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Case Study – Methods, Examples and Guide

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A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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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.

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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Methodology or method? A critical review of qualitative case study reports

Despite on-going debate about credibility, and reported limitations in comparison to other approaches, case study is an increasingly popular approach among qualitative researchers. We critically analysed the methodological descriptions of published case studies. Three high-impact qualitative methods journals were searched to locate case studies published in the past 5 years; 34 were selected for analysis. Articles were categorized as health and health services ( n= 12), social sciences and anthropology ( n= 7), or methods ( n= 15) case studies. The articles were reviewed using an adapted version of established criteria to determine whether adequate methodological justification was present, and if study aims, methods, and reported findings were consistent with a qualitative case study approach. Findings were grouped into five themes outlining key methodological issues: case study methodology or method, case of something particular and case selection, contextually bound case study, researcher and case interactions and triangulation, and study design inconsistent with methodology reported. Improved reporting of case studies by qualitative researchers will advance the methodology for the benefit of researchers and practitioners.

Case study research is an increasingly popular approach among qualitative researchers (Thomas, 2011 ). Several prominent authors have contributed to methodological developments, which has increased the popularity of case study approaches across disciplines (Creswell, 2013b ; Denzin & Lincoln, 2011b ; Merriam, 2009 ; Ragin & Becker, 1992 ; Stake, 1995 ; Yin, 2009 ). Current qualitative case study approaches are shaped by paradigm, study design, and selection of methods, and, as a result, case studies in the published literature vary. Differences between published case studies can make it difficult for researchers to define and understand case study as a methodology.

Experienced qualitative researchers have identified case study research as a stand-alone qualitative approach (Denzin & Lincoln, 2011b ). Case study research has a level of flexibility that is not readily offered by other qualitative approaches such as grounded theory or phenomenology. Case studies are designed to suit the case and research question and published case studies demonstrate wide diversity in study design. There are two popular case study approaches in qualitative research. The first, proposed by Stake ( 1995 ) and Merriam ( 2009 ), is situated in a social constructivist paradigm, whereas the second, by Yin ( 2012 ), Flyvbjerg ( 2011 ), and Eisenhardt ( 1989 ), approaches case study from a post-positivist viewpoint. Scholarship from both schools of inquiry has contributed to the popularity of case study and development of theoretical frameworks and principles that characterize the methodology.

The diversity of case studies reported in the published literature, and on-going debates about credibility and the use of case study in qualitative research practice, suggests that differences in perspectives on case study methodology may prevent researchers from developing a mutual understanding of practice and rigour. In addition, discussion about case study limitations has led some authors to query whether case study is indeed a methodology (Luck, Jackson, & Usher, 2006 ; Meyer, 2001 ; Thomas, 2010 ; Tight, 2010 ). Methodological discussion of qualitative case study research is timely, and a review is required to analyse and understand how this methodology is applied in the qualitative research literature. The aims of this study were to review methodological descriptions of published qualitative case studies, to review how the case study methodological approach was applied, and to identify issues that need to be addressed by researchers, editors, and reviewers. An outline of the current definitions of case study and an overview of the issues proposed in the qualitative methodological literature are provided to set the scene for the review.

Definitions of qualitative case study research

Case study research is an investigation and analysis of a single or collective case, intended to capture the complexity of the object of study (Stake, 1995 ). Qualitative case study research, as described by Stake ( 1995 ), draws together “naturalistic, holistic, ethnographic, phenomenological, and biographic research methods” in a bricoleur design, or in his words, “a palette of methods” (Stake, 1995 , pp. xi–xii). Case study methodology maintains deep connections to core values and intentions and is “particularistic, descriptive and heuristic” (Merriam, 2009 , p. 46).

As a study design, case study is defined by interest in individual cases rather than the methods of inquiry used. The selection of methods is informed by researcher and case intuition and makes use of naturally occurring sources of knowledge, such as people or observations of interactions that occur in the physical space (Stake, 1998 ). Thomas ( 2011 ) suggested that “analytical eclecticism” is a defining factor (p. 512). Multiple data collection and analysis methods are adopted to further develop and understand the case, shaped by context and emergent data (Stake, 1995 ). This qualitative approach “explores a real-life, contemporary bounded system (a case ) or multiple bounded systems (cases) over time, through detailed, in-depth data collection involving multiple sources of information … and reports a case description and case themes ” (Creswell, 2013b , p. 97). Case study research has been defined by the unit of analysis, the process of study, and the outcome or end product, all essentially the case (Merriam, 2009 ).

The case is an object to be studied for an identified reason that is peculiar or particular. Classification of the case and case selection procedures informs development of the study design and clarifies the research question. Stake ( 1995 ) proposed three types of cases and study design frameworks. These include the intrinsic case, the instrumental case, and the collective instrumental case. The intrinsic case is used to understand the particulars of a single case, rather than what it represents. An instrumental case study provides insight on an issue or is used to refine theory. The case is selected to advance understanding of the object of interest. A collective refers to an instrumental case which is studied as multiple, nested cases, observed in unison, parallel, or sequential order. More than one case can be simultaneously studied; however, each case study is a concentrated, single inquiry, studied holistically in its own entirety (Stake, 1995 , 1998 ).

Researchers who use case study are urged to seek out what is common and what is particular about the case. This involves careful and in-depth consideration of the nature of the case, historical background, physical setting, and other institutional and political contextual factors (Stake, 1998 ). An interpretive or social constructivist approach to qualitative case study research supports a transactional method of inquiry, where the researcher has a personal interaction with the case. The case is developed in a relationship between the researcher and informants, and presented to engage the reader, inviting them to join in this interaction and in case discovery (Stake, 1995 ). A postpositivist approach to case study involves developing a clear case study protocol with careful consideration of validity and potential bias, which might involve an exploratory or pilot phase, and ensures that all elements of the case are measured and adequately described (Yin, 2009 , 2012 ).

Current methodological issues in qualitative case study research

The future of qualitative research will be influenced and constructed by the way research is conducted, and by what is reviewed and published in academic journals (Morse, 2011 ). If case study research is to further develop as a principal qualitative methodological approach, and make a valued contribution to the field of qualitative inquiry, issues related to methodological credibility must be considered. Researchers are required to demonstrate rigour through adequate descriptions of methodological foundations. Case studies published without sufficient detail for the reader to understand the study design, and without rationale for key methodological decisions, may lead to research being interpreted as lacking in quality or credibility (Hallberg, 2013 ; Morse, 2011 ).

There is a level of artistic license that is embraced by qualitative researchers and distinguishes practice, which nurtures creativity, innovation, and reflexivity (Denzin & Lincoln, 2011b ; Morse, 2009 ). Qualitative research is “inherently multimethod” (Denzin & Lincoln, 2011a , p. 5); however, with this creative freedom, it is important for researchers to provide adequate description for methodological justification (Meyer, 2001 ). This includes paradigm and theoretical perspectives that have influenced study design. Without adequate description, study design might not be understood by the reader, and can appear to be dishonest or inaccurate. Reviewers and readers might be confused by the inconsistent or inappropriate terms used to describe case study research approach and methods, and be distracted from important study findings (Sandelowski, 2000 ). This issue extends beyond case study research, and others have noted inconsistencies in reporting of methodology and method by qualitative researchers. Sandelowski ( 2000 , 2010 ) argued for accurate identification of qualitative description as a research approach. She recommended that the selected methodology should be harmonious with the study design, and be reflected in methods and analysis techniques. Similarly, Webb and Kevern ( 2000 ) uncovered inconsistencies in qualitative nursing research with focus group methods, recommending that methodological procedures must cite seminal authors and be applied with respect to the selected theoretical framework. Incorrect labelling using case study might stem from the flexibility in case study design and non-directional character relative to other approaches (Rosenberg & Yates, 2007 ). Methodological integrity is required in design of qualitative studies, including case study, to ensure study rigour and to enhance credibility of the field (Morse, 2011 ).

Case study has been unnecessarily devalued by comparisons with statistical methods (Eisenhardt, 1989 ; Flyvbjerg, 2006 , 2011 ; Jensen & Rodgers, 2001 ; Piekkari, Welch, & Paavilainen, 2009 ; Tight, 2010 ; Yin, 1999 ). It is reputed to be the “the weak sibling” in comparison to other, more rigorous, approaches (Yin, 2009 , p. xiii). Case study is not an inherently comparative approach to research. The objective is not statistical research, and the aim is not to produce outcomes that are generalizable to all populations (Thomas, 2011 ). Comparisons between case study and statistical research do little to advance this qualitative approach, and fail to recognize its inherent value, which can be better understood from the interpretive or social constructionist viewpoint of other authors (Merriam, 2009 ; Stake, 1995 ). Building on discussions relating to “fuzzy” (Bassey, 2001 ), or naturalistic generalizations (Stake, 1978 ), or transference of concepts and theories (Ayres, Kavanaugh, & Knafl, 2003 ; Morse et al., 2011 ) would have more relevance.

Case study research has been used as a catch-all design to justify or add weight to fundamental qualitative descriptive studies that do not fit with other traditional frameworks (Merriam, 2009 ). A case study has been a “convenient label for our research—when we ‘can't think of anything ‘better”—in an attempt to give it [qualitative methodology] some added respectability” (Tight, 2010 , p. 337). Qualitative case study research is a pliable approach (Merriam, 2009 ; Meyer, 2001 ; Stake, 1995 ), and has been likened to a “curious methodological limbo” (Gerring, 2004 , p. 341) or “paradigmatic bridge” (Luck et al., 2006 , p. 104), that is on the borderline between postpositivist and constructionist interpretations. This has resulted in inconsistency in application, which indicates that flexibility comes with limitations (Meyer, 2001 ), and the open nature of case study research might be off-putting to novice researchers (Thomas, 2011 ). The development of a well-(in)formed theoretical framework to guide a case study should improve consistency, rigour, and trust in studies published in qualitative research journals (Meyer, 2001 ).

Assessment of rigour

The purpose of this study was to analyse the methodological descriptions of case studies published in qualitative methods journals. To do this we needed to develop a suitable framework, which used existing, established criteria for appraising qualitative case study research rigour (Creswell, 2013b ; Merriam, 2009 ; Stake, 1995 ). A number of qualitative authors have developed concepts and criteria that are used to determine whether a study is rigorous (Denzin & Lincoln, 2011b ; Lincoln, 1995 ; Sandelowski & Barroso, 2002 ). The criteria proposed by Stake ( 1995 ) provide a framework for readers and reviewers to make judgements regarding case study quality, and identify key characteristics essential for good methodological rigour. Although each of the factors listed in Stake's criteria could enhance the quality of a qualitative research report, in Table I we present an adapted criteria used in this study, which integrates more recent work by Merriam ( 2009 ) and Creswell ( 2013b ). Stake's ( 1995 ) original criteria were separated into two categories. The first list of general criteria is “relevant for all qualitative research.” The second list, “high relevance to qualitative case study research,” was the criteria that we decided had higher relevance to case study research. This second list was the main criteria used to assess the methodological descriptions of the case studies reviewed. The complete table has been preserved so that the reader can determine how the original criteria were adapted.

Framework for assessing quality in qualitative case study research.

Adapted from Stake ( 1995 , p. 131).

Study design

The critical review method described by Grant and Booth ( 2009 ) was used, which is appropriate for the assessment of research quality, and is used for literature analysis to inform research and practice. This type of review goes beyond the mapping and description of scoping or rapid reviews, to include “analysis and conceptual innovation” (Grant & Booth, 2009 , p. 93). A critical review is used to develop existing, or produce new, hypotheses or models. This is different to systematic reviews that answer clinical questions. It is used to evaluate existing research and competing ideas, to provide a “launch pad” for conceptual development and “subsequent testing” (Grant & Booth, 2009 , p. 93).

Qualitative methods journals were located by a search of the 2011 ISI Journal Citation Reports in Social Science, via the database Web of Knowledge (see m.webofknowledge.com). No “qualitative research methods” category existed in the citation reports; therefore, a search of all categories was performed using the term “qualitative.” In Table II , we present the qualitative methods journals located, ranked by impact factor. The highest ranked journals were selected for searching. We acknowledge that the impact factor ranking system might not be the best measure of journal quality (Cheek, Garnham, & Quan, 2006 ); however, this was the most appropriate and accessible method available.

International Journal of Qualitative Studies on Health and Well-being.

Search strategy

In March 2013, searches of the journals, Qualitative Health Research , Qualitative Research , and Qualitative Inquiry were completed to retrieve studies with “case study” in the abstract field. The search was limited to the past 5 years (1 January 2008 to 1 March 2013). The objective was to locate published qualitative case studies suitable for assessment using the adapted criterion. Viewpoints, commentaries, and other article types were excluded from review. Title and abstracts of the 45 retrieved articles were read by the first author, who identified 34 empirical case studies for review. All authors reviewed the 34 studies to confirm selection and categorization. In Table III , we present the 34 case studies grouped by journal, and categorized by research topic, including health sciences, social sciences and anthropology, and methods research. There was a discrepancy in categorization of one article on pedagogy and a new teaching method published in Qualitative Inquiry (Jorrín-Abellán, Rubia-Avi, Anguita-Martínez, Gómez-Sánchez, & Martínez-Mones, 2008 ). Consensus was to allocate to the methods category.

Outcomes of search of qualitative methods journals.

In Table III , the number of studies located, and final numbers selected for review have been reported. Qualitative Health Research published the most empirical case studies ( n= 16). In the health category, there were 12 case studies of health conditions, health services, and health policy issues, all published in Qualitative Health Research . Seven case studies were categorized as social sciences and anthropology research, which combined case study with biography and ethnography methodologies. All three journals published case studies on methods research to illustrate a data collection or analysis technique, methodological procedure, or related issue.

The methodological descriptions of 34 case studies were critically reviewed using the adapted criteria. All articles reviewed contained a description of study methods; however, the length, amount of detail, and position of the description in the article varied. Few studies provided an accurate description and rationale for using a qualitative case study approach. In the 34 case studies reviewed, three described a theoretical framework informed by Stake ( 1995 ), two by Yin ( 2009 ), and three provided a mixed framework informed by various authors, which might have included both Yin and Stake. Few studies described their case study design, or included a rationale that explained why they excluded or added further procedures, and whether this was to enhance the study design, or to better suit the research question. In 26 of the studies no reference was provided to principal case study authors. From reviewing the description of methods, few authors provided a description or justification of case study methodology that demonstrated how their study was informed by the methodological literature that exists on this approach.

The methodological descriptions of each study were reviewed using the adapted criteria, and the following issues were identified: case study methodology or method; case of something particular and case selection; contextually bound case study; researcher and case interactions and triangulation; and, study design inconsistent with methodology. An outline of how the issues were developed from the critical review is provided, followed by a discussion of how these relate to the current methodological literature.

Case study methodology or method

A third of the case studies reviewed appeared to use a case report method, not case study methodology as described by principal authors (Creswell, 2013b ; Merriam, 2009 ; Stake, 1995 ; Yin, 2009 ). Case studies were identified as a case report because of missing methodological detail and by review of the study aims and purpose. These reports presented data for small samples of no more than three people, places or phenomenon. Four studies, or “case reports” were single cases selected retrospectively from larger studies (Bronken, Kirkevold, Martinsen, & Kvigne, 2012 ; Coltart & Henwood, 2012 ; Hooghe, Neimeyer, & Rober, 2012 ; Roscigno et al., 2012 ). Case reports were not a case of something, instead were a case demonstration or an example presented in a report. These reports presented outcomes, and reported on how the case could be generalized. Descriptions focussed on the phenomena, rather than the case itself, and did not appear to study the case in its entirety.

Case reports had minimal in-text references to case study methodology, and were informed by other qualitative traditions or secondary sources (Adamson & Holloway, 2012 ; Buzzanell & D'Enbeau, 2009 ; Nagar-Ron & Motzafi-Haller, 2011 ). This does not suggest that case study methodology cannot be multimethod, however, methodology should be consistent in design, be clearly described (Meyer, 2001 ; Stake, 1995 ), and maintain focus on the case (Creswell, 2013b ).

To demonstrate how case reports were identified, three examples are provided. The first, Yeh ( 2013 ) described their study as, “the examination of the emergence of vegetarianism in Victorian England serves as a case study to reveal the relationships between boundaries and entities” (p. 306). The findings were a historical case report, which resulted from an ethnographic study of vegetarianism. Cunsolo Willox, Harper, Edge, ‘My Word’: Storytelling and Digital Media Lab, and Rigolet Inuit Community Government (2013) used “a case study that illustrates the usage of digital storytelling within an Inuit community” (p. 130). This case study reported how digital storytelling can be used with indigenous communities as a participatory method to illuminate the benefits of this method for other studies. This “case study was conducted in the Inuit community” but did not include the Inuit community in case analysis (Cunsolo Willox et al., 2013 , p. 130). Bronken et al. ( 2012 ) provided a single case report to demonstrate issues observed in a larger clinical study of aphasia and stroke, without adequate case description or analysis.

Case study of something particular and case selection

Case selection is a precursor to case analysis, which needs to be presented as a convincing argument (Merriam, 2009 ). Descriptions of the case were often not adequate to ascertain why the case was selected, or whether it was a particular exemplar or outlier (Thomas, 2011 ). In a number of case studies in the health and social science categories, it was not explicit whether the case was of something particular, or peculiar to their discipline or field (Adamson & Holloway, 2012 ; Bronken et al., 2012 ; Colón-Emeric et al., 2010 ; Jackson, Botelho, Welch, Joseph, & Tennstedt, 2012 ; Mawn et al., 2010 ; Snyder-Young, 2011 ). There were exceptions in the methods category ( Table III ), where cases were selected by researchers to report on a new or innovative method. The cases emerged through heuristic study, and were reported to be particular, relative to the existing methods literature (Ajodhia-Andrews & Berman, 2009 ; Buckley & Waring, 2013 ; Cunsolo Willox et al., 2013 ; De Haene, Grietens, & Verschueren, 2010 ; Gratton & O'Donnell, 2011 ; Sumsion, 2013 ; Wimpenny & Savin-Baden, 2012 ).

Case selection processes were sometimes insufficient to understand why the case was selected from the global population of cases, or what study of this case would contribute to knowledge as compared with other possible cases (Adamson & Holloway, 2012 ; Bronken et al., 2012 ; Colón-Emeric et al., 2010 ; Jackson et al., 2012 ; Mawn et al., 2010 ). In two studies, local cases were selected (Barone, 2010 ; Fourie & Theron, 2012 ) because the researcher was familiar with and had access to the case. Possible limitations of a convenience sample were not acknowledged. Purposeful sampling was used to recruit participants within the case of one study, but not of the case itself (Gallagher et al., 2013 ). Random sampling was completed for case selection in two studies (Colón-Emeric et al., 2010 ; Jackson et al., 2012 ), which has limited meaning in interpretive qualitative research.

To demonstrate how researchers provided a good justification for the selection of case study approaches, four examples are provided. The first, cases of residential care homes, were selected because of reported occurrences of mistreatment, which included residents being locked in rooms at night (Rytterström, Unosson, & Arman, 2013 ). Roscigno et al. ( 2012 ) selected cases of parents who were admitted for early hospitalization in neonatal intensive care with a threatened preterm delivery before 26 weeks. Hooghe et al. ( 2012 ) used random sampling to select 20 couples that had experienced the death of a child; however, the case study was of one couple and a particular metaphor described only by them. The final example, Coltart and Henwood ( 2012 ), provided a detailed account of how they selected two cases from a sample of 46 fathers based on personal characteristics and beliefs. They described how the analysis of the two cases would contribute to their larger study on first time fathers and parenting.

Contextually bound case study

The limits or boundaries of the case are a defining factor of case study methodology (Merriam, 2009 ; Ragin & Becker, 1992 ; Stake, 1995 ; Yin, 2009 ). Adequate contextual description is required to understand the setting or context in which the case is revealed. In the health category, case studies were used to illustrate a clinical phenomenon or issue such as compliance and health behaviour (Colón-Emeric et al., 2010 ; D'Enbeau, Buzzanell, & Duckworth, 2010 ; Gallagher et al., 2013 ; Hooghe et al., 2012 ; Jackson et al., 2012 ; Roscigno et al., 2012 ). In these case studies, contextual boundaries, such as physical and institutional descriptions, were not sufficient to understand the case as a holistic system, for example, the general practitioner (GP) clinic in Gallagher et al. ( 2013 ), or the nursing home in Colón-Emeric et al. ( 2010 ). Similarly, in the social science and methods categories, attention was paid to some components of the case context, but not others, missing important information required to understand the case as a holistic system (Alexander, Moreira, & Kumar, 2012 ; Buzzanell & D'Enbeau, 2009 ; Nairn & Panelli, 2009 ; Wimpenny & Savin-Baden, 2012 ).

In two studies, vicarious experience or vignettes (Nairn & Panelli, 2009 ) and images (Jorrín-Abellán et al., 2008 ) were effective to support description of context, and might have been a useful addition for other case studies. Missing contextual boundaries suggests that the case might not be adequately defined. Additional information, such as the physical, institutional, political, and community context, would improve understanding of the case (Stake, 1998 ). In Boxes 1 and 2 , we present brief synopses of two studies that were reviewed, which demonstrated a well bounded case. In Box 1 , Ledderer ( 2011 ) used a qualitative case study design informed by Stake's tradition. In Box 2 , Gillard, Witt, and Watts ( 2011 ) were informed by Yin's tradition. By providing a brief outline of the case studies in Boxes 1 and 2 , we demonstrate how effective case boundaries can be constructed and reported, which may be of particular interest to prospective case study researchers.

