Saunders Research Onion Model: Understanding and Application (UPDATED)

Introduction

Over the years, the research onion has become a widely recognised framework in academic and professional research. First developed by Saunders et al. (2007), the Saunders research onion remains an invaluable tool for guiding the development of a research methodology. By 2025, it continues to evolve with changing research paradigms while retaining its relevance across disciplines. Known formally as the research onion model, it provides a multi-layered framework that outlines the various stages researchers must progress through when designing and implementing a research strategy.

The layered structure of the research onion effectively demonstrates the logical flow of methodological decision-making. Each layer builds upon the previous one, starting with the research philosophy and culminating in techniques for data collection and analysis. It facilitates a comprehensive approach to research design, placing equal emphasis on philosophical positioning, methodological rigour, and data strategy.

This article revisits and updates the Saunders onion model for 2025, highlighting recent trends and academic refinements while maintaining accessibility. It explains each layer, outlines key concepts, and offers insight into how the research onion Saunders framework continues to align with contemporary academic research practices. Where possible, practical examples are included to illustrate its application across contexts.

1. Understanding the Research Onion Model

The research onion model, developed by Saunders, Lewis and Thornhill (2007), structures the research process into six major layers:

1. Research philosophy.
2. Research approach.
3. Methodological choice.
4. Research strategy.
5. Time horizon.
6. Techniques and procedures (data collection and analysis).

When visualised, these layers resemble the concentric shells of an onion. Each inner layer builds upon the outer one, symbolising a narrowing focus as the researcher transitions from broad conceptual understandings to specific methodological decisions. The research onion ensures coherence between all research elements, thereby enhancing the validity and credibility of findings.

As of 2025, its widespread use across disciplines – from business management to education, health sciences, and social policy – demonstrates the model’s continued relevance. Moreover, it supports both qualitative and quantitative research designs.

2. Research Philosophy: Shaping the Foundations

The outermost layer of the research onion Saunders framework is the research philosophy. This defines how the researcher perceives the world and what constitutes valid knowledge in the context of inquiry.

2.1 Defining Research Philosophy

In essence, a research philosophy outlines assumptions about the nature of reality (ontology) and how knowledge of that reality can be obtained (epistemology) (Bryman, 2021). These assumptions influence the selection of research approaches, strategies, and tools. Understanding one’s philosophical stance is thus crucial for methodological transparency.

By 2025, the following philosophies remain central to the research onion:

• Positivism: Reality is objective and measurable. Truth can be discovered via logical reasoning and empirical observation.
• Interpretivism or Constructivism: Reality is subjective. Knowledge is constructed through human interaction and experience.
• Pragmatism: Truth is viewed in terms of practical outcomes. A mixed approach is justified if it supports the research aim.
• Critical Realism: Reality exists independently but can only be imperfectly understood due to social, historical, and cultural influences.

2.2 Choosing a Philosophy

There is no one-size-fits-all philosophy. The philosophical stance should align with the nature of the research question. For example, a study investigating consumer behaviour may adopt an interpretivist stance to understand subjective experiences, whereas a study measuring supply chain efficiencies may lean towards positivism for quantifiable data.

Importantly, the choice of philosophy frames the researcher’s perspective and determines what is considered valid knowledge. As modern research becomes more interdisciplinary and impact-driven, pragmatism has grown in popularity due to its flexibility.

3. Research Approaches: Deductive, Inductive, and Beyond

Moving inward, the second layer of the research onion concerns the approach to theory development. This layer determines how existing knowledge and theoretical frameworks will influence the research design.

3.1 Deductive Approach

The deductive approach starts with a theory or hypothesis and tests it through empirical observation. It is associated with the positivist philosophy, and primarily uses quantitative methods. Researchers aim to confirm or refute hypotheses using statistical tools.

This approach is common in fields where relationships between variables can be clearly defined. For instance, in a study measuring the effect of leadership styles on employee retention, researchers may deduce a hypothesis based on leadership theories and collect organisational data to test it.

3.2 Inductive Approach

In contrast, the inductive approach begins with data collection, from which patterns and themes are used to generate theories. It is closely associated with interpretivism and is prevalent in qualitative research.

For example, a researcher conducting interviews with refugee communities to understand their experiences might adopt an inductive approach. Rather than testing a preconceived theory, the aim would be to develop theoretical insights grounded in participants’ lived realities.

3.3 Abductive Approach

Emerging prominently in recent years—especially post-2020—is the abductive approach, which combines features of both deduction and induction. It involves iterating between theory and data, making it especially useful in modern-day complex problem-solving scenarios.

