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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview
• What is qualitative research?
• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews

Research question

• Conceptual framework
• Conceptual vs. theoretical framework

Data collection

• Qualitative research methods
• Focus groups
• Observational research

What is a case study?

Applications for case study research, what is a good case study, process of case study design, benefits and limitations of case studies.

• Ethnographical research
• Ethical considerations
• Confidentiality and privacy
• Power dynamics
• Reflexivity

Case studies

Case studies are essential to qualitative research , offering a lens through which researchers can investigate complex phenomena within their real-life contexts. This chapter explores the concept, purpose, applications, examples, and types of case studies and provides guidance on how to conduct case study research effectively.

Whereas quantitative methods look at phenomena at scale, case study research looks at a concept or phenomenon in considerable detail. While analyzing a single case can help understand one perspective regarding the object of research inquiry, analyzing multiple cases can help obtain a more holistic sense of the topic or issue. Let's provide a basic definition of a case study, then explore its characteristics and role in the qualitative research process.

Definition of a case study

A case study in qualitative research is a strategy of inquiry that involves an in-depth investigation of a phenomenon within its real-world context. It provides researchers with the opportunity to acquire an in-depth understanding of intricate details that might not be as apparent or accessible through other methods of research. The specific case or cases being studied can be a single person, group, or organization – demarcating what constitutes a relevant case worth studying depends on the researcher and their research question .

Among qualitative research methods , a case study relies on multiple sources of evidence, such as documents, artifacts, interviews , or observations , to present a complete and nuanced understanding of the phenomenon under investigation. The objective is to illuminate the readers' understanding of the phenomenon beyond its abstract statistical or theoretical explanations.

Characteristics of case studies

Case studies typically possess a number of distinct characteristics that set them apart from other research methods. These characteristics include a focus on holistic description and explanation, flexibility in the design and data collection methods, reliance on multiple sources of evidence, and emphasis on the context in which the phenomenon occurs.

Furthermore, case studies can often involve a longitudinal examination of the case, meaning they study the case over a period of time. These characteristics allow case studies to yield comprehensive, in-depth, and richly contextualized insights about the phenomenon of interest.

The role of case studies in research

Case studies hold a unique position in the broader landscape of research methods aimed at theory development. They are instrumental when the primary research interest is to gain an intensive, detailed understanding of a phenomenon in its real-life context.

In addition, case studies can serve different purposes within research - they can be used for exploratory, descriptive, or explanatory purposes, depending on the research question and objectives. This flexibility and depth make case studies a valuable tool in the toolkit of qualitative researchers.

Remember, a well-conducted case study can offer a rich, insightful contribution to both academic and practical knowledge through theory development or theory verification, thus enhancing our understanding of complex phenomena in their real-world contexts.

What is the purpose of a case study?

Case study research aims for a more comprehensive understanding of phenomena, requiring various research methods to gather information for qualitative analysis . Ultimately, a case study can allow the researcher to gain insight into a particular object of inquiry and develop a theoretical framework relevant to the research inquiry.

Why use case studies in qualitative research?

Using case studies as a research strategy depends mainly on the nature of the research question and the researcher's access to the data.

Conducting case study research provides a level of detail and contextual richness that other research methods might not offer. They are beneficial when there's a need to understand complex social phenomena within their natural contexts.

The explanatory, exploratory, and descriptive roles of case studies

Case studies can take on various roles depending on the research objectives. They can be exploratory when the research aims to discover new phenomena or define new research questions; they are descriptive when the objective is to depict a phenomenon within its context in a detailed manner; and they can be explanatory if the goal is to understand specific relationships within the studied context. Thus, the versatility of case studies allows researchers to approach their topic from different angles, offering multiple ways to uncover and interpret the data .

The impact of case studies on knowledge development

Case studies play a significant role in knowledge development across various disciplines. Analysis of cases provides an avenue for researchers to explore phenomena within their context based on the collected data.

This can result in the production of rich, practical insights that can be instrumental in both theory-building and practice. Case studies allow researchers to delve into the intricacies and complexities of real-life situations, uncovering insights that might otherwise remain hidden.

Types of case studies

In qualitative research , a case study is not a one-size-fits-all approach. Depending on the nature of the research question and the specific objectives of the study, researchers might choose to use different types of case studies. These types differ in their focus, methodology, and the level of detail they provide about the phenomenon under investigation.

Understanding these types is crucial for selecting the most appropriate approach for your research project and effectively achieving your research goals. Let's briefly look at the main types of case studies.

Exploratory case studies

Exploratory case studies are typically conducted to develop a theory or framework around an understudied phenomenon. They can also serve as a precursor to a larger-scale research project. Exploratory case studies are useful when a researcher wants to identify the key issues or questions which can spur more extensive study or be used to develop propositions for further research. These case studies are characterized by flexibility, allowing researchers to explore various aspects of a phenomenon as they emerge, which can also form the foundation for subsequent studies.

Descriptive case studies

Descriptive case studies aim to provide a complete and accurate representation of a phenomenon or event within its context. These case studies are often based on an established theoretical framework, which guides how data is collected and analyzed. The researcher is concerned with describing the phenomenon in detail, as it occurs naturally, without trying to influence or manipulate it.

Explanatory case studies

Explanatory case studies are focused on explanation - they seek to clarify how or why certain phenomena occur. Often used in complex, real-life situations, they can be particularly valuable in clarifying causal relationships among concepts and understanding the interplay between different factors within a specific context.

Intrinsic, instrumental, and collective case studies

These three categories of case studies focus on the nature and purpose of the study. An intrinsic case study is conducted when a researcher has an inherent interest in the case itself. Instrumental case studies are employed when the case is used to provide insight into a particular issue or phenomenon. A collective case study, on the other hand, involves studying multiple cases simultaneously to investigate some general phenomena.

Each type of case study serves a different purpose and has its own strengths and challenges. The selection of the type should be guided by the research question and objectives, as well as the context and constraints of the research.

The flexibility, depth, and contextual richness offered by case studies make this approach an excellent research method for various fields of study. They enable researchers to investigate real-world phenomena within their specific contexts, capturing nuances that other research methods might miss. Across numerous fields, case studies provide valuable insights into complex issues.

Critical information systems research

Case studies provide a detailed understanding of the role and impact of information systems in different contexts. They offer a platform to explore how information systems are designed, implemented, and used and how they interact with various social, economic, and political factors. Case studies in this field often focus on examining the intricate relationship between technology, organizational processes, and user behavior, helping to uncover insights that can inform better system design and implementation.

Health research

Health research is another field where case studies are highly valuable. They offer a way to explore patient experiences, healthcare delivery processes, and the impact of various interventions in a real-world context.

Case studies can provide a deep understanding of a patient's journey, giving insights into the intricacies of disease progression, treatment effects, and the psychosocial aspects of health and illness.

Asthma research studies

Specifically within medical research, studies on asthma often employ case studies to explore the individual and environmental factors that influence asthma development, management, and outcomes. A case study can provide rich, detailed data about individual patients' experiences, from the triggers and symptoms they experience to the effectiveness of various management strategies. This can be crucial for developing patient-centered asthma care approaches.

Other fields

Apart from the fields mentioned, case studies are also extensively used in business and management research, education research, and political sciences, among many others. They provide an opportunity to delve into the intricacies of real-world situations, allowing for a comprehensive understanding of various phenomena.

Case studies, with their depth and contextual focus, offer unique insights across these varied fields. They allow researchers to illuminate the complexities of real-life situations, contributing to both theory and practice.

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Understanding the key elements of case study design is crucial for conducting rigorous and impactful case study research. A well-structured design guides the researcher through the process, ensuring that the study is methodologically sound and its findings are reliable and valid. The main elements of case study design include the research question , propositions, units of analysis, and the logic linking the data to the propositions.

The research question is the foundation of any research study. A good research question guides the direction of the study and informs the selection of the case, the methods of collecting data, and the analysis techniques. A well-formulated research question in case study research is typically clear, focused, and complex enough to merit further detailed examination of the relevant case(s).

Propositions

Propositions, though not necessary in every case study, provide a direction by stating what we might expect to find in the data collected. They guide how data is collected and analyzed by helping researchers focus on specific aspects of the case. They are particularly important in explanatory case studies, which seek to understand the relationships among concepts within the studied phenomenon.

Units of analysis

The unit of analysis refers to the case, or the main entity or entities that are being analyzed in the study. In case study research, the unit of analysis can be an individual, a group, an organization, a decision, an event, or even a time period. It's crucial to clearly define the unit of analysis, as it shapes the qualitative data analysis process by allowing the researcher to analyze a particular case and synthesize analysis across multiple case studies to draw conclusions.

Argumentation

This refers to the inferential model that allows researchers to draw conclusions from the data. The researcher needs to ensure that there is a clear link between the data, the propositions (if any), and the conclusions drawn. This argumentation is what enables the researcher to make valid and credible inferences about the phenomenon under study.

Understanding and carefully considering these elements in the design phase of a case study can significantly enhance the quality of the research. It can help ensure that the study is methodologically sound and its findings contribute meaningful insights about the case.

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Conducting a case study involves several steps, from defining the research question and selecting the case to collecting and analyzing data . This section outlines these key stages, providing a practical guide on how to conduct case study research.

Defining the research question

The first step in case study research is defining a clear, focused research question. This question should guide the entire research process, from case selection to analysis. It's crucial to ensure that the research question is suitable for a case study approach. Typically, such questions are exploratory or descriptive in nature and focus on understanding a phenomenon within its real-life context.

Selecting and defining the case

The selection of the case should be based on the research question and the objectives of the study. It involves choosing a unique example or a set of examples that provide rich, in-depth data about the phenomenon under investigation. After selecting the case, it's crucial to define it clearly, setting the boundaries of the case, including the time period and the specific context.

Previous research can help guide the case study design. When considering a case study, an example of a case could be taken from previous case study research and used to define cases in a new research inquiry. Considering recently published examples can help understand how to select and define cases effectively.

Developing a detailed case study protocol

A case study protocol outlines the procedures and general rules to be followed during the case study. This includes the data collection methods to be used, the sources of data, and the procedures for analysis. Having a detailed case study protocol ensures consistency and reliability in the study.

The protocol should also consider how to work with the people involved in the research context to grant the research team access to collecting data. As mentioned in previous sections of this guide, establishing rapport is an essential component of qualitative research as it shapes the overall potential for collecting and analyzing data.

Collecting data

Gathering data in case study research often involves multiple sources of evidence, including documents, archival records, interviews, observations, and physical artifacts. This allows for a comprehensive understanding of the case. The process for gathering data should be systematic and carefully documented to ensure the reliability and validity of the study.

Analyzing and interpreting data

The next step is analyzing the data. This involves organizing the data , categorizing it into themes or patterns , and interpreting these patterns to answer the research question. The analysis might also involve comparing the findings with prior research or theoretical propositions.

Writing the case study report

The final step is writing the case study report . This should provide a detailed description of the case, the data, the analysis process, and the findings. The report should be clear, organized, and carefully written to ensure that the reader can understand the case and the conclusions drawn from it.

Each of these steps is crucial in ensuring that the case study research is rigorous, reliable, and provides valuable insights about the case.

The type, depth, and quality of data in your study can significantly influence the validity and utility of the study. In case study research, data is usually collected from multiple sources to provide a comprehensive and nuanced understanding of the case. This section will outline the various methods of collecting data used in case study research and discuss considerations for ensuring the quality of the data.

Interviews are a common method of gathering data in case study research. They can provide rich, in-depth data about the perspectives, experiences, and interpretations of the individuals involved in the case. Interviews can be structured , semi-structured , or unstructured , depending on the research question and the degree of flexibility needed.

Observations

Observations involve the researcher observing the case in its natural setting, providing first-hand information about the case and its context. Observations can provide data that might not be revealed in interviews or documents, such as non-verbal cues or contextual information.

Documents and artifacts

Documents and archival records provide a valuable source of data in case study research. They can include reports, letters, memos, meeting minutes, email correspondence, and various public and private documents related to the case.

These records can provide historical context, corroborate evidence from other sources, and offer insights into the case that might not be apparent from interviews or observations.

Physical artifacts refer to any physical evidence related to the case, such as tools, products, or physical environments. These artifacts can provide tangible insights into the case, complementing the data gathered from other sources.

Ensuring the quality of data collection

Determining the quality of data in case study research requires careful planning and execution. It's crucial to ensure that the data is reliable, accurate, and relevant to the research question. This involves selecting appropriate methods of collecting data, properly training interviewers or observers, and systematically recording and storing the data. It also includes considering ethical issues related to collecting and handling data, such as obtaining informed consent and ensuring the privacy and confidentiality of the participants.

Data analysis

Analyzing case study research involves making sense of the rich, detailed data to answer the research question. This process can be challenging due to the volume and complexity of case study data. However, a systematic and rigorous approach to analysis can ensure that the findings are credible and meaningful. This section outlines the main steps and considerations in analyzing data in case study research.

Organizing the data

The first step in the analysis is organizing the data. This involves sorting the data into manageable sections, often according to the data source or the theme. This step can also involve transcribing interviews, digitizing physical artifacts, or organizing observational data.

Categorizing and coding the data

Once the data is organized, the next step is to categorize or code the data. This involves identifying common themes, patterns, or concepts in the data and assigning codes to relevant data segments. Coding can be done manually or with the help of software tools, and in either case, qualitative analysis software can greatly facilitate the entire coding process. Coding helps to reduce the data to a set of themes or categories that can be more easily analyzed.

Identifying patterns and themes

After coding the data, the researcher looks for patterns or themes in the coded data. This involves comparing and contrasting the codes and looking for relationships or patterns among them. The identified patterns and themes should help answer the research question.

Interpreting the data

Once patterns and themes have been identified, the next step is to interpret these findings. This involves explaining what the patterns or themes mean in the context of the research question and the case. This interpretation should be grounded in the data, but it can also involve drawing on theoretical concepts or prior research.

Verification of the data

The last step in the analysis is verification. This involves checking the accuracy and consistency of the analysis process and confirming that the findings are supported by the data. This can involve re-checking the original data, checking the consistency of codes, or seeking feedback from research participants or peers.

Like any research method , case study research has its strengths and limitations. Researchers must be aware of these, as they can influence the design, conduct, and interpretation of the study.

Understanding the strengths and limitations of case study research can also guide researchers in deciding whether this approach is suitable for their research question . This section outlines some of the key strengths and limitations of case study research.

Benefits include the following:

• Rich, detailed data: One of the main strengths of case study research is that it can generate rich, detailed data about the case. This can provide a deep understanding of the case and its context, which can be valuable in exploring complex phenomena.
• Flexibility: Case study research is flexible in terms of design , data collection , and analysis . A sufficient degree of flexibility allows the researcher to adapt the study according to the case and the emerging findings.
• Real-world context: Case study research involves studying the case in its real-world context, which can provide valuable insights into the interplay between the case and its context.
• Multiple sources of evidence: Case study research often involves collecting data from multiple sources , which can enhance the robustness and validity of the findings.

On the other hand, researchers should consider the following limitations:

• Generalizability: A common criticism of case study research is that its findings might not be generalizable to other cases due to the specificity and uniqueness of each case.
• Time and resource intensive: Case study research can be time and resource intensive due to the depth of the investigation and the amount of collected data.
• Complexity of analysis: The rich, detailed data generated in case study research can make analyzing the data challenging.
• Subjectivity: Given the nature of case study research, there may be a higher degree of subjectivity in interpreting the data , so researchers need to reflect on this and transparently convey to audiences how the research was conducted.

Being aware of these strengths and limitations can help researchers design and conduct case study research effectively and interpret and report the findings appropriately.

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• Open access
• Published: 27 June 2011

The case study approach

• Sarah Crowe 1 ,
• Kathrin Cresswell 2 ,
• Ann Robertson 2 ,
• Guro Huby 3 ,
• Anthony Avery 1 &
• Aziz Sheikh 2  

BMC Medical Research Methodology volume  11 , Article number:  100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

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Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

Yin RK: Case study research, design and method. 2009, London: Sage Publications Ltd., 4

Google Scholar  

Keen J, Packwood T: Qualitative research; case study evaluation. BMJ. 1995, 311: 444-446.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Sheikh A, Halani L, Bhopal R, Netuveli G, Partridge M, Car J, et al: Facilitating the Recruitment of Minority Ethnic People into Research: Qualitative Case Study of South Asians and Asthma. PLoS Med. 2009, 6 (10): 1-11.

Article   Google Scholar  

Pinnock H, Huby G, Powell A, Kielmann T, Price D, Williams S, et al: The process of planning, development and implementation of a General Practitioner with a Special Interest service in Primary Care Organisations in England and Wales: a comparative prospective case study. Report for the National Co-ordinating Centre for NHS Service Delivery and Organisation R&D (NCCSDO). 2008, [ http://www.sdo.nihr.ac.uk/files/project/99-final-report.pdf ]

Robertson A, Cresswell K, Takian A, Petrakaki D, Crowe S, Cornford T, et al: Prospective evaluation of the implementation and adoption of NHS Connecting for Health's national electronic health record in secondary care in England: interim findings. BMJ. 2010, 41: c4564-

Pearson P, Steven A, Howe A, Sheikh A, Ashcroft D, Smith P, the Patient Safety Education Study Group: Learning about patient safety: organisational context and culture in the education of healthcare professionals. J Health Serv Res Policy. 2010, 15: 4-10. 10.1258/jhsrp.2009.009052.

Article   PubMed   Google Scholar  

van Harten WH, Casparie TF, Fisscher OA: The evaluation of the introduction of a quality management system: a process-oriented case study in a large rehabilitation hospital. Health Policy. 2002, 60 (1): 17-37. 10.1016/S0168-8510(01)00187-7.

Stake RE: The art of case study research. 1995, London: Sage Publications Ltd.

Sheikh A, Smeeth L, Ashcroft R: Randomised controlled trials in primary care: scope and application. Br J Gen Pract. 2002, 52 (482): 746-51.

PubMed   PubMed Central   Google Scholar  

King G, Keohane R, Verba S: Designing Social Inquiry. 1996, Princeton: Princeton University Press

Doolin B: Information technology as disciplinary technology: being critical in interpretative research on information systems. Journal of Information Technology. 1998, 13: 301-311. 10.1057/jit.1998.8.

George AL, Bennett A: Case studies and theory development in the social sciences. 2005, Cambridge, MA: MIT Press

Eccles M, the Improved Clinical Effectiveness through Behavioural Research Group (ICEBeRG): Designing theoretically-informed implementation interventions. Implementation Science. 2006, 1: 1-8. 10.1186/1748-5908-1-1.

Article   PubMed Central   Google Scholar  

Netuveli G, Hurwitz B, Levy M, Fletcher M, Barnes G, Durham SR, Sheikh A: Ethnic variations in UK asthma frequency, morbidity, and health-service use: a systematic review and meta-analysis. Lancet. 2005, 365 (9456): 312-7.

Sheikh A, Panesar SS, Lasserson T, Netuveli G: Recruitment of ethnic minorities to asthma studies. Thorax. 2004, 59 (7): 634-

CAS   PubMed   PubMed Central   Google Scholar  

Hellström I, Nolan M, Lundh U: 'We do things together': A case study of 'couplehood' in dementia. Dementia. 2005, 4: 7-22. 10.1177/1471301205049188.

Som CV: Nothing seems to have changed, nothing seems to be changing and perhaps nothing will change in the NHS: doctors' response to clinical governance. International Journal of Public Sector Management. 2005, 18: 463-477. 10.1108/09513550510608903.

Lincoln Y, Guba E: Naturalistic inquiry. 1985, Newbury Park: Sage Publications

Barbour RS: Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?. BMJ. 2001, 322: 1115-1117. 10.1136/bmj.322.7294.1115.

Mays N, Pope C: Qualitative research in health care: Assessing quality in qualitative research. BMJ. 2000, 320: 50-52. 10.1136/bmj.320.7226.50.

Mason J: Qualitative researching. 2002, London: Sage

Brazier A, Cooke K, Moravan V: Using Mixed Methods for Evaluating an Integrative Approach to Cancer Care: A Case Study. Integr Cancer Ther. 2008, 7: 5-17. 10.1177/1534735407313395.

Miles MB, Huberman M: Qualitative data analysis: an expanded sourcebook. 1994, CA: Sage Publications Inc., 2

Pope C, Ziebland S, Mays N: Analysing qualitative data. Qualitative research in health care. BMJ. 2000, 320: 114-116. 10.1136/bmj.320.7227.114.

Cresswell KM, Worth A, Sheikh A: Actor-Network Theory and its role in understanding the implementation of information technology developments in healthcare. BMC Med Inform Decis Mak. 2010, 10 (1): 67-10.1186/1472-6947-10-67.

Article   PubMed   PubMed Central   Google Scholar  

Malterud K: Qualitative research: standards, challenges, and guidelines. Lancet. 2001, 358: 483-488. 10.1016/S0140-6736(01)05627-6.

Article   CAS   PubMed   Google Scholar  

Yin R: Case study research: design and methods. 1994, Thousand Oaks, CA: Sage Publishing, 2

Yin R: Enhancing the quality of case studies in health services research. Health Serv Res. 1999, 34: 1209-1224.

Green J, Thorogood N: Qualitative methods for health research. 2009, Los Angeles: Sage, 2

Howcroft D, Trauth E: Handbook of Critical Information Systems Research, Theory and Application. 2005, Cheltenham, UK: Northampton, MA, USA: Edward Elgar

Book   Google Scholar  

Blakie N: Approaches to Social Enquiry. 1993, Cambridge: Polity Press

Doolin B: Power and resistance in the implementation of a medical management information system. Info Systems J. 2004, 14: 343-362. 10.1111/j.1365-2575.2004.00176.x.

Bloomfield BP, Best A: Management consultants: systems development, power and the translation of problems. Sociological Review. 1992, 40: 533-560.

Shanks G, Parr A: Positivist, single case study research in information systems: A critical analysis. Proceedings of the European Conference on Information Systems. 2003, Naples

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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Crowe, S., Cresswell, K., Robertson, A. et al. The case study approach. BMC Med Res Methodol 11 , 100 (2011). https://doi.org/10.1186/1471-2288-11-100

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What is a Case Study in Research? Definition, Methods, and Examples

Case study methodology offers researchers an exciting opportunity to explore intricate phenomena within specific contexts using a wide range of data sources and collection methods. It is highly pertinent in health and social sciences, environmental studies, social work, education, and business studies. Its diverse applications, such as advancing theory, program evaluation, and intervention development, make it an invaluable tool for driving meaningful research and fostering positive change.[ 1]  

Table of Contents

What is a Case Study?  

A case study method involves a detailed examination of a single subject, such as an individual, group, organization, event, or community, to explore and understand complex issues in real-life contexts. By focusing on one specific case, researchers can gain a deep understanding of the factors and dynamics at play, understanding their complex relationships, which might be missed in broader, more quantitative studies.  

When to do a Case Study?  

