Realism Research Philosophy
Realism is a research philosophy that argues that reality exists independently of human thoughts, beliefs, or perceptions. However, realism also recognises that our understanding of reality may be imperfect because it is influenced by social, cultural, historical, and personal factors. As a result, realist researchers seek to study both objective reality and the ways in which people experience and interpret that reality.
On this page:
- Realism Explained Simply
- What is Realism Research Philosophy?
- Realism vs Positivism vs Interpretivism
- Direct Realism vs Critical Realism
- Key Characteristics of Realism
- Realism in Business Research
- Common Mistakes
- Advantages and Limitations
- Realism in the Age of AI and Digital Research
- When to Use Realism Research Philosophy
- Exam Tip
| Feature | Realism | Positivism | Interpretivism |
|---|---|---|---|
| View of reality | Reality exists independently, but understanding may be imperfect | Objective reality exists and can be measured | Reality is socially constructed |
| Main focus | Underlying structures and causal mechanisms | Observable facts and measurement | Meanings and experiences |
| Typical methods | Mixed methods | Quantitative | Qualitative |
| Researcher role | Objective but aware of limitations | Independent observer | Active interpreter |
| Main aim | Explain reality and causal mechanisms | Measure and predict | Understand subjective meanings |
| View of knowledge | Reality can be known imperfectly | Reality can be measured objectively | Reality is interpreted differently by individuals |
Research Philosophies (Comparison Table)
Realism Explained Simply
Imagine that employee turnover suddenly increases within an organisation.
A positivist researcher may focus on measurable indicators such as turnover rates, absenteeism levels, salaries, and employee satisfaction scores.
An interpretivist researcher may focus on employees’ experiences and perceptions by conducting interviews and exploring how individuals interpret workplace conditions.
A realist researcher would attempt to examine both perspectives. They would recognise that employee turnover is a real phenomenon that exists independently of the researcher, but they would also acknowledge that people’s experiences, perceptions, and interpretations help explain why turnover occurs.
For example, a study of employee retention at Unilever may analyse workforce data while simultaneously exploring how employees perceive leadership, career opportunities, and organisational culture. Realist researchers seek to understand both the observable outcomes and the underlying mechanisms that generate those outcomes.
In simple terms, realism argues that reality exists independently of us, but understanding that reality requires looking beyond surface observations.
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.
Realism is a philosophical position that occupies a middle ground between positivism and interpretivism. Like positivism, realism accepts that reality exists independently of human perception. However, like interpretivism, realism recognises that people’s understanding of reality is influenced by context, experiences, beliefs, and social structures.
Realist researchers therefore reject the idea that reality can always be understood perfectly through direct observation alone. They argue that many important organisational, social, and economic mechanisms operate beneath the surface and cannot always be observed directly.
For example, declining employee engagement may be visible through survey results and productivity data. However, the underlying causes may involve organisational culture, leadership style, communication practices, or workplace trust. A realist researcher seeks to uncover these deeper mechanisms rather than simply describing observable outcomes.
This focus on underlying causal structures makes realism particularly useful when studying complex business and organisational phenomena.
Unlike strict positivism, realism does not assume that all important aspects of reality can be measured directly. At the same time, unlike interpretivism, realism maintains that an objective reality continues to exist regardless of individual perceptions.
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 organisations involve both objective structures and subjective human experiences.
For example, researchers studying organisational culture may analyse company policies, performance metrics, and organisational structures while also examining how employees interpret and experience those structures.
Similarly, studies of digital transformation often require researchers to investigate both technological systems and employee reactions to those systems.
A researcher examining the digital transformation strategy of Adobe may analyse measurable outcomes such as productivity improvements, customer growth, and operational efficiency while simultaneously exploring how employees perceive organisational change.
Realism is also frequently applied to research involving leadership, innovation, organisational performance, employee turnover, customer behaviour, sustainability initiatives, and AI adoption.
