Suits research exploring:• Changing behaviours within health contexts to address patient and carer practices• Changing behaviours regarding environmental concerns• Barriers and enablers to behaviour/ practice/ implementation• Intervention planning and implementation• Post-evaluation• Promoting physical activity
As discussed in Chapter 3, qualitative research is not an absolute science. While not all research may need a framework or theory (particularly descriptive studies, outlined in Chapter 5), the use of a framework or theory can help to position the research questions, research processes and conclusions and implications within the relevant research paradigm. Theories and frameworks also help to bring to focus areas of the research problem that may not have been considered.
Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Tess Tsindos is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.
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Qualitative health care research can provide insights into health care practices that quantitative studies cannot. However, the potential of qualitative research to improve health care is undermined by reporting that does not explain or justify the research questions and design. The vital role of research frameworks for designing and conducting quality research is widely accepted, but despite many articles and books on the topic, confusion persists about what constitutes an adequate underpinning framework, what to call it, and how to use one. This editorial clarifies some of the terminology and reinforces why research frameworks are essential for good-quality reporting of all research, especially qualitative research.
Qualitative research provides valuable insights into health care interactions and decision-making processes – for example, why and how a clinician may ignore prevailing evidence and continue making clinical decisions the way they always have. 1 The perception of qualitative health care research has improved since a 2016 article by Greenhalgh et al. highlighted the higher contributions and citation rates of qualitative research than those of contemporaneous quantitative research. 2 The Greenhalgh et al. article was subsequently supported by an open letter from 76 senior academics spanning 11 countries to the editors of the British Medical Journal . 3 Despite greater recognition and acceptance, qualitative research continues to have an “uneasy relationship with theory,” 4 which contributes to poor reporting.
As an editor for the Journal of Patient-Centered Research and Reviews , as well as Human Resources for Health , I have seen several exemplary qualitative articles with clear and coherent reporting. On the other hand, I have often been concerned by a lack of rigorous reporting, which may reflect and reinforce the outdated perception of qualitative research as the “soft option.” 5 Qualitative research is more than conducting a few semi-structured interviews, transcribing the audio recordings verbatim, coding the transcripts, and developing and reporting themes, including a few quotes. Qualitative research that benefits health care is time-consuming and labor-intensive, requires robust design, and is rooted in theory, along with comprehensive reporting. 6
So fundamental is theory to qualitative research that I initially toyed with titling this editorial, “ Theory: the missing link in qualitative health care research articles ,” before deeming that focus too broad. As far back as 1967, Merton 6 warned that “the word theory threatens to become meaningless.” While it cannot be overstated that “atheoretical” studies lack the underlying logic that justifies researchers’ design choices, the word theory is so overused that it is difficult to understand what constitutes an adequate theoretical foundation and what to call it.
Theory, as used in the term theoretical foundation , refers to the existing body of knowledge. 7 , 8 The existing body of knowledge consists of more than formal theories , with their explanatory and predictive characteristics, so theory implies more than just theories . Box 1 9 – 12 defines the “building blocks of formal theories.” 9 Theorizing or theory-building starts with concepts at the most concrete, experiential level, becoming progressively more abstract until a higher-level theory is developed that explains the relationships between the building blocks. 9 Grand theories are broad, representing the most abstract level of theorizing. Middle-range and explanatory theories are progressively less abstract, more specific to particular phenomena or cases (middle-range) or variables (explanatory), and testable.
words we assign to mental representations of events or phenomena , | |
higher-order clusters of concepts | |
expressions of relationships among several constructs | |
“sets of interrelated constructs, definitions, and propositions that present a systematic view of phenomena by specifying relations among variables and phenomena” general sets “of principles that are independent of the specific case, situation, phenomenon or observation to be explained” |
Researchers may draw on several elements to frame their research. Generally, a framework is regarded as “a set of ideas that you use when you are forming your decisions and judgements” 13 or “a system of rules, ideas, or beliefs that is used to plan or decide something.” 14 Research frameworks may consist of a single formal theory or part thereof, any combination of several theories or relevant constructs from different theories, models (as simplified representations of formal theories), concepts from the literature and researchers’ experiences.
Although Merriam 15 was of the view that every study has a framework, whether explicit or not, there are advantages to using an explicit framework. Research frameworks map “the territory being investigated,” 8 thus helping researchers to be explicit about what informed their research design, from developing research questions and choosing appropriate methods to data analysis and interpretation. Using a framework makes research findings more meaningful 12 and promotes generalizability by situating the study and interpreting data in more general terms than the study itself. 16
The variation in how the terms theoretical and conceptual frameworks are used may be confusing. Some researchers refer to only theoretical frameworks 17 , 18 or conceptual frameworks, 19 – 21 while others use the terms interchangeably. 7 Other researchers distinguish between the two. For example, Miles, Huberman & Saldana 8 see theoretical frameworks as based on formal theories and conceptual frameworks derived inductively from locally relevant concepts and variables, although they may include theoretical aspects. Conversely, some researchers believe that theoretical frameworks include formal theories and concepts. 18 Others argue that any differences between the two types of frameworks are semantic and, instead, emphasize using a research framework to provide coherence across the research questions, methods and interpretation of the results, irrespective of what that framework is called.
Like Ravitch and Riggan, 22 I regard conceptual frameworks (CFs) as the broader term. Including researchers’ perspectives and experiences in CFs provides valuable sources of originality. Novel perspectives guard against research repeating what has already been stated. 23 The term theoretical framework (TF) may be appropriate where formal published and identifiable theories or parts of such theories are used. 24 However, existing formal theories alone may not provide the current state of relevant concepts essential to understanding the motivation for and logic underlying a study. Some researchers may argue that relevant concepts may be covered in the literature review, but what is the point of literature reviews and prior findings unless authors connect them to the research questions and design? Indeed, Sutton & Straw 25 exclude literature reviews and lists of prior findings as an adequate foundation for a study, along with individual lists of variables or constructs (even when the constructs are defined), predictions or hypotheses, and diagrams that do not propose relationships. One or more of these aspects could be used in a research framework (eg, in a TF), and the literature review could (and should) focus on the theories or parts of theories (constructs), offer some critique of the theory and point out how they intend to use the theory. This would be more meaningful than merely describing the theory as the “background” to the study, without explicitly stating why and how it is being used. Similarly, a CF may include a discussion of the theories being used (basically, a TF) and a literature review of the current understanding of any relevant concepts that are not regarded as formal theory.