A case study design is useful when you want to explore a phenomenon in-depth and in its natural context. Here are some examples of when to use a case study :[ 2]  

• Exploratory Research: When you want to explore a new topic or phenomenon, a case study can help you understand the subject deeply. For example , a researcher studying a newly discovered plant species might use a case study to document its characteristics and behavior.  
• Descriptive Research: If you want to describe a complex phenomenon or process, a case study can provide a detailed and comprehensive description. For instance, a case study design   could describe the experiences of a group of individuals living with a rare disease.  
• Explanatory Research: When you want to understand why a particular phenomenon occurs, a case study can help you identify causal relationships. A case study design could investigate the reasons behind the success or failure of a particular business strategy.  
• Theory Building: Case studies can also be used to develop or refine theories. By systematically analyzing a series of cases, researchers can identify patterns and relationships that can contribute to developing new theories or refining existing ones.  
• Critical Instance: Sometimes, a single case can be used to study a rare or unusual phenomenon, but it is important for theoretical or practical reasons. For example , the case of Phineas Gage, a man who survived a severe brain injury, has been widely studied to understand the relationship between the brain and behavior.  
• Comparative Analysis: Case studies can also compare different cases or contexts. A case study example involves comparing the implementation of a particular policy in different countries to understand its effectiveness and identifying best practices.  

How to Create a Case Study – Step by Step  

Step 1: select a case  .

Careful case selection ensures relevance, insight, and meaningful contribution to existing knowledge in your field. Here’s how you can choose a case study design :[ 3]  

• Define Your Objectives: Clarify the purpose of your case study and what you hope to achieve. Do you want to provide new insights, challenge existing theories, propose solutions to a problem, or explore new research directions?  
• Consider Unusual or Outlying Cases: Focus on unusual, neglected, or outlying cases that can provide unique insights.  
• Choose a Representative Case: Alternatively, select a common or representative case to exemplify a particular category, experience, or phenomenon.   
• Avoid Bias: Ensure your selection process is unbiased using random or criteria-based selection.  
• Be Clear and Specific: Clearly define the boundaries of your study design , including the scope, timeframe, and key stakeholders.   
• Ethical Considerations: Consider ethical issues, such as confidentiality and informed consent.  

Step 2: Build a Theoretical Framework  

To ensure your case study has a solid academic foundation, it’s important to build a theoretical framework:   

• Conduct a Literature Review: Identify key concepts and theories relevant to your case study .  
• Establish Connections with Theory: Connect your case study with existing theories in the field.  
• Guide Your Analysis and Interpretation: Use your theoretical framework to guide your analysis, ensuring your findings are grounded in established theories and concepts.   

Step 3: Collect Your Data  

To conduct a comprehensive case study , you can use various research methods. These include interviews, observations, primary and secondary sources analysis, surveys, and a mixed methods approach. The aim is to gather rich and diverse data to enable a detailed analysis of your case study .  

Step 4: Describe and Analyze the Case  

How you report your findings will depend on the type of research you’re conducting. Here are two approaches:   

• Structured Approach: Follows a scientific paper format, making it easier for readers to follow your argument.  
• Narrative Approach: A more exploratory style aiming to analyze meanings and implications.  

Regardless of the approach you choose, it’s important to include the following elements in your case study :   

• Contextual Details: Provide background information about the case, including relevant historical, cultural, and social factors that may have influenced the outcome.  
• Literature and Theory: Connect your case study to existing literature and theory in the field. Discuss how your findings contribute to or challenge existing knowledge.  
• Wider Patterns or Debates: Consider how your case study fits into wider patterns or debates within the field. Discuss any implications your findings may have for future research or practice.  

What Are the Benefits of a Case Study   

Case studies offer a range of benefits , making them a powerful tool in research.  

1. In-Depth Analysis  

• Comprehensive Understanding: Case studies allow researchers to thoroughly explore a subject, understanding the complexities and nuances involved.  
• Rich Data: They offer rich qualitative and sometimes quantitative data, capturing the intricacies of real-life contexts.  

2. Contextual Insight  

• Real-World Application: Case studies provide insights into real-world applications, making the findings highly relevant and practical.  
• Context-Specific: They highlight how various factors interact within a specific context, offering a detailed picture of the situation.  

3. Flexibility  

• Methodological Diversity: Case studies can use various data collection methods, including interviews, observations, document analysis, and surveys.  
• Adaptability: Researchers can adapt the case study approach to fit the specific needs and circumstances of the research.  

4. Practical Solutions  

• Actionable Insights: The detailed findings from case studies can inform practical solutions and recommendations for practitioners and policymakers.  
• Problem-Solving: They help understand the root causes of problems and devise effective strategies to address them.  

5. Unique Cases  

• Rare Phenomena: Case studies are particularly valuable for studying rare or unique cases that other research methods may not capture.  
• Detailed Documentation: They document and preserve detailed information about specific instances that might otherwise be overlooked.  

What Are the Limitations of a Case Study   

While case studies offer valuable insights and a detailed understanding of complex issues, they have several limitations .  

1. Limited Generalizability  

• Specific Context: Case studies often focus on a single case or a small number of cases, which may limit the generalization of findings to broader populations or different contexts.  
• Unique Situations: The unique characteristics of the case may not be representative of other situations, reducing the applicability of the results.  

2. Subjectivity  

• Researcher Bias: The researcher’s perspectives and interpretations can influence the analysis and conclusions, potentially introducing bias.  
• Participant Bias: Participants’ responses and behaviors may be influenced by their awareness of being studied, known as the Hawthorne effect.  

3. Time-Consuming  

• Data Collection and Analysis: Gathering detailed, in-depth data requires significant time and effort, making case studies more time-consuming than other research methods.  
• Longitudinal Studies: If the case study observes changes over time, it can become even more prolonged.  

4. Resource Intensive  

• Financial and Human Resources: Conducting comprehensive case studies may require significant financial investment and human resources, including trained researchers and participant access.  
• Access to Data: Accessing relevant and reliable data sources can be challenging, particularly in sensitive or proprietary contexts.  

5. Replication Difficulties  

• Unique Contexts: A case study’s specific and detailed context makes it difficult to replicate the study exactly, limiting the ability to validate findings through repetition.  
• Variability: Differences in contexts, researchers, and methodologies can lead to variations in findings, complicating efforts to achieve consistent results.  

By acknowledging and addressing these limitations , researchers can enhance the rigor and reliability of their case study findings.  

Key Takeaways  

Case studies are valuable in research because they provide an in-depth, contextual analysis of a single subject, event, or organization. They allow researchers to explore complex issues in real-world settings, capturing detailed qualitative and quantitative data. This method is useful for generating insights, developing theories, and offering practical solutions to problems. They are versatile, applicable in diverse fields such as business, education, and health, and can complement other research methods by providing rich, contextual evidence. However, their findings may have limited generalizability due to the focus on a specific case.  

Frequently Asked Questions  

Q: What is a case study in research?  

A case study in research is an impactful tool for gaining a deep understanding of complex issues within their real-life context. It combines various data collection methods and provides rich, detailed insights that can inform theory development and practical applications.  

Q: What are the advantages of using case studies in research?  

Case studies are a powerful research method, offering advantages such as in-depth analysis, contextual insights, flexibility, rich data, and the ability to handle complex issues. They are particularly valuable for exploring new areas, generating hypotheses, and providing detailed, illustrative examples that can inform theory and practice.  

Q: Can case studies be used in quantitative research?  

While case studies are predominantly associated with qualitative research, they can effectively incorporate quantitative methods to provide a more comprehensive analysis. A mixed-methods approach leverages qualitative and quantitative research strengths, offering a powerful tool for exploring complex issues in a real-world context. For example , a new medical treatment case study can incorporate quantitative clinical outcomes (e.g., patient recovery rates and dosage levels) along with qualitative patient interviews.  

Q: What are the key components of a case study?  

A case study typically includes several key components:   

• Introductio n, which provides an overview and sets the context by presenting the problem statement and research objectives;  
• Literature review , which connects the study to existing theories and prior research;  
• Methodology , which details the case study design , data collection methods, and analysis techniques;   
• Findings , which present the data and results, including descriptions, patterns, and themes;   
• Discussion and conclusion , which interpret the findings, discuss their implications, and offer conclusions, practical applications, limitations, and suggestions for future research.  

Together, these components ensure a comprehensive, systematic, and insightful exploration of the case.  

References  

• de Vries, K. (2020). Case study methodology. In  Critical qualitative health research  (pp. 41-52). Routledge.  
• Fidel, R. (1984). The case study method: A case study.  Library and Information Science Research ,  6 (3), 273-288.  
• Thomas, G. (2021). How to do your case study.  How to do your case study , 1-320.  

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Writing a Case Study

What is a case study?

A Case study is: 

• An in-depth research design that primarily uses a qualitative methodology but sometimes​​ includes quantitative methodology.
• Used to examine an identifiable problem confirmed through research.
• Used to investigate an individual, group of people, organization, or event.
• Used to mostly answer "how" and "why" questions.

What are the different types of case studies?

Note: These are the primary case studies. As you continue to research and learn

about case studies you will begin to find a robust list of different types. 

Who are your case study participants?

What is triangulation ? 

Validity and credibility are an essential part of the case study. Therefore, the researcher should include triangulation to ensure trustworthiness while accurately reflecting what the researcher seeks to investigate.

How to write a Case Study?

When developing a case study, there are different ways you could present the information, but remember to include the five parts for your case study.

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• What is a case study?
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• Roberta Heale 1 ,
• Alison Twycross 2
• 1 School of Nursing , Laurentian University , Sudbury , Ontario , Canada
• 2 School of Health and Social Care , London South Bank University , London , UK
• Correspondence to Dr Roberta Heale, School of Nursing, Laurentian University, Sudbury, ON P3E2C6, Canada; rheale{at}laurentian.ca

https://doi.org/10.1136/eb-2017-102845

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What is it?

Case study is a research methodology, typically seen in social and life sciences. There is no one definition of case study research. 1 However, very simply… ‘a case study can be defined as an intensive study about a person, a group of people or a unit, which is aimed to generalize over several units’. 1 A case study has also been described as an intensive, systematic investigation of a single individual, group, community or some other unit in which the researcher examines in-depth data relating to several variables. 2

Often there are several similar cases to consider such as educational or social service programmes that are delivered from a number of locations. Although similar, they are complex and have unique features. In these circumstances, the evaluation of several, similar cases will provide a better answer to a research question than if only one case is examined, hence the multiple-case study. Stake asserts that the cases are grouped and viewed as one entity, called the quintain . 6  ‘We study what is similar and different about the cases to understand the quintain better’. 6

The steps when using case study methodology are the same as for other types of research. 6 The first step is defining the single case or identifying a group of similar cases that can then be incorporated into a multiple-case study. A search to determine what is known about the case(s) is typically conducted. This may include a review of the literature, grey literature, media, reports and more, which serves to establish a basic understanding of the cases and informs the development of research questions. Data in case studies are often, but not exclusively, qualitative in nature. In multiple-case studies, analysis within cases and across cases is conducted. Themes arise from the analyses and assertions about the cases as a whole, or the quintain, emerge. 6

Benefits and limitations of case studies

If a researcher wants to study a specific phenomenon arising from a particular entity, then a single-case study is warranted and will allow for a in-depth understanding of the single phenomenon and, as discussed above, would involve collecting several different types of data. This is illustrated in example 1 below.

Using a multiple-case research study allows for a more in-depth understanding of the cases as a unit, through comparison of similarities and differences of the individual cases embedded within the quintain. Evidence arising from multiple-case studies is often stronger and more reliable than from single-case research. Multiple-case studies allow for more comprehensive exploration of research questions and theory development. 6

Despite the advantages of case studies, there are limitations. The sheer volume of data is difficult to organise and data analysis and integration strategies need to be carefully thought through. There is also sometimes a temptation to veer away from the research focus. 2 Reporting of findings from multiple-case research studies is also challenging at times, 1 particularly in relation to the word limits for some journal papers.

Examples of case studies

Example 1: nurses’ paediatric pain management practices.

One of the authors of this paper (AT) has used a case study approach to explore nurses’ paediatric pain management practices. This involved collecting several datasets:

Observational data to gain a picture about actual pain management practices.

Questionnaire data about nurses’ knowledge about paediatric pain management practices and how well they felt they managed pain in children.

Questionnaire data about how critical nurses perceived pain management tasks to be.

These datasets were analysed separately and then compared 7–9 and demonstrated that nurses’ level of theoretical did not impact on the quality of their pain management practices. 7 Nor did individual nurse’s perceptions of how critical a task was effect the likelihood of them carrying out this task in practice. 8 There was also a difference in self-reported and observed practices 9 ; actual (observed) practices did not confirm to best practice guidelines, whereas self-reported practices tended to.

Example 2: quality of care for complex patients at Nurse Practitioner-Led Clinics (NPLCs)

The other author of this paper (RH) has conducted a multiple-case study to determine the quality of care for patients with complex clinical presentations in NPLCs in Ontario, Canada. 10 Five NPLCs served as individual cases that, together, represented the quatrain. Three types of data were collected including:

Review of documentation related to the NPLC model (media, annual reports, research articles, grey literature and regulatory legislation).

Interviews with nurse practitioners (NPs) practising at the five NPLCs to determine their perceptions of the impact of the NPLC model on the quality of care provided to patients with multimorbidity.

Chart audits conducted at the five NPLCs to determine the extent to which evidence-based guidelines were followed for patients with diabetes and at least one other chronic condition.

The three sources of data collected from the five NPLCs were analysed and themes arose related to the quality of care for complex patients at NPLCs. The multiple-case study confirmed that nurse practitioners are the primary care providers at the NPLCs, and this positively impacts the quality of care for patients with multimorbidity. Healthcare policy, such as lack of an increase in salary for NPs for 10 years, has resulted in issues in recruitment and retention of NPs at NPLCs. This, along with insufficient resources in the communities where NPLCs are located and high patient vulnerability at NPLCs, have a negative impact on the quality of care. 10

These examples illustrate how collecting data about a single case or multiple cases helps us to better understand the phenomenon in question. Case study methodology serves to provide a framework for evaluation and analysis of complex issues. It shines a light on the holistic nature of nursing practice and offers a perspective that informs improved patient care.

• Gustafsson J
• Calanzaro M
• Sandelowski M

Competing interests None declared.

Provenance and peer review Commissioned; internally peer reviewed.

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• Knowledge Base
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• Case Study | Definition, Examples & Methods

Case Study | Definition, Examples & Methods

Published on 5 May 2022 by Shona McCombes . Revised on 30 January 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organisation, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating, and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyse the case.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

Unlike quantitative or experimental research, a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

If you find yourself aiming to simultaneously investigate and solve an issue, consider conducting action research . As its name suggests, action research conducts research and takes action at the same time, and is highly iterative and flexible. 

However, you can also choose a more common or representative case to exemplify a particular category, experience, or phenomenon.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data .

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis, with separate sections or chapters for the methods , results , and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyse its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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Case Study Research Method in Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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On This Page:

Case studies are in-depth investigations of a person, group, event, or community. Typically, data is gathered from various sources using several methods (e.g., observations & interviews).

The case study research method originated in clinical medicine (the case history, i.e., the patient’s personal history). In psychology, case studies are often confined to the study of a particular individual.

The information is mainly biographical and relates to events in the individual’s past (i.e., retrospective), as well as to significant events that are currently occurring in his or her everyday life.

The case study is not a research method, but researchers select methods of data collection and analysis that will generate material suitable for case studies.

Freud (1909a, 1909b) conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

This makes it clear that the case study is a method that should only be used by a psychologist, therapist, or psychiatrist, i.e., someone with a professional qualification.

There is an ethical issue of competence. Only someone qualified to diagnose and treat a person can conduct a formal case study relating to atypical (i.e., abnormal) behavior or atypical development.

 Famous Case Studies

• Anna O – One of the most famous case studies, documenting psychoanalyst Josef Breuer’s treatment of “Anna O” (real name Bertha Pappenheim) for hysteria in the late 1800s using early psychoanalytic theory.
• Little Hans – A child psychoanalysis case study published by Sigmund Freud in 1909 analyzing his five-year-old patient Herbert Graf’s house phobia as related to the Oedipus complex.
• Bruce/Brenda – Gender identity case of the boy (Bruce) whose botched circumcision led psychologist John Money to advise gender reassignment and raise him as a girl (Brenda) in the 1960s.
• Genie Wiley – Linguistics/psychological development case of the victim of extreme isolation abuse who was studied in 1970s California for effects of early language deprivation on acquiring speech later in life.
• Phineas Gage – One of the most famous neuropsychology case studies analyzes personality changes in railroad worker Phineas Gage after an 1848 brain injury involving a tamping iron piercing his skull.

Clinical Case Studies

• Studying the effectiveness of psychotherapy approaches with an individual patient
• Assessing and treating mental illnesses like depression, anxiety disorders, PTSD
• Neuropsychological cases investigating brain injuries or disorders

Child Psychology Case Studies

• Studying psychological development from birth through adolescence
• Cases of learning disabilities, autism spectrum disorders, ADHD
• Effects of trauma, abuse, deprivation on development

Types of Case Studies

• Explanatory case studies : Used to explore causation in order to find underlying principles. Helpful for doing qualitative analysis to explain presumed causal links.
• Exploratory case studies : Used to explore situations where an intervention being evaluated has no clear set of outcomes. It helps define questions and hypotheses for future research.
• Descriptive case studies : Describe an intervention or phenomenon and the real-life context in which it occurred. It is helpful for illustrating certain topics within an evaluation.
• Multiple-case studies : Used to explore differences between cases and replicate findings across cases. Helpful for comparing and contrasting specific cases.
• Intrinsic : Used to gain a better understanding of a particular case. Helpful for capturing the complexity of a single case.
• Collective : Used to explore a general phenomenon using multiple case studies. Helpful for jointly studying a group of cases in order to inquire into the phenomenon.

Where Do You Find Data for a Case Study?

There are several places to find data for a case study. The key is to gather data from multiple sources to get a complete picture of the case and corroborate facts or findings through triangulation of evidence. Most of this information is likely qualitative (i.e., verbal description rather than measurement), but the psychologist might also collect numerical data.

1. Primary sources

• Interviews – Interviewing key people related to the case to get their perspectives and insights. The interview is an extremely effective procedure for obtaining information about an individual, and it may be used to collect comments from the person’s friends, parents, employer, workmates, and others who have a good knowledge of the person, as well as to obtain facts from the person him or herself.
• Observations – Observing behaviors, interactions, processes, etc., related to the case as they unfold in real-time.
• Documents & Records – Reviewing private documents, diaries, public records, correspondence, meeting minutes, etc., relevant to the case.

2. Secondary sources

• News/Media – News coverage of events related to the case study.
• Academic articles – Journal articles, dissertations etc. that discuss the case.
• Government reports – Official data and records related to the case context.
• Books/films – Books, documentaries or films discussing the case.

3. Archival records

Searching historical archives, museum collections and databases to find relevant documents, visual/audio records related to the case history and context.

Public archives like newspapers, organizational records, photographic collections could all include potentially relevant pieces of information to shed light on attitudes, cultural perspectives, common practices and historical contexts related to psychology.

4. Organizational records

Organizational records offer the advantage of often having large datasets collected over time that can reveal or confirm psychological insights.

Of course, privacy and ethical concerns regarding confidential data must be navigated carefully.

However, with proper protocols, organizational records can provide invaluable context and empirical depth to qualitative case studies exploring the intersection of psychology and organizations.

• Organizational/industrial psychology research : Organizational records like employee surveys, turnover/retention data, policies, incident reports etc. may provide insight into topics like job satisfaction, workplace culture and dynamics, leadership issues, employee behaviors etc.
• Clinical psychology : Therapists/hospitals may grant access to anonymized medical records to study aspects like assessments, diagnoses, treatment plans etc. This could shed light on clinical practices.
• School psychology : Studies could utilize anonymized student records like test scores, grades, disciplinary issues, and counseling referrals to study child development, learning barriers, effectiveness of support programs, and more.

How do I Write a Case Study in Psychology?

Follow specified case study guidelines provided by a journal or your psychology tutor. General components of clinical case studies include: background, symptoms, assessments, diagnosis, treatment, and outcomes. Interpreting the information means the researcher decides what to include or leave out. A good case study should always clarify which information is the factual description and which is an inference or the researcher’s opinion.

1. Introduction

• Provide background on the case context and why it is of interest, presenting background information like demographics, relevant history, and presenting problem.
• Compare briefly to similar published cases if applicable. Clearly state the focus/importance of the case.

2. Case Presentation

• Describe the presenting problem in detail, including symptoms, duration,and impact on daily life.
• Include client demographics like age and gender, information about social relationships, and mental health history.
• Describe all physical, emotional, and/or sensory symptoms reported by the client.
• Use patient quotes to describe the initial complaint verbatim. Follow with full-sentence summaries of relevant history details gathered, including key components that led to a working diagnosis.
• Summarize clinical exam results, namely orthopedic/neurological tests, imaging, lab tests, etc. Note actual results rather than subjective conclusions. Provide images if clearly reproducible/anonymized.
• Clearly state the working diagnosis or clinical impression before transitioning to management.

3. Management and Outcome

• Indicate the total duration of care and number of treatments given over what timeframe. Use specific names/descriptions for any therapies/interventions applied.
• Present the results of the intervention,including any quantitative or qualitative data collected.
• For outcomes, utilize visual analog scales for pain, medication usage logs, etc., if possible. Include patient self-reports of improvement/worsening of symptoms. Note the reason for discharge/end of care.

4. Discussion

• Analyze the case, exploring contributing factors, limitations of the study, and connections to existing research.
• Analyze the effectiveness of the intervention,considering factors like participant adherence, limitations of the study, and potential alternative explanations for the results.
• Identify any questions raised in the case analysis and relate insights to established theories and current research if applicable. Avoid definitive claims about physiological explanations.
• Offer clinical implications, and suggest future research directions.

5. Additional Items

• Thank specific assistants for writing support only. No patient acknowledgments.
• References should directly support any key claims or quotes included.
• Use tables/figures/images only if substantially informative. Include permissions and legends/explanatory notes.
• Provides detailed (rich qualitative) information.
• Provides insight for further research.
• Permitting investigation of otherwise impractical (or unethical) situations.

Case studies allow a researcher to investigate a topic in far more detail than might be possible if they were trying to deal with a large number of research participants (nomothetic approach) with the aim of ‘averaging’.

Because of their in-depth, multi-sided approach, case studies often shed light on aspects of human thinking and behavior that would be unethical or impractical to study in other ways.

Research that only looks into the measurable aspects of human behavior is not likely to give us insights into the subjective dimension of experience, which is important to psychoanalytic and humanistic psychologists.

Case studies are often used in exploratory research. They can help us generate new ideas (that might be tested by other methods). They are an important way of illustrating theories and can help show how different aspects of a person’s life are related to each other.

The method is, therefore, important for psychologists who adopt a holistic point of view (i.e., humanistic psychologists ).

Limitations

• Lacking scientific rigor and providing little basis for generalization of results to the wider population.
• Researchers’ own subjective feelings may influence the case study (researcher bias).
• Difficult to replicate.
• Time-consuming and expensive.
• The volume of data, together with the time restrictions in place, impacted the depth of analysis that was possible within the available resources.

Because a case study deals with only one person/event/group, we can never be sure if the case study investigated is representative of the wider body of “similar” instances. This means the conclusions drawn from a particular case may not be transferable to other settings.

Because case studies are based on the analysis of qualitative (i.e., descriptive) data , a lot depends on the psychologist’s interpretation of the information she has acquired.