Abductive reasoning often arises in design thinking, user experience research, or interdisciplinary innovation studies – all of which are increasingly influential in 2025’s research landscape. Researchers may encounter an anomaly in the data, then return to theory to seek plausible explanations and further refine their understanding.

4. Methodological Choice: Mono, Mixed, and Multi

The third layer of the research onion relates to the methodological choice. This refers to the decision about whether to use a quantitative, qualitative, or combined approach in the study.

4.1 Mono-Method

A mono-method involves using a single method, either qualitative or quantitative. Researchers working within a single paradigm and aiming for methodological consistency often prefer this choice, particularly when their data needs are straightforward.

4.2 Mixed-Methods

Often recommended in complex studies, the mixed-method approach integrates both qualitative and quantitative techniques in a single study. Mixed-methods foster triangulation, thereby improving validity. It is especially common in public health and education research, where understanding both statistics and stakeholder perspectives can lead to more comprehensive findings.

The rise of AI and big data analytics in 2025 has further enhanced the feasibility and desirability of mixed-methods research, particularly where large datasets require contextual understanding provided by qualitative inquiry.

4.3 Multi-Method

The multi-method approach also combines different methodologies, but unlike the mixed-method, it separates them into distinct strands. Each strand produces independent datasets that are analysed using their respective paradigms. This is ideal for large-scale studies involving multiple objectives or departments — for example, a national education policy evaluation combining student performance metrics with teacher interviews and curriculum analysis.

In summary, the research onion Saunders model encourages researchers to justify their choice of method in relation to their philosophical stance and research goals.

5. Research Strategy: Implementing the Plan

Once the approach and method have been defined, the researcher must decide how to execute the study. The fourth layer of the research onion is the research strategy, which describes how data will be collected and analysed.

5.1 Experimental Research

Closely aligned with the positivist and deductive traditions, experimental research involves manipulating variables to observe effects. This strategy is common in scientific environments or technology-focused studies, where control over variables and replication are critical. For example, testing the performance of a new AI algorithm on predictive forecasting could follow an experimental design.

5.2 Case Study

Case study research focuses on in-depth analysis of a particular subject, organisation, place, or event. It is ideal for capturing detail and understanding complex phenomena within real-world contexts. A researcher exploring the implementation of a sustainability initiative in one corporation might use this method — combining interviews, observations, and document reviews.

Case studies lend themselves well to qualitative, quantitative, or mixed-methods approaches, offering flexibility.

5.3 Action Research

Action research involves a cyclical process where the researcher collaborates with stakeholders to initiate practical change. It is particularly suitable for fields like education, healthcare, and community development. A team of hospital staff and researchers working to reduce patient wait times would produce iterative solutions, assess outcomes, and revise practice accordingly.

This strategy closely aligns with critical realism and pragmatism, as it seeks not only to understand the world but to actively improve it.

5.4 Grounded Theory

Grounded theory is a qualitative strategy particularly aligned with inductive reasoning. Here, theory emerges from systematic analysis of data. Researchers code interviews or narrative data to derive categories and conceptual models. This strategy is popular in sociology, psychology, and organisation studies.

5.5 Ethnography

Ethnographic research requires immersion in a cultural or social group to understand perspectives from within. Researchers spend extended periods observing behaviour, conducting interviews, and participating in context. For example, a study on gender norms in rural healthcare settings in Sub-Saharan Africa might use this method.

As society grows increasingly multicultural and globalised in 2025, ethnography remains a vital approach to bridge cultural insights in policy-making.

5.6 Survey Research

Survey strategies allow for collection of standardised data from larger populations. Often used in conjunction with the deductive approach, surveys provide a snapshot of opinions, behaviours or demographics. Automated survey tools, now widely available online in 2025, have increased accessibility and participation across borders.

5.7 Archival and Documentary Research

Researchers can also use existing records to explore historical or contemporary phenomena. Archival research includes texts, reports, statistical databases, social media archives, and previously published datasets. In 2025, digital platforms have drastically improved access to archival material, offering researchers a robust alternative or supplement to primary data collection.

Each of these research strategies aligns with certain philosophical and methodological choices and must be selected carefully to support the study’s objectives.

6. Time Horizons: Cross-Sectional vs Longitudinal

The fifth layer of the research onion addresses time horizons. This relates to the time frame over which a study is designed and conducted.

6.1 Cross-Sectional Studies

A cross-sectional design involves data collection at a specific point in time. These studies are often descriptive or correlational and are commonly used when time or funding constraints exist. For example, a business might evaluate employee satisfaction after a quarterly organisational change through a one-time survey.

The popularity of cross-sectional studies has been reinforced in 2025 due to the availability of AI tools that enable rapid survey design, distribution, and analysis.