It may be helpful for authors to specify whether they are using a theoretical or a conceptual framework, but more importantly, authors should make explicit how they constructed and used their research framework. Some studies start with research frameworks of one type and end up with another type, 8 , 22 underscoring the need for authors to clarify the type of framework used and how it informed their research. Accepting the sheer complexity surrounding research frameworks and lamenting the difficulty of reducing the confusion around these terms, Box 2 26 – 31 and Box 3 offer examples highlighting the fundamental elements of theoretical and conceptual frameworks while acknowledging that they share a common purpose.
The Southern African Association of Health Educationalist’s best publication of 2023 reported on a non-inferiority randomized control trial comparing video demonstrations and bedside tutorials for teaching pediatric clinical skills. The authors combined the social cognitive of sequential skill acquisition , and Peyton’s approach to teaching procedural and physical examination skills , to provide the justification for skill demonstrations forming the first step in bedside teaching. This premise formed the basis for the study and informed the interpretation of the results. | |
Maxwell describes how a researcher used a theoretical framework based on three formal theories to understand the “day-to-day work” of a medical group practice and to emphasize aspects of his results. This example illustrates the use of existing formal theories (one of which Maxwell describes as being less “identified than the other two”) to understand the phenomenon of interest and provide a frame of reference for interpreting the results. |
There is complexity around how conceptual frameworks are developed and used to inform research design, so consider the following examples: the first is based on the work of one of my doctoral students in medical education (with permission from Dr. Neetha Erumeda). The second is a fictitious account based on the normalization process model, which has been used in qualitative health care research. | |
In a study evaluating a postgraduate medical training program, Dr. Erumeda constructed a conceptual framework based on a logic . Logic models graphically represent causal relationships between programmatic inputs, activities, outputs, and outcomes linearly, and they can be based on different , eg, theories of action, which focus on programmatic inputs and activities, or theories of change, which focus on programmatic outcomes. Dr. Erumeda based her initial CF on a formal of change. She then selected to include in her logic model, based on the literature and of teaching in the program being evaluated. Once she had a diagrammatic representation of her logic model and the concepts she would focus on, she discussed the current understanding of each concept from the literature. After an analysis of her results, Dr. Erumeda modified her initial CF by incorporating her findings and the insights. Her final logic model represented a theory of action, allowing her to offer recommendations to improve the training program. | |
To study the implementation of a complex innovation into a health care system, one might employ the normalization process , which is a representation of . The model consists of four constructs regarding the innovation: 1) how it is enacted by the people doing it (interactional workability), 2) how it is understood within the networks of people around it (relational integration), 3) how it fits with existing divisions of labor (skill set workability), and 4) how it is sponsored or controlled by the organization in which it is taking place (contextual integration). Constructing a would require researchers to consider how the innovation relates to each of the constructs in the model, to identify that make up the constructs and to consider their of the concepts (eg, how they conceive the prevailing work ethic or experience the managerial hierarchy). They may also be able to postulate between different constructs or concepts or decide to focus on particular aspects of the model, which they could explore conceptually using the literature. Their research design would be influenced by their areas of interest, which would, in turn, determine their research methods. The findings could allow them to modify their model with evidence-based relationships and new concepts. |
Qualitative research’s “uneasy relationship with theory” 4 may be due to several misconceptions. One possible misconception is that qualitative research aims to build theory and thus does not need theoretical grounding. The reality is that all qualitative research methods, not just Grounded Theory studies focused on theory building, may lead to theory construction. 16 Similarly, all types of qualitative research, including Grounded Theory studies, should be guided by research frameworks. 16
Not using a research framework may also be due to misconceptions that qualitative research aims to understand people’s perspectives and experiences without examining them from a particular theoretical perspective or that theoretical foundations may influence researchers’ interpretations of participants’ meanings. In fact, in the same way that participants’ meanings vary, qualitative researchers’ interpretations (as opposed to descriptions) of participants’ meaning-making will differ. 32 , 33 Research frameworks thus provide a frame of reference for “making sense of the data.” 34
Studies informed by well-defined research frameworks can make a world of difference in alleviating misconceptions. Good qualitative reporting requires research frameworks that make explicit the combination of relevant theories, theoretical constructs and concepts that will permeate every aspect of the research. Irrespective of the term used, research frameworks are critical components of reporting not only qualitative but also all types of research.
In memory of Martie Sanders: supervisor, mentor, and colleague. My deepest gratitude for your unfailing support and guidance. I feel your loss.
Conflicts of Interest: None.
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Published on 4 May 2022 by Bas Swaen and Tegan George. Revised on 18 March 2024.
A conceptual framework illustrates the expected relationship between your variables. It defines the relevant objectives for your research process and maps out how they come together to draw coherent conclusions.
Keep reading for a step-by-step guide to help you construct your own conceptual framework.
Developing a conceptual framework in research, step 1: choose your research question, step 2: select your independent and dependent variables, step 3: visualise your cause-and-effect relationship, step 4: identify other influencing variables, frequently asked questions about conceptual models.
A conceptual framework is a representation of the relationship you expect to see between your variables, or the characteristics or properties that you want to study.
Conceptual frameworks can be written or visual and are generally developed based on a literature review of existing studies about your topic.
Your research question guides your work by determining exactly what you want to find out, giving your research process a clear focus.
However, before you start collecting your data, consider constructing a conceptual framework. This will help you map out which variables you will measure and how you expect them to relate to one another.
In order to move forward with your research question and test a cause-and-effect relationship, you must first identify at least two key variables: your independent and dependent variables .
Note that causal relationships often involve several independent variables that affect the dependent variable. For the purpose of this example, we’ll work with just one independent variable (‘hours of study’).
