Realism Research Philosophy
Realism research philosophy argues that reality exists independently from human thoughts or perceptions, but our understanding of that reality may be imperfect and influenced by social, cultural, and historical factors
On this page:
- What is Realism Research Philosophy?
- Direct Realism vs Critical Realism
- Key Characteristics of Realism
- Realism in Business Research
- Advantages and Limitations
- Realism in the Age of AI and Digital Research
- When to Use Realism Research Philosophy
| Feature | Realism | Positivism | Interpretivism |
|---|---|---|---|
| View of reality | Reality exists independently, but may be interpreted imperfectly | Objective and measurable reality | Reality is socially constructed |
| Research focus | Underlying structures and mechanisms | Observable facts and measurement | Meanings and experiences |
| Typical methods | Mixed methods | Quantitative | Qualitative |
| Researcher role | Attempts objectivity while recognising limitations | Independent observer | Active interpreter |
| Main aim | Explain reality and causal mechanisms | Measure and predict | Understand subjective meanings |
Research Philosophies (Comparison Table)
What is Realism Research Philosophy?
Realism research philosophy relies on the idea of independence of reality from the human mind. This philosophy is based on the assumption of a scientific approach to the development of knowledge. Realism argues that the real world exists whether researchers observe it or not. However, human understanding of reality is not always completely accurate because people interpret the world through their senses, experiences, culture, and beliefs.
For example, employees within the same organisation may experience the same workplace differently even though the organisational structures themselves objectively exist.
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Realist researchers therefore attempt to study both:
- objective structures and events
- human interpretation of those events
This makes realism more flexible than strict positivism.
Direct Realism vs Critical Realism
Realism can be divided into two groups: direct and critical.
Direct realism, also known as naive realism, can be described as “what you see is what you get”[1]. In other words, direct realism portrays the world through personal human senses.
Critical realism, on the other hand, argues that humans do experience the sensations and images of the real world. According to critical realism, sensations and images of the real world can be deceptive and they usually do not portray the real world.[2]
An example of an optical illusion below can be used to illustrate the difference between direct and critical realism. Squares A and B appear to be different colours because of neighbouring contrasting squares, but actually they are the same colour. Direct realists would state that squares A and B have different colours, because this is what they see.
Illustration of direct realism and critical realism[3]
Critical realists, on the other hand, recognise that our senses and other factors may get in the way between us as researchers and researched reality. Therefore, critical realists may notice that squares A and B are actually the same colour.
Direct realists accept the world as relatively unchanging. They concentrate on only one level, be it individual, group or an organization. Critical realists, on the other hand, appreciate the importance of multi-level study. Specifically, as a researcher following critical realism research philosophy you have to appreciate the influence and interrelationship between the individual, the group and the organization.
There is a consensus among researchers that critical realist is more popular and appropriate than direct realist approach due to its ability to capture the fuller picture when studying a phenomenon. Accordingly, if you have chosen realism as your research philosophy you are advised to assume the role of critical realist, rather than direct realist.
Key Characteristics of Realism
The following are key characteristics of realism in research:
- Ontological realism. Belief in the existence of an objective reality independent of the observer.
- Epistemological realism. Belief that we can acquire knowledge about this objective reality through rigorous research methods.
- Focus on underlying mechanisms. Aiming to identify the causal structures and processes that generate the phenomena observed.
- Multiplism. Accepting that there can be multiple perspectives and interpretations of reality, but believing that there is ultimately a single objective reality.
- Critical reflexivity. Researchers should be aware of their own biases and limitations and how they may influence their research
Realism in Business Research
Realism is widely used in business and management research because business environments involve both objective structures and subjective human experiences.
Examples of realist business studies include:
- analysing organisational culture and employee behaviour
- examining causes of employee turnover
- studying digital transformation within organisations
- investigating AI adoption in workplaces
- exploring leadership and organisational performance
For example, a realist researcher studying remote working may examine:
- measurable productivity data
- employee experiences and perceptions
- organisational structures influencing behaviour
This combination allows researchers to develop more complete understanding of complex business phenomena.
Realism is particularly useful when researchers seek to explain not only what is happening, but also why it is happening beneath the surface level.
Advantages and Limitations
One of the main strengths of realism is its balanced approach between objectivity and subjectivity. Unlike positivism, which focuses mainly on measurable facts, or interpretivism, which focuses mainly on subjective meanings, realism attempts to examine both observable events and the deeper social, organisational, and contextual factors influencing those events.
Additional important advantage of realism is its flexibility. Realist researchers can combine quantitative and qualitative methods depending on the nature of the research problem. This makes realism particularly useful for studying complex organisational environments where multiple factors interact simultaneously.
Because realism focuses on identifying underlying causal mechanisms rather than only describing surface-level observations, it often generates deeper and more practical insights compared to purely descriptive research approaches.
Along with its strengths, realism also has limitations. One challenge is that identifying hidden causal mechanisms may be difficult because many organisational factors interact simultaneously. Realist studies may also become methodologically complex due to combining different types of data and analytical approaches.
In addition, realism does not always provide clear procedural guidelines compared to more structured positivist approaches. Some critics also argue that realism may become too broad philosophically because it attempts to combine elements of both positivism and interpretivism.
Realism in the Age of AI and Digital Research
Realism has become increasingly important in the age of AI, big data, and digital transformation because modern business environments involve both objective technological systems and subjective human interpretation of those systems.
For example, AI-powered management systems may objectively influence productivity, performance measurement, and decision-making processes. However, employees may interpret these technologies very differently depending on trust, organisational culture, technological experience, and perceptions of fairness. Similarly, algorithmic decision-making systems may appear objective, while still being influenced by hidden biases within training data or organisational assumptions.
Realist researchers therefore attempt to examine both:
- observable digital systems and outcomes
- deeper social, organisational, and psychological mechanisms behind them
This makes realism particularly valuable for studying:
- AI adoption in organisations
- digital transformation
- remote working environments
- algorithmic management
- technology-driven organisational change
As business environments become increasingly data-driven and technologically complex, realism provides a useful framework for combining objective analysis with contextual understanding.
When to Use Realism Research Philosophy
You can use relism in your dissertation if your research aims to understand both objective reality and contextual influences.
You should use realism when:
- the research problem involves complex organisational systems
- both measurable and social factors are important
- underlying causal mechanisms need to be explored
- mixed methods may be useful
- understanding context and structure simultaneously is important
Realism is especially common in:
- organisational behaviour research
- management studies
- digital transformation research
- leadership studies
- AI and technology adoption research
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John Dudovskiy
[1] Saunders, M., Lewis, P. & Thornhill, A. (2012) “Research Methods for Business Students” 6th edition, Pearson Education Limited
[2] Novikov, A.M. &Novikov, D.A. (2013) “Research Methodology: From Philosophy of Science to Research Design” CRC Press
[3] Photo Credit: Edward H. Adelson (1995)