6.2 Longitudinal Studies

By contrast, longitudinal studies investigate change over time. These involve repeated observations of the same variables and are critical for identifying trends, patterns, and cause-effect relationships.

In 2025, sectors such as healthcare, climate science, and education increasingly adopt longitudinal methods to inform evidence-based policy. Technological advancements have improved the feasibility of remote, real-time, long-term tracking, making this strategy more attractive than in the past.

7. Data Collection and Analysis Techniques

As the innermost layer of the research onion model, data collection and analysis methods are directly shaped by the research strategy, time horizon, and philosophical position adopted earlier.

7.1 Primary Data

Primary data is original data collected first-hand, such as through surveys, interviews, or experiments. In 2025, digital tools – from mobile apps to AI-driven chatbots – have revolutionised the ways researchers gather primary data. Efficient and flexible tools such as online forms, remote interviewing platforms, and real-time tracking apps allow researchers to reach broader audiences both locally and globally.

However, ethical considerations, such as informed consent and data protection under updated frameworks like GDPR 2.0, must be rigorously observed.

7.2 Secondary Data

Secondary data refers to existing data, including published literature, government databases, open-access datasets, and previous research outcomes. Although indirect, it is often easier to access and more cost-effective than primary data.

In 2025, researchers commonly rely on platforms like Statista, GovData, and OpenAIRE to access reliable secondary sources.

7.3 Data Analysis

Data analysis methods vary depending on the approach:

• Quantitative data is analysed using statistical packages such as SPSS, R, or Python. Techniques include regression, ANOVA, or factor analysis.
• Qualitative data is processed using thematic analysis, content analysis or narrative approaches, often aided by software like NVivo or Atlas.ti.
• Mixed-methods research integrates both datasets, typically comparing numerical trends with qualitative explanations.

Modern researchers must ensure that their chosen method of analysis aligns with their research question and ethical responsibilities, especially regarding anonymisation and confidentiality.

8. Research Design

The research design encompasses the entire plan — clearly defining how the study proceeds. It considers the research objective, target population, data collection tools, ethical concerns, and analytical framework.

8.1 Descriptive Design

Descriptive research provides a snapshot of characteristics or facts. For instance, documenting the socioeconomic profiles of gig economy workers would suit a descriptive method.

8.2 Explanatory Design

Explanatory design aims to clarify relationships between variables. This is common in policy evaluation and business modelling, where understanding causality is vital – such as measuring how training investment affects staff productivity.

8.3 Exploratory Design

Exploratory design helps uncover insights in new or weakly understood areas. Often adopted early in a research cycle, it may inform future experimental or explanatory studies. It proves especially useful in emerging fields such as AI ethics or green fintech.

9. Sampling: Ensuring Representativeness

Sampling is critical to ensuring the validity and reliability of a study.

9.1 Sample Size

In quantitative research, larger samples yield greater statistical power. Surveys involving thousands of participants can now be administered digitally in hours. Conversely, qualitative studies focus on data saturation rather than quantity, with 10–30 participants often sufficient.

9.2 Sampling Techniques

• Random sampling ensures each member of a population has an equal chance of inclusion.
• Stratified sampling accounts for key demographic proportions.
• Purposive and snowball sampling are commonplace in qualitative research, especially in hard-to-reach or specialised populations.

Choice of sampling method should align with overall research objectives and ensure ethical integrity.

Conclusion

The Saunders research onion model presents a cohesive and adaptable structure for designing robust research methodologies. In 2025, its relevance across disciplines — from data science to education and healthcare — is more pronounced than ever. By reinforcing the layered logic of research planning, the research onion guides scholars through philosophical reflection, methodological coherence, and strategic execution.

Each layer — from philosophy to data technique — reshapes the research journey, advancing it from abstraction to application. Whether choosing a mono-method or engaging in complex mixed-methods research, the research onion Saunders framework empowers researchers to align method with purpose and adapt to the evolving expectations of research in a digital and globalised era.

By leveraging this model, researchers avoid inconsistencies and enhance the reliability, validity, and impact of their work. As academic and applied research continues to evolve through new technologies and methodologies, the Saunders onion remains an essential foundation for methodological excellence.

References for The Saunders Research Onion

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M-Z Sources

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Dan Strayer

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Dan Strayer is a professional SEO and Digital Marketing consultant for Business Bliss. He’s written previously for LinkedIn, Medium and various websites about content marketing and SEO principles. A Communications graduate of Shippensburg University, he holds various digital marketing certifications from HubSpot, SEMrush and Google. Reach out to him at https://about.me/dan_strayer or https://about.me/dan_strayer/.

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