Now that you’ve figured out your research question and variables, the first step in designing your conceptual framework is visualising your expected cause-and-effect relationship.
It’s crucial to identify other variables that can influence the relationship between your independent and dependent variables early in your research process.
Some common variables to include are moderating, mediating, and control variables.
Moderating variable (or moderators) alter the effect that an independent variable has on a dependent variable. In other words, moderators change the ‘effect’ component of the cause-and-effect relationship.
Let’s add the moderator ‘IQ’. Here, a student’s IQ level can change the effect that the variable ‘hours of study’ has on the exam score. The higher the IQ, the fewer hours of study are needed to do well on the exam.
Let’s take a look at how this might work. The graph below shows how the number of hours spent studying affects exam score. As expected, the more hours you study, the better your results. Here, a student who studies for 20 hours will get a perfect score.
But the graph looks different when we add our ‘IQ’ moderator of 120. A student with this IQ will achieve a perfect score after just 15 hours of study.
Below, the value of the ‘IQ’ moderator has been increased to 150. A student with this IQ will only need to invest five hours of study in order to get a perfect score.
Here, we see that a moderating variable does indeed change the cause-and-effect relationship between two variables.
Now we’ll expand the framework by adding a mediating variable . Mediating variables link the independent and dependent variables, allowing the relationship between them to be better explained.
Here’s how the conceptual framework might look if a mediator variable were involved:
In this case, the mediator helps explain why studying more hours leads to a higher exam score. The more hours a student studies, the more practice problems they will complete; the more practice problems completed, the higher the student’s exam score will be.
It’s important not to confuse moderating and mediating variables. To remember the difference, you can think of them in relation to the independent variable:
Lastly, control variables must also be taken into account. These are variables that are held constant so that they don’t interfere with the results. Even though you aren’t interested in measuring them for your study, it’s crucial to be aware of as many of them as you can be.
A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.
No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both.
Yes, but including more than one of either type requires multiple research questions .
For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.
You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .
To ensure the internal validity of an experiment , you should only change one independent variable at a time.
A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.
A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.
A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.
In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.
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Mediator vs moderator variables | differences & examples, independent vs dependent variables | definition & examples, what are control variables | definition & examples.
Introduction.
Research is a fundamental aspect of any academic or scientific endeavor. It involves the systematic investigation of a particular topic or problem to generate new knowledge or validate existing theories. However, conducting research can be a complex and challenging process, requiring careful planning and organization. This is where research frameworks come into play.
In this comprehensive guide, we will explore the concept of research frameworks and how they can help researchers in their work. We will discuss the components of a research framework, the different types of frameworks, and the methodology behind developing and implementing a research framework. Additionally, we will provide examples of research frameworks as samples to guide researchers in designing their own projects. For researchers looking to collaborate and enhance their research framework strategies, platforms like Researchmate.net offer valuable resources and networking opportunities.
A research framework refers to the overall structure, approach, and theoretical underpinnings that guide a research study. It is a systematic and organized plan that outlines the key elements of a research project, including the research questions , objectives, methodology, data collection methods, and data analysis techniques.
A research framework provides researchers with a roadmap to follow throughout the research process, ensuring that the study is conducted in a logical and coherent manner. It helps researchers to organize their thoughts, identify gaps in existing knowledge, and develop a clear research plan. By establishing a research framework, researchers can ensure that their study is rigorous, valid, and reliable, and that it contributes to the existing body of knowledge in their field. Overall, a research framework serves as a foundation for the research study, guiding the researcher in every step of the research process.
A research framework consists of several key components that work together to guide the research process. It is essentially a structured outline that serves as a guide for researchers to organize their thoughts, define research objectives, and plan the research process comprehensively. While there are various research framework templates available, they typically include the following components:
The problem statement defines the research problem or question that the study aims to address. It provides a clear and concise statement of the issue that needs to be investigated. This often emerges from identifying a research gap in the existing literature, highlighting areas that lack sufficient study or have not been explored at all.
The research objectives outline the specific goals and outcomes that the study aims to achieve. These objectives help to focus the research and provide a clear direction for the study. The objectives should be measurable and aligned with the research question to ensure that the study is targeted and relevant.
The literature review is a critical component of a research framework. It involves reviewing existing research and literature related to the research topic. This helps to identify gaps in the current knowledge and provides a foundation for the study.
The phrases ‘ conceptual framework ‘ and ‘ theoretical framework ‘ are often used to describe the overall structure that defines and outlines a research project. These frameworks are composed of theories, concepts, and models that serve as the foundation and guide for the research process.
The research methodology outlines the methods and techniques that will be used to collect and analyze data. It includes details on the research design, data collection methods, and data analysis techniques.
Data collection method is a component of research methodology which involves collecting data from various sources, such as surveys, interviews , observations, or existing datasets. The data collected should be relevant to the research objectives and provide insights into the research problem.
Data analysis involves organizing, interpreting, and analyzing the collected data. This can include statistical analysis, qualitative analysis, or a combination of both, depending on the research objectives and data collected.
The findings and conclusion section presents the results of the data analysis and discusses the implications of the findings. It summarizes the key findings, draws conclusions, and provides recommendations for future research or practical applications. It highlights the contribution of the study to the existing body of knowledge and suggests areas for further investigation.
These components work together to provide a comprehensive framework for conducting research. Each component plays a crucial role in guiding the research process and ensuring that the study is rigorous and valid.
There are two types of research frameworks: theoretical and conceptual.
A theoretical framework is a single formal theory that is used as the basis for a study. It provides a set of concepts and principles that guide the research process. On the other hand, a conceptual framework is a broader framework that includes multiple concepts and theories. It provides a unified framework for understanding and analyzing a particular research problem. The two types of frameworks relate differently to the research question and design. The theoretical framework often inspires the research question based on the existing theory, while the conceptual framework helps in organizing and structuring the research process.
Both types of frameworks have their advantages and limitations. A theoretical framework provides a solid foundation for research and allows for the testing of specific hypotheses. However, it may be limited in its applicability to a specific research problem. On the other hand, a conceptual framework allows for a more holistic and comprehensive understanding of the research problem. It provides a framework for exploring multiple perspectives and theories. However, it may lack the specificity and precision of a theoretical framework.
