Philosophy of Research & Types of Research Design

Learning Outcomes

  • Understand different philosophical assumptions underlying management research.
  • Appreciate how philosophical assumptions influence criteria for judging research quality.
  • Develop the ability to recognize and identify latent philosophical assumptions.
  • Acknowledge how different research philosophies lead to different methods.
  • Consider different approaches in scientific reasoning (deduction vs. induction).

What is Philosophy of Science?

  • Definition: Philosophy of science studies the tools scientists use to explain, predict, and understand the natural world.
  • In simpler terms, it asks:
    • How science works.
    • What makes scientific knowledge valid.
    • What methods are used to obtain it.
  • The Central Goal of Science:
    • To promote understanding of the natural world.
    • This understanding comes from:
      • Explaining natural phenomena.
      • Helping scientists make predictions about future events or situations.

Why Do We Need It?

  • Knowing the philosophical foundations of research:
    • Helps researchers design better studies.
    • Clarifies which methods and assumptions are appropriate.
    • Ensures the research is meaningful and valid.
  • In short: Philosophy of science = thinking about science itself → its logic, methods, and validity.

What is Science? (or What is Not Science)

  • Definition: Science = a tool created by humans to help us understand the world.
    • It does more than just describe; it explains how and why things happen.

The Role of the Scientist

  • Scientists don’t just list every natural event they observe.
  • Their job is to:
    • Generalize and find explanations that apply broadly to nature.
    • Find what principles or rules cause these phenomena.
  • Simply listing observations = historian’s job.
  • Explaining causes and mechanisms = scientist’s job.

Demarcation Criterion

  • Key problem: How to separate science from non-science?
  • Solution: Create a criterion (standard) that:
    • All true sciences meet ✅
    • Non-scientific approaches fail ❌
  • This separation is called → Demarcation Criterion
  • In short: Science explains why things happen, not just what happened.
    • To call something science, there needs to be clear rules (demarcation) to separate it from non-science.

Demarcation Criterion

  • The key difference between science and non-science = falsifiability.
  • Falsifiable = it must be possible to imagine an observation or experiment that could show the theory is wrong.
  • A scientific statement:
    • Must be testable.
    • Does not have to be actually proven wrong, but it must be possible to prove it wrong.
  • Example: "All swans are white."
    • Falsifiable: finding one black swan would falsify the theory.

Two Types of Non-Falsifiable Statements

  • Tautologies
    • Always true by definition.
    • Example: "Triangles have three sides."
    • Cannot be tested or falsified → not scientific.
  • Statements about unobservable phenomena
    • Cannot be tested through observation or experiment.
    • Examples: "God exists." "God created the world."
    • Not falsifiable → not scientific.
  • Important: This does not mean they are false or nonsense. They just lie outside the scope of science.

Mini Exam Summary

  • What is Philosophy of Science?
    • A discipline that studies:
      • The tools and methods used to explain, predict, and understand the natural world.
    • Central goal of science:
      • Promote understanding through explanations of natural phenomena.
      • Allow predictions for future phenomena.
    • Why important:
      • Provides the philosophical foundation for research design.

What is Science (vs. Non-Science)?

  • Science = human tool to understand the world.
    • Explains both how and why things happen.
  • Science ≠ simply listing facts → the goal is to generalize about nature.
  • Scientists create theories explaining the causes behind phenomena.
  • Simply cataloging events = historian’s job, not scientist’s.

Demarcation Criterion

  • Key concept: Falsifiability (Karl Popper).
  • A statement is scientific if:
    • It can be tested.
    • It is possible to imagine an observation that would prove it wrong.
  • Falsifiable ≠ already proven false — only testable.
  • Purpose: separate science from non-science.

Two Types of Non-Falsifiable Statements

  • Tautologies
    • True by definition.
    • Example: "Triangles have three sides."
    • Not testable → not scientific.
  • Statements about Unobservable Phenomena
    • Cannot be observed or tested.
    • Examples: "God exists." "God created the world."
    • Not falsifiable → not scientific.
  • Note: Being non-scientific ≠ being false.

