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Exam Summary for Qualitative Innovation Analytics (QIA)

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This summary can be used as preparation for the final exam of the course: "Qualitative Innovation Analytics" in the Sustainable Business and Innovation Master's at Utrecht University. It contains summaries of the lectures used in the course during and core concepts. This exam summary contains only...

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  • 14 maart 2020
  • 30
  • 2018/2019
  • Samenvatting
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Qualitative Innovation Analytics

Content:

PART I: EPISTEMOLOGICAL BASIS AND RESEARCH DESIGN

• Introduction and Epistemological Basis
• Research Design – Criteria of a ‘Good’ Research Question (Relevance) and Literature Review
(Disciplinary Embedding)
• Research Design – Formulating Propositions or Hypotheses (Precision) & Choosing the Right
Method (Methodical Functionality)

PART II: INDUCTIVE QUALITATIVE METHODS

• Qualitative data collection - Interviews and how to complement them with other sources of data
• Qualitative data analysis and introduction to grounded theory
• Theorizing from qualitative data and ensuring the quality and rigor in inductive research

PART III: DEDUCTIVE QUALITATIVE METHODS

• Case Selection and Data Analysis for Mono-Causal Relationships: Comparative Case Studies
• Case Selection and Data Analysis for Multi-Causal Relationships: Introduction to QCA
• Case Selection and Data Analysis for Multi-Causal Relationships: Further Features of QCA + Exam
Preparation

Literature summaries:

• Corbin, Juliet and Anselm Strauss (1990), ‘Grounded Theory Research: Procedures, Canons, and
Evaluative Criteria’. Qualitative Sociology 13 (1), 3-21
• Eisenhardt, K. M. (1989), ‘Building theories from case study research’. Academy of Management
Review, 14(4), 532-550

,PART I: EPISTEMOLOGICAL BASIS AND RESEARCH DESIGN
Session 1: Introduction and Epistemological Basis
Lecture

Figure 1: Research Design Cycle




What is Theory?
Theory: attempts to provide an explanation to phenomena. Some sense of causality
• Grand theories: wide applicable but difficult to operationalize Figure 2: Trade-offs of Theories
(high abstraction)
• Middle range theories: More operational but less widely
applicable (lower abstraction)

Theories help to understand:

1. Whether research question is relevant (see session 2)
2. Which literature needs to be reviewed (see session 2)
3. How to operationalize concepts (see session 3)
4. What kind of data should be collected (see session 6)
5. How to interpret the results (see session 6)

Bottom Line: The whole research design process.

Most importantly: theory helps us to understand real-world phenomena in a parsimonious way.

Deductive research: theory to data Inductive research: data to theory

, Epistemology and Ontology
Reproducibility
From reliability stems the principle of reproducibility: a different researcher should arrive at the same
results using the same methods. However, it is not always possible to completely reproduce research that
for example uses interviews as a method (paradox of reproducibility).

• Is reproducibility an important principle in our view for qualitative research?

Reproducibility can be dealt with in many ways (transcribing, data triangulation, increasing
methodological transparency). However, for a researcher using qualitative methods, the exercise
[question] emphasizes the importance of being comfortable with discussing his/her ontological and
epistemological assumptions to be able to justify the rigor of the research.

Ontology
• Deals with questions starting with “what” - what exists? And how can it be grouped, related within
a hierarchy, and subdivided according to similarities and differences?

Considerations in Research Design:

• What kind of objects, subjects, and social entities exist in the social world?
• Do social entities exist independently of our perceptions of them?
• Is social reality external to social actors or constructed by them?
• Two opposing ends of perception:
o Objectivism versus constructionism

Objectivism: an ontological position that asserts that social phenomena and their meanings have an
existence that is independent of social actors. It is often an underlying assumption of deductive designs.

• Culture influences social actors, rather than that social actors influence culture

Constructivism: an ontological position that asserts that social phenomena and their meanings are
continually being accomplished by social actors. It implies that social phenomena and categories are not
only produced through social interaction but that they are in a constant state of revision. It is often an
underlying assumption of inductive designs.

• Social actors shape culture within which they live.

Epistemology
• Is about the theory of knowledge. Deals with “how” questions - how can we know things?

Considerations for Research Designs:

• What is (or should be) considered acceptable knowledge?
• Can the social world be studied ‘scientifically’?
• Is it appropriate to apply the methods of the natural sciences to social science research?
• Two opposing ends in perception:
o Positivism versus interpretivism

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