In practice, researchers often use a combination of theoretical and conceptual frameworks to guide their research. They may start with a theoretical framework to establish a foundation and then use a conceptual framework to explore and analyze the research problem from different angles. The choice of research framework depends on the nature of the research problem, the research question, and the goals of the study. Researchers should carefully consider the advantages and limitations of each type of framework and select the most appropriate one for their specific research context.
Methodology is an essential component of a research framework as it provides a structured approach to conducting research projects. The methodology section of a research framework includes the research design, sampling design, data collection techniques, analysis, and interpretation of the data. These elements are crucial in ensuring the validity and reliability of the research finding as follows:
Example 1: Tourism Research Framework
One example of a research framework is a tourism research framework. This framework includes various components such as tourism systems and development models, the political economy and political ecology of tourism, and community involvement in tourism. By using this framework, researchers can analyze and understand the complex dynamics of tourism and its impact on communities and the environment.
Example 2: Educational Research Framework
Another example of a research framework is an educational research framework. This framework focuses on studying various aspects of education, such as teaching methods, curriculum development, and student learning outcomes. It may include components like educational theories, pedagogical approaches, and assessment methods. Researchers can use this framework to guide their studies and gain insights into improving educational practices and policies.
Example 3: Health Research Framework
A health research framework is another common example. This framework is used to investigate different aspects of health, such as disease prevention, healthcare delivery, and patient outcomes. It may include components like epidemiological models, healthcare systems analysis, and health behavior theories. Researchers can utilize this framework to design studies that contribute to the understanding and improvement of healthcare practices and policies.
Example 4: Business Research Framework
In the field of business, a research framework can be developed to study various aspects of business operations, management strategies, and market dynamics. This framework may include components like organizational theories, market analysis models, and strategic planning frameworks. Researchers can apply this framework to investigate business-related phenomena and provide valuable insights for decision-making and industry development.
Example 5: Social Science Research Framework
A social science research framework is designed to study human behavior, social structures, and societal issues. It may include components like sociological theories, psychological models, and qualitative research methods. Researchers in the social sciences can use this framework to explore and analyze various social phenomena, contributing to the understanding and improvement of society as a whole.
In conclusion, a research framework provides a structured approach to organizing and analyzing research data, allowing researchers to make informed decisions and draw meaningful conclusions. Throughout this guide, we have delved into the nature of research frameworks, including their components, types, methodologies, and practical examples. These frameworks are essential tools for conducting effective and efficient research, helping researchers streamline processes, enhance the quality of findings, and contribute significantly to their fields.
However, it is important to recognize that research frameworks are not a one-size-fits-all solution; they may need to be tailored to suit the specific objectives, scope, and context of individual research projects. While these frameworks provide essential structure, they should not replace critical thinking and creativity. Researchers are encouraged to remain open to new ideas and perspectives, adapting frameworks to meet their unique needs and navigate the complexities of the research process, thereby advancing knowledge within their disciplines.
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Over the course of 7 days, you will receive bite-sized lessons in your email about researching theoretical and conceptual frameworks.
If you are looking for a document in the Dissertation Center or Applied Doctoral Center and can't find it please contact your Chair or The Center for Teaching and Learning at [email protected]
Research frameworks provide a foundation for your study and keeps it focused and concise. Think of a framework as a roadmap or blueprint for developing your study and supporting research.
This short video series will help you help you identify, locate, and retrieve theoretical and conceptual frameworks through the library databases and/or Google.
Theoretical frameworks provide a particular perspective, or lens, through which to examine a topic. There are many different lenses, such as psychological theories, social theories, organizational theories and economic theories, which may be used to define concepts and explain phenomena. Sometimes these frameworks may come from an area outside of your immediate academic discipline. Using a theoretical framework for your dissertation can help you to better analyze past events by providing a particular set of questions to ask, and a particular perspective to use when examining your topic.
Traditionally, Ph.D. and Applied Degree research must include relevant theoretical framework(s) to frame, or inform, every aspect of the dissertation. Further, Ph.D. dissertations should make an original contribution to the field by adding support for the theory, or, conversely, demonstrating ways in which the theory may not be as explanatory as originally thought. You can learn more about the theoretical framework requirements in the NU Dissertation Center .
It can be difficult to find scholarly work that takes a particular theoretical approach because articles, books, and book chapters are typically described according to the topics they tackle rather than the methods they use to tackle them. Further, there is no single database or search technique for locating theoretical information. However, the suggestions below provide techniques for locating possible theoretical frameworks and theorists in the Library databases. In addition to your Library research, you should discuss possible theories your Dissertation Chair to ensure they align with your study. Also, keep in mind that you will probably find and discard several potential theoretical frameworks before one is finally chosen.
A conceptual framework provides the concept or set of related concepts supporting the basis or foundation of a study. It creates a conceptual model for possible strategies or courses of action identified as important for researching a particular problem or issue. While a conceptual framework is often referred to interchangeably with a theoretical framework, it maintains a distinct purpose. A conceptual framework is used to clarify concepts, organize ideas, and identify relationships with which to frame a study. Concepts are logically developed and organized to support an overall framework and often exhibited graphically within dissertation research. Note that a dissertation may include both a theoretical framework and a conceptual framework.
The suggestions below provide techniques for locating possible conceptual frameworks in the Library databases. Note when examples may use the term "theoretical framework," you may change your search terms to "conceptual framework." In addition to your Library research, you should discuss possible frameworks your Dissertation Chair to ensure they align with your study. Also, keep in mind that you will probably find and discard several potential conceptual frameworks before one is finally chosen.
Biographical dictionaries can be useful if you are looking for basic background information on a particular theorist or scientist.
Content: A reference database useful for accessing scholarly definitions, background and contextual information. Subjects covered include art, biography, business, economics, education, history, literature, music, psychology, religion, and science and technology.
Purpose: An excellent starting point for brainstorming a research topic and building out your initial search terms list.