Key formula to remember:

  • Science = Falsifiable, Testable, Explains HOW and WHY

The Hallmarks of Scientific Research

  • These are the key characteristics of what makes research good or scientific.
  • Term
    • Purposiveness
      • Meaning: Clear goal or objective for the research.
    • Rigor
      • Meaning: Logical, careful, and thorough process; conclusions based on solid reasoning.
    • Testability
      • Meaning: Hypotheses must be testable through empirical methods.
    • Replicability
      • Meaning: Other researchers should be able to repeat the study and get similar results.
    • Precision and Confidence
      • Meaning: Results should be accurate and findings reported with certainty levels.
    • Objectivity
      • Meaning: Results should not be influenced by personal biases.
    • Generalizability
      • Meaning: Findings should apply to other contexts or populations beyond the sample studied.
    • Parsimony
      • Meaning: Explanations should be simple and not unnecessarily complicated. (Use as few assumptions as possible.)
  • Short trick for exam memory: P.R.T.R.P.O.G.P. (Purposiveness, Rigor, Testability, Replicability, Precision, Objectivity, Generalizability, Parsimony)
    • You can also remember this as: "Proper Research Tries Really Precisely On Giving Parsimony."

Paradigms in Social Research

  • Level
    • Ontology
      • What it asks: What is reality?
      • Short explanation: The nature of reality and social beings. Are facts objective or socially constructed?
    • Epistemology
      • What it asks: How can we know reality?
      • How we gain knowledge about reality. What counts as valid knowledge?
    • Methodology
      • What it asks: How to answer research questions?
      • The strategies or plans for research (qualitative, quantitative, mixed).
    • Methods and Techniques
      • What it asks: Which tools to collect data?
      • The specific instruments (surveys, interviews, experiments, observations).
  • Ontology = the nature of reality and existence.
    • In research, we ask:
      • "What kind of reality am I studying? Is there one reality or many?"

Ontology Type

  • What is Truth?
  • What about Facts?
    • Realism
      • What is Truth?: There is one single truth.
      • What about Facts?: Facts exist and can be discovered.
    • Internal Realism
      • What is Truth?: Truth exists, but it is not fully accessible (hidden or complicated).
      • What about Facts?: Facts exist but cannot always be directly observed.
    • Relativism
      • What is Truth?: There are many "truths" (depends on people).
      • What about Facts?: Facts depend on observer’s viewpoint.
    • Nominalism
      • What is Truth?: There is no universal truth.
      • What about Facts?: Facts are fully constructed by humans.
  • Situations
    • Realism
      • "Is climate change real?" Yes — one scientific truth exists.
    • Relativism
      • "Is climate change real?" Depends — different groups may interpret data differently.

Epistemology

  • Epistemology = how we know what we know.
  • Two big approaches:
    • Positivism
      • Reality exists objectively (independent of people).
      • We observe it using scientific, objective measurements.
      • Our knowledge comes from observation, measurement, experiments.
    • Social Constructionism
      • Reality is constructed by people.
      • Knowledge comes from people’s interactions, discussions, and shared meanings.
      • It’s about understanding how people create reality.

Epistemology: Positivism vs Social Constructionism (Comparison)

