MNGT-E3001 - Researching Entrepreneurship and Innovation (Master's thesis seminar), Lecture, 4.11.2022-16.12.2022
This course space end date is set to 16.12.2022 Search Courses: MNGT-E3001
Topic outline
-
Welcome to the Researching Entrepreneurship and Innovation (Master's thesis seminar) course (MNGT-E3001)!
The primary aim of this course is to introduce Master's students in Entrepreneurship and Innovation Management to the basic steps of conducting research in their field of specialisation and help them to apply it to their planned master thesis.
In essence, research in entrepreneurship and innovation consists of asking important questions and using scientific methods to design and implement ways of answering these questions. There is generally no one right way to approach these issues (although there are definitely wrong ways!). Instead, research in entrepreneurship and innovation consists of numerous choices that involve trade-offs. We will start the course by introducing relevant topic areas in entrepreneurship and innovation to have an idea about what is 'hot' in this field at the moment. We then address the research process (including identifying research questions, philosophy of science) and trade-offs among research designs such as the choice to use qualitative versus quantitative methods. We will also cover main qualitative and quantitative methods applied in entrepreneurship and innovation research.
It is important to know that it is up to your own interest and needs to choose the order in which you proceed in this course. The topics are already organized in a suitable order. Each topic comes with a set of readings and in most cases short introductory videos. Please note that neither the topics nor the readings covered should be considered to be exhaustive. We simply cannot cover everything in the time allotted. The purpose is to provide a solid foundation in the important issues related to each topic and the field as a whole. The overall goal of the course is to help prepare students to develop interesting research questions and pursue answers to them by using appropriate scientific methods. These are essential prerequisites for a successful Master's thesis.
Before engaging with the sessions of this course please take a look at the more detailed description of the course structure below, and see the Syllabus (top-right corner on this page) and familiarise yourself with the main requirements to complete the course successfully.
Please make sure to familiarize yourself with the preliminary assignment. Without submitting this short piece of work, it will not be possible for you to go on with this course.
For any enquiry regarding this course please contact thomas.hoeger@aalto.fi
Happy learning!Course Structure and Materials
The course consists of 8 topics that cover the basics of qualitative and quantitative research:
Basics of Qualitative Research in Entrepreneurship and Innovation Management
- Topic 1: Different qualitative approaches to inquiry
- Topic 2: Observation and Note-Taking
- Topic 3: Interviewing and non-mainstream approaches to collect data
- Topic 4: Qualitative Data Analysis
Basics of Quantitative Research in Entrepreneurship and Innovation Management
- Topic 5: Quantitative Data
- Topic 6: From Theory to Empirics
- Topic 7: Quantitative Analysis
- Topic 8: Interpreting and Publishing Research Findings
Each topic will include reading materials and videos that will introduce you to the core themes. In addition, you may be asked to independently look for more information.
Online sessions
Introductory session on Friday 4.11.
We will organize an online introductory session on the course on Friday 4th of November at 14.15-16.00. Participation is voluntary, but recommended.
Feedback sessions on 25.11. and 16.12.
The course includes two obligatory assignments (20 points for assignment 1 and 32 points for assignment 2) that allows you to develop your skills as a researcher, and to work on a topic that is of interest to you. We offer an online feedback session after each of the obligatory assignments:
- The first feedback session is on Friday 25th of November at 14.15-16.00.
- The second feedback session is on Friday 16th of December at 14.15-16.00.
Participation in the feedback sessions is voluntary.
The Introductory and Feedback sessions will be organized in a Zoom Meeting.
To join the meeting:
https://aalto.zoom.us/j/6435483326
----------------
Assignments
Preliminary Assignment
Since this course should help and assist you in the process of your master thesis paper, you should already have a rough idea about the topic you want to proceed. Small changes might still occur, but the overall direction should already be present. Therefore you need to submit till tuesday the 1st of November already some first ideas about your future research project. You will find more information about it under the "Assignments" section. And don't stress out too much about this. This should just be a kind of ground work we will then work on, allocate you to your supervisor and know which research methods you should focus on for the upcoming assignments.
Obligatory Assignments – due 22.11. and 16.12.
The course includes two obligatory assignments (20 points and 32 points) that allow you to develop your skills as a researcher, and to work on a topic that is of interest to you. The first assignment focuses on either on Qualitative or Quantitative Research (depending on your planned master thesis topic) and it is due Tuesday the 22nd of November at 10 am. The second assignment will focus on your particular research methodology and it is due Friday the 16th of December at 10 am.
You must complete and pass these 2 obligatory assignments in order to pass the course. However, note that they only amount to a maximum of 52 points of the total 100 points of this course and it might be very difficult to reach the maximum amount of points. In order to pass the course, you need to collect a minimum of 50 points from these exercises and the assignments. Therefore I strongly encourage you to also work on the exercises.
We offer an online feedback session after each of the obligatory assignments (see information above). The participation is voluntary.
Topic Exercises – due 22.11. and 16.12.
In association to each topic there are two small exercises (3 points each), which test the comprehension of the topic and help you to develop skills to critically read and evaluate scientific research. These exercises are voluntary. However, if you do not complete them, you will automatically loose the points they are worth, and cannot get the highest grade from the course. Altogether the voluntary exercises amount to a maximum of 48 points of the total 100 points of this course (8 session, 2 exercises in each session, 3 points for each exercise).
The first set of exercises (Either 1-4 (Qualitative) or 5-8 (Quantitative); depending on your planned master thesis research framework) are due Tuesday the 22nd of November, and the second set of exercises are due due till Friday the 16th of December at 10 am.
Elaboration of the Evaluation Criteria and Methods
In order to pass the course, you need to collect a minimum of 50 points in the exercises and assignments. Note, it is mandatory to submit both assignments, but you may choose the number of exercises. If you decide to skip some exercises you will no longer have the possibility to score maximum points, as in each exercise you can score a maximum of 3 points of the final grade.