Special Features: Mindmap; related articles; image search
Content: Ebooks with coverage across all academic disciplines. The collection offers a critical mass of more than 150,000 foundational scholarly ebooks with balanced quantity and quality to improve teaching, learning and research workflow and outcomes.
Purpose: Provides access to multidisciplinary ebooks for download or to be read online.
Special Features: Browse by subject option; highlight and take notes in text.
Help using this database.
Content: Reference e-book collection
Purpose: Users may read the full text of e-books from a range of academic disciplines
Special Features: Includes a visualization tool and browse-by-topic feature that aids in brainstorming topics, a Lexile feature that filters texts by difficulty, the ability to highlight and add notes to text, and a read-aloud feature.
Content : Books, chapters, and peer-reviewed content about a diverse range of topics.
Purpose: Users may access full text, and authoritative information about many topics.
Special Features: Users may explore topics and subjects.
Content: Reference sources, primarily books but also videos and business cases.
Purpose: Use for finding reference sources like encyclopedias and handbooks that provide contextual or explanatory material.
Special Features: Includes Sage Navigator
Use the Library’s e-book databases to gather background information on a particular theory or theorist. Since the e-book databases will contain fewer resources than a database containing thousands of scholarly journal articles, it is best to keep your search terms a little more broad.
For example, a search for education theory in the Ebook Central database results in many relevant e-books, as shown below. Expanding the Table of Contents will provide additional details about the e-book.
Encyclopedias and handbooks will also provide reliable background information on particular theories. For example, a search for cognitive developmental theory in the Credo Reference database results in a number of reference entries which discuss the history of the theory, identify relevant theorists, and cite seminal research studies.
You may search for theorists and theoretical information using Google and Google Scholar , as well. However, please keep in mind that you will need to be more discriminating when it comes to using material found on open access websites. We recommend reviewing the Website Evaluation guidelines when considering online sources.
One method that may be used in Google is limiting your search by a particular domain name. If a website ends in .org, .gov, or .edu, it is more likely to be a scholarly source. If it ends in .com or .net it is less likely to be a scholarly source. In the search below, for example, we have limited our search for "leadership theories" to just those websites ending with .edu. You may also find this domain limiter under Tools>Advanced Search.
Note: Limiting to a particular domain is not necessary in Google Scholar, as all results in Google Scholar may be considered scholarly. This may include articles, theses, books, abstracts and court opinions, material from academic publishers, professional societies, online repositories, universities and other web sites.
For additional information, see the following:
Content: National University & NCU student dissertations and literature reviews.
Purpose: Use for foundational research, to locate test instruments and data, and more.
Special Features: Search by advisor (chair), degree, degree level, or department. Includes a read-aloud feature.
Content: Global student dissertations and literature reviews.
Special Features: Search by advisor (chair), degree, degree level, or department. Includes a read-aloud feature
The ProQuest Dissertations & Theses database (PQDT) is the world's most comprehensive collection of dissertations and theses. It is the database of record for graduate research, with over 2.3 million dissertations and theses included from around the world.
Since most doctoral research requires a theoretical framework, looking at completed dissertations related to your topic is an effective way to identify relevant theories and theorists. ProQuest Dissertations & Theses Global provides access to over 3 million full text doctoral dissertations and graduate theses. You may limit your search to only doctoral dissertations by using the Advanced Search screen. Look at the table of contents or abstract for reference to theoretical framework, as shown below. The dissertation’s references/bibliography will have a full citation to the original theorist’s research.
Content: Scholarly journals, e-books, videos and more.
Purpose: A key multidisciplinary database for most topics. It is one of the library’s main search engines and the most comprehensive single search.
Note: Certain library databases and publisher content are not searchable in NavigatorSearch, and individual databases may need to be searched to retrieve information due to unique content. NavigatorSearch can be found at https://resources.nu.edu .
On the NavigatorSearchscreen, include theor* as one your search terms, as shown below. It will retrieve results that include one of the following keywords: theory, theories, theoretical, theorist, or theorists . It is important to keep in mind, however, that this is not a foolproof method for locating theoretical frameworks. Scholars will often cite theory or theorists in order to refute them, or because they are saying something that's tangentially related, or they may even just refer to theory briefly in passing. In our example, we have selected the field for AB Abstract because if theory is mentioned within the abstract, the study is more likely to take a theoretical approach.
As shown below, results from our example search clearly include articles which apply theory to the topic of curriculum design.
Remember to look past the article title. Theoretical information may be mentioned in a subheading, or referred to elsewhere in the document. Use the FIND feature in your PDF viewer or internet browser to scan the document for terms such as theor* (to pull up theory, theorist, theoretical), framework, conceptual, perspective , etc., as shown below.
Content: Books, reference works, journal articles, and instructional videos on research methods and design.
Purpose: Use to learn more about qualitative, quantitative, and mixed methods research.
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SAGE Research Methods is a multimedia database containing more than 1,000 books, reference works, journal articles, and instructional videos covering every step of the research process. It includes e-books and e-book chapters which may help you better understand the theoretical framework aspect of your research study. A selection of resources is included below:
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Use the main search bar to locate information about theoretical frameworks. Search the general phrase "theoretical frameworks," or the name of a specific theoretical framework like "social cognitive theory," in quotation marks to yield results with that specific phrase. See the example below.
You may also browse content in this database by Discipline . Select Browse on the top navigation to view a list of key topics.
Content: Citations and articles in multi-disciplines not found through a NavigatorSearch.
Purpose: Used to conduct topic searches as well as find additional resources that have cited a specific resource (citation network).
You may conduct a Cited Reference Search in Web of Science to find articles that cite a primary theorist in your area. These articles are likely to tackle your topic through your theoretical lens, or will point you toward another article that does. To access Web of Knowledge, go to A-Z Databases from the Library’s home page.
On the Web of Science home page, click on Cited Reference Search to search for articles that cite a person's work.
Enter the name of a key theorist in your area (in our example, John Dewey) in the format they specify (in this case Dewey J*), as shown below, and press "Search."
On the results screen, select the appropriate Web of Science category under Refine Results. For example, we could select “Education Educational Research” and then click “Refine.” You may wish to further refine by Document Type, Research Area, Author, etc. (also located on the left hand menu). Sorting your results by “Times Cited - Oldest to Newest" is an effective way to discover the most frequently cited works.