For example, studies examining AI implementation at DBS Bank may investigate both objective business outcomes and employee perceptions of algorithmic decision-making.
Because realism accommodates both quantitative and qualitative evidence, it is particularly compatible with mixed methods research.
Common Mistakes
One common mistake is assuming that realism is simply another version of positivism. While both philosophies recognise the existence of objective reality, realism places much greater emphasis on hidden structures, contextual influences, and imperfect knowledge.
Another frequent misunderstanding is confusing realism with interpretivism. Interpretivists focus primarily on subjective meanings and experiences, whereas realists accept the existence of an objective reality beyond those interpretations.
Students also sometimes claim to adopt realism without discussing underlying causal mechanisms. Merely collecting both quantitative and qualitative data does not automatically make a study realist. Researchers should explicitly explain how they intend to investigate the deeper structures and mechanisms influencing the phenomenon under study.
Finally, many students mention realism in the methodology chapter but fail to demonstrate realist thinking throughout their research design and data analysis.
Advantages and Limitations
One of the greatest strengths of realism is its balanced perspective. Rather than focusing exclusively on measurable facts or subjective interpretations, realism seeks to understand both observable events and the deeper mechanisms that generate them.
Another important advantage is methodological flexibility. Realist researchers can combine qualitative and quantitative methods when appropriate, making realism particularly useful for studying complex organisational environments.
A further benefit is the emphasis on explanation rather than simple description. By investigating underlying structures and causal processes, realism often generates insights that are highly valuable for both academics and practitioners.
Despite these strengths, realism can be challenging to apply in practice. Identifying hidden mechanisms and causal structures is often more difficult than measuring observable variables. Researchers may need to collect and analyse multiple forms of evidence before drawing conclusions.
The philosophy can also create methodological complexity because realist studies frequently involve mixed methods approaches and multi-level analysis.
Another limitation is that realism does not always provide the clear procedural guidance associated with more structured positivist research designs. As a result, novice researchers sometimes struggle to translate realist assumptions into practical methodological decisions.
Nevertheless, realism remains one of the most influential philosophies for studying complex business and organisational phenomena.
Realism in the Age of AI and Digital Research
Realism has become increasingly relevant in the age of artificial intelligence, big data, digital transformation, and algorithmic decision-making because modern organisations operate within environments where both objective systems and human interpretations matter.
For example, AI-powered performance management systems may objectively influence productivity, efficiency, and decision-making processes. However, employees may experience those systems very differently depending on organisational culture, trust levels, technological literacy, and perceptions of fairness.
Similarly, recommendation algorithms used by companies such as Spotify or Netflix may generate measurable behavioural outcomes, but understanding why users respond to recommendations often requires exploring deeper psychological and social mechanisms.
Realist researchers therefore attempt to examine both the observable outcomes of technology and the underlying organisational, social, and behavioural factors that influence those outcomes.
This makes realism particularly valuable for studying AI adoption, digital transformation, remote working, algorithmic management, organisational resilience, and technology-driven change.
As businesses become increasingly dependent on data and AI systems, realism provides a powerful framework for understanding not only what is happening, but also the deeper mechanisms that explain why it is happening.
When to Use Realism Research Philosophy
You should use realism if:
- the research problem involves complex organisational systems
- both objective and subjective factors are important
- underlying causal mechanisms need to be explored
- context and structure influence outcomes
- mixed methods research may be appropriate
- understanding deeper explanations is more important than simple description
Realism is particularly common in:
- organisational behaviour research
- management studies
- leadership research
- digital transformation research
- AI adoption studies
- innovation research
- organisational change research
Exam Tip
Students often struggle to distinguish realism from positivism and interpretivism.
A useful way to remember the difference is that positivists focus primarily on observable facts, interpretivists focus primarily on subjective meanings, whereas realists seek to understand both observable reality and the deeper mechanisms that produce it. If your dissertation aims to explain complex organisational phenomena using both objective evidence and contextual understanding, realism may be an appropriate choice.
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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)