For more information on the points to grade conversion, see Table below:
Course Evaluation Overview
Course Requirements
Weighting (Points)
1. Qualitative Research Exercises
24
- Assignment 1
20
- Quantitative Research Exercises
24
- Assignment 2
32
- Total
100
Points conversion scale
Final grade
(official scale)
90 - 100
5
80 - 89
4
70 - 79
3
60 - 69
2
50 - 59
1
0 - 49
0 (Fail)
Suggested schedule
This is a self-paced online course, and as such the students can decide how to allocate the topics according to their own speed, needs and interests. Just keep in mind the exercise and assignment deadlines
Class Schedule
04 November – 16 December 2022
Week 1
01.11.-06.11.
Preliminary Exercise due till Tuesday 1st of November at 10 am
Introduction session on Friday, 4th of November at 14.15 Finish time zone (optional)
Week 2
07.11.-13.11.
Topic 1: Different qualitative approaches to inquiry and Sampling
Topic 2: Observation and Note-takingWeek 3
14.11.-20.11.
Topic 3: Interviewing and non-mainstream approaches to collect data
Topic 4: Qualitative Data AnalysisWeek 4
21.11.-27.11.
Assignment 1 and Exercises (Topics 1-4) due on Tuesday 22.11. at 10 am
Feedback Session on Friday 25.11. at 14.15 Finish time zone (optional)Week 5
28.11.-04.12.
Topic 5: Quantitative Data
Topic 6: From Theory to EmpiricsWeek 6
05.12.-11.12.
Topic 7: Quantitative Analysis
Topic 8: Interpreting and Publishing Research FindingsWeek 7
12.12.-16.12.
Assignment 2 and Exercises (Topics 5-8) due on Friday 16.12. at 10am
Feedback Session on Friday 16.12. at 14.15 Finish time zone (optional)Some suggestions to create learning synergies with other Aalto courses:
This course works complementary to other Aalto research method courses, such as ‘26E02900 Doing Quantitative Analysis’ or ‘21E00011 Doing Qualitative Research’.
If you are interested in learning more about entrepreneurship and innovation management research, or you are writing a thesis on the topic, we recommend to combine this course with the online course ‘25E55000 Entrepreneurship and Society’ to strengthen your theoretical understanding of entrepreneurship research.
- Topic 1: Different qualitative approaches to inquiry
-
-
COURSE REQUIREMENTS AND DEADLINES
Preliminary Assignment - due Tuesday 1st of November
Since this course should help and assist you in the process of your master thesis paper, you should already have a rough idea about the topic you want to proceed.
Therefore please put these onto paper and hand them in before we start with our first session. This should be the ground work we will then work on, allocate you to your supervisor and know which research methods you should focus on for the upcoming obligatory assignments. You will find more details about the preliminary assignment further down. The submission guidelines also apply for the preliminary exercise.
Topic Exercises – due 22.11. and 16.12.
In association to each topic there are two small exercises (3 points each), which test the comprehension of the topic and help you to develop skills to critically read and evaluate scientific research. These exercises are voluntary. However, if you do not complete them, you will automatically loose the points they are worth, and cannot get the highest grade from the course. Altogether the voluntary exercises amount to a maximum of 48 points of the total 100 points of this course (8 session, 2 exercises in each session, 3 points for each exercise).
The first set of exercises are due on 22nd of November. The second set of exercises are done by 16th of December. Depending if your future master thesis is focusing on qualitative or quantitative research, you should submit the exercises in from your research field. Working with this groundwork will help prepare you to do obligatory assignment number 1. The other topics, even though not part of your future master thesis research, are still part of your class and the topic exercises. These have to be done after obligatory assignment number 1 with the deadline of 22nd of November.
Obligatory assignments - due 22.11. and 16.12.
The course includes two obligatory assignments (20 points and 32 points) that allow you to develop your skills as a researcher, and to work on a topic that is of interest to you. The first assignment focuses on either on Qualitative or Quantitative Research (depending on your planned master thesis topic) and it is due Tuesday the 22nd of November. The second assignment will focus on your particular research methodology and it is due Friday the 16th of December.
You must complete and pass these 2 obligatory assignments in order to pass the course. However, note that they only amount to a maximum of 52 points of the total 100 points of this course and it might be very difficult to reach the maximum amount of points. In order to pass the course, you need to collect a minimum of 50 points from these exercises and the assignments. Therefore I strongly encourage you to also work on the exercises.
We offer an online feedback session after each of the obligatory assignments (see information above). The participation is voluntary.
READINGSPlease, see the Materials section for all the readings and research papers that are referred to in the exercises.
SUBMISSION GUIDELINES
Submission formats
Submit all exercises and assignments on MyCourses. Note that the two assignments will be submitted via Turnitin (to check for plagiarism) in MyCourses.
Format
The work must be presented in the following format:
- Font: Times New Roman
- Size: 12
- Spacing: 1.5
- Alignment: justified
- Pages: numbered
- Margins: ‘normal’ in MS Word
- Filename format: SURNAME First Name-Year-ID-ASSIGNMENT INITIALS.docx (example: Hoeger Thomas-2020-1234567-A1.docx)
Late/Non-Submissions
- Late assignments will lose 10 points per 24-hour period: this will be enforced as soon as the deadline is missed, as indicated by the timestamp in Turnitin. If an assignment is three or more days late, the grade will be converted to a zero for that assignment.
Referencing and Research Ethics
Citation Style: Harvard Referencing (See Materials section)
Academic excellence and high achievement levels are only possible in an environment where the highest standards of academic honesty and integrity are maintained. Students are expected to abide by the Aalto University Code of Academic Integrity, other relevant codes and regulations, as well as the canons of ethical conduct within the disciplines of business and management education. -
Welcome to this online course on qualitative and quantitative research methods!