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Educational resources and simple solutions for your research journey
A strong conceptual framework underpins good research. A conceptual framework in research is used to understand a research problem and guide the development and analysis of the research. It serves as a roadmap to conceptualize and structure the work by providing an outline that connects different ideas, concepts, and theories within the field of study. A conceptual framework pictorially or verbally depicts presumed relationships among the study variables.
The purpose of a conceptual framework is to serve as a scheme for organizing and categorizing knowledge and thereby help researchers in developing theories and hypotheses and conducting empirical studies.
In this post, we explain what is a conceptual framework, and provide expert advice on how to make a conceptual framework, along with conceptual framework examples.
Table of Contents
Definition of a conceptual framework.
A conceptual framework includes key concepts, variables, relationships, and assumptions that guide the academic inquiry. It establishes the theoretical underpinnings and provides a lens through which researchers can analyze and interpret data. A conceptual framework draws upon existing theories, models, or established bodies of knowledge to provide a structure for understanding the research problem. It defines the scope of research, identifying relevant variables, establishing research questions, and guiding the selection of appropriate methodologies and data analysis techniques.
Conceptual frameworks can be written or visual. Other types of conceptual framework representations might be taxonomic (verbal description categorizing phenomena into classes without showing relationships between classes) or mathematical descriptions (expression of phenomena in the form of mathematical equations).
Figure 1: Definition of a conceptual framework explained diagrammatically
The term conceptual framework appears to have originated in philosophy and systems theory, being used for the first time in the 1930s by the philosopher Alfred North Whitehead. He bridged the theological, social, and physical sciences by providing a common conceptual framework. The use of the conceptual framework began early in accountancy and can be traced back to publications by William A. Paton and John B. Canning in the first quarter of the 20 th century. Thus, in the original framework, financial issues were addressed, such as useful features, basic elements, and variables needed to prepare financial statements. Nevertheless, a conceptual framework approach should be considered when starting your research journey in any field, from finance to social sciences to applied sciences.
The importance of a conceptual framework in research cannot be understated, irrespective of the field of study. It is important for the following reasons:
Essential elements that a conceptual framework should include are as follows:
The sources for these elements of a conceptual framework are literature, theory, and experience or prior knowledge.
Now that you know the essential elements, your next question will be how to make a conceptual framework.
For this, start by identifying the most suitable set of questions that your research aims to answer. Next, categorize the various variables. Finally, perform a rigorous analysis of the collected data and compile the final results to establish connections between the variables.
In short, the steps are as follows:
Be sure to make use of arrows and lines to depict the presence or absence of correlational linkages among the variables.
Researchers should be adept at developing a conceptual framework. Here are the steps for developing a conceptual framework:
Your research question guides your entire study, making it imperative to invest time and effort in formulating a question that aligns with your research goals and contributes to the existing body of knowledge. This step involves the following:
The dependent variable is the main outcome you want to measure, explain, or predict in your study. It should be a variable that can be observed, measured, or assessed quantitatively or qualitatively. Independent variables are the factors or variables that may influence, explain, or predict changes in the dependent variable.
Choose independent and dependent variables for your study according to the research objectives, the nature of the phenomenon being studied, and the specific research design. The identification of variables is rooted in existing literature, theories, or your own observations.
To better understand and communicate the relationships between variables in your study, cause-and-effect relationships need to be visualized. This can be done by using path diagrams, cause-and-effect matrices, time series plots, scatter plots, bar charts, or heatmaps.
Besides the independent and dependent variables, researchers must understand and consider the following types of variables:
Let us examine the following conceptual framework example. Let’s say your research topic is “ The Impact of Social Media Usage on Academic Performance among College Students .” Here, you want to investigate how social media usage affects academic performance in college students. Social media usage (encompassing frequency of social media use, time spent on social media platforms, and types of social media platforms used) is the independent variable, and academic performance (covering grades, exam scores, and class attendance) is the dependent variable.
This conceptual framework example also includes a mediating variable, study habits, which may explain how social media usage affects academic performance. Study habits (time spent studying, study environment, and use of study aids or resources) can act as a mechanism through which social media usage influences academic outcomes. Additionally, a moderating variable, self-discipline (level of self-control and self-regulation, ability to manage distractions, and prioritization skills), is included to examine how individual differences in self-control and discipline may influence the relationship between social media usage and academic performance.
Confounding variables are also identified (socioeconomic status, prior academic achievement), which are potential factors that may influence both social media usage and academic performance. These variables need to be considered and controlled in the study to ensure that any observed effects are specifically attributed to social media usage. A visual representation of this conceptual framework example is seen in Figure 2.
Figure 2: Visual representation of a conceptual framework for the topic “The Impact of Social Media Usage on Academic Performance among College Students”
Here is a snapshot of the basics of a conceptual framework in research:
What is the difference between a moderating variable and a mediating variable.
Moderating and mediating variables are easily confused. A moderating variable affects the direction and strength of this relationship, whereas a mediating explains how two variables relate.
Independent variables are the variables manipulated to affect the outcome of an experiment (e.g., the dose of a fat-loss drug administered to rats). Dependent variables are variables being measured or observed in an experiment (e.g., changes in rat body weight as a result of the drug). A confounding variable distorts or masks the effects of the variables being studied because it is associated both with dependent variable and with the independent variable. For instance, in this example, pre-existing metabolic dysfunction in some rats could interact differently with the drug being studied and also affect rat body weight.
The need for more than one dependent or independent variable in a study depends on the research question, study design, and relationships being investigated. Note the following when making this decision for your research:
A confounding variable is not the main focus of the study but can unintentionally influence the relationship between the independent and dependent variables. Confounding variables can introduce bias and give rise to misleading conclusions. These variables must be controlled to ensure that any observed relationship is genuinely due to the independent variable.
