first half of the class starts on week 3, then the other half will alternate and present their data visualisation on week 4, etc. You need to send your visual on Thursday the week before the session at noon. No delays will be accepted. It is where you can experience a bit with the code (resources: Homework #2 from this website, [ggplot2] and [htmlwidgets]).
Overall, you will do 5 visuals presented orally in 1 min max (originality of the data, creativity of the graph, comparative dimension, etc.). The whole class will vote.
To submit your R code, please use this form [here]
Here is an example of what is expected in terms of content organisation, with the proper title, sources, etc.
| Grading | Grade |
|---|---|
| You succeeded at creating a good visual with relevant data, and an interesting intuition | B |
| You did the above with a more complex graph | B+ |
| You did the above with a visual particularly adapted to the nature of the data (some very good creative thinking) | A- |
| You did the above with a real impact on the readers | A |
5. Final exam (35%)
The final exam covers all the material seen in class, in proportion to the time spent on each. The final exam will consist of fifteen questions to be answered in a developed form. Students must choose ten of these. For each question:
- 6/10 points will go to the analytical content and
- 4/10 points will be given for a good use of the case studies in your answers and the integration with the content.
The length of the answers is about 350 words or a page on the answer book.
In a Google Sheet, you’ll find all the questions you’ve worked on during homework each week. This gives you an idea in a single file of the nature of the questions that will be asked to you in the final exam. By doing the work each week, this makes this assessment not just an exercise to get a grade, but a real exercise in learning and focusing on important concepts.
Click [here] for all the questions!
The slides on which the answers are found are shown in the second column of the table.
The date of the final exam is on the Zone Cours / En ligne website.
For more details about the overall evaluation dynamics and expectations, please: [click on the following link]
Schedule
Part I. Clusters, competitiveness and connectivity
Session 1. Competitiveness: overview of industries and classifications Cliquez ici
International and technological context (the 4IR)
Session 2. International Industrial Comparative Analysis and Global MNE Strategies Cliquez ici
What is an industrial analysis?
Session 3. The connectivity concept: inter-enterprise networks within industry and cluster development Cliquez ici
The concept of connectivity, subsidiaries and inter-company networks.
Session 4. Industrial clusters: prospects for economic development and innovation Cliquez ici
Value chains, connectivity and geographic gravity (clusters).
Session 5. Value chains, networks and internationalization of industrial clusters Cliquez ici
The concept of competitiveness in industrial analysis = an analysis focused on institutional aspects (the quality of institutions, the Triple-Helix).
Part II. A geographical economic perspective
Session 6. Globalization: macroeconomic and policy effects on industry behavior; policies and the role of institutions for collaboration Cliquez ici
Analysis of competitiveness in the developed countries and lessons for business.
Session 7. Effects of industry on business behavior and performance - a look at developed countries Cliquez ici
After competitiveness in developed countries, competitiveness in emerging countries and lessons for business.
Session 8. Effects of industry on international expansion strategy - a look at emerging markets Cliquez ici
After competitiveness in emerging countries, competitiveness must consider the “megacity” geographical unit and the lessons for companies.
Session 9. Characteristics of internationalization of enterprises in different industries and effects of industry on choice of mode of entry into a foreign market - a look at the role of mega cities Cliquez ici
Analysis of competitiveness and the issue of international economic agreements (trade, finance and migration flows) and lessons for business.
Session 10. Particularities of internationalization of enterprises in large economic regions Cliquez ici
Conclusion on economic development and competitiveness in the context of innovation in the 4IR.
Community Values & Honor Code
Community Values
It Is Essential to Foster a Supportive Online Learning Environment.
At HEC Montreal, we believe it is essential for all participants to exemplify and uphold HEC’s Community Values in order to foster a supportive online learning environment where individuals can have open discussion, reflect on their thinking, and learn from each other.
Our Lab’s Honor Code
A Commitment of Honor, Honesty, and Stewardship.
Our Honor Code supplements the statement of Community Values and reflects the commitment participants make as members of the learning community to participate in, foster, and uphold our Lab’s Learning Model.
Our platform
You will find some of the tools we will use here: www.lab.nuance-r.com:
- first, our main portal: www.lab.nuance-r.com
- The Chat: an open source version of Slack, we will communicate exclusively through this platform. Our whole team will be here for you. You need to create your account.
- The Virtual Campus: a set of reference courses that you will use during our breakout sessions. We will provide you with your credentials by email before class.
- Nüance-R: the computing platform. We will provide you with your credentials by email before class. It will be useful for your R-based project.
- Our nuage.lab.nuance-r.com: you will have access to an open source version of Dropbox where you will put your assignments. We will provide you with your credentials by email before class.
- A Github folder to coodinate your work with your teammates.
TL;DR
Before Friday, you need to:
- Read this syllabus thoroughly
- Create your account on our Chat platform here www.lab.nuance-r.com. Help here
That’s it!