This Master-level course introduces you to research methods applied in the fields of entrepreneurship and innovation management. It follows self-paced learning principles, and offers some flexibility in deciding the order of learning topics. In particular, the course focuses on how to collect reliable data by providing exercises for different data collection techniques, such as interviews, observations, surveys, and secondary data (e.g. news articles, tweets, Facebook/Youtube comments). Further, the course focuses on how to do good qualitative research by introducing and challenging you on some of the most common qualitative research designs, as well as offering training in techniques for qualitative analysis, for example the ‘Gioia method’, discourse analysis, critical incident analysis, visual methods, and qualitative comparative analysis (QCA). The course also addresses how to do good quantitative research by learning some basic techniques for conducting statistical analysis, such as descriptive statistics, bivariate and partial correlations, factor analysis, reliability tests, linear regression and logistic regression, as well as challenging you to review its application in published studies. In sum, through a number of small exercises you engage with the basics of qualitative as well as quantitative research, and engage more deeply in one research methodology of your interest in the final assignment.
We hope this course supports developing your academic knowledge and skills.
Some suggestions to create learning synergies with other Aalto courses:
This course works complementary to other Aalto research method courses, such as ‘26E02900 Doing Quantitative Analysis’ or ‘21E00011 Doing Qualitative Research’.
If you are interested in learning more about entrepreneurship and innovation management research, or you are writing a thesis on the topic, we recommend to combine this course with the online course ‘25E55000 Entrepreneurship and Society’ to strengthen your theoretical understanding of entrepreneurship research.
-
‘What are the most important factors in your life?’or ‘Why do you prefer listening to heavy metal music more than other music genres?’
or applied to the study of entrepreneurship: ‘What challenges are encountered by people who switch careers and become entrepreneurs later in life?’ or ‘How do entrepreneurs manage volunteer retention in prosocial business venturing?’
In order to answer these questions, it would be most appropriate to turn to qualitative research. However, qualitative research is not a single body of methods and techniques that help you to understand real world phenomena. Instead qualitative research is characterized by a wide variety of qualitative approaches to inquiry, i.e. research designs, including numerous, sometimes competing, paradigm (worldviews). Therefore, it is absolutely necessary to first clarify the research approach, often called the research design, you follow when conducting a qualitative research project. Therefore, the first session on qualitative research will familiarize you with some of the more common qualitative research designs.
Ones you have gained clarity on the research design and you have formulated a research question that you want to study, you are confronted with an important question: ‘What kind of data should I collect?’
Oftentimes, in qualitative research the data is collected from multiple sources that can be categorized as interview data, observations and secondary data, such as reports and statistics. As secondary data is collected by someone other than the researcher him-/herself, we have dedicated two sessions to develop a more systematic approach to collecting interview data and data from non-participant observations. You might still wonder, is asking people their opinions really ‘research’?
Miles, Huberman and Saldana (2014, p. 24) clarify that “qualitative data are a source of well-grounded, rich descriptions and explanations of human processes. With qualitative data, one can preserve chronological flow, see which events led to which consequences, and derive fruitful explanations. Then, too, good qualitative data are more likely to lead to serendipitous findings and to new integrations; they help researchers get beyond initial conceptions and generate or revise conceptual frameworks. Finally, the findings from well analysed qualitative studies have a quality of ‘undeniability’. Words, especially organized into incidents or stories, have a concrete, vivid, and meaningful flavour that often proves far more convincing to a reader—another researcher, a policymaker, or a practitioner—than pages of summarized numbers.”
Thus, after finishing the qualitative research module you can confidently say, yes qualitative research is a scientific mode of inquiry that can help us to understand the world. However, we are still missing an important part of qualitative research, that is qualitative data analysis. While this demands several courses, in one module we want you to at least get a glimpse of the methodological repertoire that has been developed, and is constantly developing. Put simply, we want you to get a better understanding of how we can draw valid and trustworthy meaning from qualitative data and what methods of analysis we can use that are practical and will get us knowledge that we and others can rely on.
-
When you hear people speak about qualitative research, it is easy to think that there is one approach to learn about. But, as you will learn about quantitative research, many varieties of qualitative research exist. Among the most common approaches is grounded theory and variations thereof. A reason for that could be that grounded theory tries to provide explanations and develop a theory behind the activities and events that management scholars study. Another reason might be that over the years, different ontological and epistemological foundations, have been assumed that have amended grounded theories into camps of researchers. But perhaps, it is simply because grounded theory assumes that the researcher has no interpretative frame (or theory) in his mind when studying a phenomenon. This is also sometimes phrased to mean that the researcher has no ‘a priori’ knowledge about what he/she studies. However, this is misleading, because even the most senior researchers can apply a grounded theory approach to study the world. Therefore, it is important to learn what grounded theory means and some of the different approaches that are used. Hence, in this session you will learn about three of the most prominent grounded theory scholars referenced in entrepreneurship and innovation management studies: Ann Langley, Dennis Gioia, and Kathy Eisenhardt.
However, this session will not stop with introducing one approach; it will also include some other common approaches, namely phenomenology, narrative research (hermeneutics), ethnography and case study. According to Creswell (2007), trying to allocate and categorize qualitative research into these five groups, provides you with a solid basis for becoming well-versed in qualitative research.
Readings
Gehman, J., Glaser, V. L., Eisenhardt, K. M., Gioia, D., Langley, A., & Corley, K. G. (2017). Finding theory–method fit: A comparison of three qualitative approaches to theory building. Journal of Management Inquiry, 27(3) 284-300.
Creswell, J. (2007). Chapter 4: Five Qualitative Approaches to Inquiry, in Qualitative Inquiry and Research Design: Choosing Among Five Approaches.p.53-84
If you are interested to learn more about research design, look at the additional reading:
Corbin, J., & Strauss, A. (1990). Grounded theory research: Procedures, canons and evaluative criteria. Qualitative Sociology, 13(1), 3-21.