  • Question
    • "What is marriage?"
      • Positivist answer: Legal and biological facts.
      • Social Constructionist answer: Social institution created and interpreted by cultures.
  • Aspect
    • The observer
      • Positivism: Must stay independent.
      • Social Constructionism: Is part of what is studied.
    • Human interests
      • Positivism: Should be irrelevant.
      • Social Constructionism: Are central to understanding reality.
    • Explanations
      • Positivism: Must find causality (X causes Y).
      • Social Constructionism: Focus on understanding situations as they are.
    • Research progresses
      • Positivism: Form hypothesis → test it.
      • Social Constructionism: Collect data → develop ideas.
    • Concepts
      • Positivism: Must be clearly defined to measure.
      • Social Constructionism: Should reflect different stakeholder views.
    • Units of analysis
      • Positivism: Simplify into measurable parts.
      • Social Constructionism: Study full complexity of situations.
    • Generalization
      • Positivism: Based on statistics.
      • Social Constructionism: Based on theory-building.
    • Sampling
      • Positivism: Large random samples.
      • Social Constructionism: Small, carefully chosen cases.
  • Objective
    • Positivism: Objective
    • Social Constructionism: Subjective
  • Scope:
    • Positivism: Quantitative
    • Social Constructionism: Qualitative
  • Relations of researcher with subject:
    • Positivism: Independent
    • Social Constructionism: Interactive
  • Explanation:
    • Positivism: Cause-Effect
    • Social Constructionism: Understanding meaning

Positivist Research Design

  • Positivist methods incorporate the assumption that there are true answers, and the job of the researcher is to start with a hypothesis and seek data to confirm/disconfirm it (to develop theory)
  • Rigor and replicability, reliability and generalizability
  • Methodologies: experimental/ quasi- experimental methods and surveys ( Quantitative approaches)

The Positivistic Approach (Process Flow)

  • Feature
    • Assumption
      • Explanation: There is one true answer that can be discovered.
    • Role of researcher
      • Explanation: Start with theory → collect data to confirm/disconfirm it.
    • Goal
      • Explanation: Develop theory based on testing.
    • Focus on:
      • Explanation: Rigor, replicability, reliability, generalizability.
    • Methodologies:
      • Explanation: Experiments, quasi-experiments, surveys (= quantitative methods).
  • Main Idea: Reality exists objectively → one truth.
    • Researcher tries to find this truth.
    • Research starts with a hypothesis (already existing theory).
    • The researcher collects data to test whether this hypothesis is true or false.
  • Key characteristics:
    • This is called: Deductive Approach
    • Deductive = start with theory → test with data.

Constructionist Research Design

  • Feature Explanation
    • Assumption: Reality is socially constructed.
    • Role of researcher: Explore and understand different interpretations.
    • Goal: Illuminate how reality is created by people.
    • Methodologies: Action research, archival research, ethnography, narrative methods (= qualitative methods).
  • Main Idea: Reality is not fixed → different people may have different interpretations.
    • The researcher's role is to explore multiple "truths".
    • Focus on understanding people’s perspectives and social meanings.
    • No strict hypothesis at the start → researcher is open to what data reveals.
  • Key characteristics:
    • This is called: Inductive Approach
    • Inductive = start with data → build theory from findings.

Deductive vs Inductive Reasoning

  • Deductive Reasoning
    • You start with theory.
    • Based on the theory, you create hypotheses (predictions).
    • Theory → Hypothesis → Data → Test
    • Theory: "Stress causes lower productivity."
      • Hypothesis: "Employees under stress will have lower performance scores."
    • Collect data: Analyze if results support the hypothesis.
      • This is the typical process in positivist research.
    • Theory comes first.
  • Inductive Reasoning
    • You start with observations/data.
    • You analyze data and look for patterns.
    • From the patterns, you generate a theory.
    • Data → Pattern → Theory
    • You interview 50 managers about leadership styles.
      • You find that most talk about empathy.
      • From this, you develop a theory that "Empathetic leadership improves team satisfaction."
    • You collect data to check if these predictions are confirmed.
      • This is used in constructivist research.
    • Observations come first.

Why is Pure Induction Problematic?

  • Because if you start only with data, you may never falsify anything.
    • You only build theories without challenging them.
  • Induction helps build theories, but later we still need deduction to test them.