A control variable is something not of interest to the study’s objectives but is kept constant because it could influence the outcomes. Control variables can help prevent research biases and allow for a more accurate assessment of the relationship between the independent and dependent variables. Examples are (i) testing all participants at the same time (e.g., in the morning) to minimize the potential effects of circadian rhythms, (ii) ensuring that instruments are calibrated consistently before each measurement to minimize the influence of measurement errors, and (iii) randomization of participants across study groups.
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Theoretical & conceptual frameworks.
The terms theoretical framework and conceptual framework are often used interchangeably to mean the same thing. Although they are both used to understand a research problem and guide the development, collection, and analysis of research, it's important to understand the difference between the two. When working on coursework or dissertation research, make sure to clarify what is being asked and any specific course or program requirements.
A theoretical framework is a single formal theory. When a study is designed around a theoretical framework, the theory is the primary means in which the research problem is understood and investigated. Although theoretical frameworks tend to be used in quantitative studies, you will also see this approach in qualitative research.
A conceptual framework includes one or more formal theories (in part or whole) as well as other concepts and empirical findings from the literature. It is used to show relationships among these ideas and how they relate to the research study. Conceptual frameworks are commonly seen in qualitative research in the social and behavioral sciences, for example, because often one theory cannot fully address the phenomena being studied.
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Identifying and learning about theories requires a different search strategy than other types of research. Even though the steps are different, you will still use many of the same skills and tools you’ve used for other library research.
Identifying a theory that aligns with your dissertation or doctoral study takes time. It’s never too early to start exploratory research. The process of identifying an appropriate theory can seem daunting, so try breaking down the process into smaller steps.
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Home » Methodological Framework – Types, Examples and Guide
Table of Contents
Definition:
Methodological framework is a set of procedures, methods, and tools that guide the research process in a systematic and structured manner. It provides a structure for conducting research, collecting and analyzing data, and drawing conclusions. The framework outlines the steps to be taken in a research project, including the research question, hypothesis, data collection methods, data analysis techniques, and the interpretation of the results.
There are different types of methodological frameworks that researchers can use depending on the nature of their research question, the type of data they want to collect, and the research methodology they want to employ. Some common types of methodological frameworks include:
This type of framework uses numerical data and statistical analysis to test hypotheses and draw conclusions. It involves the collection of structured data through surveys, experiments, or other quantitative methods.
This framework is used to explore complex social phenomena and involves the collection of non-numerical data through methods such as interviews, observation, and document analysis. Qualitative research typically involves the use of open-ended questions and in-depth analysis of data.
This framework combines quantitative and qualitative research methods to address research questions from multiple angles. It involves collecting both numerical and non-numerical data and using both statistical analysis and interpretive techniques to analyze the data.
This framework involves the collaboration between researchers and participants to identify and address practical problems in real-world settings. It involves a cyclical process of planning, action, reflection, and evaluation to improve a specific situation or practice.
This framework involves the in-depth investigation of a specific case or phenomenon, often using qualitative methods. It aims to understand the complexity of the case and draw generalizations from the findings.
Developing a methodological framework involves a series of steps that help to guide the research process in a systematic and structured manner. Here are the general steps involved in developing a methodological framework:
Here are some examples of how a methodological framework can be applied in various fields:
Here are some specific situations when a methodological framework can be particularly useful:
Here are some real-time examples of how methodological frameworks are used in various fields:
The purpose of a methodological framework is to provide a structured and systematic approach to designing, conducting, and analyzing research. The framework serves as a guide for researchers to follow, ensuring that the research is conducted in a rigorous and transparent manner, and that the results are reliable, valid, and generalizable. Some key purposes of a methodological framework are:
Here are some common characteristics of a methodological framework:
There are several advantages to using a methodological framework in research:
While there are many advantages to using a methodological framework in research, there are also some limitations to be aware of:
Researcher, Academic Writer, Web developer
A theoretical framework is a conceptual model that provides a systematic and structured way of thinking about a research problem or question. It helps to identify key variables and the relationships between them and to guide the selection and interpretation of data. Theoretical frameworks draw on existing theories and research and can be used to develop new hypotheses or test existing ones. They provide a foundation for research design, data collection, and analysis and can help to ensure that research is relevant, rigorous, and coherent. Theoretical frameworks are common in many disciplines, including social sciences, natural sciences, and humanities, and are essential for building knowledge and advancing understanding in a field.
This article explains the importance of frameworks in a thesis study and the differences between conceptual frameworks and theoretical frameworks. It provides guidelines on how to write a thesis framework, definitions of variable types, and examples of framework types.
When planning your thesis study, you need to justify your research and explain its design to your readers. This is called the research framework.
When planning your thesis study, you need to justify your research and explain its design to your readers. This is called the research framework. Think of it as the foundation of a building. A good building needs a strong foundation. Similarly, your research needs to be supported by reviewing and explaining the existing knowledge in the field, describing how your research study will fit within or contribute to the existing literature (e.g., it could challenge or test an existing theory or address a knowledge gap), and informing the reader how your study design aligns with your thesis question or hypothesis.
Important components of the framework are a literature review of recent studies associated with your thesis topic as well as theories/models used in your field of research. The literature review acts as a filtering tool to select appropriate thesis questions and guide data collection, analysis, and interpretation of your findings. Think broadly! Apart from reviewing relevant published papers in your field of research, also explore theories that you have come across in your undergraduate courses, other published thesis studies, encyclopedias, and handbooks.
There are two types of research frameworks: theoretical and conceptual .
A conceptual framework is a written or visual representation that explains the study variables and their relationships with each other. The starting point is a literature review of existing studies and theories about your topic.
When developing a conceptual framework, you will need to identify the following:
First, identify the independent (cause) and dependent (effect) variables in your study. Then, identify variables that influence this relationship, such as moderating variables, mediating variables, and control variables. A moderating variable changes the relationship between independent and dependent variables when its value increases or decreases. A mediating variable links independent and dependent variables to better explain the relationship between them. A control variable could potentially impact the cause-and-effect relationship but is kept constant throughout the study so that its effects on the findings/outcomes can be ruled out.
You want to investigate the hours spent exercising (cause) on childhood obesity (effect).
Now, you need to consider moderating variables that affect the cause-and-effect relationship. In our example, the amount of junk food eaten would affect the level of obesity.
