INN3044 Social network analysis and green regional development
- Number of credits7,5
- Teaching semester2024 Spring
- Language of instructionEnglish
- CampusLillehammer
- Required prerequisite knowledge
None
- SNA concepts and real case studies will be illustrated throughout the course with the aim of showing how this methodological tool can be successfully applied in the fields of geography of sustainability transition, green regional development and economic restructuring. Greening the economy is a key topic for policymakers everywhere and especially in a country such as Norway due to its strong reliance on oil and gas exports.
- Real datasets based on secondary data will be provided and the students will be stimulated to develop and tackle original research questions.
- At the end of the course, they will be able to adopt several SNA measures and use a software specifically created for conducting network studies such as Ucinet.
Learning Outcome
Upon successful completion of the course, the student will have achieved the following learning outcomes:
The student
- will acquire knowledge on strands of literature such as transition studies, economic geography and innovation studies.
- will be able to apply social network analysis (SNA) in economic studies with a geographical background.
The student
- will be able to build a network matrix.
- will be able to use a broad range of SNA methods and techniques.
- will be able to interpret SNA outputs.
- will be able to use a widely used proprietary software, specifically designed for conducting network studies, such as Ucinet.
- will be able to formulate original research questions on the topics tackled throughout the course.
- will be able to carry out appropriate and in-depth scientific analyses about geography of sustainability transition, green regional development and economic restructuring.
The student
- will acquire relevant and up-to-date knowledge on transition studies, economic geography and innovation studies.
- will be able to identify knowledge gaps in the extant literature and tackle original research questions.
- will understand network theories and methods.
- will be able to examine real large relational datasets, adopt many different SNA techniques and successfully conduct economic studies with a geographical background.
- will possess adequate skills and competences for applying the same research method in cognate fields (e.g., economics, sociology, management, marketing).
Lectures, SNA applications (with Ucinet), individual or group work, self-study.
Attendance is mandatory. The students must attend at least 75% of the total hours making up the course. Moreover, they must attend all the seminars/workshops, i.e., when SNA methods are taught and how to use Ucinet is illustrated. Expected workload in the course is 187,5-225 hours (according to ECTS-standard).
IMPORTANT: The students must be aware that UCINET is Windows software only. There is no Mac or Linux version. The only way to run UCINET on a Mac is to use a Windows emulator such as Parallels or Oracle VM VirtualBox. Mac users must contact the teacher before the start of the course in order to discuss how they can attend the course regularly. Students should use their own laptop during seminars/workshops. They must install a trial version of Ucinet (which can be downloaded and used free for 60 days). It is expected that students work from home throughout the week.
- 75 % attendance in lectures
- Course papers (formative essay + final paper)
- Presentation and discussion
Final paper: The final paper is a revised version of the formative essay handed in as a coursework requirement, for which the student has received detailed comments. The final grade is based both on the overall quality of the final course paper, as well as on the ability showed by the student in addressing the written comments they received on their formative paper.
| Form of assessment | Grading scale | Grouping | Duration of assessment | Support materials | Proportion | Comment |
|---|---|---|---|---|---|---|
Written assignment | ECTS - A-F | Group/Individual |
| 100 |