This means that there is a lot of scope for Anna O , and it could be that the subjective opinions of the psychologist intrude in the assessment of what the data means.

For example, Freud has been criticized for producing case studies in which the information was sometimes distorted to fit particular behavioral theories (e.g., Little Hans ).

This is also true of Money’s interpretation of the Bruce/Brenda case study (Diamond, 1997) when he ignored evidence that went against his theory.

Breuer, J., & Freud, S. (1895).  Studies on hysteria . Standard Edition 2: London.

Curtiss, S. (1981). Genie: The case of a modern wild child .

Diamond, M., & Sigmundson, K. (1997). Sex Reassignment at Birth: Long-term Review and Clinical Implications. Archives of Pediatrics & Adolescent Medicine , 151(3), 298-304

Freud, S. (1909a). Analysis of a phobia of a five year old boy. In The Pelican Freud Library (1977), Vol 8, Case Histories 1, pages 169-306

Freud, S. (1909b). Bemerkungen über einen Fall von Zwangsneurose (Der “Rattenmann”). Jb. psychoanal. psychopathol. Forsch ., I, p. 357-421; GW, VII, p. 379-463; Notes upon a case of obsessional neurosis, SE , 10: 151-318.

Harlow J. M. (1848). Passage of an iron rod through the head.  Boston Medical and Surgical Journal, 39 , 389–393.

Harlow, J. M. (1868).  Recovery from the Passage of an Iron Bar through the Head .  Publications of the Massachusetts Medical Society. 2  (3), 327-347.

Money, J., & Ehrhardt, A. A. (1972).  Man & Woman, Boy & Girl : The Differentiation and Dimorphism of Gender Identity from Conception to Maturity. Baltimore, Maryland: Johns Hopkins University Press.

Money, J., & Tucker, P. (1975). Sexual signatures: On being a man or a woman.

Further Information

• Case Study Approach
• Case Study Method
• Enhancing the Quality of Case Studies in Health Services Research
• “We do things together” A case study of “couplehood” in dementia
• Using mixed methods for evaluating an integrative approach to cancer care: a case study

The Case Study as Research Method: A Practical Handbook

Qualitative Research in Accounting & Management

ISSN : 1176-6093

Article publication date: 21 June 2011

Scapens, R.W. (2011), "The Case Study as Research Method: A Practical Handbook", Qualitative Research in Accounting & Management , Vol. 8 No. 2, pp. 201-204. https://doi.org/10.1108/11766091111137582

Emerald Group Publishing Limited

Copyright © 2011, Emerald Group Publishing Limited

This book aims to provide case‐study researchers with a step‐by‐step practical guide to “help them conduct the study with the required degree of rigour” (p. xi).

It seeks to “demonstrate that the case study is indeed a scientific method” (p. 104) and to show “the usefulness of the case method as one tool in the researcher's methodological arsenal” (p. 105). The individual chapters cover the various stages in conducting case‐study research, and each chapter sets out a number of practical steps which have to be taken by the researcher. The following are the eight stages/chapters and, in brackets, the number of steps in each stages:

Assessing appropriateness and usefulness (4).

Ensuring accuracy of results (21).

Preparation (6).

Selecting cases (4).

Collecting data (7).

Analyzing data (4).

Interpreting data (3).

Reporting results (4).

It is particularly noticeable that ensuring accuracy of results has by far the largest number of number of steps – 21 steps compared to seven or fewer steps in the other stages. This reflects Gagnon's concern to demonstrate the scientific rigour of case‐study research. In the forward, he explains that the book draws on his experience in conducting his own PhD research, which was closely supervised by three professors, one of whom was inclined towards quantitative research. Consequently, his research was underpinned by the principles and philosophy of quantitative research. This is clearly reflected in the approach taken in this book, which seeks to show that case‐study research is just as rigorous and scientific as quantitative research, and it can produce an objective and accurate representation of the observed reality.

There is no discussion of the methodological issues relating to the use of case‐study research methods. This is acknowledged in the forward, although Gagnon refers to them as philosophical or epistemological issues (p. xii), as he tends to use the terms methodology and method interchangeably – as is common in quantitative research. Although he starts (step 1.1) by trying to distance case and other qualitative research from the work of positivists, arguing that society is socially constructed, he nevertheless sees social reality as objective and independent of the researcher. So for Gagnon, the aim of case research is to accurately reflect that reality. At various points in the book the notion of interpretation is used – evidence is interpreted and the (objective) case findings have to be interpreted.

So although there is a distancing from positivist research (p. 1), the approach taken in this book retains an objective view of the social reality which is being researched; a view which is rather different to the subjective view of reality taken by many interpretive case researchers. This distinction between an objective and a subjective view of the social reality being researched – and especially its use in contrasting positivist and interpretive research – has its origins the taxonomy of Burrell and Morgan (1979) . Although there have been various developments in the so‐called “objective‐subjective debate”, and recently some discussion in relation to management accounting research ( Kakkuri‐Knuuttila et al. , 2008 ; Ahrens, 2008 ), this debate is not mentioned in the book. Nevertheless, it is clear that Gagnon is firmly in the objective camp. In a recent paper, Johnson et al. (2006, p. 138) provide a more contemporary classification of the different types of qualitative research. In their terms, the approach taken in this book could be described as neo‐empiricist – an approach which they characterise as “qualitative positivists”.

The approach taken in this handbook leaves case studies open to the criticisms that they are a small sample, and consequently difficult to generalise, and to arguments that case studies are most appropriate for exploratory research which can subsequently be generalised though quantitative research. Gagnon explains that this was the approach he used after completing his thesis (p. xi). The handbook only seems to recognise two types of case studies, namely exploratory and raw empirical case studies – the latter being used where “the researcher is interested in a subject without having formed any preconceived ideas about it” (p. 15) – which has echoes of Glaser and Strauss (1967) . However, limiting case studies to these two types ignores other potential types; in particular, explanatory case studies which are where interpretive case‐study research can make important contributions ( Ryan et al. , 2002 ).

This limited approach to case studies comes through in the practical steps which are recommended in the handbook, and especially in the discussion of reliability and validity. The suggested steps seem to be designed to keep very close to the notions of reliability and validity used in quantitative research. There is no mention of the recent discussion of “validity” in interpretive accounting research, which emphasises the importance of authenticity and credibility and their implications for writing up qualitative and case‐study research ( Lukka and Modell, 2010 ). Although the final stage of Gagnon's handbook makes some very general comments about reporting the results, it does not mention, for example, Baxter and Chua's (2008) paper in QRAM which discusses the importance of demonstrating authenticity, credibility and transferability in writing qualitative research.

Despite Gagnon's emphasis on traditional notions of reliability and validity the handbook provides some useful practical advice for all case‐study researchers. For example, case‐study research needs a very good research design; case‐study researchers must work hard to gain access to and acceptance in the research settings; a clear strategy is needed for data collection; the case researcher should create field notes (in a field notebook, or otherwise) to record all the thoughts, ideas, observations, etc. that would not otherwise be collected; and the vast amount of data that case‐study research can generate needs to be carefully managed. Furthermore, because of what Gagnon calls the “risk of mortality” (p. 54) (i.e. the risk that access to a research site may be lost – for instance, if the organisation goes bankrupt) it is crucial for some additional site(s) to be selected at the outset to ensure that the planned research can be completed. This is what I call “insurance cases” when talking to my own PhD students. Interestingly, Gagnon recognises the ethical issues involved in doing case studies – something which is not always mentioned by the more objectivist type of case‐study researchers. He emphasises that it is crucial to honour confidentiality agreements, to ensure data are stored securely and that commitments are met and promises kept.

There is an interesting discussion of the advantages and disadvantages of using computer methods in analysing data (in stage 6). However, the discussion of coding appears to be heavily influenced by grounded theory, and is clearly concerned with producing an accurate reflection of an objective reality. In addition, Gagnon's depiction of case analysis is overly focussed on content analysis – possibly because it is a quantitative type of technique. There is no reference to the other approaches available to qualitative researchers. For example, there is no mention of the various visualisation techniques set out in Miles and Huberman (1994) .

To summarise, Gagnon's book is particularly useful for case‐study researchers who see the reality they are researching as objective and researcher independent. However, this is a sub‐set of case‐study researchers. Although some of the practical guidance offered is relevant for other types of case‐study researchers, those who see multiple realities in the social actors and/or recognise the subjectivity of the research process might have difficulty with some of the steps in this handbook. Gagnon's aim to show that the case study is a scientific method, gives the handbook a focus on traditional (quantitatively inspired) notions rigour and validity, and a tendency to ignore (or at least marginalise) other types of case study research. For example, the focus on exploratory cases, which need to be supplemented by broad based quantitative research, overlooks the real potential of case study research which lies in explanatory cases. Furthermore, Gagnon is rather worried about participant research, as the researcher may play a role which is “not consistent with scientific method” (p. 42), and which may introduce researcher bias and thereby damage “the impartiality of the study” (p. 53). Leaving aside the philosophical question about whether any social science research, including quantitative research, can be impartial, this stance could severely limit the potential of case‐study research and it would rule out both the early work on the sociology of mass production and the recent calls for interventionist research. Clearly, there could be a problem where a researcher is trying to sell consulting services, but there is a long tradition of social researchers working within organisations that they are studying. Furthermore, if interpretive research is to be relevant for practice, researchers may have to work with organisations to introduce new ideas and new ways of analysing problems. Gagnon would seem to want to avoid all such research – as it would not be “impartial”.

Consequently, although there is some good practical advice for case study researchers in this handbook, some of the recommendations have to be treated cautiously, as it is a book which sees case‐study research in a very specific way. As mentioned earlier, in the Forward Gagnon explicitly recognises that the book does not take a position on the methodological debates surrounding the use of case studies as a research method, and he says that “The reader should therefore use and judge this handbook with these considerations in mind” (p. xii). This is very good advice – caveat emptor .

Ahrens , T. ( 2008 ), “ A comment on Marja‐Liisa Kakkuri‐Knuuttila ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 291 ‐ 7 , Kari Lukka and Jaakko Kuorikoski.

Baxter , J. and Chua , W.F. ( 2008 ), “ The field researcher as author‐writer ”, Qualitative Research in Accounting & Management , Vol. 5 No. 2 , pp. 101 ‐ 21 .

Burrell , G. and Morgan , G. ( 1979 ), Sociological Paradigms and Organizational Analysis , Heinneman , London .

Glaser , B.G. and Strauss , A.L. ( 1967 ), The Discovery of Grounded Theory: Strategies for Qualitative Research , Aldine , New York, NY .

Johnson , P. , Buehring , A. , Cassell , C. and Symon , G. ( 2006 ), “ Evaluating qualitative management research: towards a contingent critieriology ”, International Journal of Management Reviews , Vol. 8 No. 3 , pp. 131 ‐ 56 .

Kakkuri‐Knuuttila , M.‐L. , Lukka , K. and Kuorikoski , J. ( 2008 ), “ Straddling between paradigms: a naturalistic philosophical case study on interpretive research in management accounting ”, Accounting, Organizations and Society , Vol. 33 Nos 2/3 , pp. 267 ‐ 91 .

Lukka , K. and Modell , S. ( 2010 ), “ Validation in interpretive management accounting research ”, Accounting, Organizations and Society , Vol. 35 , pp. 462 ‐ 77 .

Miles , M.B. and Huberman , A.M. ( 1994 ), Qualitative Data Analysis: A Source Book of New Methods , 2nd ed. , Sage , London .

Ryan , R.J. , Scapens , R.W. and Theobald , M. ( 2002 ), Research Methods and Methodology in Finance and Accounting , 2nd ed. , Thomson Learning , London .

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The case study approach

Sarah crowe.

1 Division of Primary Care, The University of Nottingham, Nottingham, UK

Kathrin Cresswell

2 Centre for Population Health Sciences, The University of Edinburgh, Edinburgh, UK

Ann Robertson

3 School of Health in Social Science, The University of Edinburgh, Edinburgh, UK

Anthony Avery

Aziz sheikh.

The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables ​ Tables1, 1 , ​ ,2, 2 , ​ ,3 3 and ​ and4) 4 ) and those of others to illustrate our discussion[ 3 - 7 ].

Example of a case study investigating the reasons for differences in recruitment rates of minority ethnic people in asthma research[ 3 ]

Example of a case study investigating the process of planning and implementing a service in Primary Care Organisations[ 4 ]

Example of a case study investigating the introduction of the electronic health records[ 5 ]

Example of a case study investigating the formal and informal ways students learn about patient safety[ 6 ]

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table ​ (Table5), 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Definitions of a case study

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table ​ (Table1), 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables ​ Tables2, 2 , ​ ,3 3 and ​ and4) 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 - 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table ​ (Table2) 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables ​ Tables2 2 and ​ and3, 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table ​ (Table4 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table ​ (Table6). 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

Example of epistemological approaches that may be used in case study research

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table ​ Table7 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

Example of a checklist for rating a case study proposal[ 8 ]

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table ​ (Table3), 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table ​ (Table1) 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table ​ Table3) 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 - 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table ​ (Table2 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table ​ (Table1 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table ​ (Table3 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table ​ (Table4 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table ​ Table3, 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table ​ (Table4), 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table ​ Table8 8 )[ 8 , 18 - 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table ​ (Table9 9 )[ 8 ].

Potential pitfalls and mitigating actions when undertaking case study research

Stake's checklist for assessing the quality of a case study report[ 8 ]

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

AS conceived this article. SC, KC and AR wrote this paper with GH, AA and AS all commenting on various drafts. SC and AS are guarantors.

Pre-publication history

The pre-publication history for this paper can be accessed here:

http://www.biomedcentral.com/1471-2288/11/100/prepub

Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

• Yin RK. Case study research, design and method. 4. London: Sage Publications Ltd.; 2009. [ Google Scholar ]
• Keen J, Packwood T. Qualitative research; case study evaluation. BMJ. 1995; 311 :444–446. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Sheikh A, Halani L, Bhopal R, Netuveli G, Partridge M, Car J. et al. Facilitating the Recruitment of Minority Ethnic People into Research: Qualitative Case Study of South Asians and Asthma. PLoS Med. 2009; 6 (10):1–11. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pinnock H, Huby G, Powell A, Kielmann T, Price D, Williams S, The process of planning, development and implementation of a General Practitioner with a Special Interest service in Primary Care Organisations in England and Wales: a comparative prospective case study. Report for the National Co-ordinating Centre for NHS Service Delivery and Organisation R&D (NCCSDO) 2008. http://www.sdo.nihr.ac.uk/files/project/99-final-report.pdf
• Robertson A, Cresswell K, Takian A, Petrakaki D, Crowe S, Cornford T. et al. Prospective evaluation of the implementation and adoption of NHS Connecting for Health's national electronic health record in secondary care in England: interim findings. BMJ. 2010; 41 :c4564. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pearson P, Steven A, Howe A, Sheikh A, Ashcroft D, Smith P. the Patient Safety Education Study Group. Learning about patient safety: organisational context and culture in the education of healthcare professionals. J Health Serv Res Policy. 2010; 15 :4–10. doi: 10.1258/jhsrp.2009.009052. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• van Harten WH, Casparie TF, Fisscher OA. The evaluation of the introduction of a quality management system: a process-oriented case study in a large rehabilitation hospital. Health Policy. 2002; 60 (1):17–37. doi: 10.1016/S0168-8510(01)00187-7. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stake RE. The art of case study research. London: Sage Publications Ltd.; 1995. [ Google Scholar ]
• Sheikh A, Smeeth L, Ashcroft R. Randomised controlled trials in primary care: scope and application. Br J Gen Pract. 2002; 52 (482):746–51. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• King G, Keohane R, Verba S. Designing Social Inquiry. Princeton: Princeton University Press; 1996. [ Google Scholar ]
• Doolin B. Information technology as disciplinary technology: being critical in interpretative research on information systems. Journal of Information Technology. 1998; 13 :301–311. doi: 10.1057/jit.1998.8. [ CrossRef ] [ Google Scholar ]
• George AL, Bennett A. Case studies and theory development in the social sciences. Cambridge, MA: MIT Press; 2005. [ Google Scholar ]
• Eccles M. the Improved Clinical Effectiveness through Behavioural Research Group (ICEBeRG) Designing theoretically-informed implementation interventions. Implementation Science. 2006; 1 :1–8. doi: 10.1186/1748-5908-1-1. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Netuveli G, Hurwitz B, Levy M, Fletcher M, Barnes G, Durham SR, Sheikh A. Ethnic variations in UK asthma frequency, morbidity, and health-service use: a systematic review and meta-analysis. Lancet. 2005; 365 (9456):312–7. [ PubMed ] [ Google Scholar ]
• Sheikh A, Panesar SS, Lasserson T, Netuveli G. Recruitment of ethnic minorities to asthma studies. Thorax. 2004; 59 (7):634. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hellström I, Nolan M, Lundh U. 'We do things together': A case study of 'couplehood' in dementia. Dementia. 2005; 4 :7–22. doi: 10.1177/1471301205049188. [ CrossRef ] [ Google Scholar ]
• Som CV. Nothing seems to have changed, nothing seems to be changing and perhaps nothing will change in the NHS: doctors' response to clinical governance. International Journal of Public Sector Management. 2005; 18 :463–477. doi: 10.1108/09513550510608903. [ CrossRef ] [ Google Scholar ]
• Lincoln Y, Guba E. Naturalistic inquiry. Newbury Park: Sage Publications; 1985. [ Google Scholar ]
• Barbour RS. Checklists for improving rigour in qualitative research: a case of the tail wagging the dog? BMJ. 2001; 322 :1115–1117. doi: 10.1136/bmj.322.7294.1115. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mays N, Pope C. Qualitative research in health care: Assessing quality in qualitative research. BMJ. 2000; 320 :50–52. doi: 10.1136/bmj.320.7226.50. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mason J. Qualitative researching. London: Sage; 2002. [ Google Scholar ]
• Brazier A, Cooke K, Moravan V. Using Mixed Methods for Evaluating an Integrative Approach to Cancer Care: A Case Study. Integr Cancer Ther. 2008; 7 :5–17. doi: 10.1177/1534735407313395. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Miles MB, Huberman M. Qualitative data analysis: an expanded sourcebook. 2. CA: Sage Publications Inc.; 1994. [ Google Scholar ]
• Pope C, Ziebland S, Mays N. Analysing qualitative data. Qualitative research in health care. BMJ. 2000; 320 :114–116. doi: 10.1136/bmj.320.7227.114. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cresswell KM, Worth A, Sheikh A. Actor-Network Theory and its role in understanding the implementation of information technology developments in healthcare. BMC Med Inform Decis Mak. 2010; 10 (1):67. doi: 10.1186/1472-6947-10-67. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Malterud K. Qualitative research: standards, challenges, and guidelines. Lancet. 2001; 358 :483–488. doi: 10.1016/S0140-6736(01)05627-6. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yin R. Case study research: design and methods. 2. Thousand Oaks, CA: Sage Publishing; 1994. [ Google Scholar ]
• Yin R. Enhancing the quality of case studies in health services research. Health Serv Res. 1999; 34 :1209–1224. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Green J, Thorogood N. Qualitative methods for health research. 2. Los Angeles: Sage; 2009. [ Google Scholar ]
• Howcroft D, Trauth E. Handbook of Critical Information Systems Research, Theory and Application. Cheltenham, UK: Northampton, MA, USA: Edward Elgar; 2005. [ Google Scholar ]
• Blakie N. Approaches to Social Enquiry. Cambridge: Polity Press; 1993. [ Google Scholar ]
• Doolin B. Power and resistance in the implementation of a medical management information system. Info Systems J. 2004; 14 :343–362. doi: 10.1111/j.1365-2575.2004.00176.x. [ CrossRef ] [ Google Scholar ]
• Bloomfield BP, Best A. Management consultants: systems development, power and the translation of problems. Sociological Review. 1992; 40 :533–560. [ Google Scholar ]
• Shanks G, Parr A. Proceedings of the European Conference on Information Systems. Naples; 2003. Positivist, single case study research in information systems: A critical analysis. [ Google Scholar ]

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• Published: 10 August 2024

Mapping biomimicry research to sustainable development goals

• Raghu Raman 1 ,
• Aswathy Sreenivasan 2 ,
• M. Suresh 2 &
• Prema Nedungadi 3  

Scientific Reports volume  14 , Article number:  18613 ( 2024 ) Cite this article

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This study systematically evaluates biomimicry research within the context of sustainable development goals (SDGs) to discern the interdisciplinary interplay between biomimicry and SDGs. The alignment of biomimicry with key SDGs showcases its interdisciplinary nature and potential to offer solutions across the health, sustainability, and energy sectors. This study identified two primary thematic clusters. The first thematic cluster focused on health, partnership, and life on land (SDGs 3, 17, and 15), highlighting biomimicry's role in healthcare innovations, sustainable collaboration, and land management. This cluster demonstrates the potential of biomimicry to contribute to medical technologies, emphasizing the need for cross-sectoral partnerships and ecosystem preservation. The second thematic cluster revolves around clean water, energy, infrastructure, and marine life (SDGs 6, 7, 9, and 14), showcasing nature-inspired solutions for sustainable development challenges, including energy generation and water purification. The prominence of SDG 7 within this cluster indicates that biomimicry significantly contributes to sustainable energy practices. The analysis of thematic clusters further revealed the broad applicability of biomimicry and its role in enhancing sustainable energy access and promoting ecosystem conservation. Emerging research topics, such as metaheuristics, nanogenerators, exosomes, and bioprinting, indicate a dynamic field poised for significant advancements. By mapping the connections between biomimicry and SDGs, this study provides a comprehensive overview of the field's trajectory, emphasizing its importance in advancing global sustainability efforts.

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Introduction.

Biomimicry, which combines 'bio' (life) and 'mimicry' (imitation), uses nature's patterns to solve human problems, aligning with the SDGs by fostering innovations 1 . This discipline studies natural processes to inspire sustainable designs and promote responsible consumption and production 2 . Biomimicry emphasizes sustainability, ideation, and education in reconnecting with nature to achieve the SDGs 3 . Collaboration among designers, technologists, and business experts is vital for translating natural mechanisms into commercial solutions 4 . Biomimetics, which aims for radical innovations by replicating living systems, strives for breakthroughs in economic growth 5 . By promoting systemic change through the emulation of nature's regenerative processes, biomimicry's alignment with the SDGs could enhance sustainability efforts. Merging biomimicry insights with SDGs could exceed sustainability benchmarks.

Integrating biomimicry with sustainable development goals (SDGs) is crucial for addressing global challenges. The SDGs offer a blueprint for global well-being and environmental stewardship by 2030 6 . They aim to protect the environment and foster social and economic development. Biomimicry provides innovative approaches to these objectives, drawing from natural strategies. While SDGs offer clear targets, biomimicry complements these by providing a unique lens for solutions 7 . The investigation of biomimicry in conjunction with the SDGs is based on the understanding that the development of biologically inspired materials, structures, and systems offers a novel and sustainable solution to design problems, particularly in the built environment 8 . By mimicking nature's answers to complicated challenges, biomimicry produces creative, clever, long-lasting, and environmentally responsible ideas.