Videos
1. Moerman – Introducing Hermeneutics (Narrative Research)
2. Moerman – Introducing Phenomenology
3. Moerman – Introducing Ethnography
4. Moerman – Introducing Grounded Theory
5. Moerman – Variations of Grounded Theory
Exercises
Exercise 1.1 – Comprehend
After getting acquainted with the materials (readings, videos), formulate five research questions that suit the following research designs (one research question per research design): an ethnography, a hermeneutic study, a phenomenology, a case study, and a grounded theory study.
Exercise 1.2 – Critique
Carefully read Gehman et al. (2017) and explain what are differences between Langley, Eisenhardt and Gioia in their methodological approaches to grounded theory research.
Self-Assessment Checklist
Please check. Did you gain an understanding of the following?
- you know what is ethnography, grounded theory, phenomenology, hermeneutics and case study
- you can broadly distinguish qualitative research into 5 categories (as suggested by Creswell)
- you are aware of different approaches to grounded theory research
If you can answer everything with a confident Yes! then you have achieved the learning objective of this session.
- you know what is ethnography, grounded theory, phenomenology, hermeneutics and case study
-
Observation is a systematic data collection approach. Researchers use all of their senses to examine people in natural settings or naturally occurring situations. Some qualitative research methodologies, in particular ethnography, rely on such participant and non-participant observationas an essential data source. Having its origins in anthropology and ethnology, nowadays social sciences, in general, increasingly apply observation techniques. This session is designed to provide a brief introduction to this well-established data collection method often applied to study cultural phenomena.
The benefits of systematically collecting participant and non-participant observation data is that it fosters an in-depth and rich understanding of a phenomenon, situation and/or setting, and the behaviour of the participants in that setting. As such, a deeper immersion or at least a prolonged involvement in a setting helps you to develop rapport and might further lead to a free and open discussion with people. In entrepreneurship and innovation management research, observations are part of the foundation for theory and proposition/hypothesis development. These are just the most obvious benefits of including observation data in a research project.
But what should you observe, when being in a field-setting? That, of course, depends on your research question. Nonetheless, observation data often involves the following:
- clearly expressed, self-conscious notations of how observing is done
- methodical and tactical improvisation in order to develop a full understanding of the setting of interest
- imparting attention in ways that is in some sense 'standardized'
While one may consider digital devices to collect observation data, the most typical form is writing fieldnotes. When browsing the web, you will find numerous examples on how to prepare them, so that they are most helpful when analysing your data later on. One possible way is to consider developing a template to guide your data collection from observations. In particular, templates can be useful when data is collected by inexperienced observers. However, at the same time, templates can deflect attention from unnamed categories, unimagined and unanticipated activities that can be very important to understanding a phenomenon and a setting. Therefore, it is important to be conscious and reflective upon one’s strategy of how to collect observation data.
Readings
Emerson, R., Fretz, R. I. and L. L. Shaw(1995). Writing Ethnographic Field Notes.University of Chicago Press. (Chapters 1 and 2 are uploaded on MyCourses)
Roulston, K. (2017). “Tips for observing and taking field notes in qualitative studies”. Available online:
https://qualpage.com/2017/04/07/tips-for-observing-and-taking-field-notes-in-qualitative-studies/If you are interested to learn more about observation and field-work, look at the additional readings:
Van Maanen, J. (2011). Tales of the Field: On Writing Ethnography. (2ndedition). University of Chicago Press.
Videos
1. Moerman – Introduction to Ethnography (same as in Topic 3)
2. Moerman – Views on Observation Data
3. Moerman – Collecting Data from Participant Observation
4. Moerman - Field Notes
Exercises
Exercise 2.1 – Comprehend
After getting acquainted with the materials (readings, videos), discuss the advantages and disadvantages theoretical knowledge, prolonged stay in a setting and fieldnote templates can have for collecting observation data, for instance, in ethnographic research.
Exercise 2.2 – Critique
Read the “methods” section in Stigliani and Ravasi (2012), and explain how observation data and field notes are collected and used in the analysis. How does observation data help to answer their research question?
Self-Assessment Checklist
Please check. Did you gain an understanding of the following?
- what are field notes
- the usefulness of field notes in answering some research questions
- the difference between writing jotting notes, detailed field notes and memos
If you can answer everything with a confident Yes! then you have achieved the learning objective of this session.
-
Interviews are the most common method of data collection used in qualitative research in entrepreneurship and innovation management. As such, interviews are used to explore views, experiences and beliefs of people in general, as well as their specific motivations to be entrepreneurs and innovators. In qualitative studies, interviews are often seen as one of the best ways to “enter into the other person's perspective” (Patton, 2002, p. 341), which helps to develop thick descriptions, and to analyse culturally sensitive patterns and themes in our social world.
Most likely, if you were to choose a qualitative approach in your Master’s thesis, the data collection method would include interviewing key actors. But how come interviews are so popular? And what is a good interview? How can I make sure that my interview is able to reveal the information that I am looking for? To combat these challenges, this session introduces general considerations to acknowledge before, and techniques to apply during an interview. It gives guidance on questions, such as: ‘should I use a structured, semi-structured or open interview design?’, ‘what is an interview guide?’, and ‘how can I be sure that the informant tells the truth?’.
Readings
Alvesson, M. (2003). Beyond neopositivists, romantics, and localists: A reflexive approach to interviews in organizational research. Academy of management review, 28(1), 13-33.
Edwards, R., & Holland, J. (2013). ‘Chapter 3: What forms can qualitative interviews take?’, in What is qualitative interviewing?. A&C Black, pp. 29-42.
Edwards, R., & Holland, J. (2013). ‘Chapter 6: What are the practicalities involved in conducting qualitative interviews?’, in What is qualitative interviewing?. A&C Black, pp. 65-75.
Roulston, K. (2010). Considering quality in qualitative interviewing. Qualitative Research, 10(2), 199-228.
If you are interested to learn more about interviewing, some additional readings are:
Baker, Sarah Elsieand Edwards, Rosalind(2012) How many qualitative interviews is enough.Discussion Paper. NCRM. Available online at: http://eprints.ncrm.ac.uk/2273/
Gubrium, J. F., Holstein, J. A., Marvast, A. B., & McKinney, K. D. (2012). The SAGE handbook of interview research: The complexity of the craft. Sage.