Article synopsis of case study research using Stake's tradition

Ledderer ( 2011 ) used a qualitative case study research design, informed by modern ethnography. The study is bounded to 10 general practice clinics in Denmark, who had received federal funding to implement preventative care services based on a Motivational Interviewing intervention. The researcher question focussed on “why is it so difficult to create change in medical practice?” (Ledderer, 2011 , p. 27). The study context was adequately described, providing detail on the general practitioner (GP) clinics and relevant political and economic influences. Methodological decisions are described in first person narrative, providing insight on researcher perspectives and interaction with the case. Forty-four interviews were conducted, which focussed on how GPs conducted consultations, and the form, nature and content, rather than asking their opinion or experience (Ledderer, 2011 , p. 30). The duration and intensity of researcher immersion in the case enhanced depth of description and trustworthiness of study findings. Analysis was consistent with Stake's tradition, and the researcher provided examples of inquiry techniques used to challenge assumptions about emerging themes. Several other seminal qualitative works were cited. The themes and typology constructed are rich in narrative data and storytelling by clinic staff, demonstrating individual clinic experiences as well as shared meanings and understandings about changing from a biomedical to psychological approach to preventative health intervention. Conclusions make note of social and cultural meanings and lessons learned, which might not have been uncovered using a different methodology.

Article synopsis of case study research using Yin's tradition

Gillard et al. ( 2011 ) study of camps for adolescents living with HIV/AIDs provided a good example of Yin's interpretive case study approach. The context of the case is bounded by the three summer camps of which the researchers had prior professional involvement. A case study protocol was developed that used multiple methods to gather information at three data collection points coinciding with three youth camps (Teen Forum, Discover Camp, and Camp Strong). Gillard and colleagues followed Yin's ( 2009 ) principles, using a consistent data protocol that enhanced cross-case analysis. Data described the young people, the camp physical environment, camp schedule, objectives and outcomes, and the staff of three youth camps. The findings provided a detailed description of the context, with less detail of individual participants, including insight into researcher's interpretations and methodological decisions throughout the data collection and analysis process. Findings provided the reader with a sense of “being there,” and are discovered through constant comparison of the case with the research issues; the case is the unit of analysis. There is evidence of researcher immersion in the case, and Gillard reports spending significant time in the field in a naturalistic and integrated youth mentor role.

This case study is not intended to have a significant impact on broader health policy, although does have implications for health professionals working with adolescents. Study conclusions will inform future camps for young people with chronic disease, and practitioners are able to compare similarities between this case and their own practice (for knowledge translation). No limitations of this article were reported. Limitations related to publication of this case study were that it was 20 pages long and used three tables to provide sufficient description of the camp and program components, and relationships with the research issue.

Researcher and case interactions and triangulation

Researcher and case interactions and transactions are a defining feature of case study methodology (Stake, 1995 ). Narrative stories, vignettes, and thick description are used to provoke vicarious experience and a sense of being there with the researcher in their interaction with the case. Few of the case studies reviewed provided details of the researcher's relationship with the case, researcher–case interactions, and how these influenced the development of the case study (Buzzanell & D'Enbeau, 2009 ; D'Enbeau et al., 2010 ; Gallagher et al., 2013 ; Gillard et al., 2011 ; Ledderer, 2011 ; Nagar-Ron & Motzafi-Haller, 2011 ). The role and position of the researcher needed to be self-examined and understood by readers, to understand how this influenced interactions with participants, and to determine what triangulation is needed (Merriam, 2009 ; Stake, 1995 ).

Gillard et al. ( 2011 ) provided a good example of triangulation, comparing data sources in a table (p. 1513). Triangulation of sources was used to reveal as much depth as possible in the study by Nagar-Ron and Motzafi-Haller ( 2011 ), while also enhancing confirmation validity. There were several case studies that would have benefited from improved range and use of data sources, and descriptions of researcher–case interactions (Ajodhia-Andrews & Berman, 2009 ; Bronken et al., 2012 ; Fincham, Scourfield, & Langer, 2008 ; Fourie & Theron, 2012 ; Hooghe et al., 2012 ; Snyder-Young, 2011 ; Yeh, 2013 ).

Study design inconsistent with methodology

Good, rigorous case studies require a strong methodological justification (Meyer, 2001 ) and a logical and coherent argument that defines paradigm, methodological position, and selection of study methods (Denzin & Lincoln, 2011b ). Methodological justification was insufficient in several of the studies reviewed (Barone, 2010 ; Bronken et al., 2012 ; Hooghe et al., 2012 ; Mawn et al., 2010 ; Roscigno et al., 2012 ; Yeh, 2013 ). This was judged by the absence, or inadequate or inconsistent reference to case study methodology in-text.

In six studies, the methodological justification provided did not relate to case study. There were common issues identified. Secondary sources were used as primary methodological references indicating that study design might not have been theoretically sound (Colón-Emeric et al., 2010 ; Coltart & Henwood, 2012 ; Roscigno et al., 2012 ; Snyder-Young, 2011 ). Authors and sources cited in methodological descriptions were inconsistent with the actual study design and practices used (Fourie & Theron, 2012 ; Hooghe et al., 2012 ; Jorrín-Abellán et al., 2008 ; Mawn et al., 2010 ; Rytterström et al., 2013 ; Wimpenny & Savin-Baden, 2012 ). This occurred when researchers cited Stake or Yin, or both (Mawn et al., 2010 ; Rytterström et al., 2013 ), although did not follow their paradigmatic or methodological approach. In 26 studies there were no citations for a case study methodological approach.

The findings of this study have highlighted a number of issues for researchers. A considerable number of case studies reviewed were missing key elements that define qualitative case study methodology and the tradition cited. A significant number of studies did not provide a clear methodological description or justification relevant to case study. Case studies in health and social sciences did not provide sufficient information for the reader to understand case selection, and why this case was chosen above others. The context of the cases were not described in adequate detail to understand all relevant elements of the case context, which indicated that cases may have not been contextually bounded. There were inconsistencies between reported methodology, study design, and paradigmatic approach in case studies reviewed, which made it difficult to understand the study methodology and theoretical foundations. These issues have implications for methodological integrity and honesty when reporting study design, which are values of the qualitative research tradition and are ethical requirements (Wager & Kleinert, 2010a ). Poorly described methodological descriptions may lead the reader to misinterpret or discredit study findings, which limits the impact of the study, and, as a collective, hinders advancements in the broader qualitative research field.

The issues highlighted in our review build on current debates in the case study literature, and queries about the value of this methodology. Case study research can be situated within different paradigms or designed with an array of methods. In order to maintain the creativity and flexibility that is valued in this methodology, clearer descriptions of paradigm and theoretical position and methods should be provided so that study findings are not undervalued or discredited. Case study research is an interdisciplinary practice, which means that clear methodological descriptions might be more important for this approach than other methodologies that are predominantly driven by fewer disciplines (Creswell, 2013b ).

Authors frequently omit elements of methodologies and include others to strengthen study design, and we do not propose a rigid or purist ideology in this paper. On the contrary, we encourage new ideas about using case study, together with adequate reporting, which will advance the value and practice of case study. The implications of unclear methodological descriptions in the studies reviewed were that study design appeared to be inconsistent with reported methodology, and key elements required for making judgements of rigour were missing. It was not clear whether the deviations from methodological tradition were made by researchers to strengthen the study design, or because of misinterpretations. Morse ( 2011 ) recommended that innovations and deviations from practice are best made by experienced researchers, and that a novice might be unaware of the issues involved with making these changes. To perpetuate the tradition of case study research, applications in the published literature should have consistencies with traditional methodological constructions, and deviations should be described with a rationale that is inherent in study conduct and findings. Providing methodological descriptions that demonstrate a strong theoretical foundation and coherent study design will add credibility to the study, while ensuring the intrinsic meaning of case study is maintained.

The value of this review is that it contributes to discussion of whether case study is a methodology or method. We propose possible reasons why researchers might make this misinterpretation. Researchers may interchange the terms methods and methodology, and conduct research without adequate attention to epistemology and historical tradition (Carter & Little, 2007 ; Sandelowski, 2010 ). If the rich meaning that naming a qualitative methodology brings to the study is not recognized, a case study might appear to be inconsistent with the traditional approaches described by principal authors (Creswell, 2013a ; Merriam, 2009 ; Stake, 1995 ; Yin, 2009 ). If case studies are not methodologically and theoretically situated, then they might appear to be a case report.

Case reports are promoted by university and medical journals as a method of reporting on medical or scientific cases; guidelines for case reports are publicly available on websites ( http://www.hopkinsmedicine.org/institutional_review_board/guidelines_policies/guidelines/case_report.html ). The various case report guidelines provide a general criteria for case reports, which describes that this form of report does not meet the criteria of research, is used for retrospective analysis of up to three clinical cases, and is primarily illustrative and for educational purposes. Case reports can be published in academic journals, but do not require approval from a human research ethics committee. Traditionally, case reports describe a single case, to explain how and what occurred in a selected setting, for example, to illustrate a new phenomenon that has emerged from a larger study. A case report is not necessarily particular or the study of a case in its entirety, and the larger study would usually be guided by a different research methodology.

This description of a case report is similar to what was provided in some studies reviewed. This form of report lacks methodological grounding and qualities of research rigour. The case report has publication value in demonstrating an example and for dissemination of knowledge (Flanagan, 1999 ). However, case reports have different meaning and purpose to case study, which needs to be distinguished. Findings of our review suggest that the medical understanding of a case report has been confused with qualitative case study approaches.

In this review, a number of case studies did not have methodological descriptions that included key characteristics of case study listed in the adapted criteria, and several issues have been discussed. There have been calls for improvements in publication quality of qualitative research (Morse, 2011 ), and for improvements in peer review of submitted manuscripts (Carter & Little, 2007 ; Jasper, Vaismoradi, Bondas, & Turunen, 2013 ). The challenging nature of editor and reviewers responsibilities are acknowledged in the literature (Hames, 2013 ; Wager & Kleinert, 2010b ); however, review of case study methodology should be prioritized because of disputes on methodological value.

Authors using case study approaches are recommended to describe their theoretical framework and methods clearly, and to seek and follow specialist methodological advice when needed (Wager & Kleinert, 2010a ). Adequate page space for case study description would contribute to better publications (Gillard et al., 2011 ). Capitalizing on the ability to publish complementary resources should be considered.

Limitations of the review

There is a level of subjectivity involved in this type of review and this should be considered when interpreting study findings. Qualitative methods journals were selected because the aims and scope of these journals are to publish studies that contribute to methodological discussion and development of qualitative research. Generalist health and social science journals were excluded that might have contained good quality case studies. Journals in business or education were also excluded, although a review of case studies in international business journals has been published elsewhere (Piekkari et al., 2009 ).

The criteria used to assess the quality of the case studies were a set of qualitative indicators. A numerical or ranking system might have resulted in different results. Stake's ( 1995 ) criteria have been referenced elsewhere, and was deemed the best available (Creswell, 2013b ; Crowe et al., 2011 ). Not all qualitative studies are reported in a consistent way and some authors choose to report findings in a narrative form in comparison to a typical biomedical report style (Sandelowski & Barroso, 2002 ), if misinterpretations were made this may have affected the review.

Case study research is an increasingly popular approach among qualitative researchers, which provides methodological flexibility through the incorporation of different paradigmatic positions, study designs, and methods. However, whereas flexibility can be an advantage, a myriad of different interpretations has resulted in critics questioning the use of case study as a methodology. Using an adaptation of established criteria, we aimed to identify and assess the methodological descriptions of case studies in high impact, qualitative methods journals. Few articles were identified that applied qualitative case study approaches as described by experts in case study design. There were inconsistencies in methodology and study design, which indicated that researchers were confused whether case study was a methodology or a method. Commonly, there appeared to be confusion between case studies and case reports. Without clear understanding and application of the principles and key elements of case study methodology, there is a risk that the flexibility of the approach will result in haphazard reporting, and will limit its global application as a valuable, theoretically supported methodology that can be rigorously applied across disciplines and fields.

Conflict of interest and funding

The authors have not received any funding or benefits from industry or elsewhere to conduct this study.

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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 case study research?

Last updated

8 February 2023

Reviewed by

Cathy Heath

Short on time? Get an AI generated summary of this article instead

Suppose a company receives a spike in the number of customer complaints, or medical experts discover an outbreak of illness affecting children but are not quite sure of the reason. In both cases, carrying out a case study could be the best way to get answers.

Organization

Case studies can be carried out across different disciplines, including education, medicine, sociology, and business.

Most case studies employ qualitative methods, but quantitative methods can also be used. Researchers can then describe, compare, evaluate, and identify patterns or cause-and-effect relationships between the various variables under study. They can then use this knowledge to decide what action to take. 

Another thing to note is that case studies are generally singular in their focus. This means they narrow focus to a particular area, making them highly subjective. You cannot always generalize the results of a case study and apply them to a larger population. However, they are valuable tools to illustrate a principle or develop a thesis.

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• What are the different types of case study designs?

Researchers can choose from a variety of case study designs. The design they choose is dependent on what questions they need to answer, the context of the research environment, how much data they already have, and what resources are available.

Here are the common types of case study design:

Explanatory

An explanatory case study is an initial explanation of the how or why that is behind something. This design is commonly used when studying a real-life phenomenon or event. Once the organization understands the reasons behind a phenomenon, it can then make changes to enhance or eliminate the variables causing it. 

Here is an example: How is co-teaching implemented in elementary schools? The title for a case study of this subject could be “Case Study of the Implementation of Co-Teaching in Elementary Schools.”

Descriptive

An illustrative or descriptive case study helps researchers shed light on an unfamiliar object or subject after a period of time. The case study provides an in-depth review of the issue at hand and adds real-world examples in the area the researcher wants the audience to understand. 

The researcher makes no inferences or causal statements about the object or subject under review. This type of design is often used to understand cultural shifts.

Here is an example: How did people cope with the 2004 Indian Ocean Tsunami? This case study could be titled "A Case Study of the 2004 Indian Ocean Tsunami and its Effect on the Indonesian Population."

Exploratory

Exploratory research is also called a pilot case study. It is usually the first step within a larger research project, often relying on questionnaires and surveys . Researchers use exploratory research to help narrow down their focus, define parameters, draft a specific research question , and/or identify variables in a larger study. This research design usually covers a wider area than others, and focuses on the ‘what’ and ‘who’ of a topic.

Here is an example: How do nutrition and socialization in early childhood affect learning in children? The title of the exploratory study may be “Case Study of the Effects of Nutrition and Socialization on Learning in Early Childhood.”

An intrinsic case study is specifically designed to look at a unique and special phenomenon. At the start of the study, the researcher defines the phenomenon and the uniqueness that differentiates it from others. 

In this case, researchers do not attempt to generalize, compare, or challenge the existing assumptions. Instead, they explore the unique variables to enhance understanding. Here is an example: “Case Study of Volcanic Lightning.”

This design can also be identified as a cumulative case study. It uses information from past studies or observations of groups of people in certain settings as the foundation of the new study. Given that it takes multiple areas into account, it allows for greater generalization than a single case study. 

The researchers also get an in-depth look at a particular subject from different viewpoints.  Here is an example: “Case Study of how PTSD affected Vietnam and Gulf War Veterans Differently Due to Advances in Military Technology.”

Critical instance

A critical case study incorporates both explanatory and intrinsic study designs. It does not have predetermined purposes beyond an investigation of the said subject. It can be used for a deeper explanation of the cause-and-effect relationship. It can also be used to question a common assumption or myth. 

The findings can then be used further to generalize whether they would also apply in a different environment.  Here is an example: “What Effect Does Prolonged Use of Social Media Have on the Mind of American Youth?”

Instrumental

Instrumental research attempts to achieve goals beyond understanding the object at hand. Researchers explore a larger subject through different, separate studies and use the findings to understand its relationship to another subject. This type of design also provides insight into an issue or helps refine a theory. 

For example, you may want to determine if violent behavior in children predisposes them to crime later in life. The focus is on the relationship between children and violent behavior, and why certain children do become violent. Here is an example: “Violence Breeds Violence: Childhood Exposure and Participation in Adult Crime.”

Evaluation case study design is employed to research the effects of a program, policy, or intervention, and assess its effectiveness and impact on future decision-making. 

For example, you might want to see whether children learn times tables quicker through an educational game on their iPad versus a more teacher-led intervention. Here is an example: “An Investigation of the Impact of an iPad Multiplication Game for Primary School Children.” 

• When do you use case studies?

Case studies are ideal when you want to gain a contextual, concrete, or in-depth understanding of a particular subject. It helps you understand the characteristics, implications, and meanings of the subject.

They are also an excellent choice for those writing a thesis or dissertation, as they help keep the project focused on a particular area when resources or time may be too limited to cover a wider one. You may have to conduct several case studies to explore different aspects of the subject in question and understand the problem.

• What are the steps to follow when conducting a case study?

1. Select a case

Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research.

2. Create a theoretical framework

While you will be focusing on a specific detail, the case study design you choose should be linked to existing knowledge on the topic. This prevents it from becoming an isolated description and allows for enhancing the existing information. 

It may expand the current theory by bringing up new ideas or concepts, challenge established assumptions, or exemplify a theory by exploring how it answers the problem at hand. A theoretical framework starts with a literature review of the sources relevant to the topic in focus. This helps in identifying key concepts to guide analysis and interpretation.

3. Collect the data

Case studies are frequently supplemented with qualitative data such as observations, interviews, and a review of both primary and secondary sources such as official records, news articles, and photographs. There may also be quantitative data —this data assists in understanding the case thoroughly.

4. Analyze your case

The results of the research depend on the research design. Most case studies are structured with chapters or topic headings for easy explanation and presentation. Others may be written as narratives to allow researchers to explore various angles of the topic and analyze its meanings and implications.

In all areas, always give a detailed contextual understanding of the case and connect it to the existing theory and literature before discussing how it fits into your problem area.

• What are some case study examples?

What are the best approaches for introducing our product into the Kenyan market?

How does the change in marketing strategy aid in increasing the sales volumes of product Y?

How can teachers enhance student participation in classrooms?

How does poverty affect literacy levels in children?

Case study topics

Case study of product marketing strategies in the Kenyan market

Case study of the effects of a marketing strategy change on product Y sales volumes

Case study of X school teachers that encourage active student participation in the classroom

Case study of the effects of poverty on literacy levels in children

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How to Write a Case Study | Examples & Methods

What is a case study?

A case study is a research approach that provides an in-depth examination of a particular phenomenon, event, organization, or individual. It involves analyzing and interpreting data to provide a comprehensive understanding of the subject under investigation. 

Case studies can be used in various disciplines, including business, social sciences, medicine ( clinical case report ), engineering, and education. The aim of a case study is to provide an in-depth exploration of a specific subject, often with the goal of generating new insights into the phenomena being studied.

When to write a case study

Case studies are often written to present the findings of an empirical investigation or to illustrate a particular point or theory. They are useful when researchers want to gain an in-depth understanding of a specific phenomenon or when they are interested in exploring new areas of inquiry. 

Case studies are also useful when the subject of the research is rare or when the research question is complex and requires an in-depth examination. A case study can be a good fit for a thesis or dissertation as well.

Case study examples

Below are some examples of case studies with their research questions:

These examples demonstrate the diversity of research questions and case studies that can be explored. From studying small businesses in Ghana to the ethical issues in supply chains, case studies can be used to explore a wide range of phenomena.

Outlying cases vs. representative cases

An outlying case stud y refers to a case that is unusual or deviates significantly from the norm. An example of an outlying case study could be a small, family-run bed and breakfast that was able to survive and even thrive during the COVID-19 pandemic, while other larger hotels struggled to stay afloat.

On the other hand, a representative case study refers to a case that is typical of the phenomenon being studied. An example of a representative case study could be a hotel chain that operates in multiple locations that faced significant challenges during the COVID-19 pandemic, such as reduced demand for hotel rooms, increased safety and health protocols, and supply chain disruptions. The hotel chain case could be representative of the broader hospitality industry during the pandemic, and thus provides an insight into the typical challenges that businesses in the industry faced.

Steps for Writing a Case Study

As with any academic paper, writing a case study requires careful preparation and research before a single word of the document is ever written. Follow these basic steps to ensure that you don’t miss any crucial details when composing your case study.

Step 1: Select a case to analyze

After you have developed your statement of the problem and research question , the first step in writing a case study is to select a case that is representative of the phenomenon being investigated or that provides an outlier. For example, if a researcher wants to explore the impact of COVID-19 on the hospitality industry, they could select a representative case, such as a hotel chain that operates in multiple locations, or an outlying case, such as a small bed and breakfast that was able to pivot their business model to survive during the pandemic. Selecting the appropriate case is critical in ensuring the research question is adequately explored.

