GRA 3167 Research Methodology for Entrepreneurship and Innovation

GRA 3167 Research Methodology for Entrepreneurship and Innovation

Course code: 
GRA 3167
Department: 
Strategy and Entrepreneurship
Credits: 
6
Course coordinator: 
Sheryl Winston Smith
Course name in Norwegian: 
Research Methodology for Entrepreneurship and Innovation
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2023 Autumn
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
One semester
Introduction

This course is designed to help students design and conduct good, theoretically informed, empirical research in the field of entrepreneurship and innovation. It addresses issues relevant to both qualitative and quantitative research.

The purpose of empirical research in the social sciences in general is to build theories that help us to understand the world. For strategic management in particular, the purpose of empirical research is to understand the myriad issues that pertain to the survival and success of organizations. Good research is both theoretically interesting and persuasive. Persuasiveness depends on whether the empirical evidence presented in the research convinces readers that the author’s arguments and interpretations are likely to be valid. This course aims to help students identify theoretically interesting and researchable topics within the field of strategic management, as well as to increase the students’ ability to design and conduct empirical research that will make their findings persuasive. The knowledge and skills the students are expected to acquire in this course are highly relevant for the task of successfully completing a MSc thesis for the strategic management major.

Learning outcomes - Knowledge
  • To gain knowledge of how to write a research proposal.
  • To gain knowledge of concepts and tools to collect and analyze data for innovation and entrepreneurship research.
  • To understand and appreciate the strengths and weaknesses of different research designs, methodologies and data sources.
Learning outcomes - Skills
  • To be able to compare and critique different research designs, methodologies and data collection methods.
  • To be able to design samples, research questions and research proposals appropriately.
  • To understand what a critical literature review is and how it can be designed and executed.
  • To understand strategies of information harvesting, including search techniques and critical evaluation of sources.
General Competence
  • To understand how research design influences and is influenced by the theme of interest.
  • To appreciate how to conduct original research at the MSc level and evaluate the research process.
Course content

The course content includes:

  1. Introduction to research methods and design
  2. Research questions, what makes for good research questions?
  3. Literature review, how to conduct a critical literature review, what to focus on when reviewing the literature you have identified in your literature search, as well as how to report the reviewed literature
  4. Information search, understanding of information search strategies, and develop your ability to critically evaluate information sources
  5. Research designs, common research designs, logic of quasi-experimental research designs, rival hypotheses and crucial tests
  6. Sampling, selection, concept operationalization and measurement, quantitative data sources, selection bias and case selection, operationalization and measurement, validity, reliability and generalizability issues, common method bias
  7. Qualitative research and interviewing, what is qualitative research, interviews
  8. Survey methods and analysis, survey data, sampling and response rates (self- selection, generalizability issues), measurement scales, survey items (validity and reliability issues)
  9. Qualitative data analysis Developing propositions, developing a process model, coding and inductive analysis of texts
  10. Focus groups, observation, document analysis and case studies
  11. Database/archival methods and analysis, Sampling, Creating your own database, variable construction, model building, statistical analysis
  12.  Approaching the empirical setting, getting and working with the data, approaching the empirical setting, ethical guidelines for research, consent forms, non-disclosure agreements
Teaching and learning activities

The course will be a combination of lectures, action learning activities, application, reflection and class discussions. We will apply concepts and skills associated with the topics and also critically evaluate them. We will assess applications, decisions made, and question/problematize these. We will also think about other ways to apply/alternative decisions.

Software tools
No specified computer-based tools are required.
Additional information

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.

All parts of the assessment must be passed in order to get a grade in the course.

The examination for this course has been changed from autumn 2023. It is not possible to resit the old version of the examination.

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

Basic proficiency in statistics

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 31672
Grading scale:
Pass/fail
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
0No1 Week(s)Group (2 - 3)Library assignment.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 31673
Grading scale:
ECTS
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
30No1 Semester(s)Group (2 - 3) Mid-term research idea submission.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA 31674
Grading scale:
ECTS
Grading rules:
Internal examiner
Resit:
Examination when next scheduled course
70No1 Semester(s)Group (2 - 3)Term paper based on research idea.
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:0
Invigilation:No
Grouping (size):Group (2-3)
Duration:1 Week(s)
Comment:Library assignment.
Exam code:GRA 31672
Grading scale:Pass/fail
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:30
Invigilation:No
Grouping (size):Group (2-3)
Duration:1 Semester(s)
Comment: Mid-term research idea submission.
Exam code:GRA 31673
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:70
Invigilation:No
Grouping (size):Group (2-3)
Duration:1 Semester(s)
Comment:Term paper based on research idea.
Exam code:GRA 31674
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
36 Hour(s)
Student's own work with learning resources
50 Hour(s)
Group work / Assignments
20 Hour(s)
Submission(s)
60 Hour(s)
Sum workload: 
166

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.