Kvale, S. 1996. InterViews: An introduction to qualitative research interviewing. Thousand Oaks, CA: Sage.
Roulston, K., DeMarrais, K., & Lewis, J. B. (2003). Learning to interview in the social sciences. Qualitative Inquiry, 9(4), 643-668.
Videos
1. Moerman –Interviewing Basics
2. Types of Interviews
Exercises
Exercise 3.1 – Comprehend
Consider a research phenomenon of interest (e.g. your thesis topic, or the one you choose in Assignment 1), and design three very brief interview guides: one for an unstructured interview, one for a semi-structured interview and one for a structured interview. In your opinion, which one is most suitable for studying your research phenomenon?
Exercise 3.2 – Critique
After reading Alvesson (2003) and Roulston (2010), discuss the authors’ different understandings of assuring ‘quality’ in interviews, in conducting social science research.
Please check. Did you gain an understanding of the following?
- the difference between unstructured, semi-structured and structured interviews
- the role of rapport between the interviewer and interviewee
- you are aware of alternative, non-positivistic views on interviews
If you can answer everything with a confident Yes! then you have achieved the learning objective of this session.
- the difference between unstructured, semi-structured and structured interviews
-
Miles and Huberman (2014, p.103-4) write: “Good research is not about good methods as much it is about good thinking. Good thinking means to look for and find patterns in the data. Good thinking means to construct substantial categories from an array of codes. Good thinking means to transcend the localness of a particular case to find its generalizability and transferability to other contexts. Research methods are excellent tools, but they are only as good as the craftsperson who uses them”. In this spirit, the final section on qualitative research aims to strengthen your craftmanship in looking at and working with data.
Essentially, data analysis in qualitative research can follow either inductive, abductive or deductive steps in systematically working with your data. Most common is an inductive analysis in which the researcher has (almost) no preconceived ideas about a phenomenon, but lets the ideas emerge from the data. Knowledge is accumulated from individual pieces of evidence. In both deductive and abductive analysis, the researcher has a clear idea of what it is he/she wants to explain (in quantitative research: the dependent variable). The deductive process starts with the generally accepted knowledge, and seeks to ‘deduce’ what that would mean in a particular case by testing the knowledge in an empirical context. The third type of reasoning, abduction, is a combination of inductive and deductive principles. When a researcher follows this type of analytical process, he/she looks at systematic combinations found in the data as well as the prior knowledge, and aims at identifying a plausible and logical explanation for a particular outcome.
A lot of researchers consider qualitative research to be inductive theory-building – and quantitative research to be deductive theory-testing. However, qualitative research can also follow a deductive logic to build theory.
After going through the readings and the videos, you should have a good understanding of the three different reasoning logics applied in qualitative research.
Readings
Dubois, A., & Gadde, L. E. (2002). Systematic combining: an abductive approach to case research. Journal of business research, 55(7), 553-560.
Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15-31.
Videos
1. Qualitative Data Analysis Basics
2. Deductive Coding and Retrieving of Information
3. Analytical Induction in Management Research
Exercise 4.1 – Comprehend
If you were to code interviews / observation data, explain how you would proceed. How do you decide when to stop coding the data? Explain your rationale.
Exercise 4.2 - Critique
Carefully read the methodology section of Farny et al. (2018), and explain when do the authors apply an inductive, an abductive or a deductive logic in analysing their data.
Self-Assessment Checklist
Please check. Did you gain an understanding of the following?
- what is inductive analysis
- how to inductively and deductively code qualitative data
- know the difference between deductive and abductive research
If you can answer everything with a confident Yes! then you have achieved the learning objective of this session.
- what is inductive analysis
-
- Do individuals who are concerned by issues of sustainability also exhibit stronger entrepreneurial intentions?
- What makes some entrepreneurs persist in their venture efforts while others quit?
In order to answer these types of questions, we must turn towards quantitative methods.
The key characteristic of quantitative research is that it deals with numbers. It is interested in finding out facts and generalising the research results to say something about a larger population. Quantitative research applies formal and systematic processes to generate and analyse numerical data, which is meant to offer information about reality.
After defining an important research topic and question, quantitative research first turns towards the theory; what do we know about these phenomena, what should we expect to find? The researcher then moves on to test this theory by formulating hypotheses, collecting appropriate data, analysing that with statistical methods, and interpreting the results that will give an answer to the research question.
During the next four sessions of this course, we will look into the key stages of quantitative research.
We will start by first getting acquainted with Quantitative Data and to develop an overview of what can be done with it, how it is collected and measured, and what kind of databases are available for entrepreneurship and innovation management researchers.
Second, we take a look at the move From Theory to Empirics by diving into the processes through which the interests of a researcher are converted into variables, models and hypotheses that can be analysed with statistic measures.
Third topic deals with the Quantitative Analysis and aims at offering an understanding of the ‘logic’ of statistical analysis, the tools that are used, and the most important concepts that form the basis for the abundance of statistical techniques that are available for researchers.
We conclude by asking ourselves the crucial question of “so what?”. The fourth session offers you skills needed for Interpreting and Publishing Research Findings. You will learn about research ethics and how to assess research findings (presented in academia, media, or other outlets), with a critical eye.
- Do individuals who are concerned by issues of sustainability also exhibit stronger entrepreneurial intentions?
-
What if you want to find out whether entrepreneurs are different in Finland and in Germany, or inform the UK government on the sentiments of the export industry on their anticipations on the impact of Brexit. How would you go about it? Where would you start?
Probably, you would first start asking questions on who or what it actually is that you want to learn about. In other words what is the population that your results should apply to.
But how will you get information on this population? If you cannot include all of them, you must select. Should you do the sampling so that everyone has an equal chance, a probability, of getting selected, take those that are most conveniently reached, or snowball your way forward, i.e. ask the participants to suggest further participants? How many is enough? In quantitative research, sample sizes are larger than in qualitative research; this is because most quantitative studies seek to make generalisations to a wider population.