Step 2: Create a theoretical framework

Theoretical frameworks are used to guide the analysis and interpretation of data in a case study. The framework should provide a clear explanation of the key concepts, variables, and relationships that are relevant to the research question. The theoretical framework can be drawn from existing literature, or the researcher can develop their own framework based on the data collected. The theoretical framework should be developed early in the research process to guide the data collection and analysis.

To give your case analysis a strong theoretical grounding, be sure to include a literature review of references and sources relating to your topic and develop a clear theoretical framework. Your case study does not simply stand on its own but interacts with other studies related to your topic. Your case study can do one of the following: 

• Demonstrate a theory by showing how it explains the case being investigated
• Broaden a theory by identifying additional concepts and ideas that can be incorporated to strengthen it
• Confront a theory via an outlier case that does not conform to established conclusions or assumptions

Step 3: Collect data for your case study

Data collection can involve a variety of research methods , including interviews, surveys, observations, and document analyses, and it can include both primary and secondary sources . It is essential to ensure that the data collected is relevant to the research question and that it is collected in a systematic and ethical manner. Data collection methods should be chosen based on the research question and the availability of data. It is essential to plan data collection carefully to ensure that the data collected is of high quality

Step 4: Describe the case and analyze the details

The final step is to describe the case in detail and analyze the data collected. This involves identifying patterns and themes that emerge from the data and drawing conclusions that are relevant to the research question. It is essential to ensure that the analysis is supported by the data and that any limitations or alternative explanations are acknowledged.

The manner in which you report your findings depends on the type of research you are doing. Some case studies are structured like a standard academic paper, with separate sections or chapters for the methods section , results section , and discussion section , while others are structured more like a standalone literature review.

Regardless of the topic you choose to pursue, writing a case study requires a systematic and rigorous approach to data collection and analysis. By following the steps outlined above and using examples from existing literature, researchers can create a comprehensive and insightful case study that contributes to the understanding of a particular phenomenon.

Preparing Your Case Study for Publication

After completing the draft of your case study, be sure to revise and edit your work for any mistakes, including grammatical errors , punctuation errors , spelling mistakes, and awkward sentence structure . Ensure that your case study is well-structured and that your arguments are well-supported with language that follows the conventions of academic writing .  To ensure your work is polished for style and free of errors, get English editing services from Wordvice, including our paper editing services and manuscript editing services . Let our academic subject experts enhance the style and flow of your academic work so you can submit your case study with confidence.

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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.

Prevent plagiarism, run a free check.

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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Library Guides

Dissertations 4: methodology: methods.

• Introduction & Philosophy
• Methodology

Primary & Secondary Sources, Primary & Secondary Data

When describing your research methods, you can start by stating what kind of secondary and, if applicable, primary sources you used in your research. Explain why you chose such sources, how well they served your research, and identify possible issues encountered using these sources.  

Definitions  

There is some confusion on the use of the terms primary and secondary sources, and primary and secondary data. The confusion is also due to disciplinary differences (Lombard 2010). Whilst you are advised to consult the research methods literature in your field, we can generalise as follows:  

Secondary sources 

Secondary sources normally include the literature (books and articles) with the experts' findings, analysis and discussions on a certain topic (Cottrell, 2014, p123). Secondary sources often interpret primary sources.  

Primary sources 

Primary sources are "first-hand" information such as raw data, statistics, interviews, surveys, law statutes and law cases. Even literary texts, pictures and films can be primary sources if they are the object of research (rather than, for example, documentaries reporting on something else, in which case they would be secondary sources). The distinction between primary and secondary sources sometimes lies on the use you make of them (Cottrell, 2014, p123). 

Primary data 

Primary data are data (primary sources) you directly obtained through your empirical work (Saunders, Lewis and Thornhill 2015, p316). 

Secondary data 

Secondary data are data (primary sources) that were originally collected by someone else (Saunders, Lewis and Thornhill 2015, p316).   

Comparison between primary and secondary data   

Use  

Virtually all research will use secondary sources, at least as background information. 

Often, especially at the postgraduate level, it will also use primary sources - secondary and/or primary data. The engagement with primary sources is generally appreciated, as less reliant on others' interpretations, and closer to 'facts'. 

The use of primary data, as opposed to secondary data, demonstrates the researcher's effort to do empirical work and find evidence to answer her specific research question and fulfill her specific research objectives. Thus, primary data contribute to the originality of the research.    

Ultimately, you should state in this section of the methodology: 

What sources and data you are using and why (how are they going to help you answer the research question and/or test the hypothesis. 

If using primary data, why you employed certain strategies to collect them. 

What the advantages and disadvantages of your strategies to collect the data (also refer to the research in you field and research methods literature). 

Quantitative, Qualitative & Mixed Methods

The methodology chapter should reference your use of quantitative research, qualitative research and/or mixed methods. The following is a description of each along with their advantages and disadvantages. 

Quantitative research 

Quantitative research uses numerical data (quantities) deriving, for example, from experiments, closed questions in surveys, questionnaires, structured interviews or published data sets (Cottrell, 2014, p93). It normally processes and analyses this data using quantitative analysis techniques like tables, graphs and statistics to explore, present and examine relationships and trends within the data (Saunders, Lewis and Thornhill, 2015, p496). 

Qualitative research  

Qualitative research is generally undertaken to study human behaviour and psyche. It uses methods like in-depth case studies, open-ended survey questions, unstructured interviews, focus groups, or unstructured observations (Cottrell, 2014, p93). The nature of the data is subjective, and also the analysis of the researcher involves a degree of subjective interpretation. Subjectivity can be controlled for in the research design, or has to be acknowledged as a feature of the research. Subject-specific books on (qualitative) research methods offer guidance on such research designs.  

Mixed methods 

Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. Mixed methods have gained popularity in recent years.  

When undertaking mixed-methods research you can collect the qualitative and quantitative data either concurrently or sequentially. If sequentially, you can for example, start with a few semi-structured interviews, providing qualitative insights, and then design a questionnaire to obtain quantitative evidence that your qualitative findings can also apply to a wider population (Specht, 2019, p138). 

Ultimately, your methodology chapter should state: 

Whether you used quantitative research, qualitative research or mixed methods. 

Why you chose such methods (and refer to research method sources). 

Why you rejected other methods. 

How well the method served your research. 

The problems or limitations you encountered. 

Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains mixed methods research in the following video:

LinkedIn Learning Video on Academic Research Foundations: Quantitative

The video covers the characteristics of quantitative research, and explains how to approach different parts of the research process, such as creating a solid research question and developing a literature review. He goes over the elements of a study, explains how to collect and analyze data, and shows how to present your data in written and numeric form.

Link to quantitative research video

Some Types of Methods

There are several methods you can use to get primary data. To reiterate, the choice of the methods should depend on your research question/hypothesis. 

Whatever methods you will use, you will need to consider: 

why did you choose one technique over another? What were the advantages and disadvantages of the technique you chose? 

what was the size of your sample? Who made up your sample? How did you select your sample population? Why did you choose that particular sampling strategy?) 

ethical considerations (see also tab...)  

safety considerations  

validity  

feasibility  

recording  

procedure of the research (see box procedural method...).  

Check Stella Cottrell's book  Dissertations and Project Reports: A Step by Step Guide  for some succinct yet comprehensive information on most methods (the following account draws mostly on her work). Check a research methods book in your discipline for more specific guidance.  

Experiments 

Experiments are useful to investigate cause and effect, when the variables can be tightly controlled. They can test a theory or hypothesis in controlled conditions. Experiments do not prove or disprove an hypothesis, instead they support or not support an hypothesis. When using the empirical and inductive method it is not possible to achieve conclusive results. The results may only be valid until falsified by other experiments and observations. 

For more information on Scientific Method, click here . 

Observations 

Observational methods are useful for in-depth analyses of behaviours in people, animals, organisations, events or phenomena. They can test a theory or products in real life or simulated settings. They generally a qualitative research method.  

Questionnaires and surveys 

Questionnaires and surveys are useful to gain opinions, attitudes, preferences, understandings on certain matters. They can provide quantitative data that can be collated systematically; qualitative data, if they include opportunities for open-ended responses; or both qualitative and quantitative elements. 

Interviews  

Interviews are useful to gain rich, qualitative information about individuals' experiences, attitudes or perspectives. With interviews you can follow up immediately on responses for clarification or further details. There are three main types of interviews: structured (following a strict pattern of questions, which expect short answers), semi-structured (following a list of questions, with the opportunity to follow up the answers with improvised questions), and unstructured (following a short list of broad questions, where the respondent can lead more the conversation) (Specht, 2019, p142). 

This short video on qualitative interviews discusses best practices and covers qualitative interview design, preparation and data collection methods. 

Focus groups   

In this case, a group of people (normally, 4-12) is gathered for an interview where the interviewer asks questions to such group of participants. Group interactions and discussions can be highly productive, but the researcher has to beware of the group effect, whereby certain participants and views dominate the interview (Saunders, Lewis and Thornhill 2015, p419). The researcher can try to minimise this by encouraging involvement of all participants and promoting a multiplicity of views. 

This video focuses on strategies for conducting research using focus groups.  

Check out the guidance on online focus groups by Aliaksandr Herasimenka, which is attached at the bottom of this text box. 

Case study 

Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section. Case studies give in-depth insights on the particular reality that has been examined, but may not be representative of what happens in general, they may not be generalisable, and may not be relevant to other contexts. These limitations have to be acknowledged by the researcher.     

Content analysis 

Content analysis consists in the study of words or images within a text. In its broad definition, texts include books, articles, essays, historical documents, speeches, conversations, advertising, interviews, social media posts, films, theatre, paintings or other visuals. Content analysis can be quantitative (e.g. word frequency) or qualitative (e.g. analysing intention and implications of the communication). It can detect propaganda, identify intentions of writers, and can see differences in types of communication (Specht, 2019, p146). Check this page on collecting, cleaning and visualising Twitter data.

Extra links and resources:  

Research Methods  

A clear and comprehensive overview of research methods by Emerald Publishing. It includes: crowdsourcing as a research tool; mixed methods research; case study; discourse analysis; ground theory; repertory grid; ethnographic method and participant observation; interviews; focus group; action research; analysis of qualitative data; survey design; questionnaires; statistics; experiments; empirical research; literature review; secondary data and archival materials; data collection. 

Doing your dissertation during the COVID-19 pandemic  

Resources providing guidance on doing dissertation research during the pandemic: Online research methods; Secondary data sources; Webinars, conferences and podcasts; 

• Virtual Focus Groups Guidance on managing virtual focus groups

5 Minute Methods Videos

The following are a series of useful videos that introduce research methods in five minutes. These resources have been produced by lecturers and students with the University of Westminster's School of Media and Communication. 

Case Study Research

Research Ethics

Quantitative Content Analysis 

Sequential Analysis 

Qualitative Content Analysis 

Thematic Analysis 

Social Media Research 

Mixed Method Research 

Procedural Method

In this part, provide an accurate, detailed account of the methods and procedures that were used in the study or the experiment (if applicable!). 

Include specifics about participants, sample, materials, design and methods. 

If the research involves human subjects, then include a detailed description of who and how many participated along with how the participants were selected.  

Describe all materials used for the study, including equipment, written materials and testing instruments. 

Identify the study's design and any variables or controls employed. 

Write out the steps in the order that they were completed. 

Indicate what participants were asked to do, how measurements were taken and any calculations made to raw data collected. 

Specify statistical techniques applied to the data to reach your conclusions. 

Provide evidence that you incorporated rigor into your research. This is the quality of being thorough and accurate and considers the logic behind your research design. 

Highlight any drawbacks that may have limited your ability to conduct your research thoroughly. 

You have to provide details to allow others to replicate the experiment and/or verify the data, to test the validity of the research. 

Bibliography

Cottrell, S. (2014). Dissertations and project reports: a step by step guide. Hampshire, England: Palgrave Macmillan.

Lombard, E. (2010). Primary and secondary sources.  The Journal of Academic Librarianship , 36(3), 250-253

Saunders, M.N.K., Lewis, P. and Thornhill, A. (2015).  Research Methods for Business Students.  New York: Pearson Education. 

Specht, D. (2019).  The Media And Communications Study Skills Student Guide . London: University of Westminster Press.  

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• Last Updated: Sep 14, 2022 12:58 PM
• URL: https://libguides.westminster.ac.uk/methodology-for-dissertations

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How methods to assess land-use changes influence the resulting global warming potential and cost of optimized diets: a case study on Danish pigs applying life cycle assessment methodology

• LCA FOR AGRICULTURE
• Open access
• Published: 10 August 2024

Cite this article

You have full access to this open access article

• Styrmir Gislason   ORCID: orcid.org/0000-0002-6154-4277 1 , 3 ,
• Thomas Sønderby Bruun 2 ,
• Stefan Wirsenius 4 ,
• Morten Birkved 3 ,
• Chandrakant Singh 4 ,
• Finn Udesen 1 &
• Alberto Maresca 1  

Meeting the demands of a growing and increasingly affluent population necessitates a deeper understanding of the environmental and economic implications of production. This implication is most relevant in key production sectors including agriculture and livestock. This article is intended to provide an understanding of the influence of methods of assessing land-use change (LUC) with respect to minimizing both the global warming potential (GWP) and the monetary costs of pig feed formulation.

Feed mixtures intended for slaughter pigs were generated for minimal cost and GWP impacts by applying four differing LUC assessment methods. The objective function was the Danish slaughter pig feed unit, minimized for cost in Danish crowns (DKK), with GWP impacts constrained in multiple steps. Attributional LCA methodology was applied using the Agri-footprint 6.3 database, with GWP impacts calculated excluding land use changes, including direct land-use changes and including the carbon opportunity cost. Analyses of the functional relationship between the optimal cost and the GWP impact were conducted, followed by a comparative LCA of the cost of comparable feed mixture by applying two sets of functional units: 100 slaughter pig feed units and 1 kg of pig live weight.

Results and discussion

A similar relationship between cost and GWP impact was observed across all methods, although variability of GWP impact magnitude depending on method was observed. Reducing at an equivalent cost, GWP reduction ranged from 5.6 to 27% based on the pig feed functional unit, and 2.4 to 13% based on the pig live weight functional unit. Optimizing feed mixtures for GWP impacts resulted in significantly increased contributions to other impact categories, including a 56% increase in terrestrial ecotoxicity. Despite the increased contributions to other impact categories, all optimized feed mixtures achieved a reduction in endpoint indicators and single score. Endpoint reductions to the feed unit were 2.3–25% for ecosystem damage, 7.4–15% for human health, and 6.0–16% based on a single score value.

Conclusions

The findings emphasize the key importance of addressing LUC when optimizing the GWP of agri-food production. Suggestions are provided for areas of improvement in future optimization studies applying a dietary unit as the objective function, including additional midpoint impact categories and/or extended optimization covering whole areas of protection. The findings suggest that GWP impacts may be reduced at no additional cost if included or embedded in the pig feed formulation procedure.

Avoid common mistakes on your manuscript.

1 Introduction

Recent statistics on global dietary patterns suggest that nearly 40% of human protein consumption is sourced from animal proteins originating from livestock, including eggs, dairy, and meat (FAO 2023a ).

Livestock feed, and in particular pig feed, consists primarily of plant-based ingredients like grains and sources of dietary protein, including oilseed “meals” originating from vegetable oil production, and to a lesser extent dietary protein sources of animal origin, including blood plasma, fish meal, and milk protein (Lewis and Southern  2001 , Sauber and Owens 2001 ). Dietary fat sources in pig feed are primarily plant-based, although this energy source may also be of animal origin (Azain 2001 ). Although consumption patterns differ globally, pork (i.e., pig meat) is on average the most highly consumed meat in China and the European Union, providing a substantial share of protein to the global food supply (FAO 2023c ).

Presently, the Danish agricultural sector contributes to about 26.2% of the sectoral anthropogenic GHG emissions, primarily in the form of nitrous oxide (N 2 O), methane (CH 4 ), and carbon dioxide (CO 2 ) (Nielsen et al. 2023 ). These GHG emissions contribute to a rise in global temperatures at different magnitudes referred to as the “global warming potential” (GWP), an impact category expressed in the mass of CO 2 equivalents (CO 2 -eq), usually over a 100-year time horizon (GWP-100). The European Union has set ambitious targets to reduce greenhouse gas (GHG) emissions by 70% (relative to 1990) by the year 2030 (European Commission 2020 ). The Danish government has also set a climate neutrality target for 2050, requiring major changes in national production across all sectors (Energistyrelsen 2020 ). Despite the urgency caused by rising mean global temperatures, multiple other environmental impact categories are highly relevant to the agri-food sector, including eutrophication, acidification, human toxicity, ecotoxicity, and excessive and unsustainable water and land use (Knudsen et al. 2019 ; Gislason et al. 2023 ). The relevance of global land and water use is exemplified by 70% of freshwater extraction and 44% of habitable land occupation directly attributable to the agri-food sector (FAO  2023b ; FAO 2024 ). The application of pesticides to croplands may result in the leaching of toxic substances into groundwater, posing an additional risk to local water supplies (Mateo-Sagasta et al. 2017 ). Application of fertilizers, containing nitrogen, phosphorus, and potassium, is a common practice in conventional crop production, resulting in multiple emission pathways, including air, leaching, and run-off (Hutchings et al. 2023 ). Most GWP impacts attributable to crop cultivation are N 2 O emissions traced to fertilizer applications and emissions attributable to transformations of land cover (Bennetzen et al. 2016 ).

Life cycle assessments (LCAs) enable GWP impacts to be calculated from all production activities, including upstream and downstream activities in the supply chain (Bjørn et al. 2018 ). Additionally, the LCA methodology enables the substance characterization of multiple impact categories (e.g., ecotoxicity and eutrophication), enabling comprehensive environmental impact reporting (Hauschild and Huijbregts 2015 ). The attributional and consequential LCA approaches are the two main assessment classifications in LCA methodology (Weidema 2014 ). The attributional LCA approach is mostly used in environmental accounting, product declarations, and minor systematic decisions, while the consequential approach is intended for large-scale systematic changes and policy decisions (European Commission 2010 ). Land cover transformations are major drivers of GHG emissions and biodiversity loss, and contribute substantially to the agricultural sector’s GWP impact (Rosa et al. 2014 ). The GHG emissions from these transformation activities are often the result of the clearing of forests and the conversion and degradation of land systems, which leads to atmospheric releases of carbon stored in above- and below-ground biomass and soil (Andreson-Teixeira and DeLucia 2011 ). Practitioners of LCA refer to these transformations as land-use changes (LUC), although inclusion in LCA is usually limited to the GHG emissions attributable to a specific transformation activity (BSI 2012 ). The importance of including LUC within agricultural systems has been emphasized in previous LCA studies, most notably in respect of decisions regarding biofuels (Cherubini and Jungmeier 2010 ; Woltjer et al. 2017 ). The importance of LUC in the context of pig feed has also been emphasized, as the inclusion of LUC may increase the GWP impact severalfold (Meul et al. 2012 ; Kebreab et al. 2016 ).

Common LUC classifications methods are the direct (dLUC), indirect (iLUC), and “carbon opportunity cost” (COC) classifications (Persson et al. 2014 ; Searchinger et al. 2018 ). The dLUC assessment methodology enables accounting of past land transformation carbon losses that are attributed to production over a specific time horizon (usually the last 20 years) (BSI 2012 ). The iLUC assessment methodology provides an estimate of GHG emissions as the consequences of production choices in a systematic supply-capacity context relative to static demand imposed by global consumption (Schmidt et al. 2015 ). The COC assessment methodology is based on the opportunity cost principle originating from economics, which provides an estimate of lost carbon sequestration by choosing to continue current production as opposed to abandoning land. The COC method calculates the relative difference in current and natural carbon stocks of soil and vegetation that are attributed to production over a specified time horizon (Searchinger et al. 2018 ). Although land-management practices (e.g., crop rotation and tillage) may result in changes to the carbon stocks of cropland, LCAs generally do not include these stock changes (BSI 2012 ). These LUC assessment methods are all recognized within LCA applications, which are included in the latest draft of the greenhouse gas protocol (GHG Protocol 2022 ). The dLUC assessment method is best suited in attributional LCAs based on the attribution of recent carbon losses to current production (e.g., environmental accounting). A majority of existing dLUC studies and assessment methods statistically attribute carbon losses through deforestation to commodities relying on aggregated LUC data (Carter et al. 2018 ; De Sy et al. 2019 ; Pendrill et al. 2019 ). This approach introduces uncertainties regarding the precise locations of deforestation events and the resulting carbon losses (Bontinck et al. 2020 ). However, the integration of remote-sensing datasets with finer spatio-temporal resolutions significantly enhances the accuracy of the carbon losses attributed to commodity production, offering improvements over traditional statistical methods (Singh and Persson 2024 ). The iLUC assessment method is, on the other hand, best suited for consequential LCAs, providing an estimate of the GWP impacts through the indirect consequences of production choices. The COC assessment method is aligned with the attributional LCA methodology in the context of accounting, although it may also align with consequential LCA methodology in the decision context for climate action (Searchinger et al. 2018 ). These LUC assessment methods are valuable tools within agricultural LCAs, and their inclusion (or exclusion) should be carefully considered based on the specific LCA’s goal and scope.