The SDGs outline a comprehensive sustainability agenda targeting social equity, environmental conservation, and poverty alleviation 9 . The use of biomimicry in research can lead to the development of solutions that mimic natural efficiency 10 , revolutionizing industries with resource-efficient technologies and enhancing sustainability. This synergy could lead to environmentally friendly products, improved energy solutions, and effective waste management systems. Integrating biomimicry into industry and education promotes environmental stewardship and ecological appreciation 11 . Marrying biomimicry research with SDGs has accelerated progress toward sustainable development.

Biomimicry can provide insightful and useful solutions consistent with sustainability ideals by imitating the adaptability and efficiency observed in biological systems 12 . The built environment's use of biomimicry has a greater sustainable impact when circular design features are included 13 . Reusing materials, cutting waste, and designing systems that work with natural cycles are all stressed in a circular design. Combining biomimicry and circular design promotes social inclusion, environmental resilience, resourcefulness, and compassionate governance, all of which lead to peaceful coexistence with the environment. This all-encompassing strategy demonstrates a dedication to tackling the larger social and environmental concerns that the SDGs represent and design challenges 14 . Complementing these studies, Wamane 7 examined the intersection of biomimicry, the environmental, social, and governance (ESG) framework, and circular economy principles, advocating for an economic paradigm shift toward sustainability.

A key aspect of realizing the impact of biomimicry on SDGs is the successful translation and commercialization of biomimicry discoveries. This involves overcoming barriers such as skill gaps, the engineering mindset, commercial acumen, and funding. Insights from the "The State of Nature-Inspired-Innovation in the UK" report provide a comprehensive analysis of these challenges and potential strategies to address them, underscoring the importance of integrating commercial perspectives into biomimicry research.

This research employs bibliometric techniques to assess the integration and coherence within circular economy policy-making, emphasizing the potential for a synergistic relationship between environmental stewardship, economic growth, and social equity to foster a sustainable future.

In addressing the notable gap in comprehensive research concerning the contribution of biomimicry solutions to specific SDGs, this study offers significant insights into the interdisciplinary applications of biomimicry and its potential to advance global sustainability efforts. Our investigation aims to bridge this research gap through a systematic analysis, resulting in the formulation of the following research questions:

RQ1: How does an interdisciplinary analysis of biomimicry research align with and contribute to advancing specific SDGs?

RQ2: What emerging topics within biomimicry research are gaining prominence, and how do they relate to the SDGs?

RQ3 : What are the barriers to the translation and commercialization of biomimicry innovations, and how can these barriers be overcome to enhance their impact on SDGs?

RQ4: Based on the identified gaps in research and the potential for interdisciplinary collaboration, what innovative areas within biomimicry can be further explored to address underrepresented SDGs?

The remainder of this paper is arranged as follows. Section " Literature review " focuses on the literature background of biomimicry, followed by methods (section " Methods ") and results and discussion, including emerging research topics (section " Results and discussion "). Section " Conclusion " concludes with recommendations and limitations.

Literature review

The potential of biomimicry solutions for sustainability has long been recognized, yet there is a notable lack of comprehensive studies that explore how biomimicry can address specific sustainable development goals (SDGs) (Table 1 ). This research aims to fill this gap by investigating relevant themes and building upon the literature in this field.

Biomimicry, with its roots tracing back to approximately 500 BC, began with Greek philosophers who developed classical concepts of beauty and drew inspiration from natural organisms for balanced design 15 . This foundational idea of looking to nature for design principles continued through history, as exemplified by Leonardo Da Vinci's creation of a flying machine inspired by birds in 1482. This early instance of biomimicry influenced subsequent advancements, including the Wright brothers' development of the airplane in 1948 12 , 15 . The term "bionics," coined in 1958 to describe "the science of natural systems or their analogs," evolved into "biomimicry" by 1982. Janine Benyus's 1997 book, “Biomimicry: Innovation Inspired by Nature,” and the founding of the Biomimicry Institute (Biomimicry 16 ) were pivotal, positioning nature as a guide and model for sustainable design. Benyus’s work underscores the potential of biomimicry in tackling contemporary environmental challenges such as climate change and ecosystem degradation 12 , 17 .

In recent years, the call for more targeted research in biomimicry has grown, particularly in terms of architecture and energy use. Meena et al. 18 and Varshabi et al. 19 highlighted the need for biomimicry to address energy efficiency in building design, stressing the potential of nature-inspired solutions to reduce energy consumption and enhance sustainability. This perspective aligns with that of Perricone et al. 20 , who explored the differences between artificial and natural systems, noting that biomimetic designs, which mimic the principles of organism construction, can significantly improve resource utilization and ecosystem restoration. Aggarwal and Verma 21 contributed to this discourse by mapping the evolution and applications of biomimicry through scientometric analysis, revealing the growing significance of nature-inspired optimization methodologies, especially in clustering techniques. Their work suggested that these methodologies not only provide innovative solutions but also reflect a deeper integration of biomimetic principles in technological advancements. Building on this, Pinzón and Austin 22 emphasized the infancy of biomimicry in the context of renewable energy, advocating for more research to explore how nature can inspire new energy solutions. Their work connects with that of Carniel et al. 23 , who introduced a natural language processing (NLP) technique to identify research themes in biomimicry across disciplines, facilitating a holistic understanding of current trends and future directions.

To further illustrate the practical applications of biomimicry, Nasser et al. 24 presented the Harmony Search Algorithm (HSA), a nature-inspired optimization technique. Their bibliometric analysis demonstrated the algorithm's effectiveness in reducing energy and resource consumption, highlighting the practical benefits of biomimicry in technological innovation. Rusu et al. 25 expanded on these themes by documenting significant advancements in soft robotics, showing how biomimicry influences design principles and applications in this rapidly evolving field. Their findings underscore the diverse applications of biomimetic principles, from robotics to building design. Shashwat et al. 26 emphasized the role of bioinspired solutions in enhancing energy efficiency within the built environment, promoting the use of high solar reflectance surfaces that mimic natural materials. This perspective is in line with that of Pires et al. 27 , who evaluated the application of biomimicry in dental restorative materials and identified a need for more clinical studies to realize the full potential of biomimetic innovations in healthcare. Liu et al. 28 explored the application of nature-inspired design principles in software-defined networks, demonstrating how biomimetic algorithms can optimize resource and energy utilization in complex systems. This study builds on the broader narrative of biomimicry's potential to transform various sectors by offering efficient, sustainable solutions. Finally, Hinkelman et al. 29 synthesized these insights by discussing the transdisciplinary applications of ecosystem biomimicry, which supports sustainable development goals by integrating biomimetic principles across engineering and environmental disciplines. This comprehensive approach underscores the transformative potential of biomimicry, suggesting that continued interdisciplinary research and innovation are crucial for addressing global sustainability challenges effectively.

PRISMA framework

This study utilizes the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to structure its analysis, following the established five-step protocol: formulating research questions, defining a search strategy, executing a literature search, screening identified literature, and analyzing the findings (Page et al., 2021). The application of the PRISMA guidelines across various research domains, including the SDGs, is well documented 30 .

To ensure a comprehensive search, we searched the Scopus database, a widely utilized resource for bibliometric studies 31 (Donthu et al. 82 ), which led to the discovery of 46,141 publications from 2013 to 2023. This period marked significant research activity following the introduction of the SDGs at the Rio + 20 summit in 2012. Publications were identified using the following terms in the title and abstract: “ (biomimic* OR biomimetic* OR bioinspired OR bioinsp* OR bionic* OR nature-inspired OR "biologically inspired" OR bioinspiration OR biomimesis OR biognosis).”

During the screening phase, publications lacking complete author details were reviewed, narrowing the field to 46,083 publications for further analysis. The eligibility phase utilized proprietary algorithms to map publications to the 17 SDGs, informed by initiatives such as the University of Auckland (Auckland’s SDG mapping 32 ) and Elsevier's SDG Mapping Initiatives (Elsevier's SDG Mapping 33 ). The selection of the Elsevier SDG Mapping Initiative for this study was based on its seamless integration with Scopus, facilitating the use of predefined search queries for each SDG and employing a machine learning model that has been refined through expert review. This approach has been utilized in various studies to analyze research trends within emerging fields. For example, the exploration of green hydrogen was detailed by Raman et al. 34 , while investigations into Fake News and the Dark Web were conducted by Raman et al. 35 , 36 , 37 and Rama et al. 38 , respectively. These examples demonstrate the efficacy of SDG mapping in elucidating how research outputs align with and contribute to sustainable development goals in these emerging domains. This phase identified 13,287 publications as mapped to SDGs. In the inclusion phase, stringent criteria further filtered the publications to English-language journals and review articles, culminating in 13,271 publications deemed suitable for in-depth analysis. This process ensures a comprehensive and high-quality dataset for the study, reflecting the robust and systematic approach afforded by the PRISMA framework in evaluating literature relevant to SDGs.

Our keyword search strategy, while comprehensive, may capture papers that do not genuinely contribute to the field. To mitigate this, we employed manual verification. After the automated search, the authors conducted a manual review of a subset of the final set of identified papers to assess their relevance and authenticity in the context of biomimicry. The subset was based on 20 highly cited papers from each year. We believe that papers that are frequently cited within the community are more likely to be accurately classified. The authors mainly reviewed the introduction, methodology, and results sections to confirm the relevance and authenticity of the papers. However, we acknowledge that these steps may not fully eliminate the inclusion of irrelevant papers, which could skew the results of our meta-analysis.

SDG framework

The examination of sustainable development goals (SDGs) reveals their interconnected nature, where the achievement of one goal often supports progress in others. Studies by Le Blanc (2015) and Allison et al. (2016) have mapped out the complex web of relationships among the SDGs, identifying both strong and subtle linkages across different objectives. To visualize these connections, we employed a cocitation mapping approach using VOSviewer 39 , which allows us to depict the semantic relationships between SDGs through their cocitation rates in scholarly works. This approach generates a visual map where each SDG is represented as a node, with the node size reflecting the goal's research prominence and the thickness of the lines between nodes indicating the frequency of cocitations among the goals. This visual representation reveals the SDGs as an intricate but unified framework, emphasizing the collaborative nature of global sustainability initiatives.

Topic prominence percentile

The Scopus prominence percentile is a crucial metric indicating the visibility and impact of emerging research topics within the scientific community. High-ranking topics in this percentile are rapidly gaining attention, highlighting emerging trends and areas poised for significant advancements. This tool enables researchers and policymakers to identify and focus on innovative topics, ensuring that their efforts align with the forefront of scientific development 35 , 36 , 37 . Topics above the 99.9th percentile were used in this study.

Results and discussion

Rq1: sdg framework and interdisciplinary research (rq4).

This study evaluates biomimicry research through the framework of SDGs. A cocitation SDG map shows two clusters and provides insights into the interplay between biomimicry themes and SDGs, highlighting the cross-disciplinary nature of this research (Fig.  1 ). The blue box hidden behind the “3 – Good Health and Well-being” and “7 – Affordable and Clean Energy” is “11 – Sustainable cities and Communities”. The blue box hidden behind “15 – Life on Land” is “16 – Peace, Justice and Strong institutions”.

Interdisciplinary SDG network of biomimicry research.

Cluster 1 (Red): Biomimetic innovations for health, partnership, and life on land

This cluster comprises a diverse array of research articles that explore the application of biomimicry across various SDGs 3 (health), 17 (partnership), and 15 (land). The papers in this cluster delve into innovative biomimetic ideas, each contributing uniquely to the intersection of sustainable development and biological inspiration. SDG 3, emphasizing good health and well-being for all, is significantly represented, indicating a global effort to leverage biomimicry for advancements in healthcare, such as new medication delivery systems and medical technologies. Similarly, the frequent citations of SDG 17 underscore the vital role of partnerships in achieving sustainable growth, especially where bioinspired solutions require interdisciplinary collaboration to address complex challenges. Finally, the prominence of 15 SDG citations reflects a commitment to preserving terrestrial ecosystems, where biomimicry is increasingly applied in land management, demonstrating nature's adaptability and resilience as a model for sustainable practices. Table 2 lists the top 5 relevant papers from Cluster 1, further illustrating the multifaceted application of biomimicry in addressing these SDGs.

A unique binary variant of the gray wolf optimization (GWO) technique, designed especially for feature selection in classification tasks, was presented by Emary et al. 40 . GWO is a method inspired by the social hierarchy and hunting behavior of gray wolves to find the best solutions to complex problems. This bioinspired optimization technique was used to optimize SDG15, which also highlights its ecological benefits. The results of the study highlight the effectiveness of binary gray wolf optimization in identifying the feature space for ideal pairings and promoting environmental sustainability and biodiversity. Lin et al. 41 focused on SDG 3 by examining catalytically active nanomaterials as potential candidates for artificial enzymes. While acknowledging the limits of naturally occurring enzymes, this study explores how nanobiotechnology can address problems in the food, pharmaceutical, and agrochemical sectors.

The investigation of enzymatic nanomaterials aligns with health-related objectives, highlighting the potential for major improvements in human health. Parodi et al. 42 used biomimetic leukocyte membranes to functionalize synthetic nanoparticles, extending biomimicry into the biomedical domain. To meet SDG 3, this research presents "leukolike vectors," which are nanoporous silicon particles that can communicate with cells, evade the immune system, and deliver specific payloads. In line with the SDGs about health, this study emphasizes the possible uses of biomimetic structures in cancer detection and treatments. A novel strategy for biological photothermal nanodot-based anticancer therapy utilizing peptide‒porphyrin conjugate self-assembly was presented by Zou et al. 43 . For therapeutic reasons, efficient light-to-heat conversion can be achieved by imitating the structure of biological structures. By providing a unique biomimetic approach to cancer treatment and demonstrating the potential of self-assembling biomaterials in biomedical applications, this research advances SDG 3. Finally, Wang et al. 44 presented Monarch butterfly optimization (MBO), which is a bioinspired algorithm that mimics the migration patterns of monarch butterflies to solve optimization problems effectively. This method presents a novel approach to optimization, mimicking the migration of monarch butterflies, aligning with SDG 9. Comparative analyses highlight MBO's exceptional performance and demonstrate its capacity to address intricate issues about business and innovation, supporting objectives for long-term collaboration and sector expansion.

The publications in Cluster 1 show a wide range of biomimetic developments, from ecological optimization to new optimization techniques and biomedical applications. These varied contributions highlight how biomimicry can advance sustainable development in health, symbiosis, and terrestrial life.

Cluster 2 (green): Nature-inspired solutions for clean water, energy, and infrastructure

Cluster 2, which focuses on the innovative application of biomimicry in sustainable development, represents a range of research that aligns with SDGs 6 (sanitation), 7 (energy), 9 (infrastructure), and 14 (water). This cluster is characterized by studies that draw inspiration from natural processes and structures to offer creative solutions to sustainability-related challenges. The papers in this cluster, detailed in Table 3 , demonstrate how biomimicry can address key global concerns in a varied and compelling manner.

Within this cluster, the high citation counts for SDG 7 underscore the significance of accessible clean energy, a domain where biomimicry contributes innovative energy generation and storage solutions inspired by natural processes. This aligns with the growing emphasis on sustainable energy practices. The prominence of SDG 9 citations further highlights the global focus on innovation and sustainable industry, where biomimicry's role in developing nature-inspired designs is crucial for building robust systems and resilient infrastructure. Furthermore, the substantial citations for SDG 6 reflect a dedicated effort toward ensuring access to clean water and sanitation for all. In this regard, biomimicry principles are being applied in water purification technologies, illustrating how sustainable solutions modeled after natural processes can effectively meet clean water objectives.

The study by Sydney Gladman et al. (2016), which presented the idea of shape-morphing systems inspired by nastic plant motions, is one notable addition to this cluster. This discovery creates new opportunities for tissue engineering, autonomous robotics, and smart textile applications by encoding composite hydrogel designs that exhibit anisotropic swelling behavior. The emphasis of SDG 9 on promoting industry, innovation, and infrastructure aligns with this biomimetic strategy. SDGs 7 and 13 are addressed in the study of Li et al. 45 , which is about engineering heterogeneous semiconductors for solar water splitting. This work contributes to the goals of inexpensive, clean energy and climate action by investigating methods such as band structure engineering and bionic engineering to increase the efficiency of solar water splitting. Li et al. 46 conducted a thorough study highlighting the importance of catalysts for the selective photoreduction of CO2 into solar fuels. This review offers valuable insights into the use of semiconductor catalysts for selective photocatalytic CO2 reduction. Our work advances sustainable energy solutions by investigating biomimetic, metal-based, and metal-free cocatalysts and contributes to SDGs 7 and 13. Wang et al. 47 address the critical problem of water pollution. Creating materials with superlyophilic and superlyophobic qualities offers a creative method for effectively separating water and oil. This contributes to the goals of clean water, industry, innovation, and life below the water. It also correlates with SDGs 6, 9, and 14. Singh et al. 48 also explored the 'green' synthesis of metals and their oxide nanoparticles for environmental remediation, which furthers SDG 9. This review demonstrates the environmentally benign and sustainable features of green synthesis and its potential to lessen the environmental impact of conventional synthesis methods.

Cluster 2 provides nature-inspired solutions for clean water, renewable energy, and sustainable infrastructure, demonstrating the scope and importance of biomimicry. The varied applications discussed in these papers help overcome difficult problems and advance sustainable development in line with several SDGs.

RQ2: Emerging research topics

Temporal evolution of emerging topics.

Figure  2 displays the publication counts for various emerging topics from 2013 to 2022, indicating growth trends over the years. For 'Metaheuristics', there is a notable increase in publications peaking in approximately 2020, suggesting a surge in interest. 'Strain sensor' research steadily increased, reaching its highest publication frequency toward the end of the period, which is indicative of growing relevance in the field. 'Bioprinting' sharply increased over the next decade, subsequently maintaining high interest, which highlights its sustained innovation. In contrast, 'Actuators' showed fluctuating publication counts, with a recent upward trend. 'Cancer' research, while historically a major topic, displayed a spike in publications in approximately 2018, possibly reflecting a breakthrough or increased research funding. 'Myeloperoxidase' has a smaller presence in the literature, with a modest peak in 2019. The number of 'Water '-related publications remains relatively low but shows a slight increase, suggesting a gradual but increasing recognition of its importance. Research on exosomes has significantly advanced, particularly since 2018, signifying a greater area of focus. 'Mechanical' topic publications have moderate fluctuations without a clear trend, indicating steady research interest. 'Micromotors' experienced an initial publication surge, followed by a decline and then a recent resurgence, possibly due to new technological applications. 'Nanogenerators' have shown a dramatic increase in interest, particularly in recent years, while 'Hydrogel' publications have varied, with a recent decline, which may point toward a shift in research focus or maturity of the topic.

Evolution of emerging topics according to publications (y-axis denotes the number of publications; x-axis denotes the year of publication).

Figure  3 presents the distribution of various research topics based on their prominence percentile and total number of publications. Topics above the 99.9th percentile and to the right of the vertical threshold line represent the most emergent and prolific topics of study. Next, we examine the topics within each of the four quadrants, focusing on how each topic has developed over the years in relation to SDGs and the key phrases associated with each topic.

Distribution of research topics based on prominence percentile and total number of publications.

Next, we examine each research topic in four quadrants, assessing their evolution concerning SDGs. We also analyze the keyphrase cloud to identify which keyphrases are most relevant (indicated by their font size) and whether they are growing or not. In the key phrase cloud, green indicates an increasing relevance of the key phrase, grey signifies that its relevance remains constant, and blue represents a declining relevance of the key phrase.

Niche biomimetic applications

These are topics with a lower number of publications and prominence percentiles, indicating specialized or emerging areas of research that are not yet widely recognized or pursued (Quadrant 1—bottom left).

Myeloperoxidase; colorimetric; chromogenic compounds

The inclusion of myeloperoxidase indicates that inflammation and the immune system are the main research topics. The focus on chromogenic and colorimetric molecules suggests a relationship to analytical techniques for identifying biological materials. The evolution of the research is depicted in Fig.  4 a shows an evolving emphasis on various sustainable development goals (SDGs) over time. The research trajectory, initially rooted in SDG 3 (Good Health and Well-being), has progressively branched out to encompass SDG 7 (Affordable and Clean Energy) and SDG 6 (Clean Water and Sanitation), reflecting an expanding scope of inquiry within the forestry sciences. More recently, the focus has transitioned toward SDG 15 (Life on Land), indicating an increased recognition of the interconnectedness between forest ecosystems and broader environmental and sustainability goals. This trend underscores the growing complexity and multidisciplinary nature of forestry research, highlighting the need to address comprehensive ecological concerns along with human well-being and sustainable development.

Evolution of research ( a ) and key phrases ( b ).

The word cloud in Fig.  4 b highlights key phrases such as 'Biocompatible', 'Actuator', and 'Self-healing Hydrogel', reflecting a focus on advanced materials, while terms such as 'Elastic Modulus' and 'Polymeric Networks' suggest an emphasis on the structural properties essential for creating innovative diagnostic and environmental sensing tools. Such developments are pertinent to health monitoring and water purification, resonating with SDG 3 (Good Health and Well-being) and SDG 6 (Clean Water and Sanitation). The prominence of 'Self-healing' and 'Bioinspired' indicates a shift toward materials that emulate natural processes for durability and longevity, supporting sustainable industry practices aligned with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 12 (Responsible Consumption and Production), contributing to the overarching aim of sustainable development.

Next, we analyzed the top 3 cited publications. Catalytically active nanomaterials, or nanozymes, are exciting candidates for artificial enzymes, according to Lin et al. 41 . The authors explore the structural features and biomimetics applications of these enzymes, classifying them as metal-, carbon-, and metal oxide-based nanomaterials. This study emphasizes the benefits of enzymes over natural enzymes, including their high stability, variable catalytic activity, and controlled production. Wang et al. 49 developed biomimetic nanoflowers made from nanozymes to cause intracellular oxidative damage in hypoxic malignancies. Under both normoxic and hypoxic conditions, the nanoflowers demonstrated catalytic efficiency. By overcoming the constraints of existing systems that depend on oxygen availability or external stimuli, this novel technique represents a viable treatment option for malignant neoplasms. Gao et al. 50 investigated the use of a dual inorganic nanozyme-catalyzed cascade reaction as a biomimetic approach for nanocatalytic tumor therapy. This approach produces a high level of therapeutic efficacy by cascading catalytic events inside the tumor microenvironment. This study highlights the potential of inorganic nanozymes for achieving high therapeutic efficacy and outstanding biosafety, which adds to the growing interest in nanocatalytic tumor therapy.

Water; hydrophobicity; aerogels

With an emphasis on hydrophobicity, aerogel use, and water-related features, this topic relates to materials science and indicates interest in cutting-edge materials with unique qualities. From Fig.  5 a, we can see that, initially, the focus was directed toward SDG 6 (Clean Water and Sanitation), which is intrinsically related to the research theme, as biomimetic approaches are leveraged to develop innovative water purification and management solutions. As the research progressed, the scope expanded to intersect with SDG 14 (Life Below Water) and SDG 7 (Affordable and Clean Energy), signifying a broadened impact of biomimetic innovations in marine ecosystem conservation and energy-efficient materials. The gradual involvement with SDG 9 (industry, innovation, and infrastructure) and SDG 13 (climate action) indicates the interdisciplinary reach of this research, which aims to influence industrial practices and climate change mitigation strategies.