Let’s say you have a good sample of respondents that represent the population, how would you collect data on them? Should you rely on their subjective accounts, or find something more objective? You could either collect primary data yourself, or turn to the already existing secondary data, such as archives or databases. What would be benefits and drawbacks of surveys and questionnaires, or of doing field research?
Remember, in order to do statistical analysis, you will need the data in numerical form, so observations and interviews must be structured, coded and quantified. How can you measure the elusive concepts, such as attitudes, capabilities, orientations, and emotions – or the innovativeness of a firm, or the entrepreneurial culture of a country? In order to do statistical analysis, we must transform the concepts into measurable variables.
How we measure and what kind of numbers we assign to these variables may also vary. Do we simply divide people, firms or other entities into categories? Or, is our measuring truly continuous, or is it discrete in a sense that only certain values are possible? The level of measurement will then also affect the way our data can be analysed. Different calculations can be done with binary variables (two groups) or nominal variables (more than two groups), ordinal variables (assigned order), interval variable (quantified differences) and ratio variables (percentages). As you might already realise, the level of measurement and the variables we receive also depends on the way we collect the data and the way we formulate questions.
Without even diving into the deep philosophical debates on whether there is a reality that we should discover and assign with true values (realism), or whether whatever we measure is what it then becomes central (nominalism), there are a number of issues to consider in regard to the quality of measurement. First of all, does the response in a questionnaire really tell about, for instance, ‘job satisfaction’? Is it a valid measure for that? And, what if we ask this again, would we get the same answer, or will the person tick a different box depending on the weather outside or due to her/his character – in other words, is the measure reliable? How could you test the reliability and validity of a selected measure?
The saving grace is that research does not have to represent the reality to the full extent – it is actually meant to summarize and simplify it in such a way that we can make wise decisions, and accumulate knowledge. Thus, some measurement error always exists, and this is ok. However, the data is the bedrock of our research and no fancy analysis can correct the damage (or even carried out) if the data is flawed, and contains too much random noise or systematic bias. How could you avoid bias, and gain the best possible basis for further analysis?
Readings
Kuckertz, A. & Wagner, M. 2010. The influence of sustainability orientation on entrepreneurial intentions — Investigating the role of business experience. Journal of Business Venturing, 25(5), 524-539.
Videos
Population and Sample
Sampling Methods
Collecting Data
1 - Objective and Subjective
2 - Primary and Secondary
3 - Observation, Survey, Experiment
4 - Survey
Measurement and Scales
Validity and Reliability
Summary of Quantitative Data Collection
Exercises
Exercise 5.1 – Comprehend
Take a look at two databases that are freely available and offer interesting data for entrepreneurship and innovation management researchers: Global Entrepreneurship Monitor (GEM) European Innovation Scoreboard (EIS).
- https://www.gemconsortium.org/data
- https://ec.europa.eu/info/research-and-innovation/statistics/performance-indicators/european-innovation-scoreboard_en#european-innovation-scoreboard-2021
Give a brief (max 1 page) account on your impressions on one or both of the databases. What kind of data is available? How is the data collected? What do you find interesting in the database?
If you are interested to learn more about databases, see for example:
Wennberg, K. 2005. Entrepreneurship research through databases: Measurement and design issues. New England Journal of Entrepreneurship, 8(2), 9-19.
Available online:
https://www.emeraldinsight.com/doi/pdfplus/10.1108/NEJE-08-02-2005-B002
Exercise 5.2 – Critique
Read the Data, Measures and Limitations sections of the article by Kuckertz and Wagner (2010), and answer the questions on it: What kind of data does the study use? How well do the authors describe the process of data collection? Do they assess the validity and reliability of the data, and its potential limitations? Would you have ideas for improving the data (e.g. do you think they should have used different data, or collect more data)?
Self-Assessment Checklist
Please check. Did you gain an understanding of the following?
- The meaning of population and sample
- Some data collection methods used in quantitative research
- Process of measuring and how to assess the quality of measurement
If you can answer everything with a confident Yes! then you have achieved the learning objective of this session.
- https://www.gemconsortium.org/data
-
As you have already learned, research is not the full picture of the reality; it is meant to condense the world into useful and insightful information. Importantly, it builds on the prior knowledge and accumulates to a body of literature. All these aspects mean that research needs theory.
For instance, let’s say we wish to understand what makes some entrepreneurs internationalise their business and even expand to global markets. Could be anything, right? Is there not enough demand in domestic environment, or a huge need abroad? The entrepreneurs’ personal background might influence, and the support programs by the state might give a push. As we cannot (and don’t even aspire to) explain the whole story, we turn to the most interesting insight offered by the prior theory: What is well-established knowledge that we could build on? What is something that could reasonably offer an explanation, but has not been fully explored? This offers us the theoretical model that proposes different concepts are connected to each other, and guides all our empirical research.
When moving on to the empirics, we must convert the theoretical propositions and concepts into hypotheses that describe relationships in the empirical world, and can thus be tested. For instance, we could argue that the theory suggests that start-ups heavily rely on the personal capabilities of their owners, and there is a connection between the owners’ characteristics and the internationalisation strategy of the company. To convert the theoretical concepts of “owners’ characteristics” and “internationalisation strategy” into a testable hypothesis, we could formulate that “the more international experience the owner has, the faster the start-up establishes international operations”.
But how do we actually test whether that is true? As discussed during the previous session, we must select variables that represent these empirical concepts, and collect and measure data. This means, that we could measure international experience by collecting survey data on, for instance, how many years the person has worked abroad, and/or how many times a year they travel abroad in business. Respectively, our variables for speed of internationalisation could be drawn from the financial statements of the companies by looking at when was the first time the company has e.g. revenue/personnel/affiliate in a country outside their original domicile. And, if we have too many similar variables, we could think of putting them into a common score by using factor analysis.