The majority of environmental impacts in pig production is attributable to the production of feed, the housing of animals, and manure management, with minimal contribution from capital goods (e.g., onsite energy use, machinery, and buildings) (Gislason et al. 2023 ). Although multiple mitigation areas have been discussed in existing LCA literature on pig production, changes in feeding practices are among the most promising mitigation areas (Gislason et al. 2023 ). As the GWP impact of feed mixtures for pigs is mainly influenced by its feed ingredients, including ingredient GWP impacts in the diet formulation may enable cost-effective impact reductions. Feed mixtures for pigs differ in their requirements depending on the developmental stage, which are usually split into three stages for slaughter pig production. The stages are as follows: (1) sow management and nursing of piglets until they reach weaning weight, (2) weaning of piglets from sows until attaining fattening weight, and (3) the fattening of pigs until slaughter weight. In Denmark, after giving birth to a litter of piglets, the sow will typically nurse these piglets for close to 28 days until weaning weight of approximately 7.0 kg. When piglets are weaned, they enter the nursery stage, where they will be housed in temperature-controlled stables until they reach a weight of about 30 kg. Throughout this period, piglets will typically consume two or three different diets that gradually adapt the piglet to consuming vegetable protein and grains. The final stage is the grower-finisher phase, where pigs are fattened from 30 kg to their slaughter weight, which ranges from 110 to 140 kg depending on the system in question. In this period, pigs consume dry feed dispensed in automatic feeders or using a liquid feeding system. The slaughter pig diets primarily consist of plant feed as a mixture of grains (primarily wheat and barley) in addition to the meal by-product of oil-pressing beans or seeds (primarily soybean meal).

Studies that formulate pig’s diet are based on multiple-linear programming, taking into account the individual ingredient cost and nutrient content (van Zanten et al. 2018 ). In multiple-linear programming, an objective function defines the output variable (e.g., the unit of feed), while the decision variables are input variables (e.g., the cost) that are subjected to minimization or maximization (Stark 2012 ). In diet formulation for pigs, the objective function is defined as a feed unit that includes specific nutritional requirements based on constraints, while the decision variable is typically cost.

The nutrient constraints are typically retrieved from nutrient standards at a national level, i.e., the American “Nutrient Requirements of Swine,” the Dutch “Booklet of Feeding Tables for Pigs,” or the Danish Nutrient Standards (National Research Council 2012 ; Tybirk. 2022 ; CVB 2023 ). Formulation of pig feed mixtures require the combined knowledge of nutritional requirements and ideal ingredient compositions in pig feed mixtures, since certain ingredients may only be introduced in minimal quantities without risking digestive issues resulting in reduced growth performance (Landbrug & Fødevarer 2019 ). In modern pig production, the feed conversion ratio and average daily gain and mortality rates are typically monitored to utilize the feed as efficiently as possible. The primary source of non-feed-related GWP impacts consists of emissions of CH 4 , which occur during manure storage and animal housing through enteric (gut) fermentation. Various nitrogen-based emissions (e.g., N 2 O, NH 3 ) originate from pig excretions during housing and manure storage, contributing to multiple environmental impact categories (Sørensen et al. 2023 ). Other emissions of stated significance include non-methane volatile organic compounds (NMVOCs) and particulate matter formation (Amon et al. 2019 ).

This study aims to provide an analysis of the optimal cost and GWP impact of feed mixtures fed to slaughter pigs by applying multiple common LUC assessment methods in calculation of the GWP impact. The analysis results will be used to investigate the potential reduction in the GWP impacts of Danish pig production by means of an LCA comparison of optimized feed mixtures at a cost equal to the average feed mixture used in Denmark.

2 Methodology

This study investigates the optimization of slaughter pig feed mixtures through the minimization of cost and the constraining of GWP impacts based on four separately applied LUC assessment methods. Each LUC assessment method was individually analyzed in terms of changes in cost and ingredient composition based on constraining the GWP impact in multiple steps. Thereafter, cost-equivalent and GWP-minimized feed mixtures were then generated for each applied LUC assessment method and subjected to an LCA comparison with an average feed mixture (baseline) used in Denmark. Two functional units were defined, the first comparing the slaughter pig feed mixtures directly as a slaughter pig feed unit and the second comparing the average Danish pig production system using the optimized feed mixtures. Animal performance and life cycle inventory data were based on published statistics that are representative of Danish production in 2021. Feed ingredients were subjected to sensitivity analyses of the ingredient’s production origins and ingredient constraints applied during feed optimization and formulation. Methodological sensitivity analysis included the addition of iLUC and an investigation of the correlations across the applied LUC assessment methods.

2.1 LCA methodology

All environmental impacts were calculated using attributional LCA methodology, following the ISO 14040 and 14,044 standards (ISO 2006a , b ). Cost-equivalent slaughter pig feed mixtures were compared in two LCAs, the first comparing the slaughter pig feed directly and the second comparing slaughter pig production systems differing only in slaughter pig feed mixtures. The functional unit directly assessing the slaughter pig feed was defined as 100 Danish feed units for slaughter pigs (FU pig ), a comparable unit fulfilling the nutrient requirements of slaughter pigs comparable to 1 kg of barley when completely oxidized (Tybirk et al. 2006 ). The functional unit assessing the slaughter pig production systems is defined as the mass of pig live weight in kilograms (kg*LW) exiting the farm gate, hence excluding all slaughterhouse activities. The system boundary assessing the functional unit of pig live weight does not account for changes in animal performance resulting from the different feed mixtures and is only included to provide a reference of reduction potential towards the entire pig production system. The product system applying the functional unit of FU pig is commonly referred to as “cradle to feed-gate,” including all activities required in the production of a ready-to-eat feed mixture. The product system for assessing slaughter pigs is commonly referred to as “cradle to farm-gate,” including all growth stages and activities from sow to slaughter weight (0–115 kg). The defined baseline slaughter pig feed mixture and the feed mixtures for sows and weaners were based on estimated feed mixtures typically used in Denmark (Tybirk. 2022 ). Feed ingredients are purchased with no crop production as part of the foreground system; therefore, a minimal contribution is expected from capital goods (stable construction, electricity, farming equipment) resulting in the exclusion of these activities from the foreground system. Feed ingredient life cycle inventories were retrieved from the Agri-footprint 6.3 economically allocated database, using Danish market mix processes (Mérieux NutriSciences | Blonk 2024 ). Statistical data on housing types and manure management systems were collected from the Danish national inventory reports for 2022 and 2023, verified by specialists at SEGES Innovation P/S (Nielsen et al. 2022 , Nielsen et al. 2023 ). Productivity data on feed conversion ratios, litter sizes, and growth and mortality rates for Danish pig production in 2021 was retrieved from a SEGES Innovation P/S report (Hansen 2021 ).

Figure  1 illustrates the product systems, including the emissions modelled as part of the foreground system for animal housing and manure storage, all based on IPCC and EMEP calculation models (Amon et al. 2019 ; Gavrilova et al. 2019 ). Sow multifunctionality (culled sows and piglets) is treated by economic allocations based on previous 5-year average prices, while manure is treated as a waste flow (cutoff) in alignment with the Agri-footprint 6.3 methodology (Landbrug & Fødevarer 2024 , Blonk et al. 2022 ). OpenLCA 2.0.3 software was used for inventory modelling and impact assessments using a modified version of the ReCiPe 2016 (H) midpoints and endpoints (Huijbregts et al. 2016 , Green Delta 2023 ). The modification of the ReCiPe 2016 (H) methods included the implementation of multiple GWP impact (sub-)categories for characterizing GWP impacts depending on the LUC assessment method in question. Additionally, as regionalized characterization factors for Danish NH 3 flows were missing for acidification impacts, they were included manually to provide representative acidification impacts. Details of inventory data, emission models, applied emission factors, and the characterization factors applied in modifications to the impact assessment method are available in the supplementary information (Online resource 1 & 2 ).

Illustration of the system boundaries of the two product systems expressed in the functional units of slaughter pig feed unit (FU pig ) and kilograms of pig live weight (kg*LW)

2.2 Methods assessing land-use change

Four LUC assessment methods were defined and included in this study as specified in Table  1 , resulting in four separate feed optimization analyses and LCA results. Feed ingredient inventories were based on Danish market mix processes for the Agri-footprint 6.3 feed database, which provide disaggregated elementary flows (e.g., emissions), including separated dLUC flows. These elementary flows were utilized in addition to newly added LUC flows generated for the additional LUC assessment methods, all expressed as CO 2 emissions. The differences between the LUC assessment methods were only the characterization values applied to the GWP impact for the existing or newly generated LUC flows. The additions of new LUC flows were limited to crop ingredient inventory processes, with no additional LUC flows provided for supplements (minerals and free amino acids). Applying the modifications of ReCiPe 2016 (H) midpoint in the various LUC flows enabled the environmental impacts of all LUC methods for ingredients, feed mixtures, and the entire pig production system to be calculated. The “noLUC” assessment method included no characterization of LUC flows on the GWP impact, while including the standard characterization values of all other elementary flows, including peat-soil drainage emissions. All subsequent LUC assessment methods include the characterization of flows described for “noLUC,” in addition to their unique LUC flows characterized towards the GWP impact. The “dLUC a ” assessment method included characterization of the default land transformation flows provided by the Agri-footprint 6.3 database. The “dLUC b ” assessment method characterized a newly added flow based on a model that combined geospatial datasets with agricultural statistics (i.e., a combination of direct and statistical land attribution approaches) for assessing the carbon losses through land transformation (Singh and Persson 2024 ). To ensure homogeneity and fair comparability of the two dLUC assessment methods, carbon losses attributable to crop land (in hectares) were attributed to individual crop products (in kg) for the dLUC b assessment method using the PAS2050 guideline and FAO statistics data (BSI 2012 ; FAO 2022 ). The LUC assessment method “COC” characterized a newly added flow that was calculated using the LPJmL model to estimate current and native carbon stocks (Searchinger et al. 2018 ). Calculation of COC requires the difference in carbon stock to be attributed over a specific time horizon by discounting or amortization over a period of 80 and 30 years, respectively. Details of the newly added flows and value per kg of ingredient are available in the supplementary information (Online resource 2 )

2.3 Feed optimization

The steps needed to enable feed optimization, including the cost and GWP impacts, are presented visually in Fig.  2 . The first steps were development of the life cycle inventory for ingredients including LUC (Sect. 2.2), and was followed by a calculation of the resulting GWP impacts for all ingredients and LUC assessment methods. Data on the ingredient’s nutrients, cost, and GWP impacts were then uploaded to the software WinOpti (v2023.1.8628.14970), where optimized feed mixtures were generated and analyzed (AgroVision 2024 ). Verification of successful implementation was performed by comparing feed mixture GWP impacts reported by WinOpti to those reported by OpenLCA. After verification that optimization had been successful, analysis was performed and followed by an LCA comparison of cost-equivalent and GWP impact-optimized slaughter pig feed mixtures using the two different functional units. Table 2 displays the feed ingredients used in this study, which consist of 15 crop products as feed ingredients, in addition to five essential free amino acid supplements and three mineral supplements. Free amino acids only balance the amino acid profile of the feed mix, being limited to this purpose within the feed mixture formulation. The analysis and changes were limited to slaughter pig feed to avoid increasing the study’s complexity, but were substantiated by majority of feed-related environmental impacts attributed to slaughter pig feed (Gislason et al. 2023 ). Data sources of ingredient nutrient contents were based on a combination of primary data and proprietary and/or confidential data (AgroVision 2024 ). Data on average feed ingredient prices for 2021 were acquired through correspondence with the Danish feed distributor Vestjyllands Andel A.m.b.a. (Ehmsen 2023 ).

The framework showcasing the steps needed to enable the inclusion of GWP impacts into the feed formulation for optimization

The objective function was defined as 100 slaughter pig feed units (FU pig ), a unit that typically ranges between 1.0 and 1.1 FU pig per kg of total ingredient mass (wet weight). As a technical limitation of WinOpti required constraining the GWP impacts relative to the ingredients’ dry matter (DM) content, an additional constraint was applied at precisely 1.20 FU pig /kg*DM to ensure accuracy. The allowable ingredient limits within the feed mixtures were based on nutritional guidelines published by SEGES innovation P/S and the specialists participating in this study (Online resource 1 ). These include a minimum mass constraint of 15% barley and 1% vegetable oil content, representing local feeding practices (barley) and ensuring that feed pellets can be produced from the feed mixtures (oil). The analysis began by formulating an “economic-optimum” feed mixture, which represents the lowest cost feed mixture that can be achieved while still satisfying all nutritional and ingredient constraints. The economic-optimum feed mixture is identical for all LUC assessment methods since no maximum constraints are placed on GWP impacts. The economic-optimum feed mixtures resulting in a GWP impact were then gradually constrained for each LUC assessment method until arriving at each LUC assessment method’s “GWP-optimum,” representing the lowest GWP impact achievable for a feed mixture while still satisfying all nutritional and ingredient constraints. These two “optimum” feed mixtures provided two key pieces of information about all optimized feed mixtures. The first is the lowest cost achievable and its resulting GWP impact, and the second is the lowest GWP impact achievable and its resulting cost. This means that all possible combinations of optimal feed mixtures are within these GWP impact values, providing valuable information of the relationship of minimal cost and GWP impact. Multiple feed mixtures were generated for each LUC assessment method at GWP impact constraints between the two optimum values, followed by collecting data on feed mixture costs, GWP impacts, and ingredient compositions. The data collected on the feed mixtures were then analyzed to identity relationships, followed by formulation of the cost-equivalent and GWP-minimized slaughter pig feed mixtures used in the LCA comparison.

2.4 Sensitivity analyses

Feed ingredient constraints and production origins were subjected to sensitivity analyses to investigate their influence on the resulting cost and their GWP impacts. An important distinction among ingredient constraints is that they are not defined by the feed unit and therefore do not influence the nutritional requirements, since there are applied specifically to avoid potential digestive issues that result in reduced growth performance. This influence of the ingredient constraints was investigated by repeating the analysis with the complete removal of ingredient constraints, followed by a comparison with the main results. The removal of constraints is aimed at providing insights into the importance of nutritional research, and more specifically to its influence when minimizing the costs and GWP impacts of pig feed mixtures. Although these feed mixtures are currently unusable in practice, their analysis provides insights into the potential that future nutritional research may unlock. Since the individual feed ingredients may differ in production origins and exhibit substantial differences in nutritional composition and GWP impacts, the ingredient production origin was subjected to a sensitivity analysis performed by replacing Danish market mix ingredients with multiple macro-regional ingredient processes at continental scales and resolutions (e.g., European barley, South American maize). These macro-regional processes were modelled for ingredients originating from different regions, included if a specific region supplies a minimum of 1% of a specific ingredient global net supply, as further explained in the supplementary information (Online resource 1 ). Since the influence of LUC assessment methodologies on the results is an area of interest for this research, the iLUC assessment method was included, and a correlation investigation of all LUC assessment methods was performed. The iLUC assessment method was not combined with the consequential LCA methodology (as recommended) and applied the identical attributional system boundary as applied for the other LUC assessment methodologies. The decision to include iLUC for sensitivity analysis with attributional LCA methodology is due to the interest in comparison of results to the other LUC assessment methods through the correlation investigation. The correlation investigation was performed through comparing optimized feed mixtures for each LUC assessment method to the results when applying the other LUC assessment methods. The correlation investigation was performed to investigate the potential correlation between methods, as optimizing for one LUC assessment method may have positive, negative, or alternatively no influence towards another LUC assessment method.

3 Results and discussion

3.1 feed analysis: gwp impacts and costs.

Figure  3 provides a visual representation of the relationship between the cost and GWP impact for each LUC assessment method, based on collected data from the various feed mixtures. To reiterate, the economic-optimum feed mixture is indicated by the lowest cost, while the GWP-optimum feed mixture is indicated by the lowest GWP impact (see Sect. 2.3 for details). The economic-optimum feed mixture is identical across LUC assessment methods and resulted in a cost of 148.4 DKK per 100 FU pig . The noLUC assessment method resulted in the lowest GWP impacts, with respective GWP optima and economic optima at 50.4 and 54.6 kg CO 2 -eq per 100 FU pig , respectively. The dLUC a assessment method exhibited a higher GWP impact of both optima and their range compared to the noLUC assessment method, with the GWP optimum and economic optimum at 55.1 and 77.6 kg CO 2 -eq per 100 FU pig , respectively. The dLUC b assessment method showed somewhat lower GWP impacts compared to its dLUC a counterpart, with the GWP optimum and economic optimum at 53.1 and 56.5 kg CO 2 -eq per 100 FU pig , respectively. The GWP impacts applying the COC assessment method were substantially higher compared to the other LUC assessment methods, with the GWP optimum and economic optimum at 240 and 390 kg CO 2 -eq per 100 FU pig , respectively.

Plotted data collected from optimized feed mixtures, displaying the GWP impact (y-axis) and cost in DKK per 100 FU pig (x-axis). The datapoints represent the optimal diet composition with the lowest cost at differing ranges of maximally constrained GWP impact, with the baseline cost of 152.2 indicated by the dashed line. Note that GWP impact (y-axis) differs in scale between A and B

Figure  4 displays changes in compositions of optimal feed mixtures based on changes in GWP impacts. The economical optimum feed mixture is composed of 40% rye, 39% barley, 10% rapeseed meal, 7.7% soybean meal, 0.7% palm oil, and 0.3% soy oil. All feed mixtures contained precisely 1.0% vegetable oil and approximately 2.0% mineral supplements and roughly 0.5% of free amino acids. Reductions in GWP impacts applying the noLUC assessment method (Fig.  4 A) are first attributed to changing the oil source from palm to soy (not visible on graph), followed by a reduction in barley content and increases in both triticale and soybean meal. Achieving maximum reductions by applying the noLUC assessment method required introducing wheat, broad beans, maize, and sunflower meal into the feed mix. Reductions in GWP impacts applying the dLUC a assessment method (Fig.  4 B) begin with a shift from palm to soy oil and are then followed by replacing barley and soybean meal in exchange for triticale and sunflower meal, and later including broad beans and replacing the soy oil with rapeseed oil. Reductions in GWP impacts applying the dLUC b assessment method (Fig.  4 C) follow a similar pattern to the noLUC assessment method, although including sunflower meal rather than broad beans. This similarity in ingredient changes in noLUC and dLUC b is primarily due to GWP impacts driven by non-LUC-related emissions for both methods. Reductions in GWP impacts applying the COC assessment method (Fig.  4 D) are achieved by replacing rye content with barley, followed by the addition of triticale and wheat, and finally reductions of barley in favor of increased maize and sunflower meal.

Changes in the composition of optimal feed mixtures relative to changes in GWP impact

The resulting cost of the baseline feed mixture (average in Denmark) was 152.2 DKK per 100 FU pig ; therefore, optimized feed mixtures were generated at this cost for the LCA comparison. Feed mixtures generated for LCA at the baseline price are indicated in Figs. 3 and 4 by the dotted line and detailed in the supplementary data file (Online resource 2 ). In particular, all feed mixtures generated for LCA comparisons included the maximum content of rapeseed meal at 10%, with most preferring soybean oil to satisfy the oil requirements and often including a high rye content. The feed mixture generated by applying the noLUC assessment method included soybean oil and grains of rye, barley, and triticale, including a 21% meal content consisting primarily of rapeseed and soybean meal, in addition to minimal sunflower meal content. The feed mixture generated by applying the dLUC a assessment method consisted of a similar grain profile, although including rapeseed oil to satisfy oil requirements in addition to a 9% broad bean content. Additionally, this contained a 16% meal content consisting primarily of rapeseed and sunflower meal and a minimal soybean meal content. The feed mixture generated applying the dLUC b assessment method had a similar grain profile to noLUC, although including wheat instead of triticale with a 19% meal content that consisted of rapeseed, sunflower, and soybean meals. The feed mixture generated by applying the COC assessment method included a grain profile that differed from the other methods, including the maximum allowable barley content (70%) and a 16% meal content consisting of rapeseed and soybean meal.