The word cloud in Fig.  5 b reinforces this narrative by showcasing key phrases such as 'Hydrophobic', 'Bioinspired', 'Emulsion', and 'Oil Pollution', which reflect the emphasis on developing materials and technologies that mimic natural water repellency and separation processes. 'Aerogel' and 'polydopamine', along with 'Underwater' and 'Biomimetic Cleaning', suggest a strong focus on creating lightweight, efficient materials capable of self-cleaning and oil spill remediation. These keywords encapsulate the essence of the research theme, demonstrating a clear alignment with the targeted SDGs and the overall aim of sustainable development through biomimicry.

Three highly referenced works that have made substantial contributions to the field of biomimetic materials for oil/water separation are included in the table. The development of superlyophilic and superlyophobic materials for effective oil/water separation was examined by Wang et al. 47 . This review highlights the applications of these materials in separating different oil-and-water combinations by classifying them according to their surface wettability qualities. The excellent efficiency, selectivity, and recyclability of the materials—which present a viable treatment option for industrial oily wastewater and oil spills—are highlighted in the paper. Su et al. 51 explored the evolution of super wettability systems. The studies included superhydrophobicity, superoleophobicity, and undersea counterparts, among other extreme wettabilities. The kinetics, material structures, and wetting conditions related to obtaining superwettability are covered in the article. This demonstrates the wide range of uses for these materials in chemistry and materials science, including self-cleaning fabrics and systems for separating oil and water. Zhang et al. 52 presented a bioinspired multifunctional foam with self-cleaning and oil/water separation capabilities. To construct a polyurethane foam with superhydrophobicity and superoleophobicity, this study used porous biomaterials and superhydrophobic self-cleaning lotus leaves. Foam works well for separating oil from water because of its slight weight and ability to float on water. It also shows exceptional resistance to corrosive liquids. According to the article, multifunctional foams for large-scale oil spill cleaning might be designed using a low-cost fabrication technology that could be widely adopted.

Growing interest in bioinspired healthcare

These topics have a higher prominence percentile but a lower number of publications, suggesting growing interest and importance in the field despite a smaller body of research (Quadrant 2—top left).

Exosomes; extracellular vesicles; MicroRNAs

Exosomes and extracellular vesicles are essential for intercellular communication, and reference to microRNAs implies a focus on genetic regulation. The evolution of this topic reflects an increasing alignment with specific sustainable development goals (SDGs) over the years. The initial research focused on SDG 3 (good health and well-being) has expanded to encompass SDG 9 (industry, innovation, and infrastructure) and SDG 6 (clean water and sanitation), showcasing the multifaceted impact of biomimetic research in healthcare (Fig.  6 a). The research trajectory into SDG 9 and SDG 6 suggests broader application of bioinspired technologies beyond healthcare, potentially influencing sustainable industrial processes and water treatment technologies, respectively.

The word cloud (Fig.  6 b) underscores the central role of 'Extracellular Vesicles' and 'Exosomes' as platforms for 'Targeted Drug Delivery' and 'Nanocarrier' systems, which are key innovations in medical biotechnology. The prominence of terms such as 'Bioinspired', 'Biomimetic', 'Liposome', and 'Gold Nanoparticle' illustrates the inspiration drawn from biological systems for developing advanced materials and delivery mechanisms. These key phrases indicate significant advancements in 'Controlled Drug Delivery Systems', 'Cancer Chemotherapy', and 'Molecular Imaging', which have contributed to improved diagnostics and treatment options, consistent with the objectives of SDG 3.

The work by Jang et al. 53 , which introduced bioinspired exosome-mimetic nanovesicles for improved drug delivery to tumor tissues, is one of the most cited articles. These nanovesicles, which resemble exosomes but have higher creation yields, target cells and slow the growth of tumors in a promising way. Yong et al.'s 54 work presented an effective drug carrier for targeted cancer chemotherapy, focusing on biocompatible tumor cell-exocytosed exosome-biomimetic porous silicon nanoparticles. A paper by Cheng et al. 55 discussed the difficulties in delivering proteins intracellularly. This study suggested a biomimetic nanoparticle platform that uses extracellular vesicle membranes and metal–organic frameworks. These highly cited studies highlight the importance of biomimetic techniques in improving drug delivery systems for improved therapeutic interventions.

Nanogenerators; piezoelectric; energy harvesting

This topic advises concentrating on technology for energy harvesting, especially for those that use piezoelectric materials and nanogenerators. We see a rising focus on medical applications of biomimetics, from diagnostics to energy harvesting mimicking biological systems.

The evolution of this research topic reflects a broader contribution to the SDGs by not only addressing healthcare needs but also by promoting sustainable energy practices and supporting resilient infrastructure through biomimetic innovation (Fig.  7 a). Initially, the emphasis on SDG 3 (Good Health and Well-being) suggested the early application of biomimetic principles in healthcare, particularly in medical devices and diagnostics leveraging piezoelectric effects. Over time, the transition toward SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure) indicates an expansion of bioinspired technologies into sustainable energy solutions and industrial applications. Nanogenerators and energy harvesting techniques draw inspiration from biological processes and structures, aiming to optimize energy efficiency and contribute to clean energy initiatives.

The word cloud in Fig.  7 b emphasizes key phrases such as 'Piezoelectric', 'Energy Harvesting', 'Tactile Sensor', 'Triboelectricity', and 'Nanogenerators', highlighting the core technologies that are being developed. These terms, along with 'Bioinspired', 'Wearable Electronic Devices', and 'Energy Conversion Efficiency', illustrate the convergence of natural principles with advanced material science to create innovative solutions for energy generation and sensor technology.

Yang et al.'s 56 study in Advanced Materials presented the first triboelectrification-based bionic membrane sensor. Wearable medical monitoring and biometric authentication systems will find new uses for this sensor since it allows self-powered physiological and behavioral measurements, such as noninvasive human health evaluation, anti-interference throat voice recording, and multimodal biometric authentication. A thorough analysis of the state-of-the-art in piezoelectric energy harvesting was presented by Sezer and Koç 57 . This article addresses the fundamentals, components, and uses of piezoelectric generators, highlighting their development, drawbacks, and prospects. It also predicts a time when piezoelectric technology will power many electronics. The 2021 paper by Zhao et al. 58 examines the use of cellulose-based materials in flexible electronics. This section describes the benefits of these materials and the latest developments in intelligent electronic device creation, including biomimetic electronic skins, optoelectronics, sensors, and optoelectronic devices. This review sheds light on the possible drawbacks and opportunities for wearable technology and bioelectronic systems based on cellulose.

Leading edge of biomimetic sensing and electronics

This quadrant represents topics with both a high number of publications and a prominence percentile, indicating well-established and influential research areas (Quadrant 3—top right).

Strain sensor; flexible electronics; sensor

Figure  8 a highlights the progress of research on bioinspired innovations, particularly in the development of strain sensors and flexible electronics for adaptive sensing technologies. Initially, concentrated on health applications aligned with SDG 3 (Good Health and Well-being), the focus has expanded. The integration of SDG 9 (Industry, Innovation, and Infrastructure) indicates a shift toward industrial applications, while the incorporation of SDG 7 (Affordable and Clean Energy) suggests a commitment to energy-efficient solutions. Additionally, the mention of SDG 11 (Sustainable Cities and Communities) and SDG 12 (Responsible Consumption and Production) reflects the broadening scope to include urban sustainability and eco-friendly manufacturing practices.

Figure  8 b provides insight into the key phrases associated with this research topic, highlighting terms such as 'Bioinspired', 'Self-healing', 'Wearable Electronic Devices', 'Flexible Electronics', and 'Pressure Sensor'. These key phrases speak to the innovative approaches for creating sensors and electronics that are not only inspired by biological systems but also capable of seamlessly integrating human activity and environmental needs. The mention of 'Wearable Sensors' and 'Tactile Sensor' indicates a focus on user interaction and sensitivity, which is crucial for medical applications and smart infrastructure.

The top three articles with the most citations represent the cutting edge of this topic’s study. Chortos et al. 59 investigated how skin characteristics can be replicated for medicinal and prosthetic uses. Kim et al. 60 focused on creating ultrathin silicon nanoribbon sensors for smart prosthetic skin, opening up new possibilities for bionic systems with many sensors. A bioinspired microhairy sensor for ultraconformability on nonflat surfaces was introduced in Pang et al.'s 61 article, which significantly improved signal-to-noise ratios for accurate physiological measurements.

Cancer; photoacoustics; theranostic nanomedicine

Modern technologies such as photoacoustics, theranostic nanomedicine, and cancer research suggest that novel cancer diagnosis and therapy methods are highly needed. Figure  9 a traces the research focus that has evolved across various SDGs over time, commencing with SDG 3 (Good Health and Well-being), which is indicative of the central role of health in biomimetic research. It then extends into SDG 9 (Industry, Innovation, and Infrastructure) and SDG 7 (Affordable and Clean Energy), illustrating the cross-disciplinary applications of biomimetic technologies from healthcare to the energy and industrial sectors.

Figure  9 b provides a snapshot of the prominent keywords within this research theme, featuring terms such as “photodynamic therapy”, “photothermal chemotherapy”, “nanocarrier”, and “controlled drug delivery”. These terms underscore the innovative therapeutic strategies that mimic biological mechanisms for targeted cancer treatment. 'Bioinspired' and 'Biomimetic Synthesis' reflect the approach of deriving design principles from natural systems for the development of advanced materials and medical devices. 'Theranostic nanomedicine' integrates diagnosis and therapy, demonstrating a trend toward personalized and precision medicine.

A study conducted by Yu et al. 62 presented a novel approach for synergistic chemiexcited photodynamic-starvation therapy against metastatic tumors: a biomimetic nanoreactor, or bio-NR. Bio-NRs use hollow mesoporous silica nanoparticles to catalyze the conversion of glucose to hydrogen peroxide for starvation therapy while also producing singlet oxygen for photodynamic therapy. Bio-NR is promising for treating cancer metastasis because its coating on cancer cells improves its biological qualities. Yang et al.'s 63 study focused on a biocompatible Gd-integrated CuS nanotheranostic agent created via a biomimetic approach. This drug has low systemic side effects and good photothermal conversion efficiency, making it suitable for skin cancer therapy. It also performs well in imaging. The ultrasmall copper sulfide nanoparticles generated within ferritin nanocages are described in Wang et al.’s 64 publication. This work highlights the possibility of photoacoustic imaging-guided photothermal therapy with improved therapeutic efficiency and biocompatibility. These highly referenced articles highlight the significance of biomimetic techniques in furthering nanotheranostics and cancer therapy.

Established biomimetic foundations

Here, there are topics with a greater number of publications but a lower prominence percentile, which may imply areas where there has been significant research but that may be waning in influence or undergoing a shift in focus (Quadrant 4—bottom right).

Metaheuristics; Fireflies; Chiroptera

This topic is a fascinating mix of subjects. Using Firefly and Chiroptera in metaheuristic optimization algorithms provides a bioinspired method for resolving challenging issues. The thematic progression of research papers suggests the maturation of biomimetic disciplines that resonate with several SDGs (Fig.  10 a). The shift from initially aligning with SDG 3 (Good Health and Well-being) extends to intersecting with goals such as SDG 9 (Industry, Innovation, and Infrastructure), SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land). This diversification reflects the expansive utility of biomimetic approaches, from health applications to broader environmental and societal challenges.

The top keyphrases, such as 'Swarm Intelligence', 'Global Optimization', 'Cuckoo Search Algorithm', and 'Particle Swarm Optimization', are shown in Fig.  10 b highlights the utilization of nature-inspired algorithms for solving complex optimization problems. These terms, along with the 'Firefly Algorithm' and 'Bat Algorithm', underscore the transition of natural phenomena into computational algorithms that mimic the behavioral patterns of biological organisms, offering robust solutions in various fields, including resource management, logistics, and engineering design.

The three highly referenced metaheuristic publications centered around the “Moth Flame Optimization (MFO),” Salp Swarm Algorithm (SSA),” and Whale Optimization Algorithm (WOA).” The WOA, authored by Mirjalili and Lewis 65 , is a competitive solution for mathematical optimization and structural design issues because it emulates the social behavior of humpback whales. Inspired by the swarming behavior of salps, Mirjalili et al. 66 introduced the SSA and multiobjective SSA. This shows how well they function in optimizing a variety of engineering design difficulties. Finally, Mirjalili 67 suggested the MFO algorithm, which is modeled after the navigational strategy of moths and exhibits competitive performance in resolving benchmark and real-world engineering issues.

Bioprinting; three-dimensional printing; tissue engineering

The emphasis on sophisticated manufacturing methods for biological applications in this field suggests a keen interest in the nexus of biology and technology, especially in tissue engineering. As shown in Fig.  11 a, the topic's evolution encompasses Sustainable Development Goals (SDGs) that have transitioned over the years, including SDG 3 (Good Health and Well-being), which is inherently connected to the advancement of medical technologies and tissue engineering for health applications. This research also touches upon SDG 6 (Clean Water and Sanitation) and SDG 7 (Affordable and Clean Energy), suggesting applications of bioprinting technologies in the environmental sustainability and energy sectors. The progression toward SDG 9 (Industry, Innovation, and Infrastructure) and SDG 15 (Life on Land) reflects a broader impact, where biomimetic principles are applied to foster innovation in industrial processes and contribute to the preservation of terrestrial ecosystems.

Key phrases emerging from the word cloud in Fig.  11 b, such as “Hydrogel”, “Biofabrication”, “Tissue Scaffold”, and “Regenerative Medicine”, highlight the specialized methodologies and materials that are inspired by natural processes and structures. Terms such as 'Three-Dimensional Printing' and 'Bioprinting' underscore the technological advancements in creating complex biological structures, aiming to revolutionize the field of tissue engineering and regenerative medicine.

Three widely referenced papers about advances in 3D printing—particularly in bioprinting, soft matter, and the incorporation of biological tissue with functional electronics—are described next. Truby and Lewis’s 68 review of light- and ink-based 3D printing techniques is ground-breaking. This highlights the technology's capacity to create soft matter with tunable properties and its potential applications in robotics, shape-morphing systems, biologically inspired composites, and soft sensors. Ozbolat, and Hospodiuk 69 provide a thorough analysis of “extrusion-based bioprinting (EBB).” The adaptability of EBB in printing different biologics is discussed in the paper, with a focus on its uses in pharmaceutics, primary research, and clinical contexts. Future directions and challenges in EBB technology are also discussed. Using 3D printing, Mannoor et al. 70 presented a novel method for fusing organic tissue with functioning electronics. In the proof-of-concept, a hydrogel matrix seeded with cells and an interwoven conductive polymer containing silver nanoparticles are 3D printed to create a bionic ear. The improved auditory sensing capabilities of the printed ear show how this novel technology allows biological and nanoelectronic features to work together harmoniously.

RQ3: Translation and commercialization

Biomimicry offers promising solutions for sustainability in commercial industries with environmentally sustainable product innovation and energy savings with reduced resource commitment 71 . However, translating biomimicry innovations from research to commercialization presents challenges, including product validation, regulatory hurdles, and the need for strategic investment, innovative financial models, and interdisciplinary collaboration 71 , 72 , 73 , 74 . Ethical considerations highlight the need for universally applicable ethical guidelines regarding the moral debates surrounding biomimicry, such as motivations for pursuing such approaches and the valuation of nature 75 .

Addressing these barriers requires interdisciplinary collaboration, targeted education, and training programs. Strategic investment in biomimicry research and development is also crucial. Encouraging an engineering mindset that integrates biomimicry principles into conventional practices and developing commercial acumen among researchers is essential for navigating the market landscape 76 . Securing sufficient funding is essential for the development, testing, and scaling of these innovations 76 .

Successful case studies illustrate that the strategic integration of biomimicry enhances corporate sustainability and innovation (Larson & Meier 2017). In biomedical research, biomimetic approaches such as novel scaffolds and artificial skins have made significant strides (Zhang 2012). Architecture benefits through energy-efficient building facades modeled after natural cooling systems (Webb et al. 2017). The textile industry uses biomimicry to create sustainable, high-performance fabrics 77 .

RQ4: Interdisciplinary collaboration

Agricultural innovations (sdgs 1—no poverty and 2—zero hunger).

Environmental degradation, biodiversity loss, poverty, and hunger highlight the need for sustainable agricultural methods to mimic natural ecosystems. This includes computational models for ecological interactions, field experiments for biomimetic techniques, and novel materials inspired by natural soil processes. Research can develop solutions such as artificial photosynthesis for energy capture, polyculture systems mimicking ecosystem diversity, and bioinspired materials for soil regeneration and water retention 28 . These innovations can improve sustainability and energy efficiency in agriculture, addressing poverty and hunger through sustainable farming practices.

Educational models (SDG 4—Quality education)

Integrating sustainability principles and biomimicry into educational curricula at all levels presents opportunities for innovation. Collaborations between educators, environmental scientists, and designers can create immersive learning experiences that promote sustainability. This includes interdisciplinary curricula with biomimicry case studies, digital tools, and simulations for exploring biomimetic designs, and participatory learning approaches for engaging students with natural environments. Designing biomimicry-based educational tools and programs can help students engage in hands-on, project-based learning 10 , fostering a deeper understanding of sustainable living and problem-solving.

Gender-inclusive design (SDG 5—Gender inequality)

Gender biases in design and innovation call for research into biomimetic designs and technologies that facilitate gender equality. This includes participatory design processes involving women as cocreators, studying natural systems for inclusive strategies, and applying biomimetic principles to develop technologies supporting gender equality. Bioinspired technologies can address women's specific needs, enhancing access to education, healthcare, and economic opportunities. Interdisciplinary approaches involving gender studies, engineering, and environmental science can uncover new pathways for inclusive innovation.

Inclusive urban solutions (SDG 11—Sustainable cities and communities)

Rapid urbanization challenges such as housing shortages, environmental degradation, and unsustainable transportation systems require innovative solutions. Methodologies include systems thinking in urban planning, simulation tools for modeling biomimetic solutions, and pilot projects testing bioinspired urban innovations. Research on biomimetic architecture for affordable housing, green infrastructure for climate resilience, and bioinspired transportation systems can offer solutions. Collaborative efforts among architects, urban planners, ecologists, and sociologists are essential 78 .

Peace and justice (SDG 16—Peace, justice and institutions)

Social conflicts and weak institutions necessitate innovative approaches that integrate political science, sociology, and biology. Methods involve case studies, theoretical modeling, and participatory action research to develop strategies for peacebuilding and institutional development.

This research provides a comprehensive exploration of the multifaceted dimensions of biomimicry, SDG alignment, and interdisciplinary topics, demonstrating a clear trajectory of growth and relevance. Interdisciplinary collaboration has emerged as a pivotal strategy for unlocking the full potential of biomimicry in addressing underexplored SDGs.

While answering RQ1, the interdisciplinary analysis underscores the significant alignment of biomimicry research with several SDGs. This reflects the interdisciplinary nature of biomimicry and its ability to generate solutions for societal challenges. The analysis of two thematic clusters revealed the broad applicability of biomimicry across various sustainable development goals (SDGs). The first cluster includes health, partnership, and life on land (SDGs 3, 17, and 15), highlighting biomimicry's potential in medical technologies, sustainability collaborations, and land management. The second cluster encompasses clean water, energy, infrastructure, and marine life (SDGs 6, 7, 9, and 14), demonstrating innovative approaches to clean energy generation, sustainable infrastructure, and water purification.

In response to RQ2, this study highlights emerging topics within biomimicry research, such as metaheuristics and nanogenerators, which reflect a dynamic and evolving field that is swiftly gaining attention. These topics, alongside sensors, flexible electronics, and strain sensors, denote evolving research objectives and societal demands, pointing to new areas of study and innovation. This focus on interdisciplinary topics within biomimicry underscores the field’s adaptability and responsiveness to the shifting landscapes of technological and societal challenges.

In addressing RQ3, biomimicry holds potential for sustainable innovation but faces challenges in commercialization. Biomimicry inspires diverse technological and product innovations, driving sustainable advancements (Lurie-Luke 84 ). Overcoming these barriers through strategic investment, training, interdisciplinary collaboration, and ethical guidelines is essential for unlocking their full potential.

For RQ4 , the recommendations are formulated based on underexplored SDGs like 1, 4, 5, and 10 where biomimicry could play a pivotal role.

Future research could apply generative AI models to this dataset to validate the findings and explore additional insights. While our current study did not explore this topic, we see significant potential for this approach. Generative AI models can process extensive datasets and reveal patterns, potentially offering insights into biomimetic research correlations. The interpretation required for context-specific analysis remains challenging for generative AI 36 , 37

Our study provides valuable insights, but some limitations are worth considering. The chosen database might limit the comprehensiveness of the research captured, potentially excluding relevant work from other sources. Additionally, while the combination of cocitation mapping and BERTopic modeling provides a powerful analysis, both methods have inherent limitations. They may oversimplify the complexities of the field or introduce bias during theme interpretation, even with advanced techniques. Furthermore, our use of citations to thematically clustered publications as a proxy for impact inherits the limitations of citation analysis, such as biases toward established ideas and potential misinterpretations 79 , 80 . Another limitation of our study is the potential for missing accurate SDG mappings, as multiple SDG mapping initiatives are available, and our reliance on a single, Scopus-integrated method may not capture all relevant associations. Consequently, this could have resulted in the exclusion of papers that were appropriately aligned with certain SDGs but were not identified by our chosen mapping approach. Given these limitations, this study provides a valuable snapshot for understanding biomimicry research.

Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information files.

El-Zeiny, R. M. A. Biomimicry as a problem solving methodology in interior architecture. Procedia. Soc. 50 , 502–512 (2012).

Article   Google Scholar  

Othmani, N. I., Sahak, N. M. & Yunos, M. Y. M. Biomimicry in agrotechnology: Future solution of water problem for the agriculture industry?. IOP Conf. Ser.: Earth Environ. Sci. 756 (1), 012051. https://doi.org/10.1088/1755-1315/756/1/012051 (2021).

Bensaude-Vincent, B. Bioinformed emerging technologies and their relation to the sustainability aims of biomimicry. Environ. Values 28 (5), 551–571 (2019).

Chirazi, J. E. Commercialization journeys: Bringing biomimetic innovation to the market. In Biomim. Mat., Des. and Habit. (pp. 393–436). Elsevier (2022).

Vincent, J. F., Bogatyreva, O. A., Bogatyrev, N. R., Bowyer, A. & Pahl, A. K. Biomimetics: Its practice and theory. J. R. Soc. Interface 3 (9), 471–482 (2006).

Article   PubMed   PubMed Central   Google Scholar  

Pogge, T. & Sengupta, M. The sustainable development goals: A plan for building a better world?. J. Glob. Ethics 11 (1), 56–64 (2015).

Wamane, G. V. A “new deal” for a sustainable future: Enhancing circular economy by employing ESG principles and biomimicry for efficiency. Manag. Environ. Qual. https://doi.org/10.1108/MEQ-07-2022-0189 (2023).

Butt, A. N. & Dimitrijević, B. Multidisciplinary and transdisciplinary collaboration in nature-based design of sustainable architecture and urbanism. Sustainability 14 (16), 10339 (2022).

Gupta, J. & Vegelin, C. Sustainable development goals and inclusive development. INEA 16 , 433–448 (2016).

Google Scholar  

Hayes, S., Desha, C. & Baumeister, D. Learning from nature—Biomimicry innovation to support infrastructure sustainability and resilience. TFSC 161 , 120287 (2020).