Our theory and hypotheses also formulate a conceptual model that describes the relationship between the variables: what is the dependent variable that we are trying to explain and which independent variables that could help to predict the outcome? In the example we have used, this is pretty straight-forward: we suggest company internationalisation depends on owner characteristics. The first would thus be the dependent variable and the second the independent variable; and they have a direct relationship.
However, whilst this might turn out to be a significant relationship, could there not be other things that effect the internationalisation of a company? What can you think of? Maybe, for instance, ICT sector would tend to internationalise faster than, say, construction industry. In order to see whether owner background alone has statistical significance, we must rule out these “rival explanations”. To do this we define control variables, collect data on them too and include them in our model.
Also, in many cases the accumulation of knowledge has led to the situation that we already know a lot about the phenomenon and wish to gain more nuanced insight. Maybe the relationship is not as straight-forward as we suggested above. What if there are other issues that influence the relationship, such as the composition of the management team as a whole. In this case, we could include that as a moderating variable in our model; it is something that makes the relationship between owner and internationalisation different in some ways, e.g. stronger/weaker, or negative/positive. Or could it be so that there is another element through which the independent variable (owner background) produces the outcome (internationalisation)? In this case, we need mediating variables in the model.
Does that sound complicated? Remember, it is simplification after all, and actually kind of fun!
Readings
Kibler, E., Salmivaara, V, Stenholm, P., & Terjesen, S. 2018. The Evaluative Legitimacy of Social Entrepreneurship in Capitalist Welfare Systems. Journal of World Business, 53(6), 944-957.
Sparrowe, R. T., & Mayer, K. J. (2011). Publishing in AMJ--Part 4: Grounding Hypotheses. Academy of Management Journal, 54(6), 1098-1102.
Videos
Theory and Hypotheses
Research Questions, Hypothesis and Variables
Formulating Hypotheses
Variables: IV, DV, control, Mediation, Moderation
Mediation, Moderation
Exploratory Factor Analysis
Exercises
Exercise 6.1 – Comprehend
After getting acquainted with the materials (readings, videos), explain briefly (max 1 page) why it is important to pay due attention to the empirical research design when either conducting research, or reading a research report or article.
Exercise 6.2 – Critique
Read the Theory and hypotheses, and Research design and method sections of the article by Kibler et al., (2018), and answer the questions on it.
What kind of theory/-ies do the authors use to build their model and ground their hypotheses? How do they operationalise their model into variables (DV, IV, control)? Would you have ideas for improving the process of building a bridge between the theory and the empirics (e.g. do you think they argue their hypotheses convincingly, do the variables fit well with the concepts, and could they have included other control variables)?
Self-Assessment Checklist
Please check. Did you gain an understanding of the following?
- The idea of moving from the conceptual level to the empirical world
- Formulation of testable hypotheses
- The role of different types of variables (DV, IV, control)
If you can answer everything with a confident Yes! then you have achieved the learning objective of this session.
- The idea of moving from the conceptual level to the empirical world
-
Where does the analysis begin? What should you do, when you have collected the data? Well, get acquainted with it. A common point of departure is to plot and visualise the data into a graph and take a first look at it just by assessing its main features. Where are most of the observations concentrated? Is there a shape that you can recognize? Are there any outliers?
After that, you can move to calculating descriptive statistics to gain an understanding of what kind of data you are dealing with. You will find a table presenting these numbers in pretty much all research articles that apply quantitative analysis. The table typically presents measures of central tendency: the mean, median, and mode; and measures of their variability: the range, and standard deviation or variance. Beyond individual variables, we may present descriptions of the relationship between two different variables, i.e. measures of dependence, such correlation. When two variables are positively correlated, ‘the larger the first, the larger the second’; and when they are negatively correlated, ‘the larger the first, the smaller the second’.
Calculating and presenting these will give the researcher, and the reader, a first impression of the phenomenon. For instance, the table could provide information on the average revenue of the companies and how much this varies within the companies included in the sample. It indicates what might be a high or low value (typically values that are more than one standard deviation away from the mean are considered high or low). Also, it tells to which extent the variables are statistically associated.
Whereas descriptive statistics focus on the data, making predictions on the variables and their relationships within the wider population, we must immerse ourselves to the world of inferential statistics. This includes an abundance of different methods – that suit different types of questions and data, and have their specific limitations. However, the idea is to draw conclusions from the data in order to infer how different entities compare across time or groups, in reality.
Inferential statistics offers ways of testing our hypothesis. This is done through a null hypotheses significance testing (NHST).Null hypothesis means that the predictors (independent variables) that we have included in our model do not have an effect on the outcome (dependent variable). Basically, our model and hypotheses are rubbish – or, at least, they are not supported by our data. In other words, NHST estimates how likely it is that we would obtain a result that confirms the alternative hypothesis (=the one we have formulated to state that there is a particular effect), if there ultimately is no effect.
It also deals with statistic models that are used to predict an outcome. They follow the same basic procedure: choosing a model that ‘fits’ with the data, and then using that model to make predictions of the wider population. In order to fit a model, you will need a function that describes how the predictors form the outcome, and you will need to define the error function that describes the difference between your data and the model’s prediction. (Don’t worry: there are analysis that test how well your model actually fits.) Then you use this model to predict what the population values would look like.
Linear regression, multiple regression, logistic regression… These are all ways of doing predictive analysis; they predict an outcome from predictor variables and error. Choose one that is best, and has the least error: Are you interested in bivariate correlation, i.e. whether there is a relationship between two variables? Choose liner regression model. It formulates a mathematical function that describes a linear relationship between the variables. Visually, this would mean drawing a straight line through the ‘cloud of data’ in a way that best describes it.
Or do you think there is a partial correlations and multiple independent variables? Then choose multiple regression. Or is it so that your dependent variable is a binary variable and you are explaining it with nominal/ordinal/interval/ratio independent variables? Then logistic regression is the method for you.
