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
Program of study: 
Master of Science in Entrepreneurship and Innovation
Course coordinator: 
Sheryl Winston Smith
Product category: 
Master
Portfolio: 
MSc - Core course
Semester: 
2019 Autumn
Active status: 
Active
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.
  • To gain an understanding of information search strategies.
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 be able to critically evaluate information sources.
  • To understand what a critical literature review is and how it can be designed and executed.
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.

This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course starts.

At resit, all exam components must, as a main rule, be retaken during next scheduled course.

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.

Required prerequisite knowledge

Basic proficiency in statistics

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Activity
Form of assessment:
Class participation
Exam code:
GRA31671
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
35No1 Semester(s)Individual
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA31671
Grading scale:
Point scale
Grading rules:
Internal examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
10No 1 Week(s)Group/Individual ( 1 - 3)Library assignment
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
GRA31671
Grading scale:
Point scale
Grading rules:
Internal and external examiner
Resit:
All components must, as a main rule, be retaken during next scheduled course
55No1 Semester(s)Group/Individual Term paper
Exams:
Exam category:Activity
Form of assessment:Class participation
Weight:35
Invigilation:No
Grouping (size):Individual
Duration:1 Semester(s)
Comment:
Exam code:GRA31671
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:10
Invigilation:No
Grouping (size):Group/Individual (1-3)
Duration: 1 Week(s)
Comment:Library assignment
Exam code:GRA31671
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:55
Invigilation:No
Grouping (size):Group/Individual
Duration:1 Semester(s)
Comment:Term paper
Exam code:GRA31671
Grading scale:Point scale
Resit:All components must, as a main rule, be retaken during next scheduled course
Type of Assessment: 
Continuous assessment
Grading scale: 
ECTS
Total weight: 
100