Toner, J., Desha, C., Reis, K., Hes, D. & Hayes, S. Integrating ecological knowledge into regenerative design: A rapid practice review. Sustainability 15 (17), 13271 (2023).

Jamei, E. & Vrcelj, Z. Biomimicry and the built environment, learning from nature’s solutions. Appl. Sci. 11 (16), 7514 (2021).

Article   CAS   Google Scholar  

Blanco, E., Pedersen Zari, M., Raskin, K. & Clergeau, P. Urban ecosystem-level biomimicry and regenerative design: Linking ecosystem functioning and urban built environments. Sustainability 13 (1), 404 (2021).

Rebecca, C. Climate Action Through Biomimcry: Innovation for the SDGs. Sustainable Brands. https://sustainablebrands.com/read/product-service-design-innovation/climate-action-through-biomimicry-innovation-for-the-sdgs . (Accessed on January 9, 2024) (2020).

Vierra, S. Biomimicry: Designing to model nature. Whole Build. Des. Guide 1–10 (2011).

Biomimicry Institute. Case Studies of Commercialisation of Biomimicry Innovations (2023).

Fayemi, P. E., Wanieck, K., Zollfrank, C., Maranzana, N. & Aoussat, A. Biomimetics: Process, tools and practice. Bioinsp. Biomim. 12 (1), 011002. https://doi.org/10.1088/1748-3190/12/1/011002 (2017).

Article   PubMed   CAS   Google Scholar  

Meena, A. K., D’Costa, D., Bhavsar, S., Kshirsagar, M. & Kulkarni, S. Applications of biomimicry in construction and architecture: A bibliometric analysis. Lib. Phil. Prac. Lincoln, NE, USA , 1–17 (2021).

Varshabi, N., Arslan Selçuk, S. & Mutlu Avinç, G. Biomimicry for energy-efficient building design: A bibliometric analysis. Biomimetics 7 (1), 21 (2022).

Perricone, V., Langella, C. & Santulli, C. Sustainable Biomimetics: A Discussion on Differences in Scale, Complexity, and Organization Between the Natural and Artificial World. In Bion. Sustain. Design 171–193 (Springer Nature Singapore, 2022).

Aggarwal, S., & Verma, A. Scientometric analysis of research trends in clustering and nature inspired techniques. In AIP Conf. Proc. (Vol. 2576, No. 1). AIP Publishing (2022, December).

Pinzón, O. A. & Austin, M. C. Nature as an Inspiration to Exploit Renewable Energy Sources: A Review on Trends and Biological Strategies. In 2022 8th International Engineering, Sciences and Technology Conference (IESTEC) 463–468 (IEEE, 2022).

Carniel, T., Cazenille, L., Dalle, J. M. & Halloy, J. Using natural language processing to find research topics in Living Machines conferences and their intersections with Bioinspiration & Biomimetics publications. Bioinsp. & Biomim 17 (6), 065008 (2022).

Nasser, H. K. et al. Harmony search algorithm for solving combinatorial optimization problems: Bibliometric analysis. Math. Model. Eng. Prob. 10 (3), 906 (2023).

Rusu, D. M. et al. Soft robotics: A systematic review and bibliometric analysis. Micromachines 14 (2), 359. https://doi.org/10.3390/mi14020359 (2023).

Shashwat, S. et al. A review on bioinspired strategies for an energy-efficient built environment. Energy Build. 296 , 113382 (2023).

Pires, P. M. et al. Bioactive restorative materials applied over coronal dentine—A bibliometric and critical review. Bioengineering 10 (6), 731. https://doi.org/10.3390/bioengineering10060731 (2023).

Article   PubMed   PubMed Central   CAS   Google Scholar  

Liu, H., Liao, X. & Du, B. The applications of nature-inspired meta-heuristic algorithms for decreasing the energy consumption of software-defined networks: A comprehensive and systematic literature review. Sustain. Comput.: Inform. Syst. 39 , 100895. https://doi.org/10.1016/j.suscom.2023.100895 (2023).

Hinkelman, K., Yang, Y. & Zuo, W. Design methodologies and engineering applications for ecosystem biomimicry: An interdisciplinary review spanning cyber, physical, and cyber-physical systems. Bioinsp. Biomim. 18 (2), 021001 (2023).

Raman, R., Lathabhai, H., Mandal, S., Kumar, C. & Nedungadi, P. Contribution of business research to sustainable development goals: bibliometrics and science mapping analysis. Sustainability 15 (17), 12982 (2023).

Nedungadi, P. et al. Mapping autism’s research landscape: Trends in autism screening and its alignment with sustainable development goals. Front. Psychiatry 14 , 1294254 (2024).

Auckland's SDG Mapping. University of Auckland's SDG Mapping initiative from https://www.sdgmapping.auckland.ac.nz , accessed on 4th February 2024

Elsevier's SDG Mapping - Elsevier's SDG Mapping Initiative from https://www.elsevier.com/about/partnerships/sdg-research-mapping-initiative , accessed on 4th February 2024

Raman, R., Nair, V. K., Prakash, V., Patwardhan, A. & Nedungadi, P. Green-hydrogen research: What have we achieved, and where are we going? Bibliometrics analysis. Energy Rep. 8 , 9242–9260 (2022).

Raman, R. et al. Fake news research trends, linkages to generative artificial intelligence and sustainable development goals. Heliyon 10 (3), e24727 (2024).

Raman, R. et al. ChatGPT: Literate or intelligent about UN sustainable development goals?. Plos one 19 (4), e0297521 (2024).

Raman, R., Calyam, P. & Achuthan, K. ChatGPT or bard: Who is a better certified ethical hacker?. Comput. Secur. 140 , 103804 (2024).

Rama, R., Nair, V. K., Nedungadi, P., Ray, I. & Achuthan, K. Darkweb research: Past, present, and future trends and mapping to sustainable development goals. Heliyon 9 (11), e22269 (2023).

Van Eck, N. J. & Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84 , 523–538 (2010).

Article   PubMed   Google Scholar  

Emary, E., Zawbaa, H. M. & Hassanien, A. E. Binary gray wolf optimization approaches for feature selection. Neurocomputing 172 , 371–381 (2016).

Lin, Y., Ren, J. & Qu, X. Catalytically active nanomaterials: a promising candidate for artificial enzymes. Acc. Chem. Res. 47 (4), 1097–1105 (2014).

Parodi, A. et al. Synthetic nanoparticles functionalized with biomimetic leukocyte membranes possess cell-like functions. Nat. Nanotechnol. 8 (1), 61–68 (2013).

Article   ADS   PubMed   CAS   Google Scholar  

Zou, Q. et al. Biological photothermal nanodots based on self-assembly of peptide–porphyrin conjugates for antitumor therapy. J. Am. Chem. Soc. 139 (5), 1921–1927 (2017).

Wang, G. G., Deb, S. & Cui, Z. Monarch butterfly optimization. Neural Comput. Appl. 31 , 1995–2014 (2019).

Li, X. et al. Engineering heterogeneous semiconductors for solar water splitting. J. Mater. Chem. 3 (6), 2485–2534 (2015).

Li, X., Yu, J., Jaroniec, M. & Chen, X. Cocatalysts for selective photoreduction of CO 2 into solar fuels. Chem. Rev. 119 (6), 3962–4179 (2019).

Wang, B., Liang, W., Guo, Z. & Liu, W. Biomimetic superlyophobic and superlyophilic materials applied for oil/water separation: A new strategy beyond nature. Chem. Soc. Rev. 44 (1), 336–361 (2015).

Singh, J. et al. ‘Green’synthesis of metals and their oxide nanoparticles: Applications for environmental remediation. J. Nanobiotechnology 16 (1), 1–24 (2018).

Wang, Z. et al. Biomimetic nanoflowers by self-assembling nanozymes to induce intracellular oxidative damage against hypoxic tumors. Nat. Commun. 9 (1), 3334 (2018).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Gao, S. et al. Nanocatalytic tumor therapy by biomimetic dual inorganic nanozyme-catalyzed cascade reaction. Adv. Sci. 6 (3), 1801733 (2019).

Su, B., Tian, Y. & Jiang, L. Bioinspired interfaces with superwettability: From materials to chemistry. J. Am. Chem. Soc. 138 (6), 1727–1748 (2016).

Zhang, X., Li, Z., Liu, K. & Jiang, L. Bioinspired multifunctional foam with self-cleaning and oil/water separation. Adv. Funct. Mater. 23 (22), 2881–2886 (2013).

Jang, S. C. et al. Bioinspired exosome-mimetic nanovesicles for targeted delivery of chemotherapeutics to malignant tumors. ACS Nano 7 (9), 7698–7710 (2013).

Yong, T. et al. Tumor exosome-based nanoparticles are efficient drug carriers for chemotherapy. Nat. Commun. 10 (1), 3838 (2019).

Cheng, G. et al. Self-assembly of extracellular vesicle-like metal–organic framework nanoparticles for protection and intracellular delivery of biofunctional proteins. J. Am. Chem. Soc. 140 (23), 7282–7291 (2018).

Yang, J. et al. Eardrum-inspired active sensors for self-powered cardiovascular system characterization and throat-attached anti-interference voice recognition. Adv. Mater. 27 (8), 1316–1326 (2015).

Sezer, N. & Koç, M. A comprehensive review on the state-of-the-art of piezoelectric energy harvesting. Nano Energy 80 , 105567 (2021).

Zhao, D. et al. Cellulose-based flexible functional materials for emerging intelligent electronics. Adv. Mater. 33 (28), 2000619 (2021).

Chortos, A., Liu, J. & Bao, Z. Pursuing prosthetic electronic skin. Nat. mater. 15 (9), 937–950 (2016).

Kim, J. et al. Stretchable silicon nanoribbon electronics for skin prosthesis. Nat. Commun. 5 (1), 5747 (2014).

Pang, C. et al. Highly skin-conformal microhairy sensor for pulse signal amplification. Adv. Mater. 27 (4), 634–640 (2015).

Yu, Z., Zhou, P., Pan, W., Li, N. & Tang, B. A biomimetic nanoreactor for synergistic chemiexcited photodynamic and starvation therapy against tumor metastasis. Nat. Commun. 9 (1), 5044 (2018).

Yang, W. et al. Albumin-bioinspired Gd: CuS nanotheranostic agent for in vivo photoacoustic/magnetic resonance imaging-guided tumor-targeted photothermal therapy. ACS Nano 10 (11), 10245–10257 (2016).

Wang, Z. et al. Biomineralization-inspired synthesis of copper sulfide–ferritin nanocages as cancer theranostics. ACS Nano 10 (3), 3453–3460 (2016).

Mirjalili, S. & Lewis, A. The whale optimization algorithm. Adv. Eng. Softw. 95 , 51–67 (2016).

Mirjalili, S. et al. Salp Swarm Algorithm: A bioinspired optimizer for engineering design problems. Adv. Eng. Softw. 114 , 163–191 (2017).

Mirjalili, S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl. Based Syst. 89 , 228–249 (2015).

Truby, R. L. & Lewis, J. A. Printing soft matter in three dimensions. Nature 540 (7633), 371–378 (2016).

Ozbolat, I. T. & Hospodiuk, M. Current advances and future perspectives in extrusion-based bioprinting. Biomater. 76 , 321–343 (2016).

Mannoor, M. S. et al. 3D printed bionic ears. Nano Lett. 13 (6), 2634–2639 (2013).

Article   ADS   PubMed   PubMed Central   CAS   Google Scholar  

Kennedy, E. B. & Marting, T. A. Biomimicry: Streamlining the front end of innovation for environmentally sustainable products. Res. Technol. Manage. 59 (4), 40–48 (2016).

Chazhaev, M. Economic potential of biotechnologies: challenges and windows of opportunity. BIO Web Conf. 76 , 10002 (2023).

Kasatova, A. A., Vagizova, V. I. & Tufetulov, A. M. Stages of biotechnology commercialization in the system of interaction between financial and innovative industrial structures. Acad. Strateg. Manag. J. 15 , 191 (2016).

Rovalo, E., McCardle, J., Smith, E. & Hooker, G. Growing the practice of biomimicry: Opportunities for mission-based organisations based on a global survey of practitioners. Technol. Anal. Strateg. Manag. 32 (1), 71–87 (2020).

Broeckhoven, C. & Winters, S. Biomimethics: a critical perspective on the ethical implications of biomimetics in technological innovation. Bioinspir. Biomim. 18 (5), 053001 (2023).

Article   ADS   Google Scholar  

Biomimicry Innovation Lab. The State of Nature-Inspired Innovation in the UK. Retrieved from https://www.biomimicryinnovationlab.com/blog/the-state-of-nature-inspired-innovation-in-the-uk (2023).

Weerasinghe, D. U., Perera, S. & Dissanayake, D. Application of biomimicry for sustainable functionalization of textiles: Review of current status and prospectus. Textile Res. J. 89 (22), 4282–4294 (2019).

Madmar, S., Shah, M. Z., Matusin, A. M. R. A. & Ilhan, A. A. Applications of biomimicry to urban planning: interrogating the relevance of emerging approaches to design cities by inspiring from nature. IOP Conf. Ser.: Earth Environ. Sci. 1274 (1), 012015 (2023).

Letrud, K. & Hernes, S. Affirmative citation bias in scientific myth debunking: A three-in-one case study. PLoS One 14 (9), e0222213 (2019).

McCain, K. W. Obliteration by incorporation. Beyond bibliometrics: Harnessing multidimensional indicators of scholarly impact. J. Scientometr. Res. 4 (1), 129–149 (2014).

Benyus, J. M. (1997). Biomimicry: Innovation inspired by nature.

Donthu, N., Kumar, S., Pandey, N., Pandey, N., & Mishra, A. (2021). Mapping the electronic word-of-mouth (eWOM) research: A systematic review and bibliometric analysis. J. Bus. Res. , 135 , 758–773.

Sydney Gladman, A., Matsumoto, E. A., Nuzzo, R. G., Mahadevan, L., & Lewis, J. A. (2016). Biomimetic 4D printing. Nature materials , 15 (4), 413–418.

Lurie-Luke, E. Product and technology innovation: What can biomimicry inspire?. Biotechnology advances , 32 (8), 1494–1505 (2014).

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R.R.—Conceptualization; supervision; methodology; data curation; visualization; writing—original draft; and writing—review and editing. A.S.—Data curation; Writing—original draft; and Writing—review and editing. M.S.—writing—original draft; and writing—review and editing. P.N.—Data curation; writing—original draft; and writing—review and editing.

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9 Best Marketing Research Methods to Know Your Buyer Better [+ Examples]

Published: August 08, 2024

One of the most underrated skills you can have as a marketer is marketing research — which is great news for this unapologetic cyber sleuth.

From brand design and product development to buyer personas and competitive analysis, I’ve researched a number of initiatives in my decade-long marketing career.

And let me tell you: having the right marketing research methods in your toolbox is a must.

Market research is the secret to crafting a strategy that will truly help you accomplish your goals. The good news is there is no shortage of options.

How to Choose a Marketing Research Method

Thanks to the Internet, we have more marketing research (or market research) methods at our fingertips than ever, but they’re not all created equal. Let’s quickly go over how to choose the right one.

Free Market Research Kit

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1. Identify your objective.

What are you researching? Do you need to understand your audience better? How about your competition? Or maybe you want to know more about your customer’s feelings about a specific product.

Before starting your research, take some time to identify precisely what you’re looking for. This could be a goal you want to reach, a problem you need to solve, or a question you need to answer.

For example, an objective may be as foundational as understanding your ideal customer better to create new buyer personas for your marketing agency (pause for flashbacks to my former life).

Or if you’re an organic sode company, it could be trying to learn what flavors people are craving.

2. Determine what type of data and research you need.

Next, determine what data type will best answer the problems or questions you identified. There are primarily two types: qualitative and quantitative. (Sound familiar, right?)

• Qualitative Data is non-numerical information, like subjective characteristics, opinions, and feelings. It’s pretty open to interpretation and descriptive, but it’s also harder to measure. This type of data can be collected through interviews, observations, and open-ended questions.
• Quantitative Data , on the other hand, is numerical information, such as quantities, sizes, amounts, or percentages. It’s measurable and usually pretty hard to argue with, coming from a reputable source. It can be derived through surveys, experiments, or statistical analysis.

Understanding the differences between qualitative and quantitative data will help you pinpoint which research methods will yield the desired results.

For instance, thinking of our earlier examples, qualitative data would usually be best suited for buyer personas, while quantitative data is more useful for the soda flavors.

However, truth be told, the two really work together.

Qualitative conclusions are usually drawn from quantitative, numerical data. So, you’ll likely need both to get the complete picture of your subject.

For example, if your quantitative data says 70% of people are Team Black and only 30% are Team Green — Shout out to my fellow House of the Dragon fans — your qualitative data will say people support Black more than Green.

(As they should.)

Primary Research vs Secondary Research

You’ll also want to understand the difference between primary and secondary research.

Primary research involves collecting new, original data directly from the source (say, your target market). In other words, it’s information gathered first-hand that wasn’t found elsewhere.

Some examples include conducting experiments, surveys, interviews, observations, or focus groups.

Meanwhile, secondary research is the analysis and interpretation of existing data collected from others. Think of this like what we used to do for school projects: We would read a book, scour the internet, or pull insights from others to work from.

So, which is better?

Personally, I say any research is good research, but if you have the time and resources, primary research is hard to top. With it, you don’t have to worry about your source's credibility or how relevant it is to your specific objective.

You are in full control and best equipped to get the reliable information you need.

3. Put it all together.

Once you know your objective and what kind of data you want, you’re ready to select your marketing research method.

For instance, let’s say you’re a restaurant trying to see how attendees felt about the Speed Dating event you hosted last week.

You shouldn’t run a field experiment or download a third-party report on speed dating events; those would be useless to you. You need to conduct a survey that allows you to ask pointed questions about the event.

This would yield both qualitative and quantitative data you can use to improve and bring together more love birds next time around.

Best Market Research Methods for 2024

Now that you know what you’re looking for in a marketing research method, let’s dive into the best options.

Note: According to HubSpot’s 2024 State of Marketing report, understanding customers and their needs is one of the biggest challenges facing marketers today. The options we discuss are great consumer research methodologies , but they can also be used for other areas.

Primary Research

1. interviews.

Interviews are a form of primary research where you ask people specific questions about a topic or theme. They typically deliver qualitative information.

I’ve conducted many interviews for marketing purposes, but I’ve also done many for journalistic purposes, like this profile on comedian Zarna Garg . There’s no better way to gather candid, open-ended insights in my book, but that doesn’t mean they’re a cure-all.

What I like: Real-time conversations allow you to ask different questions if you’re not getting the information you need. They also push interviewees to respond quickly, which can result in more authentic answers.

What I dislike: They can be time-consuming and harder to measure (read: get quantitative data) unless you ask pointed yes or no questions.

Best for: Creating buyer personas or getting feedback on customer experience, a product, or content.

2. Focus Groups

Focus groups are similar to conducting interviews but on a larger scale.

In marketing and business, this typically means getting a small group together in a room (or Zoom), asking them questions about various topics you are researching. You record and/or observe their responses to then take action.

They are ideal for collecting long-form, open-ended feedback, and subjective opinions.

One well-known focus group you may remember was run by Domino’s Pizza in 2009 .

After poor ratings and dropping over $100 million in revenue, the brand conducted focus groups with real customers to learn where they could have done better.

It was met with comments like “worst excuse for pizza I’ve ever had” and “the crust tastes like cardboard.” But rather than running from the tough love, it took the hit and completely overhauled its recipes.

The team admitted their missteps and returned to the market with better food and a campaign detailing their “Pizza Turn Around.”

The result? The brand won a ton of praise for its willingness to take feedback, efforts to do right by its consumers, and clever campaign. But, most importantly, revenue for Domino’s rose by 14.3% over the previous year.

The brand continues to conduct focus groups and share real footage from them in its promotion:

What I like: Similar to interviewing, you can dig deeper and pivot as needed due to the real-time nature. They’re personal and detailed.

What I dislike: Once again, they can be time-consuming and make it difficult to get quantitative data. There is also a chance some participants may overshadow others.

Best for: Product research or development

Pro tip: Need help planning your focus group? Our free Market Research Kit includes a handy template to start organizing your thoughts in addition to a SWOT Analysis Template, Survey Template, Focus Group Template, Presentation Template, Five Forces Industry Analysis Template, and an instructional guide for all of them. Download yours here now.

3. Surveys or Polls

Surveys are a form of primary research where individuals are asked a collection of questions. It can take many different forms.

They could be in person, over the phone or video call, by email, via an online form, or even on social media. Questions can be also open-ended or closed to deliver qualitative or quantitative information.

A great example of a close-ended survey is HubSpot’s annual State of Marketing .

In the State of Marketing, HubSpot asks marketing professionals from around the world a series of multiple-choice questions to gather data on the state of the marketing industry and to identify trends.

The survey covers various topics related to marketing strategies, tactics, tools, and challenges that marketers face. It aims to provide benchmarks to help you make informed decisions about your marketing.

It also helps us understand where our customers’ heads are so we can better evolve our products to meet their needs.

Apple is no stranger to surveys, either.

In 2011, the tech giant launched Apple Customer Pulse , which it described as “an online community of Apple product users who provide input on a variety of subjects and issues concerning Apple.”

"For example, we did a large voluntary survey of email subscribers and top readers a few years back."

While these readers gave us a long list of topics, formats, or content types they wanted to see, they sometimes engaged more with content types they didn’t select or favor as much on the surveys when we ran follow-up ‘in the wild’ tests, like A/B testing.”  

Pepsi saw similar results when it ran its iconic field experiment, “The Pepsi Challenge” for the first time in 1975.

The beverage brand set up tables at malls, beaches, and other public locations and ran a blindfolded taste test. Shoppers were given two cups of soda, one containing Pepsi, the other Coca-Cola (Pepsi’s biggest competitor). They were then asked to taste both and report which they preferred.

People overwhelmingly preferred Pepsi, and the brand has repeated the experiment multiple times over the years to the same results.

What I like: It yields qualitative and quantitative data and can make for engaging marketing content, especially in the digital age.

What I dislike: It can be very time-consuming. And, if you’re not careful, there is a high risk for scientific error.

Best for: Product testing and competitive analysis

Pro tip:  " Don’t make critical business decisions off of just one data set," advises Pamela Bump. "Use the survey, competitive intelligence, external data, or even a focus group to give you one layer of ideas or a short-list for improvements or solutions to test. Then gather your own fresh data to test in an experiment or trial and better refine your data-backed strategy."

Secondary Research

8. public domain or third-party research.

While original data is always a plus, there are plenty of external resources you can access online and even at a library when you’re limited on time or resources.

Some reputable resources you can use include:

• Pew Research Center
• McKinley Global Institute
• Relevant Global or Government Organizations (i.e United Nations or NASA)

It’s also smart to turn to reputable organizations that are specific to your industry or field. For instance, if you’re a gardening or landscaping company, you may want to pull statistics from the Environmental Protection Agency (EPA).

If you’re a digital marketing agency, you could look to Google Research or HubSpot Research . (Hey, I know them!)

What I like: You can save time on gathering data and spend more time on analyzing. You can also rest assured the data is from a source you trust.