3.2 LCA impact results

This section compares the costs of the LCA comparison slaughter pig feed mixtures (152.2 DKK per 100 FU pig ). Impact results applying the ReCiPe 2016 (H) midpoint impact assessment method expressed per 100 FU pig and per 1 kg*LW are presented in Table  3 . The reductions achieved by applying the noLUC and dLUC b assessment methods resulted in the lowest overall GWP impact reductions when compared to the baseline, respectively at 5.6% and 6.2% to the feed unit, and 2.4% and 2.7% to the pig live weight. The dLUC a assessment method resulted in the highest overall reduction to GWP impacts of all LUC assessment methods, at 27% to the feed unit and 13% to the pig live weight when compared to the baseline. The COC assessment method resulted in a GWP impact reduction of 15% by applying the feed unit and 10% by applying the pig live weight compared to the baseline. An interesting finding is that optimizing for one LUC assessment method did not necessarily provide a performance advantage for the GWP impact of another LUC assessment method. Furthermore, while the optimized feed mixtures lowered the GWP impact of a specific LUC assessment method, increases were observed for other impact categories at various magnitudes, notably in the ecotoxicity impact categories. The largest burden shift was observed for the dLUC a optimized feed mixture, where increases of 23%, 26%, and 56% in freshwater, marine, and terrestrial ecotoxicity impact categories were observed when compared to the baseline, respectively. On the other hand, lower impacts were observed in water use, land use, marine eutrophication, terrestrial acidification, and non-carcinogenic human toxicity in the GWP-optimized feed mixtures compared to the baseline. ReCiPe 2016 (H) endpoint indicators and weighted and normalized single scores are presented for 100 FUpig and 1 kg*LW in Table 4 . Endpoint and single score results include the characterization, weighing, and normalization of LUC-induced elementary flows specific to the LUC assessment method. Despite the substantial increase in ecotoxicity impacts, an overall reduction to endpoint and single-score indicators was observable across the optimized feed mixtures, with exception of the resource depletion indicator. Applying the functional unit 100 FU pig resulted in a reduction of 2.8–24% to ecosystem damage, 7.4–15% to human health impact, and 7.4–15% applying the weighed and normalized single score when compared to the baseline. Applying the functional unit of 1 kg*LW resulted in a reduction of 1.4–11% to ecosystem damage, 4.2–8.4% to human health impact, and 4.1–8.5% applying the weighed and normalized single score when compared to the baseline. Figure  5 visualizes the contribution to GWP impacts for the baseline and optimized pig production systems for all LUC assessment methods when applying the functional unit of 1 kg*LW. The largest contributors to GWP impacts applying the noLUC and dLUC b assessment methods were feed production and manure management. Contributions from LUC-related activities were only substantial to GWP impacts when applying the dLUC a and COC assessment methods, contributing 26% of the baseline and 13% of the optimized systems when applying dLUC a , and contributing 74% to the baseline system and 64% to the optimized system when applying COC. Detailed results of the impact assessment are available in the supplementary information (Online resource 2 ).

Contribution analysis of sources to GWP impacts of the pig life cycle, applying all LUC assessment methods. A The contribution of the baseline pig production system for each LUC assessment method. B The pig production system including slaughter pig feed mixtures optimized for the applied LUC method, presented relative to the correspondent baseline scenario. GWP impacts are expressed in kg*CO 2 -eq per kg*LW, and they are provided on top of the bars, for each LUC assessment method, and for both A and B

3.3 Sensitivity analysis

3.3.1 ingredient constraints and the origin of production.

This section investigates the influence of feed ingredient constraints applied during feed formulation, and the influence of the ingredient origin of production by specifying source regions. For assessing the sensitivity of nutritional constraints, feed ingredient constraints were removed from the optimization entirely; therefore, no specific feed ingredients were defined in terms of their minimal or maximum contents. To reiterate, the removal of ingredient constraints only applied to specific feed ingredient minimum and maximum contents and does not influence any nutrients defined by the feed unit, since these constraints are only introduced to avoid potential digestive issues that reduce animal performance. For assessing the sensitivity of feed ingredients’ production origins, the Danish market mix was replaced by macro-regional ingredient processes on a continental scale (see Sect. 2.4, online resource 2 ). Figure  6 provides a visual representation of the resulting cost and GWP impacts of both ingredient changes in a side-by-side comparison of the original results, although at differing scales depending on the LUC assessment method.

Plotted data collected from optimized feed mixtures applying ingredient sensitivity parameters, displaying the GWP impact (y-axis) and cost in DKK per 100 FU pig (x-axis), although at differing scales. The four plots presented ( A , B , C , and D ) use different LUC assessment methods, as indicated by the figures’ titles. The baseline feed mixture cost is indicated by the dashed line crossing the y-axis at 152.2 DKK per 100 FU pig

Removing feed ingredient constraints resulted in an economically optimal feed mixture cost of 143.2 DKK per 100 FU pig , composed of approximately 80% rye and 17% meal content split between rapeseed and soybean meal, with the remaining ~ 3% consisting of mineral supplements and free amino acids. The preference for rye can be attributed to its nutritional content combined with the price data utilized for the optimization. Removal of ingredient constraints resulted in substantial changes to the cost and GWP impact for all applied LUC assessment methods. No feed mixture could be formulated at the baseline cost when applying the dLUC b assessment method, as the cost of the GWP optimal feed mixture was below this point. The economically optimal diet composition exhibited a greatly increased GWP impact when applying the COC assessment method, although a substantial reduction was observed when comparing GWP impacts relative to cost. Differences in feed mixtures were primarily observed through the complete absence of oils as ingredients, combined with rye and rapeseed meal content exceeding the original constraints. The low preference for oils and high preference for meal is likely to be influenced by sensitivities in applying economic allocations for calculation of the different ingredients’ GWP impacts, since prices of oils are considerably higher than the prices of meals. Therefore, applying economical allocation results in a twofold sensitivity depending on the cost ratio of the oil and meal of specific crops, resulting in both optimization objectives (GWP and cost) being determined by cost. This highlights the sensitivity of cost when applying economic allocations in optimization studies minimizing both cost and environmental impacts, since cost influences both optimization objectives that is highly relevant for oil crop ingredients.

Replacing the market mix datasets with macro-regional datasets of feed ingredients resulted in an economically optimal feed mixture cost of 148.6 DKK per 100 FU pig , composed of approximately 40% rye, 39% barley, 10% rapeseed meal, 7.5% soybean meal, and 1% soybean oil satisfying the oil requirement. The rye and soy products originated from Europe and South America, respectively, while the barley and rapeseed meal originated from a mix between North America and Denmark. Applying the noLUC and dLUC b assessment methods resulted in a higher GWP impact at the economic optimum, with relatively lower GWP impacts observed at higher cost ranges when compared to the main results. Applying the dLUC a assessment method resulted in a consistently lower GWP impact relative to cost when compared to the main results, although minimal differences are observed at approximately 155 DKK per 100 FU pig . Applying the COC assessment method provided a consistent reduction in the GWP impact relative to cost, with a 25% reduction to the economical-optimal GWP impact possible with a 1% increase in cost. This indicates that, for certain applied LUC methods, the selection of a feed ingredient’s origin may enable a greater overall reduction in GWP, although possibly at greater cost. Ingredient changes relative to GWP impact changes for both ingredient sensitivity analyses are provided in the supplementary information (Online resource 1 & 2 ).

3.3.2 LUC assessment methods

Inclusion of the iLUC assessment method was performed identically to the other methods (see Sect. 2.2), i.e., by applying iLUC data acquired from the Bonsai database currently in development at Aalborg University (Schmidt et al. 2015 , Aalborg University 2024 ). Figure  7 displays the relationship of cost, GWP impact, and ingredient changes for iLUC. The baseline feed mixture GWP impact was 88.1 kg CO 2 -eq per 100 FU pig , while the optimized impact at the same price point was 85.3 kg CO 2 -eq per 100 FU pig . This results in a 3.2% reduction when compared to the baseline, the lowest reduction achievable for all included LUC assessment methods. The optimized feed mixture at the baseline cost (152.2 DKK) contained 34% rye, 23% wheat, 15% barley, 10% rapeseed meal, 7.4% soybean meal, 6.7% oats, and 1% soybean oil content.

A Potted data collected from the optimized iLUC feed mixtures, displaying the GWP impact (y-axis) and cost in DKK per 100 FU pig (x-axis). The datapoints represent the optimal diet composition with the lowest cost at different ranges of maximally constrained GWP impact. B Changes in the composition of optimal feed mixture for iLUC relative to changes in GWP impact

To investigate potential correlations between methods, we included a second data collection exercise of GWP impacts across LUC assessment methods to investigate potential similarities (e.g., correlations) in results optimization. Figure  8 displays the relative change in GWP impact to the economic optimum (at 0%) and the GWP optimum (at − 100%), where + 100% indicates that the GWP impact has increased equal to the difference between the economic optimum and the GWP optimum. Optimizing for GWP impacts applying the noLUC assessment method resulted in relative GWP impact reductions for dLUC b and iLUC, with increased GWP impacts for dLUC a at high cost ranges. Optimizing for GWP impacts applying the dLUC a assessment method resulted in variable results depending on the method in question, and exhibited a 100% relative increase to iLUC at approximately 153 DKK per 100 FU pig , followed by a 50% relative decrease at 161 DKK per 100 FU pig . Optimizing for GWP impacts applying the dLUC b assessment method resulted in relatively linear reductions of GWP impacts when applying the noLUC and iLUC methods. Additionally, optimizing for dLUC b had no influence on COC and a variable influence on dLUC b depending on cost range. Surprisingly, optimizing for GWP impacts applying COC assessment methods resulted in suboptimal formulations with all other methods, plus a relative increase of GWP impacts to all other methods of approximately 40% at the cost point of 151 DKK per 100 FU pig . Optimizing for GWP impacts applying the iLUC assessment method varied depending on cost, although this method appears to result in a relative reduction compared to all other methods regarding GWP impact, with the exception of dLUC a at high cost range. In some cases, a non-linear relationship was observed through a GWP impact reduction at one point followed by an increase at another, which is likely explained by the introduction of a specific feed ingredient providing a mutual reduction in GWP impacts. The non-linearity may provide some insight into the best performing feed ingredients at specific price points across multiple methods. A prime example of this is the reduction of palm oil in favor of soybean oil, a change that is apparently beneficial across all applied LUC assessment methods.

Change in GWP impact across LUC assessment method optimizations. The title of each sub-figure indicates the chosen LUC assessment method for the calculation of GWP impacts that is subjected to maximization constraining. *The GWP impact of the economic-optimum feed mixture for the LUC assessment method of interest (i.e., that one reported in the titles of each sub-figure) is positioned at 0%, and the maximum reduction achieved indicated by the GWP optimum feed at − 100%

3.4 Implications of results and limitations

This study required handling multiple parameters that exhibit natural variations, many of which could not be investigated, mainly because of a lack of data combined with technical limitations. The feed ingredient cost is a key parameter with a substantial influence on the study’s results that could not be subjected to sensitivity analysis, a parameter well known to be a fluctuating variable. Inclusion of cost variations as a sensitivity scenario would have required the determination of local prices at a given time point and corrections for transport and storage. In practice, different feed mixtures can result in differences in growth rates for pigs (e.g., feed conversion ratios and growth rates), which requires large and expensive trials for performance validations that were not conducted to validate the performance of the hypothetical feed mixtures treated in this study. The LCA results and inventory data used for optimization are limited to average Danish production in 2021, and therefore represent a hypothetical improvement on the past. The results should not be used for any decision-support context, which would otherwise have required a consequential LCA. The findings provide further evidence that changes in pig feed may enable cost-effective reductions of the environmental impacts of pig production, although a key limitation of these results is that no evaluation was performed of the feed mixtures influence towards animal performance metrics (e.g., feed conversion ratios, growth rates). A key finding is the substantial influence of the applied LUC assessment method on the magnitude of reductions of GWP impacts, which are more pronounced when applying methods resulting in high LUC contributions. Applying the dLUC a assessment method resulted in the greatest reductions compared to the baseline of all the methods covered in this study, indicating that feed changes are likely to be a beneficial consideration when considering this assessment method. The achievable reduction may become apparent in future accounting studies through a growing awareness of feed suppliers, resulting in the avoidance of importing soybeans cultivated in recently deforested regions (DAKOFO 2021 ). It is important to consider the result differences between dLUC a and dLUC b should not influence the selection of one dLUC assessment method to another, but rather emphasize the need for further research and consensus of the specific dLUC assessment method to apply in agricultural LCAs. Although applying the COC assessment method resulted in lower-than-expected reductions when compared to the baseline, considering the broad range of the GWP impact for optimized feed mixtures for this LUC assessment method, the broad range of the COC assessment method indicates that greater reductions may be achievable for this method when considering a different baseline for comparison. However, we emphasize that the selection of LUC assessment methods should be aligned with the assessment’s goal and scope, as the inclusion of these methods provides additional answers in relation to a system’s GWP impact.

4 Conclusions

This study has provided an analysis of the environmental impacts of optimizing for cost and the GWP impacts of slaughter pig feed mixtures by applying multiple LUC assessment methods. Analysis of GWP and cost optima revealed that initial reductions of GWP impacts will be very cost-effective for non-optimized or cost-optimized feed mixtures, but costs will increase exponentially when approaching the lowest GWP impact feed mixtures (GWP-optima). The inclusion of GWP impacts in the cost optimizations of slaughter pig feed can provide a substantial GWP impact reduction at no additional cost, and we therefore encourage its inclusion in future feed formulation practices. Two LCAs were conducted for GWP optimized feed mixtures, generated at cost equal to the average feed mixture in Denmark that additionally served at the baseline for LCA comparison. The two LCAs compared the slaughter pig feed directly and the feed included in a pig’s life cycle. Depending on the LUC assessment method in question, GWP impacts ranged from 5.6 to 27% for the feed unit and 2.4 to 13% for the pig’s life cycle when compared to the baseline. Limiting the environmental impact optimization to GWP alone may result in increases in the impacts of other impact categories, demonstrated in this study to be up to 56%. Despite the increase in other impact categories, the optimization of GWP impacts resulted in a reduction in endpoints and single-score environmental indicators. Although the results of this study suggest that GWP impact is a promising decision variable in environmental feed optimization, future feed optimization studies should consider the inclusion of multiple impact categories, or alternatively apply endpoint indicators. In addition to the findings, we emphasize that the choice of LUC assessment method should be defined on the basis of the LCA’s goal and scope, and therefore of the intended use of the results.

Data availability

Additional data can be made available at request to the corresponding author, with exception of confidential data from WinOpti and the developing pig LCA model applied in this study.

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Acknowledgements

The authors wish to express their sincere gratitude to the Innovation Fund Denmark, the Otto Mønsted Fund, and the Augustinus Fund for providing the support and funding that made this project and study a reality. In addition, we want to thank Dr. Robert Parkin for his valuable contribution to improve the written language of this paper.

Open access funding provided by University of Southern Denmark This research is part of an industrial PhD project funded by the SEGES Innovation and Innovation Fund Denmark (grant number: 1044-00035B). The University of Southern Denmark, SEGES Innovation, and Chalmers University of Technology have contributed to the study design, data collection and analysis, the decision to publish, and the preparation of the manuscript in accordance with the author list. Additional funding was obtained from the Otto Mønsted Fund (23–70-2013) and the Augustinus Fund (23–2575) to cover the expenses of the external research trip.

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Physical Resource Theory, Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden

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Conceptualization: Styrmir Gislason, Alberto Maresca, Finn Udesen. Methodology: Styrmir Gislason, Alberto Maresca, Stefan Wirsenius, Morten Birkved, Finn Udesen. Data curation and software: Styrmir Gislason, Thomas Sønderby Bruun. Formal analysis and investigation: Styrmir Gislason, Thomas Sønderby Bruun, Stefan Wirsenius, Chandrakant Singh. Writing—original draft preparation: Styrmir Gislason, Alberto Maresca. Writing—review and editing: Alberto Maresca, Morten Birkved, Styrmir Gislason, Stefan Wirsenius, Thomas Sønderby Bruun, Chandrakant Singh, Finn Udesen. Funding acquisition: Styrmir Gislason, Finn Udesen, Morten Birkved, Alberto Maresca. Resources: Stefan Wirsenius, Chandrakant Singh, Thomas Sønderby Bruun, Finn Udesen. Supervision: Alberto Maresca, Stefan Wirsenius, Morten Birkved.

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Gislason, S., Bruun, T.S., Wirsenius, S. et al. How methods to assess land-use changes influence the resulting global warming potential and cost of optimized diets: a case study on Danish pigs applying life cycle assessment methodology. Int J Life Cycle Assess (2024). https://doi.org/10.1007/s11367-024-02356-0

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Drug repositioning based on residual attention network and free multiscale adversarial training

• Guanghui Li 1 ,
• Shuwen Li 1 ,
• Cheng Liang 2 ,
• Qiu Xiao 3 &
• Jiawei Luo 4  

BMC Bioinformatics volume  25 , Article number:  261 ( 2024 ) Cite this article

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Metrics details

Conducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently needed.

This paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease associations.

Conclusions

The comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor for identifying unobserved disease-drug associations.

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Drugs play important roles in treating diseases and promoting the health of organisms [ 1 ]. However, traditional drug development is an extremely lengthy and expensive process [ 2 ]. Recent studies have estimated that the average development cost to approve a new drug is $2.6 billion and the average development time is 10 years [ 3 ]. Drug repositioning, which involves discovering new therapeutic outcomes for previously approved drugs, is considered an important alternative to traditional drug development [ 4 , 5 , 6 , 7 , 8 ]. This approach shortens drug development and research cycles to 7 years, reduces costs to $295 million, and is more reliable than novel drug development [ 9 ]. Therefore, using known drugs for new disease treatments is gaining popularity [ 10 , 11 ]. Traditional methods of discovering abnormal clinical manifestations through manual screening of clinical drug databases requires extensive experimentation. With the continuous accumulation of a wide variety of biological data, numerous computational methods based on data mining techniques have gained traction [ 12 ].

Matrix factorization aims to approximate the initial matrix by decomposing it into the product of two low-rank matrices, which are represented by hidden factor vectors in the k -dimension. The inner product of the drug and disease vectors represents the association between them. Previous studies have shown that matrix decomposition methods are effective computational methods for drug-disease association prediction [ 13 , 14 , 15 , 16 , 17 ]. For example, the similarity constrained matrix factorization method for the drug-disease association prediction (SCMFDD) method, proposed by Zhang et al., maps the associations between diseases and drugs into two low-ranking spaces and reveals the basic features. Then, drug similarity and disease similarity are introduced as increasing constraints [ 18 ]. Furthermore, Yang et al. proposed the multisimilarities bilinear matrix factorization (MSBMF) approach, which connects multiple disease and drug similarity matrices and extracts the effective latent features in the similarity matrix to infer associations between diseases and drugs [ 19 ]. In addition, Zhang et al. proposed a new drug repositioning method by using Bayesian inductive matrix completion (DRIMC), which uses the complement of Bayesian inductive matrices. This method integrates multiple similarities into a fused similarity matrix, where similarity information is described by similarity values between a drug or disease and its k -nearest neighbors. Finally, the disease-drug association is predicted via induction matrix completion [ 20 ].

Networks can represent the complex relationships among entities, and the methods used to construct biological networks can effectively utilize information from multiple biological entities to represent the degree of association between them [ 21 ]. The network-based method has produced good results in drug repositioning [ 22 , 23 , 24 ]. For instance, Zhao et al. first constructed a heterogeneous information network by combining drug-disease, protein-disease and drug-protein bioinformatics networks with disease and drug biology information. Then, the combined features of the nodes were learned from a biological and topological perspective via different representations. Moreover, random forest classifiers can be used to predict unknown associations [ 25 ]. Zhang et al. proposed a multiscale neighborhood topology learning method for drug repositioning (MTRD) to learn and integrate multiscale neighborhood topologies. This method involves the construction of different drug-disease heterogeneous networks to discover new drug-disease associations [ 26 ]. In addition, Luo et al. proposed a method named MBiRW that uses similarity matrices and known associations to construct heterogeneous networks and predicts unknown associations via the double random walk algorithm [ 27 ].

Although matrix factorization methods achieve good performance, they are weak in the interpretability of associations between diseases and drugs, whereas network methods are biased in representing higher-order networks. To solve these problems, several pioneering studies have focused on developing deep learning-based drug repositioning models [ 28 , 29 , 30 , 31 , 32 , 33 ]. For example, Zeng et al. first integrated multiple disease-drug biological networks and designed a multimodal deep autoencoder named deep learning-based drug repositioning (deepDR) for learning higher order neighborhood information of drug-disease associations [ 34 ]. Subsequently, Yu et al. constructed a graph convolutional network (GCN) architecture with attention mechanisms, i.e., the label-aware GCN (LAGCN). First, this method uses known drug-disease associations, disease-disease similarities and drug-drug similarities to construct heterogeneous networks and applies GCNs to the network. Next, the embeddings from multiple GCN layers are integrated via layer attention mechanisms. Finally, drug-disease pairs are scored on the basis of the integrated embeddings [ 35 ]. Feng et al. proposed Protein And Drug Molecule interaction prEdiction (PADME), a novel method to combine molecular GCNs for compound featurization with protein descriptors for drug-target interaction prediction [ 36 ]. Moreover, Meng et al. proposed a drug repositioning approach based on weighted bilinear neural collaborative filtering (DRWBNCF) on the basis of neighborhood interaction and collaborative filtering. Instead of using all neighbors, this method uses only the nearest neighbors, thus filtering out noise and yielding more precise results [ 37 ]. Recently, Gu et al. proposed a method named relations-enhanced drug-disease association (REDDA) for learning node features of heterogeneous networks and topological subnetworks. This method employs heterogeneous networks as the backbone and combines the backbone with three attention mechanisms [ 38 ]. Deep learning-based methods mainly construct heterogeneous networks by using supplementary information about diseases and drugs and learn the features of diseases and drugs by applying deep learning algorithms to these networks.