The Abductive Approach

  • What is abduction?
    • Abduction happens when you observe something surprising or unexpected.
    • Instead of starting from theory or data, you try to make the best possible explanation for that unexpected observation.
    • Abduction helps to generate new theories when existing theories cannot explain what you see.
    • You observe: "A company suddenly performs extremely well during a financial crisis."
    • This is unexpected (surprising observation). You don’t have a ready theory.
    • You generate an explanation: "Maybe they invested in crisis-resistant sectors."

Comparison Table

  • Reasoning Type
    • Deduction
      • Process: Theory → Hypotheses → Test with data
      • Typical Use: Testing known theories (Positivist)
    • Induction
      • Process: Data → Patterns → Build theory
      • Typical Use: Discovering patterns (Constructivist)
    • Abduction
      • Process: Surprising data → Best possible explanation
      • Typical Use: Exploring new unexpected phenomena
  • Apply theory
    • Deduction: Test hypotheses
    • Induction: Find patterns
    • Abduction: Explain surprises
  • Deduction: Confirm existing knowledge
  • Induction: Generate new theory
  • Abduction: Propose possible explanation

Pragmatic Approach to Research

  • Key idea: Pragmatism is practical: It does not care too much about choosing between positivism or constructivism.
  • Feature
    • No strict position
      • Explanation: Does not follow one strict philosophy.
    • Objective + subjective
      • Explanation: Combines both objective (positivist) and subjective (constructivist) knowledge.
    • Theory Practice
      • Explanation: The purpose of theory is to help solve real-world problems.
    • Used by managers
      • Explanation: Helps managers make decisions based on useful knowledge, not on strict philosophical rules.
  • Main principles:
  • Mixed Methods
  • What is it?
    • Mixed methods = combining both quantitative (numbers, statistics) and qualitative (interviews, meanings) approaches in one study.
    • It combines positivist and constructionist elements.
  • Why use mixed methods?
    • Real-life problems are often complex → using only one method might not be enough.

Design Considerations in Mixed Methods

  • Design Feature
    • Sequencing
      • Alternatives: Which comes first?
    • Dominance
      • Alternatives: Which method is stronger?
  • Sequencing
    • Qualitative first
    • Quantitative first
    • Both at the same time
  • Dominance
    • Predominantly qualitative
    • Predominantly quantitative
    • Balanced
  • Arguments for and against mixed methods
    • Mixed methods research has the potential to throw new perspectives on research questions, to increase the credibility of results, to demonstrate generalizability, and to provide deeper insights.
    • But there are also plenty of reasons for being cautious about their wholesale adoption.

Types of Research Questions: Quantitative vs. Qualitative

  • Quantitative Research Questions ("To what extent?")
    • These questions ask:
      • How much does one variable influence another?
      • To what extent, how much, how strong, relationship between variables
    • They assume:
      • Variables can be measured.
      • The relationship is known or assumed from theory (deductive logic).
      • You usually have:
        • Independent variable (cause).
          • Dependent variable (effect).
      • Connected to positivism + deductive approach.
      • Examples:
        • To what extent does community-based shared value influence the success of social enterprises?
        • To what extent reciprocal altruism influences the success of social enterprises?
  • Qualitative Research Questions ("How?" and "Why?")
    • These questions ask:
      • Focuses on: Meanings, processes, perceptions.
      • Exploring new or complex phenomena.
      • No predefined variables → theory is built from data (inductive logic).
      • Connected to social constructionism + inductive approach.
      • Examples:
        • How do consumers perceive ‘Made in China’ products?
        • How does innovation take place in the software sector in emerging markets?
        • How do online consumers experience privacy issues during online transactions?
        • How do SMEs in food and drinks sector experience the use of digital warehouse for exporting in China?

Super Summary Table

  • Question Type
    • Quantitative
      • Typical Words: To what extent / How much
      • Linked Approach: Deductive / Positivist
    • Qualitative
      • Typical Words: How / Why
      • Linked Approach: Inductive / Constructivist