What I dislike: You may not find data specific to your needs.

Best for: Companies under a time or resource crunch, adding factual support to content

Pro tip: Fellow HubSpotter Iskiev suggests using third-party data to inspire your original research. “Sometimes, I use public third-party data for ideas and inspiration. Once I have written my survey and gotten all my ideas out, I read similar reports from other sources and usually end up with useful additions for my own research.”

9. Buy Research

If the data you need isn’t available publicly and you can’t do your own market research, you can also buy some. There are many reputable analytics companies that offer subscriptions to access their data. Statista is one of my favorites, but there’s also Euromonitor , Mintel , and BCC Research .

What I like: Same as public domain research

What I dislike: You may not find data specific to your needs. It also adds to your expenses.

Best for: Companies under a time or resource crunch or adding factual support to content

Which marketing research method should you use?

You’re not going to like my answer, but “it depends.” The best marketing research method for you will depend on your objective and data needs, but also your budget and timeline.

My advice? Aim for a mix of quantitative and qualitative data. If you can do your own original research, awesome. But if not, don’t beat yourself up. Lean into free or low-cost tools . You could do primary research for qualitative data, then tap public sources for quantitative data. Or perhaps the reverse is best for you.

Whatever your marketing research method mix, take the time to think it through and ensure you’re left with information that will truly help you achieve your goals.

Don't forget to share this post!

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Utilizing crowdsourced data for timely investigation of catastrophic landslide accidents: a case study of the coal mine collapse in inner Mongolia, China

• Original Paper
• Published: 12 August 2024
• Volume 83 , article number  354 , ( 2024 )

Cite this article

• Pengju An 1 ,
• Rui Yong 1 ,
• Changshuo Wang 1 ,
• Shixun Jia 2 &
• Kun Fang   ORCID: orcid.org/0000-0002-8866-4076 3 , 4  

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Catastrophic landslide accidents are a significant global issue, resulting in considerable loss of life and property damage. However, traditional landslide survey methods are typically time-consuming and require expensive equipment, which hinders timely responses to the need for landslide rescue and accident investigation. This study proposes a method for utilizing timely crowdsourced data in the preliminary investigation of catastrophic landslide accidents. Specifically, we examine the case of the Xinjing Landslide in Inner Mongolia, China, which occurred on February 23, 2023. We demonstrate the ability of crowdsourced data to provide real-time information about landslide occurrence, size, movement direction, and speed. Moreover, we analyze the possible triggers of the landslide based on the gathered data. Our findings suggest that prompt crowdsourced data can provide valuable information about landslides and potentially save lives through timely responses. This study emphasizes the potential of timely crowdsourced data in enhancing landslide investigation and calls for further research into the integration of crowdsourced data with traditional monitoring methods.

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Accident Investigation Team of the State Council of the PRC (2023) Inner Mongolia Alxa Xinjing Coal Industry Co., Ltd. open-pit coal mine 2·22 special major collapse accident investigation report [WWW Document]. URL https://www.mem.gov.cn/gk/sgcc/tbzdsgdcbg/ (accessed 9.11.23)

An P, Fang K, Jiang Q, Zhang H, Zhang Y (2021) Measurement of Rock Joint surfaces by using Smartphone structure from motion (SfM) photogrammetry. Sensors 21:922

Article   Google Scholar  

An B, Wang C, Liu C, Li P (2023) A multi-source remote sensing satellite view of the February 22nd Xinjing landslide in the mining area of Alxa left Banner, China. Landslides 20:2517–2523. https://doi.org/10.1007/s10346-023-02138-z

Bevan A, Daniel P, Chiara B, Keinan-Schoonbaert A, González DL, Sparks R, Wexler J, Wilkin N (2014) Citizen archaeologists. Online collaborative research about the human past. Hum Comput 1. https://doi.org/10.15346/hc.v1i2.9

Bunn M, Leshchinsky B, Olsen MJ (2020) Estimates of three-dimensional rupture surface geometry of deep-seated landslides using landslide inventories and high-resolution topographic data. Geomorphology 367:107332. https://doi.org/10.1016/j.geomorph.2020.107332

Callau AÀ, Albert MYP, Rota JJ, Giné DS (2019) Landscape characterization using photographs from crowdsourced platforms: content analysis of social media photographs. Open Geosci 11:558–571. https://doi.org/10.1515/geo-2019-0046

Catani F (2021) Landslide detection by deep learning of non-nadiral and crowdsourced optical images. Landslides 18:1025–1044. https://doi.org/10.1007/s10346-020-01513-4

Cheng L, Yuan Y, Xia N, Chen S, Chen Y, Yang K, Ma L, Li M (2018) Crowd-sourced pictures geo-localization method based on street view images and 3D reconstruction. ISPRS J Photogrammetry Remote Sens 141:72–85. https://doi.org/10.1016/j.isprsjprs.2018.04.006

Choi CE, Cui Y, Zhou GGD (2018) Utilizing crowdsourcing to enhance the mitigation and management of landslides. Landslides 15:1889–1899. https://doi.org/10.1007/s10346-018-1034-9

Corominas J, Moya J, Lloret A, Gili JA, Angeli MG, Pasuto A, Silvano S (2000) Measurement of landslide displacements using a wire extensometer. Eng Geol 55:149–166. https://doi.org/10.1016/S0013-7952(99)00086-1

Corradetti A, McCaffrey K, De Paola N, Tavani S (2017) Evaluating roughness scaling properties of natural active fault surfaces by means of multi-view photogrammetry. Tectonophysics 717:599–606

Crema S, Llena M, Calsamiglia A, Estrany J, Marchi L, Vericat D, Cavalli M (2020) Can inpainting improve digital terrain analysis? Comparing techniques for void filling, surface reconstruction and geomorphometric analyses. Earth Surf Proc Land 45:736–755. https://doi.org/10.1002/esp.4739

Esposito G, Salvini R, Matano F, Sacchi M, Danzi M, Somma R, Troise C (2017) Multitemporal monitoring of a coastal landslide through SfM-derived point cloud comparison. Photogram Rec 32:459–479. https://doi.org/10.1111/phor.12218

Fan X-M, Xu Q, Zhang Z-Y, Meng D, Tang R (2009) The genetic mechanism of a translational landslide. Bull Eng Geol Environ 68:231–244. https://doi.org/10.1007/s10064-009-0194-1

Fang K, Dong A, Tang H, An P, Wang Q, Jia S, Zhang B (2024) Development of an easy-assembly and low-cost multismartphone photogrammetric monitoring system for rock slope hazards. Int J Rock Mech Min Sci 174:105655. https://doi.org/10.1016/j.ijrmms.2024.105655

Fang K, Miao M, Tang H, Dong A, Jia S, An P, Zhang B, Tu J (2022) Model test on deformation and failure behaviour of arching-type slope under excavation condition. Eng Geol 302:106628. https://doi.org/10.1016/j.enggeo.2022.106628

Fang K, Tang H, Li C, Su X, An P, Sun S (2023) Centrifuge modelling of landslides and landslide hazard mitigation: a review. Geosci Front 14:101493. https://doi.org/10.1016/j.gsf.2022.101493

Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Engineering Geology, Landslide susceptibility, Hazard and Risk Zoning for Land Use Planning. 102:85–98. https://doi.org/10.1016/j.enggeo.2008.03.022

Franceschini R, Rosi A, del Soldato M, Catani F, Casagli N (2022) Integrating multiple information sources for landslide hazard assessment: the case of Italy. Sci Rep 12:20724. https://doi.org/10.1038/s41598-022-23577-z

Article   CAS   Google Scholar  

Fritz S, See L, Perger C, McCallum I, Schill C, Schepaschenko D, Duerauer M, Karner M, Dresel C, Laso-Bayas J-C, Lesiv M, Moorthy I, Salk CF, Danylo O, Sturn T, Albrecht F, You L, Kraxner F, Obersteiner M (2017) Sci Data 4:170075. https://doi.org/10.1038/sdata.2017.75 . A global dataset of crowdsourced land cover and land use reference data

Havas C, Resch B, Francalanci C, Pernici B, Scalia G, Fernandez-Marquez JL, Van Achte T, Zeug G, Mondardini MR, Rosy), Grandoni D, Kirsch B, Kalas M, Lorini V, Rüping S (2017) E2mC: improving Emergency Management Service practice through Social Media and Crowdsourcing Analysis in Near Real Time. Sensors 17:2766. https://doi.org/10.3390/s17122766

Heipke C (2010) Crowdsourcing geospatial data. ISPRS Journal of Photogrammetry and Remote Sensing. ISPRS Centenary Celebration Issue 65:550–557. https://doi.org/10.1016/j.isprsjprs.2010.06.005

Heller V, Spinneken J (2015) On the effect of the water body geometry on landslide–tsunamis: physical insight from laboratory tests and 2D to 3D wave parameter transformation. Coast Eng 104:113–134. https://doi.org/10.1016/j.coastaleng.2015.06.006

Huang R, Pei X, Fan X, Zhang W, Li S, Li B (2012) The characteristics and failure mechanism of the largest landslide triggered by the Wenchuan earthquake, May 12, 2008, China. Landslides 9:131–142. https://doi.org/10.1007/s10346-011-0276-6

Hungr O, Leroueil S, Picarelli L (2014) The Varnes classification of landslide types, an update. Landslides 11:167–194

Intrieri E, Gigli G, Mugnai F, Fanti R, Casagli N (2012) Design and implementation of a landslide early warning system. Eng Geol 147–148:124–136. https://doi.org/10.1016/j.enggeo.2012.07.017

Jaboyedoff M, Oppikofer T, Abellán A, Derron M-H, Loye A, Metzger R, Pedrazzini A (2012) Use of LIDAR in landslide investigations: a review. Nat Hazards 61:5–28. https://doi.org/10.1007/s11069-010-9634-2

James MR, Chandler JH, Eltner A, Fraser C, Miller PE, Mills JP, Noble T, Robson S, Lane SN (2019) Guidelines on the use of structure-from‐motion photogrammetry in geomorphic research. Earth Surf Process Landf 44:2081–2084

Jaud M, Le Dantec N, Parker K, Lemon K, Lendre S, Delacourt C, Gomes RC (2022) How to include crowd-sourced photogrammetry in a Geohazard Observatory—Case Study of the Giant’s Causeway Coastal cliffs. Remote Sens 14:3243. https://doi.org/10.3390/rs14143243

Juang CS, Stanley TA, Kirschbaum DB (2019) Using citizen science to expand the global map of landslides: introducing the Cooperative Open Online Landslide Repository (COOLR). PLoS ONE 14:e0218657. https://doi.org/10.1371/journal.pone.0218657

Lee S, Suh J, Choi Y (2018) Review of smartphone applications for geoscience: current status, limitations, and future perspectives. Earth Sci Inf 11:463–486. https://doi.org/10.1007/s12145-018-0343-9

Lee KA, Lee JR, Bell P (2020) A review of Citizen Science within the Earth sciences: potential benefits and obstacles. Proc Geologists’ Association 131:605–617. https://doi.org/10.1016/j.pgeola.2020.07.010

Li Z, Li W, Xu Q, Lu H, Fu H, Guo P, Zhao J, Yu C (2024) Preliminary analysis of the catastrophic February 22nd 2023 Xinjing open-pit mine landslide, Inner Mongolia, China. Landslides 21:1053–1067. https://doi.org/10.1007/s10346-024-02229-5

Lucieer A, de Jong SM, Turner D (2014) Mapping landslide displacements using structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress Phys Geography: Earth Environ 38:97–116. https://doi.org/10.1177/0309133313515293

McCann DM, Forster A (1990) Reconnaissance geophysical methods in landslide investigations. Eng Geol 29:59–78. https://doi.org/10.1016/0013-7952(90)90082-C

Miles HC, Wilson AT, Labrosse F, Tiddeman B, Griffiths S, Edwards B, Moller K, Karl R, Roberts JC (2014) Crowd-Sourced Digitisation of Cultural Heritage Assets, in: 2014 International Conference on Cyberworlds. Presented at the 2014 International Conference on Cyberworlds, pp. 361–368. https://doi.org/10.1109/CW.2014.57

Minson SE, Brooks BA, Glennie CL, Murray JR, Langbein JO, Owen SE, Heaton TH, Iannucci RA, Hauser DL (2015) Crowdsourced earthquake early warning. Sci Adv 1:e1500036. https://doi.org/10.1126/sciadv.1500036

Perrone A, Lapenna V, Piscitelli S (2014) Electrical resistivity tomography technique for landslide investigation: a review. Earth Sci Rev 135:65–82. https://doi.org/10.1016/j.earscirev.2014.04.002

Pesci A, Teza G, Kastelic V, Carafa MMC (2020) Resolution and Precision of fast long-range terrestrial photogrammetric surveying aimed at detecting slope changes. J Surv Eng 146:04020017. https://doi.org/10.1061/(ASCE)SU.1943-5428.0000328

Pollock W, Wartman J (2020) Human Vulnerability to Landslides. GeoHealth 4, e2020GH000287. https://doi.org/10.1029/2020GH000287

Ratner J, Sury J, James M, Mather T, Pyle D (2019) Crowd-sourcing structure-from- motion data for terrain modelling in a real-world disaster scenario: a proof of concept. Progress Phys Geography: Earth Environ 43:236–259. https://doi.org/10.1177/0309133318823622

Ren S, Tao Z, He M, Pang S, Li M, Xu H (2020) Stability analysis of open-pit gold mine slopes and optimization of mining scheme in Inner Mongolia, China. J Mt Sci 17:2997–3011. https://doi.org/10.1007/s11629-020-6217-x

Saralioglu E, Gungor O (2020) Crowdsourcing in Remote sensing: a review of applications and future directions. IEEE Geoscience Remote Sens Magazine 8:89–110. https://doi.org/10.1109/MGRS.2020.2975132

Scaioni M, Longoni L, Melillo V, Papini M (2014) Remote sensing for landslide investigations: an overview of recent achievements and perspectives. Remote Sens 6:9600–9652. https://doi.org/10.3390/rs6109600

See L, Comber A, Salk C, Fritz S, van der Velde M, Perger C, Schill C, McCallum I, Kraxner F, Obersteiner M (2013) Comparing the quality of Crowdsourced Data contributed by Expert and non-experts. PLoS ONE 8:e69958. https://doi.org/10.1371/journal.pone.0069958

Shi J, Wang Y (2023) Rescue operation documentary captures the collapse of an open-pit coal mine in Inner Mongolia’s Alxa League. [WWW Document]. URL http://nmg.news.cn/xwzx/ttxw/2023-02/25/c_1129395904.htm (accessed 8.1.23)

Steed RJ, Fuenzalida A, Bossu R, Bondár I, Heinloo A, Dupont A, Saul J, Strollo A (2019) Crowdsourcing triggers rapid, reliable earthquake locations. Sci Adv 5:eaau9824. https://doi.org/10.1126/sciadv.aau9824

Su X (2023) News tracking · the open-pit coal mine collapse incident in Alxa Left Banner, Inner Mongolia [WWW Document]. Central Radio Network Synthesis. URL http://news.cnr.cn/dj/20230224/t20230224_526163568.shtml (accessed 4.5.23)

Tavani S, Granado P, Riccardi U, Seers T, Corradetti A (2020) Terrestrial SfM-MVS photogrammetry from smartphone sensors. Geomorphology 367:107318. https://doi.org/10.1016/j.geomorph.2020.107318

Thielicke W, Sonntag R (2021) Particle image Velocimetry for MATLAB: Accuracy and enhanced algorithms in PIVlab. 9:12. https://doi.org/10.5334/jors.334

Tian S, Li Y, Hu G, Chen N, Wu K, Rahman M, Peng T, Ni H (2023) Preliminary analysis on the formation mechanism of the unexpected catastrophic Qijiaba landslide in a tectonically non-active hilly area of China. Landslides 20:409–420. https://doi.org/10.1007/s10346-022-01990-9

Turner AK, Schuster RL (eds) (1996) Landslides: investigation and mitigation, Special report / Transportation Research Board, National Research Council. National Academy, Washington, D.C.

Google Scholar  

Wang R-Q, Mao H, Wang Y, Rae C, Shaw W (2018) Comput Geosci 111:139–147. https://doi.org/10.1016/j.cageo.2017.11.008 . Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data

Wang Q, Xing A, Xu X, Zhou Y, Yang Q, Song H, Peng J, Zhai L, Bilal M, Liu Y (2024) Formation mechanism and dynamic process of open-pit coal mine landslides: a case study of the Xinjing landslide in Inner Mongolia, China. Landslides 21:541–556. https://doi.org/10.1007/s10346-023-02193-6

Wernette P, Miller IM, Ritchie AW, Warrick JA (2022) Crowd-sourced SfM: best practices for high resolution monitoring of coastal cliffs and bluffs. Cont Shelf Res 245:104799. https://doi.org/10.1016/j.csr.2022.104799

White DJ, Take WA, Bolton MD (2003) Soil deformation measurement using particle image velocimetry (PIV) and photogrammetry. Géotechnique 53:619–631. https://doi.org/10.1680/geot.2003.53.7.619

Wu Q, Kulatilake PHSW (2012) REV and its properties on fracture system and mechanical properties, and an orthotropic constitutive model for a jointed rock mass in a dam site in China. Comput Geotech 43:124–142. https://doi.org/10.1016/j.compgeo.2012.02.010

Wu Q, Liu Y, Tang H, Kang J, Wang L, Li C, Wang D, Liu Z (2023) Experimental study of the influence of wetting and drying cycles on the strength of intact rock samples from a red stratum in the Three Gorges Reservoir area. Eng Geol 314:107013. https://doi.org/10.1016/j.enggeo.2023.107013

Yu M, Yang C, Li Y (2018) Big Data in Natural Disaster Management: a review. Geosciences 8:165. https://doi.org/10.3390/geosciences8050165

Zhang J, Pang H (2023) Inner Mongolia coal mine collapse accident, 3D analysis [WWW Document]. BJNEWS.COM.CN. URL https://www.bjnews.com.cn/detail/167724960214081.html (accessed 4.5.23)

Zhang S, Li C, Peng J, Zhou Y, Wang S, Chen Y, Tang Y (2023) Fatal landslides in China from 1940 to 2020: occurrences and vulnerabilities. Landslides. https://doi.org/10.1007/s10346-023-02034-6

Zhao M, Liu Z (2023) The latest progress of the coal mine collapse accident in Inner Mongolia [WWW Document]. People.cn. URL http://nm.people.com.cn/n2/2023/0223/c196689-40313319.html (accessed 4.5.23)

Zheng F, Tao R, Maier HR, See L, Savic D, Zhang T, Chen Q, Assumpção TH, Yang P, Heidari B, Rieckermann J, Minsker B, Bi W, Cai X, Solomatine D, Popescu I (2018) Crowdsourcing methods for data Collection in Geophysics: state of the art, issues, and future directions. Rev Geophys 56:698–740. https://doi.org/10.1029/2018RG000616

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This research is supported by Ningbo Natural Science Foundation (Grant No. 2023J084) and National Nature Science Foundation of China (No. 42207216).

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Pengju An, Rui Yong & Changshuo Wang

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An, P., Yong, R., Wang, C. et al. Utilizing crowdsourced data for timely investigation of catastrophic landslide accidents: a case study of the coal mine collapse in inner Mongolia, China. Bull Eng Geol Environ 83 , 354 (2024). https://doi.org/10.1007/s10064-024-03848-x

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DOI : https://doi.org/10.1007/s10064-024-03848-x

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A bi-objective model for the multi-period inventory-based reverse logistics network: a case study from an automobile component distribution network.

1. Introduction

• To optimize the transportation system in the ISACO company.
• To cut down transportation costs.
• To increase customer satisfaction by increasing the supply of customer demands.
• To allow the customers to return unused parts (which are not used by customers due to seasonal variations or environmental changes and market fluctuations.
• To collect and dispose or recycle the stock parts.

2. Literature Review

2.1. a review of the literature on distribution systems in supply chain management, 2.2. a review of the literature on green logistics in supply chain management, 3. materials and methods.

• Very high transportation costs induced by long round-trip distances.
• High costs imposed on the company as a result of vehicle breakdown.
• Frequent troubles related to timely goods delivery (e.g., the cities located far from Tehran, the chances are high that the goods do not reach on time).
• To benefit from the full capacity of cars, it is required that the amount of the ordered goods reach a certain quantity and then the goods be delivered to the representatives, which leads to dissatisfaction among the representatives and losing the competitive market.
• The lack of order and prioritization in the current system.
• Not considering different scenarios in decision making.
• Not being able to return unused or low-use parts by the representatives.
• The lack of an integrated system for receiving scrap parts.
• Not able to implement strategic planning.
• Some of the expected merits of the new system are the following:
• Reducing the costs resulting from redundant transportation.
• Increasing the representatives’ satisfaction level due to goods’ timely delivery and increasing the power to supply the demanded goods and the possibility of returning low-use parts to the representative.
• Systematizing transportation system which curbs other nuisances.
• Increasing the flexibility of the system.
• Decreasing the risks such as the sensitive parts becoming faulty during long transportation or the possibility of vehicle breakdowns that impose losses on the company.
• Building regional warehouses and reducing the heavy costs of the central warehouse.
• Controlling the system better and the potential to constantly improve.

5. Discussion and Conclusions

• Employing a multi-period model along with the power of inventory management so that it leads to reduced costs and increased revenue.
• With respect to the variety of available products, the number of product groups should be increased and included in the proposed model.
• Reducing the time of ordering periods to better use the multi-period model, supplying faster and more up-to-date customer demands in the year, and removing the barriers of the inventory cost increase through modeling and making decisions at the tactical and operational level.
• Raising the number of customers and applying the proposed model to the actual number of customers. It is worth mentioning that in this model, they were integrated into the provincial centers to facilitate the modeling of customer demand.
• Constructing regional warehouses in the locations suggested by the model outputs considering the construction cost and setting up and storing the goods in these warehouses.
• Launching the central warehouse number 2 when its effectiveness gets approved in all the models to properly benefit from it.
• Regularly controlling the proposed performance evaluation indices considering the possibility of changing the supply or demand pattern and making suitable decisions accordingly.
• Investigating the demand pattern in various time periods and the possibility of presenting a supplementary model for the probability mode of demand.
• Investigating the profit from waste recycling.
• Investigating the benefits of the brand’s mental image in terms of compliance with environmental issues.
• Considering production issues in the supply chain and distribution system.
• Including the demand of the different classes of customers in the distribution system and locating facilities; accordingly, in other words, assessing the effect of marketing decisions on the strategic macro-decisions of facility location.
• Considering other location benchmarks.
• Determining the order supply deadline for all sorts of goods orders and programming to supply them within the deadline and its effect on facility location problems.
• Considering other objective functions like social aspects, employment rates, and environmental impacts according to the priorities of managers and decision-makers.

Author Contributions

Data availability statement, conflicts of interest.