However, these deep learning-based approaches tend to have oversmoothing problems caused by the homogenization of node embeddings and are highly dependent on the input quality. In this paper, we present a novel method of drug repositioning named RAFGAE. This method combines residual networks, graph attention networks (GATs), graph autoencoders (GAEs) and adversarial training to predict unknown associations between diseases and drugs. First, we use disease semantic similarity, drug structural similarity and disease-drug associations to construct the initial input features. GATs are used to facilitate the learning of disease and drug embeddings in each layer and combine the embedding of different layers via attention mechanisms. Moreover, the initial residual and adaptive residual connections are adopted to alleviate the oversmoothing problem. Then, two GAEs are constructed on the basis of the disease space and drug space, and the information in these spaces can be integrated through synergistic training. Finally, the scores of the two GAEs are linearly combined by a balancing parameter to calculate the final prediction scores. On this basis, adversarial training is introduced to reduce invalid information and data noise, improving the input quality. The main contributions of RAFGAE can be summarized as follows:

RAFGAE is a complete deep learning approach that can effectively predict the associations between diseases and drugs.

RAFGAE designs the Re_GAT framework, which includes multilayer GATs and two residual networks. Multilayer GATs are utilized to learn the node embeddings by aggregating information from multihop neighbors, and two residual networks are used to alleviate the deep network oversmoothing problem. Then, an attention mechanism is introduced to combine the node embeddings of different attention layers.

RAFGAE performs adversarial training that may eliminate abnormal values, missing values and noise, increasing the input quality and prediction accuracy when extracting associations between diseases and drugs.

Our comprehensive experimental results demonstrate that the proposed RAFGAE method significantly outperforms five state-of-the-art methods on the benchmark dataset.

Results and discussion

Algorithm performance comparison.

To verify the performance of RAFGAE, we compare it with five recently proposed methods.

DRWBNCF [ 37 ], a method for drug repositioning on the basis of neighborhood interaction and collaborative filtering, uses only the nearest neighbors, rather than all neighbors, to filter out noisy information. A new weighted bilinear GCN encoder is then proposed.

LAGCN [ 35 ], a layer attention GCN method for drug repositioning, encodes a heterogeneous network combining known drug-disease associations, disease similarity and drug similarity information. To integrate all useful information, a layer attention mechanism is introduced into multiple GCN layers.

In bounded nuclear norm regularization (BNNR) [ 39 ], a heterogeneous network is constructed. This network combines known drug-disease associations, disease similarity and drug similarity information. The method tolerates noise by adding a regularization term to balance the rank properties and approximation error.

The neural inductive matrix completion with GCN (NIMCGCN) method [ 40 ], a method for the prediction of miRNA-disease associations) first employs GCN to learn the features of diseases and miRNAs from the disease and miRNA similarity networks. Then, neural induction matrix completion is applied for association matrix completion.

SCMFDD [ 18 ] (a similarity constraint matrix completion method for the prediction of drug-disease associations) projects known drug-disease association information into two low-rank spaces, revealing potential disease and drug embeddings, and then introduces drug featured-based and disease semantic similarities as constraints for drugs and diseases in the low-rank spaces.

The above methods also involve similarity-based graph neural network models. The parameters in these methods are set to either the optimal values via a grid search (for DRWBNCF, λ is selected from {0.1, 0.2, ..., 0.9}; for BNNR, α and β are chosen from {0.01, 0.1, 1, 10}; and for SCMFDD, k is selected from{5%, 10%, ..., 50%}) or the values recommended by the authors (for LAGCN, α = 4000, β =0.6, and γ = 0.4; and for NIMCGCN, α = 0.4, l = 3, and t = 2). Furthermore, to ensure a meaningful and relevant comparison, each of the comparison methods is initially evaluated via the same 10-fold cross-validation approach and on the same benchmarking sets as those for our proposed method, RAFGAE. This approach allows us to conduct a comprehensive and rigorous assessment of the performance of all the methods.

The area under the curve (AUC) values in Fig. 1 and Table 1 show a comparison of the model performance. On the F-dataset, RAFGAE achieves the highest AUC score of 0.9343, which is 7.28%, 4.50%, 3.13%, 4.31%, and 4.01% higher than those of SCMFDD, LAGCN, BNNR, NIMGCN, and DRWBNCF, respectively. Similarly, on the C-dataset, RAFGAE achieves the highest AUC score of 0.9346. By comparing the model proposed in this paper with other models, it is evident that introducing residual connections and adversarial training can enhance the predictive performance of our model. Overall, the above experiments show that RAFGAE is an excellent predictor of disease-drug relationships.

ROC curves and PR curves of RAFGAE and other models on the F-dataset

Ablation study

To quantitatively evaluate the importance of the two modules (the Re_GAT framework and the FMAT module) to RAFGAE, ablation experiments are conducted. The details of these variants of RAFGAE are listed below:

RAFGAE: The comprehensive RAFGAE framework consists of three main components: the Re_GAT framework, the FMAT module, and the GAE module.

GAE: The RAFGAE variant that includes only the GAE module.

FGAE: The RAFGAE variant that includes the FMAT and GAE modules but excludes the Re_GAT framework.

RAGAE: The RAFGAE variant that includes Re_GAT framework and the GAE module but excludes the FMAT module.

According to Fig.  2 and Table  2 , it is clear that RAFGAE achieved the highest AUC and area under the precision–recall (AUPR) curve values on both the F-dataset and the C-dataset. The RAGAE and FGAE results show the impacts of global neighborhood node information aggregation and adversarial feature enhancement on the RAFGAE performance, respectively. In addition, the GAE results demonstrate that combining the Re_GAT framework and the FMAT module can improve the predictive performance of the RAFGAE model. In comparing FGAE and RAGAE to GAE, the performance results imply that both the Re_GAT framework and the FMAT module can improve the model performance. The poor performance of GAE suggests that the use of multilayer attention networks to aggregate global information and the incorporation of residual architectures to address the potential oversmoothing problem can enhance the accuracy of drug-disease association prediction. Furthermore, the results indicate that the inclusion of the adversarial training module improves the input quality, thereby satisfying the requirements of deep neural networks for high-quality input features. These results demonstrate that the RAFGAE structure is reasonable.

Results of RAFGAE and its variants in the ablation study on the F-dataset

Performance evaluation

To assess the effectiveness of RAFGAE in predicting known associations, tenfold cross validation (CV) is applied. In tenfold CV, the dataset is divided into ten folds. Nine folds are used as the training set, and the remaining fold is used to validate the performance of RAFGAE. This process is repeated 10 times, with each fold used as the testing fold once. Several important indicators are used to evaluate the performance of RAFGAE. The receiver operating characteristic (ROC) curve, which is based on the false-positive rate (FPR) and the true positive rate (TPR), is utilized. As the benchmark datasets used in this experiment are imbalanced, we also use the PR curve and calculate the area under the PR curve (AUPR) as two additional indicators. To further evaluate the overall performance of the prediction model from multiple perspectives, the F1 score and the Mathews correlation coefficient (MCC) are calculated.

The ROC and PR curves for the F-dataset are shown in Fig.  3 . RAFGAE achieves mean AUC and AUPR values of 0.9343 and 0.5270, respectively. The detailed results, including the F1-score and MCC, are presented in Table  3 . The results based on the C-dataset are shown in Table  4 . As shown in Tables 1 and 2 , the newly proposed RAFGAE model obtains good performance on the above two datasets, proving the effectiveness and robustness of this model.

RAFGAE ROC and PR curves via tenfold CV on the F-dataset

Parameter adjustment

Since the hyperparameter settings can influence the performance of RAFGAE, we used tenfold CV on the F-dataset to analyze the impact of different parameter settings. In the Re_GAT framework, the weight α of the initial residual connection and the weight β of the adaptive residual connection can directly affect the result of feature fusion. To fully integrate adjacent node information and mitigate the oversmoothing problem, we adjust the α and β values within the following range: α ϵ {0.1 ~ 0.9} and β ϵ {0.1 ~ 0.9}. As shown in Fig.  4 , when α  = 0.3 and β  = 0.7, the AUC reaches its maximum value.

Effect of the α and β parameters on the AUC of RAFGAE

In addition, the features of diseases and drugs are extracted via GATs. The Re_GAT framework computes and aggregates different multilayer features via the GAT. We discuss the impact of GATs with different numbers of layers on association prediction. Figure  5 presents the results of the ROC curve analysis on the basis of tenfold CV.

Effect of the number of GAT layers on the AUC of RAFGAE

To optimize the initial parameters, we use the Adam optimizer [ 41 ]. As in previous studies [ 42 , 43 ], we set the dropout and weight decay parameters to 0.5 and 10 –5 , respectively. We also evaluate the model performance by changing the dimensions of the GAE hidden layers. With the other parameters unchanged, the AUC value of RAFGAE generally increases as the embedding dimension of the GAE hidden layer increase and tends to stabilize when the dimension reaches 256. Finally, we set the embedding dimension of the hidden layer to 256. These results are shown in Fig.  6 .

Effect of the hidden vector dimension on the AUC of RAFGAE

Case studies

To evaluate the practical ability of RAFGAE to predict unknown indications of approved drugs as well as new therapies for existing diseases, we train the RAFGAE model using all known associations as training data, and predict potential associations for known diseases or drugs. The predicted ranking of unknown indications of approved drugs and unknown therapies for existing diseases is validated on the public database, namely, the Comparative Toxicogenomics Database (CTD) [ 44 ].

To assess the ability of RAFGAE to discover new indications, we select two representative medicinal products. Table 5 shows the confirmation information for the top 10 candidate diseases and the known drug-disease associations. Among them, doxorubicin is a cytotoxic anthracycline antibiotic that is widely used to treat various cancers, including Kaposi sarcoma and metastatic cancer related to AIDS. Of the top 10 positive predictions, there were 7 tumor-related diseases that have been verified via reliable databases. Levodopa is a precursor of dopamine and is commonly used in the treatment of Parkinson's syndrome and Parkinson's syndrome-related disorders because of its ability to cross the blood–brain barrier. As shown in Table  5 , reliable sources have identified 7 of the top 10 associated diseases. This evidence suggests that RAFGAE can be trained on and can learn from existing biological information and can identify association markers that are not captured in the training set.

To validate the practical ability of RAFGAE to discover novel therapies, we select breast neoplasms and small-cell lung cancer as experimental cases. On the basis of the RAFGAE prediction results, the 10 drugs with the highest prediction scores are validated via the CTD. Table 6 shows similar results for the top 10 positive predictions. Breast neoplasms are among the most common malignancies in women and the leading cause of cancer-related disease in women. As shown in Table  6 , 9 of the top 10 drugs were verified via reliable sources. The high incidence rate and high mortality of small cell lung cancer worldwide make this complex tumor a difficult medical problem. In summary, 6 drugs have been confirmed by evidence from authoritative sources among the top 10 predicted drugs ranked by prediction score. In summary, case studies have shown that RAFGAE can identify the associations between diseases and drugs that are unknown in training datasets but that have been validated in real-world studies. Moreover, RAFGAE can make reliable predictions regarding unconfirmed potential associations between diseases and drugs. Therefore, RAFGAE has a noteworthy ability to uncover novel therapies/indications for existing diseases/drugs.

In this paper, a deep-learning methodology named RAFGAE is developed for elucidating drug-disease associations. The key innovation of RAFGAE is that it combines the Re_GAT framework and the FMAT algorithm, facilitating the learning of neighbor node information and enhancing the initial node features in the disease-drug bipartite network. Then, two GAEs with collaborative training are applied to integrate the disease and drug spaces for association prediction. Notably, unlike some previous predictors that consider only low-order neighbor information, the Re_GAT framework can account for both high-order and low-order neighbor information by using multilayer GATs. Moreover, residual networks are introduced to mitigate model data oversmoothing, enabling the full employment of graph structure information hidden in the bipartite network. To enhance the initial features of nodes and make the model more robust, the FMAT algorithm is employed. This algorithm adds gradient-based adversarial perturbation to the input characteristics. In addition, we construct two GAEs with collaborative training for label propagation, enabling the full integration of the drug and disease space information for association prediction and improving the robustness of the RAFGAE model.

With tenfold CV, the RAFGAE model achieves an AUC score of 0.9343, which is better than the AUC scores of five state-of-the-art predictors. Furthermore, the case study results show that RAFGAE can reposition several representative drugs for human diseases and can be applied as a reasonable and effective tool for predicting the relationships between diseases and drugs.

We propose a computational drug repurposing method. This method can effectively identify candidate drugs with potential for treating different diseases and has the potential to uncover new indications for approved drugs that were previously unexplored. RAFGAE can guide wet laboratory experiments, accelerating drug development, reducing costs, and expanding treatment options. The method combines multilayer neural networks with residual connections to capture global information and alleviate oversmoothing problems. We also employ adversarial perturbations to improve the input quality. This novel combination of techniques provides a new perspective for future research and can also serve as a valuable reference for similar studies, such as predicting the associations between ncRNAs and diseases, microbiome-disease associations, and screening ncRNA drug targets.

However, RAFGAE has certain limitations. In this study, the negative and positive samples of the benchmark dataset are unbalanced, and we use all the negative samples as negative samples for training the proposed model. However, these unknown samples considered negative samples may be potential correlations, which greatly impacts the prediction accuracy of the model. In the future, we will select negative samples to further improve the model accuracy. In terms of biological data, we simply apply the interaction network between drugs and diseases without establishing a more informative biological regulatory network, which may further improve performance. In future research, we will introduce other biological entities, such as proteins, pathways, and genes. In scenarios where drugs share the same or similar indications but lack structural similarity, the transmission of structural similarity information through a multilayer neural network can give rise to an "information leakage" problem, leading to a distorted view of the algorithm's performance in realistic drug repurposing settings. In our future research, we plan to address the problem of information leakage further by incorporating multiple drug similarities, such as target protein domain similarity, GO target protein annotation similarity, side effect similarity, and GIP similarity. This broader range of drug similarities can provide a more comprehensive features for drug repurposing. Similarly, incorporating disease similarities, such as disease ontology similarity, can help improve the accuracy and reliability of repositioning predictions by leveraging additional disease-related information.

Data preparation

We employ two benchmark datasets established by investigators. The first dataset is the F-dataset, which corresponds to Gottlieb's gold standard dataset [ 45 ]. The F-dataset contains 1933 known associations between diseases and drugs, including 313 diseases collected from the OMIM database [ 46 ] and 593 drugs obtained from the DrugBank database [ 47 ]. The second dataset is the C-dataset [ 24 ], which includes 2532 known associations between 409 diseases collected from the OMIM database and 663 drugs obtained from the DrugBank database. Table 7 summarizes the benchmark datasets in our proposal.

In this study, we calculated the drug structure similarity matrix X dr via the simplified molecular input line entry system (SMILES) chemical structure [ 48 ], which is represented as the Tanimoto index of chemical fingerprints of the drug pair via the Chemical Development Kit [ 49 ]. The disease semantic similarity matrix X di is computed from the semantic similarity of the disease phenotypes via information from the medical descriptions of the disease pairs [ 50 ].

After collecting the required data from different sources, we propose a prediction model with three individual modules to predict potential candidate diseases for drugs of interest. We first design the Re_GAT framework, which captures global structural information from a bipartite network. For the second module, we employ GAEs that use known associations between diseases and drugs to simulate label propagation to guide and predict unknown associations. On the basis of the above, we utilize the FMAT module for adversarial training to improve the input quality and increase the prediction accuracy. Figure  7 shows the overall workflow of RAFGAE.

Flow chart of the RAFGAE calculation method

Re_GAT framework

Graph attention networks use a self-attention hidden layer to assign different attention scores to different neighbors, thus extracting the features of neighboring nodes more effectively.

The initial input to the Re_GAT framework can be described as follows:

where N represents the node count, F represents the dimension of the feature and h i ϵ R F represents the initial feature matrix of all the nodes. GATs calculate attention scores on the basis of the importance of neighbors and then aggregate neighbor features on the basis of the attention score.

The attention score is calculated as follows:

To adjust for the influence of different nodes, we use the softmax function for attention score normalization score:

By combining Formulas ( 3 ) and ( 4 ), the calculation formula for the attention score can be expressed as:

where a ij is the attention score, W is a learnable linear transformation matrix, a vector denotes the weight vector, σ () represents the LeakyReLU activation function, and ║ denotes the connection operation. After normalization, the following formula can be used to calculate the final output feature:

In this study, the drug-disease association matrix is given by matrix A , where the columns represent diseases and the rows represent drugs. The matrix A ( j , k ) = 1 if drug j is associated with disease k and 0 otherwise. Matrix A and its transposition matrix A T define the bipartite network G :

We create the initial input embedding H (0) as follows:

When combined with the bipartite network adjacency matrix G above, the graph attention network is defined as:

where H ( l ) represents the node embedding of the l -th layer, where l  = 1, …, L , and GATs () represents a single attention layer, whereas the entire Re_GAT framework consists of multiple attention layers.

This study proposes a Re_GAT framework through two main strategies for forward propagation: (I) initial residual connection and adaptive residual connection; and (II) attention mechanism layer aggregation.

To facilitate the learning of feature information from higher-order neighbors, multiple attention layers are typically used, easily homogenizing the data and thus leading to oversmoothing problems. To alleviate the oversmoothing problem of deep CNNs, residual connections, also known as skip connections was first proposed for ResNet. Inspired by ResNet [ 51 ], recent studies have attempted to apply various residual connections to GATs to alleviate the oversmoothing problem. Several studies have shown that residual connections are necessary for deep GATs [ 52 ], not only to alleviate the oversmoothing problem, but also to give GATs a more stable gradient.

We sum the H ( l ) weights with H (0) and H ( l− 1) according to the scale coefficients α and β , respectively. We use the initial skip connection and the adaptive skip connection to mitigate the oversmoothing problem and accelerate the convergence of the GATs. The GAT formula of our model can be rewritten as:

where α and β are hyperparameters.

Inspired by LAGCN [ 35 ], the embedding of each layer captures structural information from different orders of the heterogeneous network. For instance, the initial layer obtains direct connection information, whereas the higher-order layers collect information about multihop neighbors through iterative update embedding. To fuse all useful information from multiple GAT layers, we use the attention mechanism. Since the Re_GAT framework calculates the embedding of different layers and the embeddings contain different information, we define the resulting GAT layer embedding as:

where Hdr l ϵ R Ndr × kl is the embedding of the drug in layer l and Hdi l ϵ R Ndi × kl is the embedding of the disease in layer l . We use attention mechanism layer aggregation to integrate multiple embedding matrices, and the final fused embedding matrix is as follows:

where, Hdr i and Hdi i are the l -layer embeddings of drugs and diseases, respectively, a i and b i are the attention factors that can be calculated via Formulas ( 2 ), ( 3 ) and ( 4 ), and L is the number of layers.

Constructing the feature similarity graph

A previous study showed that a similarity graph constructed using drug and disease features can be used to propagate labels [ 53 ]. We use the features C dr and C di to construct feature similarity graphs for diseases and drugs, respectively. These features are used for label propagation in the disease and drug spaces. The feature similarity graphs are constructed as follows. First, the Euclidean distance between nodes is calculated and ranked. Second, for each node i , its 10 nearest neighbors are selected. Finally, the adjacency matrix is defined as M , and the set of neighbors of node i is defined as N ( i ). The matrix M satisfies M ij  = 1 when j belongs to N ( i ); otherwise, M ij  = 0.

The self-loop adjacency matrix for the similarity graph S is constructed as follows:

where ⊙ is the Hadamard product. This method can be used to obtain both the drug similarity graph S dr and the disease similarity graph S di .

• Graph autoencoder

Previous studies have shown that the graph autoencoder may simulate label propagation by iteratively propagating label information on the graph [ 54 , 55 , 56 ]. The association matrix A can be considered initial label information. The initial label information and the similarity graph S calculated via the above method are input to the GAE. The encoder layer produces a hidden layer Z , whereas the decoder outputs the score F . The encoder of the GAE can be defined as:

where Φ denotes the weight matrix. Here, we use two GAEs to propagate label information on the drug and disease graphs. We can obtain the drug hidden layer Z dr and the disease hidden layer Z di , which are expressed as follows:

where S dr and S di denote the drug similarity graph and the disease similarity graph, respectively, and A denotes the association matrix.

The decoder of the GAE is applied to decode the hidden layer representation, which is defined as follows:

Therefore, the score matrices F dr and F di can be obtained by decoding Z dr and Z di , respectively.

Since F dr and F di are both low rank matrices [ 57 ], they need to satisfy the rank-sum inequality:

By performing a linear combination of F dr and F di , the final integrated score is obtained as follows:

where α ϵ (0,1) represents the balanced weight between the drug space and the disease space.

The GAE reconstruction error is the loss of cross-entropy between the final prediction and the true value:

As the information from the disease space and the drug space influences the predicted outcome, we use a cotraining approach to train the above two GAEs. The cotraining training loss L co is defined as:

The combined loss function can be rewritten as:

where L rdr and L rdi denote the reconstruction errors of the two GAEs in the drug space and the disease space, respectively.

Free multiscale adversarial training

In this section, we investigate how to effectively improve the input quality through data augmentation [ 58 ]. When neural networks are trained, the quality of the data is far more important than the quantity. By searching for and stamping out small perturbations that cause the classifier to fail, one may hope that adversarial training could benefit standard accuracy. Adversarial training is a well-studied method that increases the robustness and interpretability of neural networks. When the data distribution is sparse and discrete, the beneficial effect of adversarial perturbations on generalizability is prominent [ 59 ]. Inspired by this, we introduce free multiscale adversarial training (FMAT) to augment the node features [ 60 ].

