Basics

Recap

I’d like to discuss the findings of your homework: the identification and study of a literature review on your topic.

What theories have been applied to study the phenomenon?
What methods have been applied to build, test, or expand the theories?
Why are the methods suitable for achieving the research aim?

Overview

The action plan

A research design is the blueprint for the collection, measurement, and analysis of data that

typically requires a combination of reasoning skills such as induction, deduction, and abduction;
and often involves different research strategies such as exploration, rationalization, and validation (Recker 2021)

Combination of strategies

Good research involves strategies for exploration, rationalization, and validation (Recker 2021).

Exploration, rationalization, and validation in research design based on Recker (2021, 43)

Figure 1: Exploration, rationalization, and validation in research design based on Recker (2021, 43)

Exploration involves investigating new areas, generating ideas, and identifying patterns or phenomena that haven’t been studied extensively.
Example: A researcher might conduct exploratory interviews with a small group of people to understand their experiences with a new technology. This helps in forming initial hypotheses and understanding the scope of the research problem1.
Rationalization involves developing logical explanations and theories based on the data collected. It often includes forming hypotheses and theoretical frameworks.
Example: After gathering data on how people use a new app, a researcher might rationalize that certain features are more popular because they align with users’ daily routines. This step helps in building a coherent narrative or theory around the observations1.
Validation is the process of confirming that the findings and theories are accurate and reliable. This often involves testing hypotheses and ensuring that the results can be replicated.
Example: To validate their findings, a researcher might conduct a follow-up study with a larger sample size or use different methods to see if the results hold true. This step is crucial for establishing the credibility of the research2.

Exploration, rationalization, and validation do not necessarily follow each other in a defined linear or temporal manner. Good research typically moves back and forth among them, as shown in Figure 1.

The emphasis of any one study can be on either end (1,2,3) or in combination (e.g., 4). Often, only so-called research programs (combinations of multiple studies) can achieve all (5).

We may find that explaining a particular behavior requires that we collect additional observations about other behaviours that we did not identify as relevant to our initial exploration. The interplay between rationalization and exploration can also provide a set of initial evidence against which we can test the outcomes of our rationalization process or evaluate a set of tentative propositions between constructs that capture a phenomenon. The rationalization should be valid in light of any observations that we collected. Once a rationalization, in which tentative general propositions are created through inductive reasoning from observations or through abduction, has been made, we can proceed to validation, where we develop testable hypotheses or propositions for a particular context or scenario from our more general theory. These hypotheses can be subjected to further empirical tests using new or existing cases. The results or evidence collected may suggest that we revise our rationalization (moving from validation back to rationalization), which could involve abduction. For example, our validation may find an observable mechanism that speaks against the logic of our propositions, requiring us to make an educated guess about why it happened or how these observations can otherwise be explained. Recker (2021, 43–44)

Research design decisions

The key benchmark against which your research design must be aligned is the problem statement as specified in the research question(s)—the research design must match logically the research question.

Research design decisions (Recker 2021, 45)
Spectrum	One end of continuum		Other end of continuum
Aim	Exploratory	vs	Explanatory
Method	Qualitative	vs	Quantitative
Boundary	Case	vs	Statistical properties ¹
Setting	Field	vs	Laboratory
Timing	Several cases, one point in time (cross-sectional)	vs	One case over time (longitudinal)
Outcome	Descriptive	vs	Causal

Other considerations

Data, risks, theory, feasibility & instrumentation

The alignment between research question(s) and design does not have to be unidirectional. In fact, most research questions are tweaked and altered over time to reflect an updated research design, although research questions should retain their prominence over the research design (Recker 2021).

Data: What type of data is required? What type of data might be available? Where can I collect observations or other forms of evidence? How will I sample the relevant data?
Risks: What are the potential dangers associated with execution of the research design? For example, what is the likelihood of a case organisation not being available for study anymore? What are strategies available to minimize or mitigate these risks?
Theory: How much literature concerning the phenomena of interest is available? What are problems with the knowledge base? What findings have been produced to date that might have an impact on my work and influence choices in my research design?
Feasibility: Can the research design be executed within the constraints associated with a study (e.g., the PhD program) such as time limitations, resource limitations, funding, experience, geographic boundaries, and others? Is guidance available to me to support me in the study?
Instrumentation: How will my constructs of interest manifest in reality? How can they be measured? Will my construct operationalisationbe appropriate given the choice of research methodology and set of data available?

Source: Recker (2021)

Requirement	Qualitative	Quantitative	Design science	Computational
Controllability	Low	Medium to high	High	Low to medium
Deducibility	Low	Medium to high	Low	High
Repeatability	Low	Medium to high	High	High
Generalisability	Low	Medium to high	Low	Low to medium
Explorability	High	Low to medium	Low to medium	High
Complexity	High	Low to medium	Medium to high	Medium to high

Research design

Basics

Recap

Overview

Deduction

Induction

Abduction

Combination of strategies

Research design decisions

Other considerations

Research methodology

Overview

Exercise

Differences

Examples

IS research

Focus

Methods overview

Homework

Q&A

References

Footnotes