• Kamalahmadi, M.; Parast, M.M. A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. Int. J. Prod. Econ. 2016 , 171 , 116–133. [ Google Scholar ] [ CrossRef ]
• Wang, G.; Gunasekaran, A.; Ngai, E.W.T.; Papadopoulos, T. Big data analytics in logistics and supply chain management: Certain investigations for research and applications. Int. J. Prod. Econ. 2016 , 176 , 98–110. [ Google Scholar ] [ CrossRef ]
• Alshubiri, F. The Impact of Green Logistics-Based Activities on the Sustainable Monetary Expansion Indicators of Oman. J. Ind. Eng. Manag. 2017 , 10 , 389–405. [ Google Scholar ] [ CrossRef ]
• Sheikh Azadi, A.H.; Shamsi Nesary, V.; Kebriyaii, O.; Khalilzadeh, M.; Antucheviciene, J. Design of a Green Supply Chain Based on the Kano Model Considering Pricing. Sustainability 2023 , 15 , 13038. [ Google Scholar ] [ CrossRef ]
• Liou, J.J.H.; Tamošaitienė, J.; Zavadskas, E.K.; Tzeng, G.-H. New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management. Int. J. Prod. Res. 2015 , 54 , 114–134. [ Google Scholar ] [ CrossRef ]
• Scott, J.; Ho, W.; Dey, P.K.; Talluri, S. A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments. Int. J. Prod. Econ. 2015 , 166 , 226–237. [ Google Scholar ] [ CrossRef ]
• Dweiri, F.; Kumar, S.; Khan, S.A.; Jain, V. Designing an integrated AHP based decision support system for supplier selection in automotive industry. Expert Syst. Appl. 2016 , 62 , 273–283. [ Google Scholar ] [ CrossRef ]
• Mogeni, L.M.; Kiarie, D.M. Effect of Green Logistics Practices on Performance of Supply Chains in Multinational Organizations in Kenya. Ind. Eng. Lett. 2016 , 6 , 40–50. [ Google Scholar ]
• Muangpan, T.; Chaowarat, M.; Neamvonk, J. The key activities of green logistics management in the Thai automotive industry. J. Arts Sci. Commer. 2016 , 7 , 1–11. [ Google Scholar ] [ CrossRef ]
• Brusset, X.; Teller, C. Supply chain capabilities, risks, and resilience. Int. J. Prod. Econ. 2017 , 184 , 59–68. [ Google Scholar ] [ CrossRef ]
• Ribeiro, J.P.; Barbosa-Povoa, A. Supply Chain Resilience: Definitions and quantitative modelling approaches. Comput. Ind. Eng. 2018 , 115 , 109–122. [ Google Scholar ] [ CrossRef ]
• Heidari, A.; Imani, D.M.; Khalilzadeh, M. A hub location model in the sustainable supply chain considering customer segmentation. J. Eng. Des. Technol. 2021 , 19 , 1387–1420. [ Google Scholar ] [ CrossRef ]
• Eydi, A.; Fazayeli, S.; Ghafouri, H. Multi-period configuration of forward and reverse integrated supply chain networks through transport mode. Sci. Iran. 2020 , 27 , 935–955. [ Google Scholar ] [ CrossRef ]
• Antucheviciene, J.; Jafarnejad, A.; Amoozad Mahdiraji, H.; Razavi Hajiagha, S.H.; Kargar, A. Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry. Symmetry 2020 , 12 , 594. [ Google Scholar ] [ CrossRef ]
• Gholamian, N.; Mahdavi, I.; Mahdavi-Amiri, N.; Tavakkoli-Moghaddam, R. Hybridization of an interactive fuzzy methodology with a lexicographic min-max approach for optimizing a multi-period multi-product multi-echelon sustainable closed-loop supply chain network. Comput. Ind. Eng. 2021 , 158 , 107282. [ Google Scholar ] [ CrossRef ]
• Alinezhad, M.; Mahdavi, I.; Hematian, M. A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries. Environ. Dev. Sustain. 2022 , 24 , 8779–8806. [ Google Scholar ] [ CrossRef ]
• Pazhani, S.; Mendoza, A.; Nambirajan, R.; Narendran, T.T.; Ganesh, K.; Olivares-Benitez, E. Multi-period multi-product closed loop supply chain network design: A relaxation approach. Comput. Ind. Eng. 2021 , 155 , 107191. [ Google Scholar ] [ CrossRef ]
• Nazari-Ghanbarloo, V.; Ghodratnama, A. Optimizing a robust tri-objective multi-period reliable supply chain network considering queuing system and operational and disruption risks. Oper. Res. 2021 , 21 , 1963–2020. [ Google Scholar ] [ CrossRef ]
• Rabe, M.; Gonzalez-Feliu, J.; Chicaiza-Vaca, J.; Tordecilla, R.D. Simulation-Optimization Approach for Multi-Period Facility Location Problems with Forecasted and Random Demands in a Last-Mile Logistics Application. Algorithms 2021 , 14 , 41. [ Google Scholar ] [ CrossRef ]
• Zarrat Dakhely Parast, Z.; Haleh, H.; Avakh Darestani, S. Green reverse supply chain network design considering location-routing-inventory decisions with simultaneous pickup and delivery. Environ. Sci. Pollut. Res. 2021 . [ Google Scholar ] [ CrossRef ]
• Abdolazimi, O.; Bahrami, F.; Shishebori, D. A multi-objective closed-loop supply chain network design problem under parameter uncertainty: Comparison of exact methods. Environ. Dev. Sustain. 2022 , 24 , 10768–10802. [ Google Scholar ] [ CrossRef ]
• Maliki, F.; Souier, M.; Dahane, M. A multi-objective optimization model for a multi-period mobile facility location problem with environmental and disruption considerations. Ann. Oper. Res. 2022 , 1–26. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Gupta, S.; Vijaygargy, L.; Sarkar, B. A bi-objective integrated transportation and inventory management under a supply chain network considering multiple distribution networks. RAIRO Oper. Res. 2022 , 56 , 3991–4022. [ Google Scholar ] [ CrossRef ]
• Bortolini, M.; Calabrese, F.; Gabriele Galizia, F.; Mora, C. A three-objective optimization model for mid-term sustainable supply chain network design. Comput. Ind. Eng. 2022 , 168 , 108131. [ Google Scholar ] [ CrossRef ]
• Mogale, D.G.; De, A.; Ghadge, A.; Aktas, E. Multi-objective modelling of sustainable closed-loop supply chain network with price-sensitive demand and consumer’s incentives. Comput. Ind. Eng. 2022 , 168 , 108105. [ Google Scholar ] [ CrossRef ]
• Al-Refaie, A.; Kokash, T. Optimization of sustainable reverse logistics network with multi-objectives under uncertainty. J. Remanuf. 2023 , 13 , 1–23. [ Google Scholar ] [ CrossRef ]
• Meidute-Kavaliauskiene, I.; Yıldırım, F.; Ghorbani, S.; Činčikaitė, R. The Design of a Multi-Period and Multi-Echelon Perishable Goods Supply Network under Uncertainty. Sustainability 2022 , 14 , 2472. [ Google Scholar ] [ CrossRef ]
• Allehashemi, T.; Hassanzadeh Amin, S.; Zolfaghari, S. A proposed multi-objective model for cellphone closed-loop supply chain optimization based on fuzzy QFD. Expert Syst. Appl. 2022 , 210 , 118577. [ Google Scholar ] [ CrossRef ]
• Abdi, F.; Farughi, H.; Sadeghi, H.; Arkat, J. Location-inventory-reliability optimisation problem in a multi-objective multi-period three-level supply chain network with stochastic demand. Eur. J. Ind. Eng. 2023 , 17 , 479–528. [ Google Scholar ] [ CrossRef ]
• Arab Momeni, M.; Jain, V.; Bagheri, M. A Multi-Objective Model for Designing a Sustainable Closed-Loop Supply Chain Logistics Network. Logistics 2024 , 8 , 29. [ Google Scholar ] [ CrossRef ]
• Varshney, N.; Gupta, S.; Ahmed, A. Addressing uncertainty in closed-loop supply chain networks: A multi-objective approach to integrated production and transportation problems. J. Model. Manag. 2024 , in press . [ Google Scholar ] [ CrossRef ]
• Xia, H.; Chen, Z.; Milisavljevic-Syed, J.; Salonitis, K. Uncertain programming model for designing multi-objective reverse logistics networks. Clean. Logist. Supply Chain 2024 , 11 , 100155. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

			α
			0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
β	0	Z1	4.82 × 10	3.50 × 10	2.19 × 10	1.90 × 10	1.34 × 10	9.49 × 10	5.34 × 10	0.00 × 10	0.00 × 10
		Z2	1	0.95	0.8368985	0.8	0.6857143	0.5714286	0.3811921	0	0
	0.1	Z1	4.82 × 10	3.64 × 10	3.19 × 10	2.27 × 10	1.98 × 10	1.64 × 10	1.18 × 10	1.10 × 10	9.38 × 10
		Z2	1	0.955	0.9228571	0.8264286	0.775	0.6464286	0.5371429	0.4857143	0.2649475
	0.2	Z1	4.82 × 10	3.78 × 10	3.37 × 10	2.59 × 10	2.33 × 10	1.87 × 10	1.62 × 10	1.55 × 10	1.43 × 10
		Z2	1	0.96	0.9314286	0.8457143	0.8	0.6857143	0.5885714	0.5428571	0.3942857
	0.3	Z1	4.82 × 10	4.11 × 10	3.55 × 10	2.90 × 10	2.68 × 10	2.28 × 10	2.06 × 10	2.00 × 10	1.90 × 10
		Z2	1	0.975	0.94	0.865	0.825	0.725	0.64	0.6	0.47
	0.4	Z1	4.82 × 10	4.21 × 10	3.73 × 10	3.31 × 10	3.07 × 10	2.69 × 10	2.50 × 10	2.44 × 10	2.36 × 10
		Z2	1	0.9785714	0.9485714	0.9014286	0.8585714	0.7642857	0.6914286	0.6571429	0.5457143
	0.5	Z1	4.82 × 10	4.31 × 10	3.92 × 10	3.56 × 10	3.36 × 10	3.09 × 10	2.94 × 10	2.88 × 10	2.81 × 10
		Z2	1	0.9821429	0.9571429	0.9178571	0.8821429	0.8142857	0.7607143	0.7214286	0.6214286
	0.6	Z1	4.82 × 10	4.41 × 10	4.10 × 10	3.82 × 10	3.65 × 10	3.43 × 10	3.32 × 10	3.27 × 10	3.21 × 10
		Z2	1	0.9857143	0.9657143	0.9342857	0.9057143	0.8514286	0.8085714	0.7771429	0.6971429
	0.7	Z1	4.82 × 10	4.51 × 10	4.28 × 10	4.07 × 10	3.94 × 10	3.78 × 10	3.69 × 10	3.66 × 10	3.61 × 10
		Z2	1	0.9892857	0.9742857	0.9507143	0.9292857	0.8885714	0.8564286	0.8328571	0.7728571
	0.8	Z1	4.82 × 10	4.62 × 10	4.46 × 10	4.32 × 10	4.24 × 10	4.13 × 10	4.07 × 10	4.05 × 10	4.01 × 10
		Z2	1	0.9928571	0.9828571	0.9671429	0.9528571	0.9257143	0.9042857	0.8885714	0.8485714
	0.9	Z1	4.82 × 10	4.72 × 10	4.64 × 10	4.57 × 10	4.53 × 10	4.47 × 10	4.44 × 10	4.43 × 10	4.42 × 10
		Z2	1	0.9964286	0.9914286	0.9832714	0.9764286	0.9628571	0.9521429	0.9442857	0.9242857
	1	Z1	4.82 × 10	4.82 × 10	4.82 × 10	4.82 × 10	4.82 × 10	4.82 × 10	4.82 × 10	4.82 × 10	4.82 × 10
		Z2	1	1	1	1	1	1	1	1	1

Variable Title	Value
Z	3.32 × 10
Z	80%
V(1,2)	(1,1)
U(1,2,3,4,5,6,7,8)	(0,0,0,0,0,1,0,1)

			α
			0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
β	0	Z1	4.84 × 10	4.70 × 10	4.52 × 10	4.20 × 10	3.68 × 10	3.54 × 10	3.41 × 10	3.15 × 10	2.65 × 10
		Z2	1	0.9928571	0.9785714	0.9428571	0.8494217	0.8142857	0.7571429	0.5928571	0
	0.1	Z1	4.84 × 10	4.72 × 10	4.55 × 10	4.27 × 10	3.82 × 10	3.70 × 10	3.58 × 10	3.35 × 10	3.31 × 10
		Z2	1	0.9935714	0.9807143	0.9485714	0.8624167	0.8328571	0.7814286	0.6335714	0.5821429
	0.2	Z1	4.84 × 10	4.73 × 10	4.58 × 10	4.22 × 10	4.15 × 10	3.87 × 10	3.76 × 10	3.55 × 10	0.5821429
		Z2	1	0.9942857	0.9828571	0.9371429	0.9257143	0.8514286	0.8057143	0.6742857	0.6285714
	0.3	Z1	4.84 × 10	4.74 × 10	4.62 × 10	4.30 × 10	4.24 × 10	4.14 × 10	3.94 × 10	3.75 × 10	3.72 × 10
		Z2	1	0.995	0.985	0.945	0.935	0.91	0.83	0.715	0.675
	0.4	Z1	4.84 × 10	4.76 × 10	4.65 × 10	4.37 × 10	4.33 × 10	4.24 × 10	4.14 × 10	3.98 × 10	3.93 × 10
		Z2	1	0.9957143	0.9871429	0.9528571	0.9442857	0.9228571	0.88	0.7814286	0.7214286
	0.5	Z1	4.84 × 10	4.77 × 10	4.68 × 10	4.45 × 10	4.41 × 10	4.34 × 10	4.26 × 10	4.12 × 10	4.11 × 10
		Z2	1	0.9964286	0.9892857	0.9607143	0.9535714	0.9357143	0.9	0.8178571	0.7928571
	0.6	Z1	4.84 × 10	4.79 × 10	4.71 × 10	4.53 × 10	4.50 × 10	4.44 × 10	4.37 × 10	4.27 × 10	4.26 × 10
		Z2	1	0.9971429	0.9914286	0.9685714	0.9628571	0.9485714	0.92	0.8542857	0.8342857
	0.7	Z1	4.84 × 10	4.80 × 10	4.75 × 10	4.61 × 10	4.58 × 10	4.54 × 10	4.49 × 10	4.41 × 10	4.40 × 10
		Z2	1	0.9978571	0.9935714	0.9764286	0.9721429	0.9614286	0.94	0.8907143	0.8757143
	0.8	Z1	4.84 × 10	4.82 × 10	4.78 × 10	4.69 × 10	4.67 × 10	4.64 × 10	4.61 × 10	4.56 × 10	4.55 × 10
		Z2	1	0.9985714	0.9957143	0.9842857	0.981286	0.9742857	0.96	0.9271429	0.9171429
	0.9	Z1	4.84 × 10	4.83 × 10	4.81 × 10	4.77 × 10	4.76 × 10	4.74 × 10	4.73 × 10	4.70 × 10	4.70 × 10
		Z2	1	0.9992857	0.9978571	0.9921429	0.9907143	0.9871429	0.98	0.9635714	0.9585714
	1	Z1	4.84 × 10	4.84 × 10	4.84 × 10	4.84 × 10	4.84 × 10	4.84 × 10	4.84 × 10	4.84 × 10	4.84 × 10
		Z2	1	1	1	1	1	1	1	1	1

Variable Title	Value
Z	3.37 × 10
Z	92%
V(1,2)	(1,1)
U(1,2,3,4,5,6,7,8)	(0,0,0,0,0,1,0,1)

α
0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
1.65 × 10	1.65 × 10	1.65 × 10	1.61 × 10	1.51 × 10	1.48 × 10	1.37 × 10	1.34 × 10	1.21 × 10
1	1	1	0.9228571	0.9714286	0.9642857	0.9150749	0.888311	0.7168279
1.65 × 10	1.65 × 10	1.65 × 10	1.61 × 10	1.52 × 10	1.50 × 10	1.40 × 10	1.37 × 10	1.26 × 10
1	1	1	0.9935714	0.9742857	0.9678571	0.9232892	0.8982267	0.7361599
1.65 × 10	1.65 × 10	1.65 × 10	1.61 × 10	1.54 × 10	1.52 × 10	1.43 × 10	1.40 × 10	1.33 × 10
1	1	1	0.9942857	0.9771429	09744286	0.9315568	0.9085202	0.8056308
1.65 × 10	1.65 × 10	1.65 × 10	0.62 × 10	1.55 × 10	1.53 × 10	1.46 × 10	1.43 × 10	1.37 × 10
1	1	1	0.995	0.98	0.975	0.939905	0.919635	0.8278087
1.65 × 10	1.65 × 10	1.65 × 10	0.62 × 10	1.57 × 10	1.55 × 10	1.49 × 10	1.47 × 10	1.41 × 10
1	1	1	0.9957143	0.9828571	0.9785714	0.9482679	0.9307498	0.8485393
1.65 × 10	1.65 × 10	1.65 × 10	1.63 × 10	1.58 × 10	1.57 × 10	1.52 × 10	1.50 × 10	1.45 × 10
1	1	1	0.9664286	0.9857143	0.9821429	0.9566604	0.9418646	0.8732143
1.65 × 10	1.65 × 10	1.65 × 10	1.63 × 10	1.57 × 10	1.48 × 10	1.43 × 10	1.41 × 10	1.39 × 10
1	1	1	0.9971429	0.9885714	0.9857143	0.9654133	0.9530132	0.8985714
1.65 × 10	1.65 × 10	1.65 × 10	1.64 × 10	1.61 × 10	1.60 × 10	1.57 × 10	1.56 × 10	1.53 × 10
1	1	1	0.9978571	0.9914286	0.9892857	0.9742857	0.964634	0.9238566
1.65 × 10	1.65 × 10	1.65 × 10	1.64 × 10	1.62 × 10	1.62 × 10	1.61 × 10	1.59 × 10	1.57 × 10
1	1	1	0.9985714	0.9942857	0.9928571	0.9885714	0.97625	0.9489286
1.65 × 10	0.2	0.3	0.4	1.64 × 10	1.64 × 10	1.63 × 10	1.63 × 10	1.62 × 10
1	1.65 × 10	1.65 × 10	1.61 × 10	0.9971429	0.9964286	0.9942857	0.9895536	0.9785714
1.65 × 10	1	1	0.9228571	1.65 × 10	1.65 × 10	1.65 × 10	1.65 × 10	1.65 × 10
1	1.65 × 10	1.65 × 10	1.61 × 10	1	1	1	1	1

Variable Title	Value
Z	1.42 × 10
Z	94%
V(1,2)	(1,1)
U(1,2,3,4,5,6,7,8)	(1,0,1,1,0,1,0,1)

			α
			0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9
β	0	Z1	1.65 × 10	1.65 × 10	1.65 × 10	1.61 × 10	1.51 × 10	1.48 × 10	1.37 × 10	1.34 × 10	1.21 × 10
		Z2	1	1	1	0.9228571	0.9714286	0.9642857	0.9150749	0.888311	0.7168279
	0.1	Z1	1.65 × 10	1.65 × 10	1.65 × 10	1.61 × 10	1.52 × 10	1.50 × 10	1.40 × 10	1.37 × 10	1.26 × 10
		Z2	1	1	1	0.9935714	0.9742857	0.9678571	0.9232892	0.8982267	0.7361599
	0.2	Z1	1.65 × 10	1.65 × 10	1.65 × 10	1.61 × 10	1.54 × 10	1.52 × 10	1.43 × 10	1.40 × 10	1.33 × 10
		Z2	1	1	1	0.9942857	0.9771429	0.9744286	0.9315568	0.9085202	0.8056308
	0.3	Z1	1.65 × 10	1.65 × 10	1.65 × 10	0.62 × 10	1.55 × 10	1.53 × 10	1.46 × 10	1.43 × 10	1.37 × 10
		Z2	1	1	1	0.995	0.98	0.975	0.939905	0.919635	0.8278087
	0.4	Z1	1.65 × 10	1.65 × 10	1.65 × 10	0.62 × 10	1.57 × 10	1.55 × 10	1.49 × 10	1.47 × 10	1.41 × 10
		Z2	1	1	1	0.9957143	0.9828571	0.9785714	0.9482679	0.9307498	0.8485393
	0.5	Z1	1.65 × 10	1.65 × 10	1.65 × 10	1.63 × 10	1.58 × 10	1.57 × 10	1.52 × 10	1.50 × 10	1.45 × 10
		Z2	1	1	1	0.9664286	0.9857143	0.9821429	0.9566604	0.9418646	0.8732143
	0.6	Z1	1.65 × 10	1.65 × 10	1.65 × 10	1.63 × 10	1.57 × 10	1.48 × 10	1.43 × 10	1.41 × 10	1.39 × 10
		Z2	1	1	1	0.9971429	0.9885714	0.9857143	0.9654133	0.9530132	0.8985714
	0.7	Z1	1.65 × 10	1.65 × 10	1.65 × 10	1.64 × 10	1.61 × 10	1.60 × 10	1.57 × 10	1.56 × 10	1.53 × 10
		Z2	1	1	1	0.9978571	0.9914286	0.9892857	0.9742857	0.964634	0.9238566
	0.8	Z1	1.65 × 10	1.65 × 10	1.65 × 10	1.64 × 10	1.62 × 10	1.62 × 10	1.61 × 10	1.59 × 10	1.57 × 10
		Z2	1	1	1	0.9985714	0.9942857	0.9928571	0.9885714	0.97625	0.9489286
	0.9	Z1	1.65 × 10	1.65 × 10	1.65 × 10	1.65 × 10	1.64 × 10	1.64 × 10	1.63 × 10	1.63 × 10	1.62 × 10
		Z2	1	1	1	1	0.9971429	0.9964286	0.9942857	0.9895536	0.9785714
	1	Z1	1.65 × 10	1.65 × 10	1.65 × 10	1.65 × 10	1.65 × 10	1.65 × 10	1.65 × 10	1.65 × 10	1.65 × 10
		Z2	1	1	1	1	1	1	1	1	1

Variable Title	Value
Z	1.43 × 10
Z	96%
V(1,2)	(1,1)
U(1,2,3,4,5,6,7,8)	(1,0,1,1,0,1,0,1)

Criterion Illustration	Criterion Components	Basic Model	Basic Model with Inventory Management	Multi-Period Basic Model with Inventory Management	Multi-Period Basic Model with Inventory Management and Green Logistics
Overall Satisfaction of Customers		85%	92%	94%	96%
Total Costs		3.32 × 10	4.37 × 10	1.42 × 10	1.43 × 10

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Share and Cite

Khalilzadeh, M.; Antucheviciene, J.; Božanić, D. A Bi-Objective Model for the Multi-Period Inventory-Based Reverse Logistics Network: A Case Study from an Automobile Component Distribution Network. Systems 2024 , 12 , 299. https://doi.org/10.3390/systems12080299

Khalilzadeh M, Antucheviciene J, Božanić D. A Bi-Objective Model for the Multi-Period Inventory-Based Reverse Logistics Network: A Case Study from an Automobile Component Distribution Network. Systems . 2024; 12(8):299. https://doi.org/10.3390/systems12080299

Khalilzadeh, Mohammad, Jurgita Antucheviciene, and Darko Božanić. 2024. "A Bi-Objective Model for the Multi-Period Inventory-Based Reverse Logistics Network: A Case Study from an Automobile Component Distribution Network" Systems 12, no. 8: 299. https://doi.org/10.3390/systems12080299

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