Adversarial training first generates adversarial perturbations, which are then integrated into the training node features. Given a learning model f θ with parameters θ , we denote the perturbed feature as H adv  =  H  +  δ . Adversarial learning follows the min–max formulation:

where A represents the real value, D represents the data distribution, L represents the objective loss function, ε represents the perturbation budget, and ║║ p represents an l p -norm distance measure.

The saddle-point optimization problem can be solved via projected gradient descent (PGD), which implements inner maximization, and stochastic gradient descent (SGD), which implements outer minimization. The parameter δ is updated after each step:

where ∏ ║δ║≤ε is projected onto the ε -sphere under the l ∞ -norm . The initial layer of the Re_GAT framework can be rewritten as:

To effectively exploit the generalizability of adversarial perturbations and improve their diversity and quality, Chen et al. emphasized the importance of adapting to different types of data enhancements [ 61 ]. To achieve this, we introduce a 'free' training approach [ 62 ].

The calculation of δ is inefficient because the N -step update requires N forward and backward channels. This update runs N times completely forward and backward to obtain the worst perturbation δ N . However, the model weight θ is updated once to use only δ N . Model training is N times slower because of this process. In contrast, the 'free' training outputs the model weights θ on the same backward channel while calculating the δ gradient, allowing model weight updates to be calculated in parallel with perturbation updates.

'Free' training has the same robustness and accuracy as standard adversarial training does. However, the training costs are the same as those of clean training. The 'free' strategy accumulates a gradient of \(\nabla_{\theta } L\) in each iteration and updates the model weight θ through this gradient. During training process, the model runs the inner circle T times, each time calculating the gradient of θ t -1 and δ t by taking a step along the average gradient at H ( l )  +  δ 0 , …, H ( l )  +  δ T- 1 . Formally, the optimization step is

Availability of data and materials

We acquired the C-dataset of disease-drug associations, from the Comparative Toxicogenomics Database [ 44 ] ( http://ctdbase.org/ ). We screened the F-dataset of disease-drug interactions from the OMIM database [ 46 ] ( https://www.omim.org/ ) and DrugBank database [ 47 ] ( https://www.drugbank.ca/ ). These two datasets and the source code are available at: https://github.com/ghli16/RAFGAE .

Abbreviations

• Graph attention network

True positive rate

False-positive rate

Receiver operating characteristic

Area under ROC curve

Cross validation

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This work is supported by the National Natural Science Foundation of China (Grant Nos. 62362034, 61862025, 62372279, and 62002116), the Natural Science Foundation of Jiangxi Province (Grant Nos. 20232ACB202010, 20212BAB202009, 20181BAB211016), and the Natural Science Foundation of Shandong Province (Grant No. ZR2023MF119).

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GL and JL conceived and designed the study. GL and SL implemented the experiments and drafted the manuscript. CL and QX analyzed the results. All the authors have read and approved the final manuscript.

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Li, G., Li, S., Liang, C. et al. Drug repositioning based on residual attention network and free multiscale adversarial training. BMC Bioinformatics 25 , 261 (2024). https://doi.org/10.1186/s12859-024-05893-5

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• 2 Department of Psychology, University of Bergen, Bergen, Norway

Introduction: To achieve substantial energy efficiency improvements in the privately owned building stock, it is important to communicate with potential renovators at the right point in time and provide them with targeted information to strengthen their renovation ambitions. The European Union recommends using one-stop-shops (OSSs), which provide information and support throughout the whole process, from planning to acquisition of funding, implementation, and evaluation as a measure to remove unnecessary barriers.

Methods: For this paper, we invited visitors of two Norwegian websites with OSS characteristics to answer an online survey about their renovation plans and energy efficiency ambitions. The participants visited the websites out of their own interest; no recruitment for the websites was conducted as part of the study ( N = 437). They also rated a range of psychological drivers, facilitators, and barriers to including energy upgrades in a renovation project. Their answers were then compared to existing data from representative samples of Norwegian households regarding home renovation in 2014, 2018, and 2023, as well as data from a sample of people who were engaged in renovation projects in 2014, which was collected by the research team with a similar online survey. Furthermore, 78 visitors completed a brief follow-up online survey one year later to report the implemented measures.

Results: We found that visitors of the websites are involved in more comprehensive renovation projects and have substantially higher ambitions for the upgrade of energy efficiency compared to the representative samples. They also perceive stronger personal and social norms, as well as have a different profile of facilitators and barriers.

Discussion: The findings suggest to policymakers that OSSs should be marketed especially to people motivated to upgrade energy efficiency but lack information and are unable to implement their plans alone. Also, the construction industry might refer interested people to such low-threshold online solutions to assist informed and more ambitious decisions.

1 Introduction

Reducing energy use in the building sector by increasing energy efficiency is a key pillar of decarbonising Europe as formulated in the EU’s “Fit for 55” legislation ( Schlacke et al., 2022 , 4). On a global level, the residential sector is the third largest energy consumer, representing 27–30% of the energy consumption, almost at the same level as transportation and industry ( Nejat et al., 2015 , 843; IEA, 2023 ). Also in Europe, the residential sector stands for 26% of final energy consumption, being the second largest consumption sector after transportation ( Tsemekidi et al., 2019 , 1). Whereas the primary energy consumption in the residential sector decreased by 4.6% between 2000 and 2016 ( Tsemekidi et al., 2019 , 9), there is still a substantial untapped potential for further improvement of energy efficiency in the sector. This can be achieved through energy efficiency renovation of the existing building stock ( Pohoryles et al., 2020 , 11–12). Realizing this potential requires that also private house owners invest in energy efficiency measures. However, the annual rate of housing renovation in Europe is only about 1% ( Biere-Arenas and Marmolejo-Duarte, 2022 , 185), which is far too slow to reach the ambitious energy conservation targets. Besides, not all of those renovations include energy efficiency improvements. This raises the question of how property owners make decisions about renovating and energy efficiency measures and how they can be efficiently supported in these processes. To alleviate this problem, one-stop-shops (OSS), which are places where interested citizens can get counseling and support for the whole process of an energy retrofit, have gained a lot of attention lately as a means to support citizens in the matter of energy retrofits also from the European Union (as for example reflected in recently finished EU projects like “EUROPE one stop” or “ProRetro”).

1.1 One-stop-shops in energy counseling

Bertoldi et al. (2021 , 3–12) analysed the role of OSSs across Europe. They concluded that OSSs may be able to address some of the main barriers that households face when deciding about energy efficiency renovations. Often, these barriers can be categorized as economic (upfront costs, need for loan, split incentives between landlords and renters/disagreement between owners), information (information asymmetries, outcome uncertainties, incorrect beliefs), and decision-making (limited attention, social invisibility of the action, cognitive burden, loss aversion, status quo bias). Their analysis of 63 OSSs over Europe showed that the services the OSSs offer differ considerably, as do their business models. Some of them are public entities that often offer services for free, others are commercial enterprises. Their clients are usually homeowners living in relatively old buildings, and only a few of them work with social housing. Also Bagaini et al. (2022 , 3–4) analysed and categorized 29 OSS initiative around Europe and formulated five key elements on which the different OSS differed: (a) value proposition, (b) services, (c) partnership management, (d) revenue stream, and (e) shared value. Based on these dimensions, they destilled three archetypes for OSS models: They refer to them as the Facilitation Model (mostly focused on providing information to homeowners without a revenue generation model behind), the Coordination Model (also taking in a project management role with the contractors and generating revenue by fixed fees), and the Development Model (similar to the Coordination Model but with a revenue generated dynamically from the shared energy savings). Along similar lines, Pardalis et al. (2022) compared publicly and privately funded OSSs. In addition to the facilitation and the coordination model they separate the development model into “all inclusive models” (where the renovation process is fully managed by the OSS under one single contract, but energy savings are not guaranteed) and “ESCO models” (where Energy Service Companies−ESCOs−manage the whole renovation package and also guarantee energy savings). Whereas publicly funded OSSs are evaluated as providing homeowners with crucial services at the right time, privately funded OSSs struggle more with generating revenue and providing access to financing.

According to Bertoldi et al. (2021) , a key activity all of the surveyed OSSs cover is the assessment of the status quo, which is done in different ways (sometimes as a guided online self-assessment). Then, a stage of guidance toward possible measures is started, usually resulting in an individual renovation plan. In the next stage, financing is secured (either directly or indirectly, for example by supporting applications for subsidies). In the implementation stage, OSSs either manage the implementation themselves or recommend contractors who will do that. Often OSSs are involved in quality assurance of the implemented measures afterwards, sometimes certifying the result. Some OSSs also monitor the building after the energy upgrade to support the clients, often through a contract where financial benefits are shared between the OSS and the client (often in ESCO models). Finally, most OSSs also engage in campaigns for energy efficiency in buildings to increase awareness.

McGinley et al. (2020 , 355–57) formulate some key considerations for OSS design. They define OSS as offering full-service retrofitting, including initial building evaluation and thorough analysis, proposal of retrofitting solutions, retrofit execution, and quality assurance. However, they also state that little is known about characteristics and motivations of households that are drawn to OSS and how household decisions are impacted by OSSs, a research gap we aim to fill with this paper.

A number of recent EU projects have addressed the issue of OSSs in detail. In particular, the “EUROPA one stop” project (europaonestop.eu) is interesting as it created an online platform (SUNShINE−savehomesave.eu) to connect homeowners, facility managers, and contractors working on energy efficiency upgrades and provide them with easy access tools to online diagnose their renovation potential. This platform is structurally comparable with the platforms analysed in this paper and can be considered a concept following the facilitation model. However, to understand how homeowners may be affected by OSSs, it is important to take a look at decision-making processes.

1.2 Psychological drivers of implementing energy efficiency in renovation of privately owned dwellings

In a detailed study of decision-making about energy retrofits in Norwegian households data of which was also used as a comparison for this study, Klöckner and Nayum (2017 , 1014) found that an extended Theory of Planned Behaviour ( Ajzen, 1991 , 182; Klöckner, 2013 , 1032) formed a viable theoretical framework to structure these decision processes. They were able to show that personal norms, positive attitudes, and high self-efficacy were the decisive factors for forming intentions to include energy efficiency upgrades in renovation projects. Social norms were closely related to personal norms and an important trigger of these. More distal factors were problem awareness, value orientations, perceived consumer effectiveness, and innovativeness. The most central concepts are briefly introduced in the next paragraph.

In this context, personal norms are a feeling of moral obligation to invest in better energy efficiency. Positive attitudes are the overall evaluation of the pros and cons of the decision to invest. That is how good or bad this would be, all taken into account. Self-efficacy captures how capable one feels to implement the investment, a factor that most likely will be directly affected by engaging with an OSS. Following the theoretical framework as outlined and tested by Klöckner and Nayum (2017 , 1014), an intention to invest will thus be formed: (a) if people feel that they are morally obliged to do that because wasting energy is a bad thing which is more likely; (b) if other people who are important to them support this view. Furthermore, c) a positive attitude to energy efficiency investments d) and a high self-efficacy (i.e., knowing how to implement these measures and/or who to contract to do it) also contribute. As attitudes are a combination of positive and negative beliefs about the behavioral alternatives that people choose between ( Ajzen, 1996 , 385–403), a closer look at assumed barriers and facilitators underlying those alternatives could help in understanding the decision process further, as discussed in the next section.

1.3 Barriers and facilitators of energy efficiency measures in buildings

A number of studies analyzed facilitators of or barriers against implementing energy efficiency in a residential building from different theoretical and methodological perspectives. In his PhD thesis, Pardalis (2021 , 60) finds, based on an online survey with almost 1000 homeowners in Sweden, that the house age and time lived in a house but also energy concern trigger the decision to renovate. These factors are, again, influenced by sociodemographic factors of the occupants. Thus, structural aspects seem of importance as drivers of the retrofit decision.

Digging deeper into the decision process, Xue et al. (2022 , 5) conducted interviews with 39 professionals in the retrofit market to identify barriers to energy retrofitting from the perspective of the public sector, the private sector, and the owners who conduct the retrofit. They found financial issues as the most important barrier in all three groups. For owners who are supposed to implement energy efficiency measures, they further named lack of information, lack of creative models or cases, risks connected to the project, trust, and negative social influence as important barriers. Also, problems of reaching an agreement, time consuming processes, limited added value, and concerns about payback time were named.

Many of these aspects were also reflected in another qualitative study. Klöckner et al. (2013 , 406–408) interviewed 70 Norwegians on drivers and barriers regarding energy efficiency behaviour. They found that economic barriers (e.g., lack of investment money), motivational barriers (e.g., too much effort, loss of comfort, low perceived efficacy), structural barriers (e.g., building structure, ownership), and informational barriers (e.g., lack of trust, uncertainty, lack of specific information) were central.

Departing from practice theory in an ethnographic study of renovation projects, Judson and Maller (2014) interviewed 49 Australians involved in renovation projects and unraveled the process of renovation even more. They found that renovation projects, to a large degree, are shaped and reshaped by the existing or evolving practices people have within their buildings. Energy efficiency is traded off against other needs and meanings, negotiation between different household members occur, and focus shifts dynamically. Some parts of the home have a meaning for its inhabitants as part of their daily practices which cannot just be changed to enhance energy efficiency.

With a quantitative perspective, Klöckner and Nayum (2016 , 5) studied barriers in different stages of renovation processes in a representative sample of Norwegian households. Their findings indicate that facilitators like perceived increase in comfort, anticipated better living conditions or increased marked value were important in the early stages of decision making. Information about subsidy schemes or trustworthy information about the procedures came out as important at a later stage when planning was more advanced. Correspondingly, some barriers like building protection regulations, planning to move soon, or not owning the building were relevant already early in the process before people started even thinking about an energy retrofit, whereas barriers like too much disturbance of everyday life, contractors with a lack of competence, the need to supervise contractors, or a lack of economic resources were turned out to be relevant barriers later in the process. A particularly important barrier appeared to be the feeling that “the right point in time for a larger renovation project has not come, yet”.

In an economic modeling approach comparing expected utility theory (which assumes that decision makers chose the alternative with the best possible utility for them) and cumulative prospect theory (which assumes that decisions about investments are strongly affected by specific decision biases), Ebrahimigharehbaghi et al. (2022) found that cumulative prospect theory, which takes biases like “reference dependence” (utility changes are interpreted differently with respect to difference reference points), “loss aversion” (losses weigh higher than gains of the same size), “diminishing sensitivity” (avoiding risk for positive outcomes but taking risks for negative outcomes), and “probability weighting” (events with low probability but more extreme outcomes are overestimated) is much better equipped to predict homeowners investments in home energy efficiency in a large sample from the Netherlands than classical expected utility theory. This shows that people’s decision-making in such cases takes other aspects than economic utility into consideration to a large degree.

Studies such as the ones briefly mentioned above show that the selection of aspects that can interfere with or facilitate a decision-making process about energy retrofits is plentiful. In addition, they even have different importance depending on where in the process a decision-maker is. This makes it demanding to provide the most helpful support for decision-makers in the residential sector. It seems important to provide the right information at the right time to the right people, which underscores the need for careful targeting and timing of information provision. Flexible and interactive online counseling systems, which can take people through all stages of the process, similar OSSs, may be a way to find a good balance between resources needed and effects achieved in targeted energy counseling. Interestingly, Pardalis (2021 , 66) asked homeowners what would be most important for them with respect to OSSs, and guarantees for costs and quality, as well as having one contact and one contract and a preliminary check and counseling were on top of the list, directly addressing some of the issues identified as barriers in many of the studies above.

1.4 The present study

Summarizing what has been outlined in the introduction, energy efficiency upgrades of residential buildings are a major contributor to reaching the targets of the energy transition of the European Union. However, the private residential sector is lagging behind in this process. Renovation rates of the aging building stock are low. Even when the buildings are renovated, energy efficiency measures are not always implemented. In cases where some energy efficiency measures are included, they are often not to the standard that would be recommendable. One-stop-shops have been heavily promoted recently as a way of removing the burden of planning, financing, and implementing a deep renovation project from the individual house owners. Consequently, many such services have been implemented around Europe with differing business models, financing, and mandate. However, relatively little is known about who uses these services and what effect they have on their users. Especially, it is unknown to a large degree how interacting with a low-threshold digital OSS following a facilitation model shapes its users’ perception of barriers and facilitators of a retrofit decision, and if it affects their motivations and ambitions for this project. This research gap is addressed by the present study. More specifically, we are analysing if visitors of energy efficiency counceling websites differ in their engagement in retrofits, their energy efficiency ambitions, the profile of psychological variables, the drivers and barriers from representative samples of the population and a sample of home renovators.

Our study is, thus, contributing to the literature by providing new insights into how natural users of websites with OSS characteristics differ from the general population of homeowners on a number of psychological and socio-demographic characteristics. This helps on the one hand to identify who are the target group for such low-threshold website services, but on the other hand, we also provide an assessment if their renovation ambitions, and especially the level to which they intend to implement energy efficiency measures in these updates differs after they visited the service. Through a one-year follow-up, we can also provide an assessment of to which degree the planned measures were implemented. Taken together, the focus on primarily psychological drivers and barriers of energy efficiency investments in homes for a very specific target group in comparison to large, representative samples of homeowners paints a new, and informative picture of who the users of these websites are not only socio-demographically, but also psychologically, what they are looking for on these websites, and to which degree the websites support them in their pathway towards more energy efficient homes. Being able to run the comparisons of a relatively large sample of website users to several, large representative comparison samples which were surveyed with the same methodology in the same country over the course of 10 years provides an unique opportunity to understand the target group.

2 Materials and Methods

2.1 study design.

For this study, we collected responses from users of two online energy efficiency counseling websites, which have a similar structure that might be conceptualized as OSS following a facilitating model. These websites offer an analysis of the current energy standard of privately owned residential buildings (either as a guided self-assessment or based on data from the Norwegian building registry). They can also suggest a rough renovation plan and connect the homeowner to potential contractors who can implement energy efficiency measures. Moreover, they can provide information about costs, pay-off rates, subsidies (incl. information on how to apply), etc. Energismart.no is promoted by the environmental organization Friends of the Earth Norway, whereas energiportalen.no is promoted by Viken county. From January 2022 until January 2023, participants for the study were recruited from natural visitors of both websites by messages on the websites and pop-up windows, which promoted participation in our study and provided a link to the online questionnaire. We thus recruited people who visited the websites out of their own interest without promoting using the websites from our end. This sampling strategy was chosen to recruit a ecologically valid group of website users.

In the online survey, participants were then asked about their plans for retrofitting their homes, recently finished or ongoing retrofitting projects, the ambitions for energy efficiency upgrades as part of these retrofits, and psychological drivers and barriers of the decisions.

Since randomization of users of the websites was not possible, as people self-assigned to the websites, we chose a comparison group design, where we compared the means and distributions of key variables in our survey against representative homeowner data collected in 2014, 2018, and 2023 ( Klöckner and Nayum, 2016 , 2017 ; Egner and Klöckner, 2021 ; Egner et al., 2021 ; Peng and Klöckner, 2024 ) with the same survey instrument (see Table 1 for an overview of the survey samples). Because of that design, we are unable to draw causal conclusions, but we can get indications for differences between the samples (for a deeper discussion, see the limitations section below). We were also not able to survey our participants before they entered the websites. Thus, we do not know if the described differences were already there before they used the website, or which differences were caused by the website visit. It is likely that people visit such counseling websites when they already have developed an interest for the information presented there. Thus, some of the differences will have existed already pre-visit. Especially some of the drivers and barriers, but also some parts of the psychological profile might fall into that category and it is important to keep this in mind when interpreting the results. Furthermore, we do not know how long people stayed on the websites, what they read, and how much they used the information to adapt their renovation strategy, which would have given us more insights into their user experience. However, we believe that comparing the visitors to representative homeowners from different historical points in time in the same country surveyed with the same questionnaire can give us some relevant insights and at least input for generating new hypotheses.

Table 1. Overview of sample statistics in the different samples.

Differences between the samples were identified by comparing 95% confidence intervals for the means. Non-overlapping confidence intervals were interpreted as significant mean differences. Effect sizes for the differences are presented in Supplementary Appendix Table 1 .

One year after the participants answered the survey, we approached them again with a short survey asking if and which retrofitting measures had been implemented in the meantime and if not, why. The follow-up survey was sent to every participant who agreed to be contacted again.

The surveys conducted in all different studies compared here were collected through an online survey platform operated by the University of Oslo (Nettskjema.no). The questions used for the analyses presented in this paper composed only part of the questionnaires; we describe only the relevant questions below. The full survey can be found in the data repository together with the dataset. 1

2.2.1 Sociodemographic information

In the surveys, participants were asked about their gender, age, highest education level, gross household income (in the 2023 data collection, individual gross income was recorded), the type of house they lived in, and if they owned or rented etheir dwellings. The categories of these variables can be found in Table 1 .