In practice, there are many statistical programs (SPSS, R, Stata, Matlab, SAS) that are used to analyse the data. And, as already mentioned, this is a whole world that you can spend a life-time studying.
Before getting excited about the enormous possibilities of statistical analysis, remember that studies relying on correlation do not necessarily tell about causality. For us to know that a particular independent variable really affects the dependent variable – ‘causes it’ – more criteria than their joint variation must be fulfilled. Think about the relationship between ice cream and drowning; they correlate but do not have a causal relationship. A causal relationship should have a temporal sequence (cause àeffect), the effect should be consistent and the strength of it should be of relevance. In fact, there is still debate on what suffices as an evidence of causality.
Readings
Cardon, M. S. & Kirk, C. P. 2013. Entrepreneurial passion as mediator of the self‐efficacy to persistence relationship. Entrepreneurship Theory and Practice, 39(5), 1027-1050.
Zhang, Y. & Shaw, J. D. 2012. Publishing in AMJ--Part 5: Crafting the Methods and Results. Academy of Management Journal, 55(1), 8-12.
Online resources:
Videos
Descriptive and Inferential Statistics
Descriptive Statistics
Visualising Data
Describing one variable (at a time): mean, median, and mode, range, and standard deviation
Correlation – describing two variables
Inferential Statistics
Testing Hypotheses: Null Hypothesis Significance Testing (NHST)
Simple Linear Regression
Multiple Regression
Logistic Regression
Correlation vs. CausationExercises
Exercise 7.1 – Comprehend
After getting acquainted with the materials (readings, videos), explain briefly (max 1 page) what is the purpose of data visualisation, descriptive and inferential statistics.
Exercise 7.2 – Critique
Read the Results and Limitations sections of the article by Cardon and Kirk (2013), and answer the questions on it: How do the authors analyse their data; which analytical techniques do they use? Do you think the authors sufficiently justify why they have chosen these methods?
Self-Assessment Checklist
Please check. Did you gain an understanding of the following?
- How to present descriptive statistics
- Basic principle of inferential statistics
- Difference between correlation and causality
If you can answer everything with a confident Yes! then you have achieved the learning objective of this session.
- How to present descriptive statistics
-
It would be great, if your research produces significant and novel findings! This can happen, when the researchers have an excellent idea, they are well-versed in theory, gather high-quality data, and analyse it in a robust and transparent manner. At this point, it is good to deepen your understanding on how you would get the most out of the hard work that you have put in your research, gain visibility, and make an impact. Also, we will adopt a critical eye towards assessing quantitative studies and research ethics.
How to report and visualise findings in a manner that they are easily understandable and communicate the key messages? And how to make sure that you fulfil the ethical requirements of science, and refrain from drawing conclusions that extend beyond the ideas that are supported by the data of the study?
Also, there is politics involved in determining what kind of research findings get published in academic journals, and which researchers are given time in the media. What do you need to know about academic publishing, and the so called ‘publishing game’, if you wish your research to be published in a premier scientific journal? How is research discussed in the media, and how do the best researchers gain a voice in the media and public debates?
As a researcher – and an educated person – you probably wish to learn from others, too. So, how can you take use of research? What, in general, are the uses of research in the academia, in policy-making, business associations and individual firms? What can you learn from the key measures in quantitative research papers? And how would you detect the problematic issues that should ring your alarm bells not to fully trust the findings of a particular study?
Some important numbers that you may always want to take a look at are p-value tells the probability that a result we obtained was due to random factors (e.g. 1% change) and R-squared that gives a good hint at the ‘goodness’ of the model; it quantifies the proportion of variance in dependent variable that is explained by the independent variables of the model.
Readings
Antonakis, J. 2017. On doing better science: From thrill of discovery to policy implications. The Leadership Quarterly, 28(1), 5-21.
Geletkanycz, M. & Tepper, B. J. 2012. Publishing in AMJ-Part 6: Discussing the Implications. Academy of Management Journal, 55(2), 256-260.
Kirsch, D. A., Goldfarb B. & Gera, A. 2009. Form or Substance? The Role of Business Plans in Venture Capital Decision Making. Strategic Management Journal, 30(5), 487-515.
Mehrotra, V., Morck, R., Shim, J. & Wiwattanakantang, Y. 2011. Must Love Kill the Family Firm? Some Exploratory Evidence. Entrepreneurship Theory and Practice, 35(6), 1121-1148.
Online resources:
Academy of Finland: Effects and impact of research.
Bowers, B. (2009). Investors Pay Business Plans Little Heed, Study Finds.The New York Times.
Rucker, M. (2017) How to Critically Read Quantitative Research Using These 10 Questions. (find in the materials section)
Zarah, L. (2018). 7 Reasons Why Research Is Important
Videos
Visualizing and Reporting Research Findings
P-value
- - P-value and Null Hypothesis
- - P-value and Research Ethics
R-squared
Research Ethics
Academic Publishing
Exercises
Exercise 8.1 – Comprehend
Look at the Introduction, Discussion, Conclusion sections of the research paper by Kirsch et al. (2009) as well as The New York Times newspaper article ‘Investors Pay Business Plans Little Heed, Study Finds’, that refers to the study by Kirsch et al. Describe briefly your impression on the differences in reporting research to the academic audience and in the media. Are there any problematic issues, or specific advantages in the way in which the newspaper article discusses the study?
Exercise 8.2 – Critique
Read the Introduction, Empirical Findings and Conclusions sections of the article by Mehrotra et al (2011), and explain briefly (max 1 page) the insight delivered by this study. What was the motivation for this study, why did the authors feel it was needed? What is the research question they wish to answer? What are the key findings of the research paper? What is your impression of this research; is it well-grounded and does it offer relevant implications?
Self-Assessment Checklist
Please check. Did you gain an understanding of the following?
- How to report research and disseminate the information
- What is ethical research
- How to critically interpret quantitative research findings
If you can answer everything with a confident Yes! then you have achieved the learning objective of this session.
- How to report research and disseminate the information