2.2.2 Deep renovation

To capture if the participants were just finished, engaged in, or planning what we refer to as a “deep renovation” project, we asked them the following questions:

(1) Within the previous three years, were you involved in a renovation project that involved (a) substantial work on the roof like replacing all tiles, (b) replacing at least 50% of the outer walls, (c) replacing at least 50% of the window area, and/or (d) substantial work on the foundation? This definition was developed for the 2014 study in a collaboration of the researchers behind the studies and the Norwegian Energy Efficiency Agency Enova and used in the same form in all data collections since. The aim of this definition was to differentiate larger renovation projects from smaller, more cosmetic renovation projects.

(2) Are you currently involved in a renovation project according to the definition above or are you planning to engage in such a renovation project within the next three years?

However, the definition does not automatically assume that energy efficiency measures are included in the deep renovation project.

The ambition level of these renovation projects was measured by how many of the four components they (are planning to) implement, and it ranges from 1 to 4.

2.2.3 Energy efficiency upgrade

If participants answered “yes” to either or both of the questions presented in the previous section, they were asked if that renovation project included, includes or is planned to include (a) additional insulation of the roof of at least 10 cm, (b) adding additional insulation to the walls of at least 5 cm, (c) energy saving windows with a μ-value of 1.0 or lower, (d) at least 5 cm additional insulation to the foundation walls, (e) installation of mechanical ventilation, and/or (f) installation of balanced ventilation. Also here, the definition of these measures was agreed upon with Enova in 2014 to represent a substantial improvement in the energy standard of the respective building component. For our analyses, we counted the number of these measures that had been/were planned to be implemented in the deep renovation project. The number could thus be between 0 and 6.

2.2.4 Personal norms, social norms, attitudes, and efficiency

Based on the Theory of Planned Behaviour ( Ajzen, 1991 , 182) extended by personal norms from the Norm-Activation Model ( Schwartz and Howard, 1981 ), four psychological variables are central to understand people’s intentions: attitudes, social norms, perceived behavioral control or behavioral efficacy, and personal norms. Each of these variables was measured by two items in the surveys, with a 7-point Likert scale from −3 to +3. Higher values indicate stronger norms, attitudes, or efficacy.

The two items to measure social norms were “People who influence my decisions think I should insulate my home” and “People who are important to me think I should retrofit my home”. The two items to measure perceived efficacy were “I know which person or company I need to contact to have my home professionally insulated” and “I know what I need to do to insulate my home”. The two items to measure personal norms were “Because of my values/principles, I feel obliged to insulate my home” and “I feel personally obliged to retrofit my home”. For each pair of items, the mean score was calculated and used in subsequent analyses.

Attitudes were measured with four semantic differentials: “Increasing the energy standard of my home would be (a) useless−useful, (b) uncomfortable−comfortable, (c) unfavorable−favorable, and (d) bad−good”. Each pair has −3 as the anchor for the negative word and +3 as the anchor for the positive word. For further analyses, the mean of the four items was calculated.

All items had been used in an identical way since the first study in 2014, as documented elsewhere ( Klöckner and Nayum, 2016 , 2017 ). In the 2023 data collection, different answering scales were used, therefore the results are not comparable and are not reported here ( Peng and Klöckner, 2024 ).

2.2.5 Barriers and facilitators

Finally, a list of potential barriers and facilitators of energy efficiency upgrades was presented in random order to the participants, asking how much they agreed with each item. The items can be found in the Supplementary Appendix . These lists were derived from a qualitative study on reasons why Norwegians upgrade or decide not to upgrade energy standards of their dwellings ( Klöckner et al., 2013 ). In the 2023 data collection, different answering scales had been used, therefore the results are not comparable and are not reported here.

2.3 Sample and comparison groups

The sample of counseling website users was recruited from the first week of January 2022 to the first week of January 2023. In total, 437 answers were collected. These answers were not equally distributed over the year, however, as ( Figure 1 ) shows. Whereas relatively many responses were collected in winter and early spring 2022, the interest was reduced in late spring and summer before it skyrocketed after summer 2022, as well as in winter 2023. This coincided with electricity price peaks in Norway (especially in the South) and media discussions about that topic. Thus, a first conclusion can already be that the interest in using energy efficiency counseling websites clearly follows the pattern of the energy price fluctuation and accompanying societal discussion.

Figure 1. Number of participants recruited for the counseling website user survey per week in 2022 (the line is the moving average).

Table 1 below shows the sociodemographic statistics of the sample from the counseling websites in comparison to the existing samples in detail. As can be seen, the samples are comparable on most of the dimensions. All samples contain close to 50% males and females (with the most deviation in the sample of renovators from 2014). The average age is around 50 years in all samples, with the youngest average age in the 2023 population sample and the oldest average age in the sample of the users of the websites. Education varies quite strongly, with the population sample from 2014 being the outlier with far lower education level than all other samples. Participants recruited from the counseling websites had the highest education level. The median household gross income category is the same in most samples. However, it is lower in the 2014 population sample and higher in the sample of people who answered the one-year follow-up after the visit on the counseling websites. Income categories of the 2023 sample cannot be compared, as individual gross income was recorded in that data collection. However, it can be extrapolated that the average household income would be comparable to the other samples. The proportion of people living in detached houses is particularly high in the sample of website users and the renovator sample from 2014. Also, the level of people owning their dwelling is close to 100% in these groups and a little lower in all other groups. As a conclusion, it can be said that the samples are comparable on most dimensions. Meanwhile, the website users are most similar to the people who were recruited as being in a renovation project in 2014. That is, they are more likely better educated, more likely to live in a detached house, and more likely to own their dwelling than representative samples of Norwegian households.

In the following section, we present the results of the comparison of the counseling website users with the other available samples. To do this, we examine the 95% confidence intervals as displayed in the figures for overlaps between the group of website users and the other groups. As the data is partly in separate datasets, we did not calculate formal significance tests, but a non-overlapping 95% confidence interval corresponds to an assumed significant difference between the respective groups. The numbers for the website users are always highlighted in the figures. Effect sizes are reported in Supplementary Appendix Table 1 . An overview of all results can be found in Table 2 .

Table 2. Summary of the differences between the website visitors and the representative homeowner samples from 2014, 2018, and 2023, as well as the renovator sample from 2014.

3.1 Engagement in deep renovation

As can be seen in Figure 2 , the percentage of people who were involved in a deep renovation project is higher in the group of counseling website users than in all three population samples. The same can be said for the ongoing or planned deep renovation projects, which are also more common for people visiting the energy counseling websites. Only the group that was specifically recruited in 2014 to only contain respondents who either just had been, were still, and/or were planning a deep renovation project in the near future has higher numbers (which is not surprising). Interestingly, the number of finished and planned projects in the population sample is lower in 2023 than in 2018 and 2014, likely an effect of renovation saturation after COVID years.

Figure 2. Percentage of households per group who were, are or plan to be in a deep renovation project (see definition in the text). The columns with the bold lines are the users of the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

Among the users of the energy counseling websites, the ambition level is higher than in any other group, both for finished, ongoing and planned projects (see Figure 3 ). This means that they are engaged in slightly larger projects, involving more of the four different potential measures (walls, windows, roof, foundation). Thus, these people probably are or plan to be involved in more comprehensive renovation projects.

Figure 3. Ambition of the deep renovation (how many different measures are included of walls, windows, roof, and basement). The columns with the bold lines are the users of the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

3.2 Energy efficiency ambitions

When looking at the level of ambitions for integrating energy efficiency upgrades in the renovation projects, the picture is even more interesting (see Figure 4 ). Among the users of the energy counseling websites, the ambition level is substantially higher than in any other group, both for finished, ongoing, and planned projects. On a side note, even if the total percentage of people involved in deep renovation was lower in the population in 2023 than in 2014 and 2018, the degree to which energy efficiency measures are included is increasing as can be seen in Figures 2 , 4 . This may be an effect of the energy crisis in Europe in 2022.

Figure 4. Ambition of the energy retrofit as part of the renovation (how many different energy efficiency measures are included of more insulation of walls, better windows, more insulation of roof and basement, balanced ventilation system, and heat pump). The columns with the bold lines are the users of the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

3.3 Psychological drivers

When comparing the psychological profiles of the website users to the population profiles from 2014 and 2018, it can be seen that the website users have substantially higher personal norms. This indicates that they feel more moral pressure to increase the energy efficiency of their dwellings (see Figure 5 ). They also feel stronger social norms, meaning more social pressure from their peers to engage in such energy upgrades. For attitudes, the differences are smaller. Meanwhile, the attitudes are slightly more positive than for the population samples, on the same level as for the renovators in 2014. Interestingly, despite small differences, the website users have the lowest perceived self-efficacy, especially compared to the renovators in 2014. In contrast to renovators in 2014, they feel less convinced that they know how to go about for the renovations.

Figure 5. Means in key psychological variables driving the decision to renovate and energy upgrade. The bold black line is the sample from the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

3.4 Facilitators and barriers of energy efficiency upgrades

Figures 6 , 7 show how the website users perceive facilitators and barriers of energy efficiency upgrades of their dwellings in comparison to people in the other samples. For some facilitators and barriers, differences are substantial: counseling website users expect more comfort, a cost reduction, a house that is better to live in, increased property value, and less waste of energy as a result of the renovation. They score the lowest of all samples, though, on availability of information, payback time, and availability of subsidy.

Figure 6. Means in key facilitators for an energy upgrade. The bold black line is the sample from the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

Figure 7. Means in key barriers towards an energy upgrade. The bold black line is the sample from the counseling websites, whiskers represent 95% confidence intervals (CI), non-overlapping CI are regarded as indicating a statistically significant difference.

For the barriers, they score particularly high on perceptions of the renovation taking too much time, on lack of money, difficulty of finding information, a lack of ability to decide what to do, and a lack of capable contractors. They score lower on perceptions of it not being the right time to act.

3.5 Implemented energy efficiency actions

In the one-year follow-up, the participants of the energy counseling website survey were contacted again and asked if they implemented the planned actions. 201 participants (46.0% of all participants) gave permission to be contacted a year after the initial survey was completed, and 78 (38.8% of all who were willing to be contacted) answered the short follow-up survey.

Of the 78 participants, 25 stated that they implemented the energy efficiency upgrades that they were planning to implement (32.1%). 29.2% of these changed at least 50% of the outer walls, 45.8% worked on the roof, 45.8% on the windows, and 37.5% on the foundation walls.

Of the 25 who implemented the measures, 15 added at least 5 cm insulation to the walls, 13 installed highly efficient windows (μ = 1.0 or smaller), 13 installed new mechanical ventilation, 12 insulated the roof with at least 10 cm additional insulation, 10 insulated the foundation walls with at least 5 additional cm of insulation, and 7 installed a balanced ventilation system. In addition to these measures, 11 installed heat pumps, 11 installed clean-burning wood stoves, and 5 installed solar panels on their houses. Overall, the measures taken were fairly ambitious.

The main reasons for not implementing the planned measures among the remaining participants of the follow-up were lack of economic funding (57.1%), lack of subsidies (42.9%), and that the time was not right, yet, to start the renovation, again reflecting some of the main barriers indicated in the introduction.

4 Discussion

The study conducted with the users of two energy efficiency counseling websites had three aims: (a) finding out if users of the website differed from representative samples of Norwegian households in terms of engagement in retrofits and have higher ambitions for their renovation projects and the energy efficiency measures embedded in them, (b) finding out if they differ in the psychological profile in central variables driving the decision-making process, and (c) finding out if they perceive facilitators and barriers in this process differently than representative samples of households. Furthermore, a follow-up study aimed to find out how many participants implement their ambitions up to a year later.

For all three main questions, we find substantial differences. Whereas the website users are mostly comparable to the general population of Norwegian households regarding socio-demographics (but have a higher education level and an even smaller percentage of people renting their dwelling, which reflects well the drivers for renovation projects as identified by Pardalis, 2021 ), their psychological profile differs in two important points. Compared to all other samples (also including the renovators studied in 2014), the website users have far higher levels of personal norms−they feel they really should do something about the energy standard of their homes−and also higher social norms. Considering the importance of these two factors for intentions to implement energy renovations ( Klöckner and Nayum, 2017 , 1014), this finding is relevant. Having such high levels of these two variables makes it more likely that people will form intentions to improve the energy standard of their homes. It also indicates that people like these are a prime target group for interventions like OSSs: They are already motivated to take action because they have high energy-related moral standards, and they feel the social pressure of their peer groups.

Since we could not survey these people before they went to the website, we do not know if they had such high personal and social norm values already before the visit to the website. On the other hand, since one of the websites is promoted by the environmental organization Friends of the Earth Norway, it can be assumed that this is the case. Interestingly, users of the counseling websites had a slightly lower level of self-efficacy, especially compared to the renovators from 2014. This implies that a lower level of self-efficacy might be a barrier to implement the intentions they form, and maybe also a reason for visiting the websites. Again, this means that this group is a very attractive target group for OSS-type interventions: Alleviating the low self-efficacy is something a well-designed OSS can achieve by reducing uncertainties, providing requested information, and not the least making the link between the urge to act on the side of the homeowners and the competence the homeowners are lacking provided by skilled and trustworthy contractors. This finding is, again, very much in line with what Pardalis (2021) found as being the most important features of OSSs from the perspective of potential users.

Also in terms of facilitators and barriers analysed, counseling website users had some values substantially different from the other groups. In particular, increased expected comfort levels, expected cost reductions, and expectations of having a better house to live in after the renovation were more important facilitators for website users than for the population samples or the renovators. Expecting an increased value of the house after the renovation was also higher than for the population samples, but at the same level as for the renovators. Perceiving the current energy standards a waste was standing out again for the website users. This indicates that they enter the process with a different, more energy interested perspective (or they get convinced of that by visiting the website). Interestingly, counseling website users score lower on perceptions that information is easy to find, and that access to subsidy is available. Maybe this is also a reason why they ended up on the websites in the first place.

Among the barriers, the website users mention a lot more often the time demand for supervision and the lack of money as the main barriers. They thereby raise the need to have a facilitator (or even a manager) of the renovation process, again a function OSSs typically fill. The websites we studied are following a facilitation model, but still leave the management of the project to the homeowners. From their answers, we can conclude that many of them would actually prefer a more comprehensive model. Also here, they reiterate that they consider information hard to find, that they cannot decide what to do, and that contractors lack competence. The latter three again might be reasons for being interested in the website services in the first place. The websites seem to partly satisfy their needs, as can be seen in that a significant amount of the website visitors implement their renovation plans within a year. However, some still sit with the same lack of support and the same barriers after a year. Maybe for them, a more comprehensive OSS model with a higher degree of process management would be more appropriate. In line with the renovators from 2014, the website users are to a lesser degree unsure if the right point in time for a renovation project has come. Overall, the order of importance of renovation facilitators and barriers to a large extent reproduces what has been found in earlier studies ( Klöckner et al., 2013 ; Klöckner and Nayum, 2016 , 2017 ; Bertoldi et al., 2021 ; Xue et al., 2022 ).

Most importantly, we found that the visitors of the websites had stronger ambitions for their renovation projects, and in particular for the implementation of energy efficiency measures as part of them. Of course, we do not know if this was caused by visiting the websites or if it was already higher before they visited. Nevertheless, we can assume that there is at least some mutual influence. People with a stronger motivation, but who are unsure about how to implement, visit the websites, which then confirm their motivations and provide hands-on counseling to remove the implementation barriers. This then eventually might result in higher ambitions. This is good news for the OSS concept, even the low-threshold version of it that these websites represent ( McGinley et al., 2020 ). However, not all visitors seem to receive from these websites what they need. For the future, it might be recommendable to use low-threshold OSSs like the ones studied here following a facilitating model as an entry point but implement an (automated, maybe AI-based) detection of who would benefit from more comprehensive OSS models to channel these people to the offers that better suit their needs.

Finally, we could at least tentatively show−even if based upon only relatively few cases and subject to large sample attrition−that about 1/3 of the participants manage to implement their energy upgrade intentions. These people usually combine several measures and implement a deep renovation. For these people, the websites seem to have pushed them in the right direction without too much effort. As such, these websites have their niche as gatekeepers for a deeper process for some people, as the final push and reassurance for others.

5 Limitations and future research needs

Even if the study presented here shows some interesting results in a field where more research is needed, there are a number of limitations that are mostly caused by the design we had to choose. The biggest limitation of this study is that the participants recruited among the website users were, for obvious reasons, not randomly assigned to use the website but self-selected, and they were not surveyed before the visit on the website, a limitation that was already discussed in the methodology section. In addition, the users of the website fall into a narrower sociodemographic category than the population samples, though they seem to be rather comparable with people engaged in renovation projects six years prior to our study. Furthermore, we do not know how long people stayed on the websites, what they read, and how much they used the information to adapt their renovation strategy.

To address these limitations, studies with more controlled experimental designs would be advisable. Assigning participants randomly to different conditions (including no OSS, and different models of OSS) would give a better understanding of what the effects of the OSS are and what differences people come with in the process. Such a study could also test, whether different forms of OSS interact with different sociodemographic and psychological profiles of homeowners. In simple words, it might answer the question, which form of OSS works for which type of homeowner.

6 Conclusion

One-stop-shops have been promoted as a measure to overcome the inertia in energy efficiency retrofitting, especially in the privately owned residential building stock. Results from our study on users of two Norwegian energy efficiency counseling websites, which offer services in many ways similar to an OSS following a facilitator model, show that the users of these websites clearly differ from representative samples of Norwegian households that were surveyed with similar instruments. Their profiles were more like a sample of people who were in the beginning or in the middle of a larger renovation project, which was surveyed in 2014. However, the results also show that they are scoring substantially lower on their perceived access to information and subsidy. Regarding the psychological profiles, they were much more strongly motivated by personal and social norms than average households. Most importantly, it appears that visitors of such low-threshold websites have substantially higher ambitions for the energy upgrades, which about 1/3 of them have implemented a year after they visited the websites. Interest in online energy efficiency counseling services seems to be impacted by societal discussions about energy and/or by energy prices, as suggested by the spike in recruitment to our survey coinciding with an energy price increase during 2022 (however, this intriguing possibility will need to be confirmed in future studies). From a policy perspective, the results are interesting because they indicate that low-threshold OSSs can be gateways capturing people who are motivated for energy efficiency upgrades but not able to make the decision for several reasons. For some of them, the services that these relatively simple online platforms can offer is already enough to reduce their uncertainty and make the missing connections. For those still not satisfied after visiting these platforms, future developments should explore whether they can be automatically directed to more comprehensive forms of OSSs.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/records/10453810 .

Ethics statement

The studies involving humans were approved by the Norwegian Agency for Shared Services in Education and Research (SIKT). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

CK: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing–original draft, Writing–review and editing. AN: Data curation, Formal analysis, Writing–original draft, Writing–review and editing. SV: Conceptualization, Funding acquisition, Writing–original draft, Writing–review and editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of the article. This study has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 957115 as part of the ENCHANT project: www.enchant-project.eu. Data for three of the comparison groups for the analyses was extracted from two previous projects funded by the Norwegian Energy Efficiency Agency, and one comparison group was extracted from data from an ongoing project funded by the Research Council of Norway (BEHAVIOUR, Project No. 308772).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1364980/full#supplementary-material

• ^ https://zenodo.org/records/12605729

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Keywords : energy efficiency, renovation, one-stop-shops, counseling, psychological drivers, theory of planned behaviour, personal norms, facilitators

Citation: Klöckner CA, Nayum A and Vesely S (2024) Understanding users of online energy efficiency counseling: comparison to representative samples in Norway. Front. Psychol. 15:1364980. doi: 10.3389/fpsyg.2024.1364980

Received: 03 January 2024; Accepted: 18 July 2024; Published: 06 August 2024.

Reviewed by:

Copyright © 2024 Klöckner, Nayum and Vesely. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Christian A. Klöckner, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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An equity evaluation of healthcare accessibility across age strata using the g2sfca method: a case study in karamay district, china, 1. introduction, 2. literature review, 2.1. measuring healthcare accessibility, 2.2. the equity of healthcare accessibility, 3. study area and data, 3.1. study area, 3.2.1. road network and healthcare facility data, 3.2.2. demographic indicators, 4.1. measuring accessibility, 4.2. spatial autocorrelation, 4.3. equity analysis, 5.1. accessibility of two types of healthcare facilities, 5.2. spatial agglomeration characteristics, 5.3. equity evaluation, 5.3.1. equality of healthcare accessibility, 5.3.2. equity across age strata, 6. discussion, 6.1. interpreting results, 6.2. planning implications, 6.3. assumptions and limitations, 7. conclusions, supplementary materials, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Liu, L.; Gao, R.; Zhang, L. An Equity Evaluation of Healthcare Accessibility across Age Strata Using the G2SFCA Method: A Case Study in Karamay District, China. Land 2024 , 13 , 1259. https://doi.org/10.3390/land13081259

Liu L, Gao R, Zhang L. An Equity Evaluation of Healthcare Accessibility across Age Strata Using the G2SFCA Method: A Case Study in Karamay District, China. Land . 2024; 13(8):1259. https://doi.org/10.3390/land13081259

Liu, Lu, Runyi Gao, and Li Zhang. 2024. "An Equity Evaluation of Healthcare Accessibility across Age Strata Using the G2SFCA Method: A Case Study in Karamay District, China" Land 13, no. 8: 1259. https://doi.org/10.3390/land13081259

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