Next, you need to consider mediating variables. In our example, the maximum heart rate during exercise would affect the child’s weight.
Finally, you need to consider control variables. In this example, because we do not want to investigate the role of age in obesity, we can use this as a control variable. Thus, the study subjects would be children of a specific age (e.g., aged 6–10 years).
A theoretical framework provides a general framework for data analysis. It defines the concepts used and explains existing theories and models in your field of research.
A theoretical framework provides a general framework for data analysis. It defines the concepts used and explains existing theories and models in your field of research. It also explains any assumptions that were used to inform your approach and your choice of specific rationales. Theoretical frameworks are often used in the fields of social sciences.
A thesis topic can be approached from a variety of angles, depending on the theories used.
The structure of a theoretical framework is fluid, and there are no specific rules that need to be followed, as long as it is clearly and logically presented.
The theoretical framework is a natural extension of your literature review. The literature review should identify gaps in the field of your research, and reviewing existing theories will help to determine how these can be addressed. The structure of a theoretical framework is fluid, and there are no specific rules that need to be followed, as long as it is clearly and logically presented. The theoretical framework is sometimes integrated into the literature review chapter of a thesis, but it can also be included as a separate chapter, depending on the complexity of the theories.
The sales staff at Company X are unmotivated and struggling to meet their monthly targets. Some members of the management team believe that this could be achieved by implementing a comprehensive product-training program, but others believe that introducing a sales commission structure will help.
Company X is not achieving their monthly sales targets
To increase monthly sales.
How can Company X motivate their sales team to achieve its monthly sales targets?
A literature search will need to be performed to understand the background of the many different theories of motivation in psychology. For example, Maslow’s Hierarchy of Needs (basic human needs—physiological, safety, love/belonging, esteem, and self-actualization—have to be fulfilled before one can live up to their true potential), Vroom’s Theory of Expectancy (people decide upon their actions based on the outcomes they expect), and Locke’s Goal-Setting Theory (goals are a key driver of one’s behavior). These theories would need to be investigated to determine which would be the best approach to increase the motivation of the sales staff in Company X so that the monthly sales targets are met.
A robust conceptual or theoretical framework is crucial when writing a thesis/dissertation. It defines your research gap, identifies your approach, and guides the interpretation of your results.
A thesis is the most important document you will write during your academic studies. For professional thesis editing and thesis proofreading services, check out Enago's Thesis Editing service s for more information.
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Theoretical frameworks are typically used in the HSS domain, while conceptual frameworks are used in the Sciences domain.
The difference between mediators and moderators can be confusing. A moderating variable is unaffected by the independent variable and can increase or decrease the strength of the relationship between the independent and dependent variables. A mediating variable is affected by the independent variable and can explain the relationship between the independent and dependent variables. T he statistical correlation between the independent and dependent variables is higher when the mediating variable is excluded.
The software program Creately provides some useful templates that can help you get started. Other recommended programs are SmartDraw , Inkscape , and diagrams.net .
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This study addresses the critical need for documented adaptation progress in mountain regions by reviewing recently implemented or ongoing adaptation solutions collected from the Adaptation at Altitude Solutions Portal (A@A Solution Portal). Using a data driven approach, the research explores the characteristics, feasibility, and transformative potential of these solutions. Findings reveal a predominant focus on addressing droughts and floods, aligning with the IPCC’s emphasis on water-related impacts in mountains. Notably, watershed management practices emerge as popular solutions, showcasing their capacity to address multiple concerns beyond climate impacts. Education and awareness, along with land use practices, dominate the types of solutions, reflecting their positive impact on project acceptability and low associated risk of maladaptation. Agricultural land and forests are the main ecosystems where solutions are reported, with an evident association with education and awareness and land use change solutions. Most SDGs and Sendai targets are found to be addressed by the solutions emphasising the importance of documenting project experiences as way to bridge previously reported gaps between policy frameworks and on-the-ground implementation. Despite community involvement being high in many of the solutions, challenges such as gender inequality persists. While solutions often demonstrate local relevance and depth of change, upscaling remains challenging, with limited evidence of mainstreaming and replication. Sustainability criteria are moderately met, incorporating inclusive decision-making but with uncertainty regarding long-term plans. Furthermore, findings underscore the significance of co-developing and maintaining adaptation solution portals, illustrating how this approach enriches our understanding of adaptation progress in mountains. Moreover, this research contributes to broadening the scope of systematic adaptation assessments by providing a nuanced perspective that integrates local needs and diverse knowledge systems. In essence, this study makes a valuable contribution to the evolving landscape of adaptation research, emphasizing the importance of practical insights and collaborative efforts to address the complex challenges posed by climate-related impacts and corresponding adaptation efforts.
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Climate change is having a significant impact on mountain ecosystems, which are home to a quarter of the world’s population and a source of freshwater for billions of people (Adler et al. 2022 ). Mountain communities are highly dependent on natural resources for their livelihoods, and changes in the mountain environment can have significant social, economic, and cultural impacts (Huss et al. 2017 ; Mengistu et al. 2020 ; Schmeller et al. 2022 ; Reader et al. 2023a ). Alongside climate and environmental change, demographic change, land use change and urbanisation also create numerous disruptions, in particular when settlements and infrastructures appear in hazard-prone areas (Viviroli et al. 2020 ; Thornton et al. 2022 ). Therefore, adapting to climate change in mountains is essential to ensure the well-being of mountain and lowland communities, as well as the long-term sustainability of mountain ecosystems (McDowell et al. 2019b ; Adler et al. 2022 ).
Evidence from mountain specific research confirms that climate adaptation is taking place in many mountain countries, often as a reaction to realised impacts, and sporadically as part of coordinated strategies and plans (McDowell et al. 2019b ; Adler et al. 2022 ). The status quo of mountain adaptation is that of small adjustments to existing risk management strategies with limited scope and extent. Yet, as risks become ever more complex and pervasive, the need to move from small adjustments to substantial innovation and systemic changes, is becoming more pressing (Colloff et al. 2017 ; Klein et al. 2019 ; Palomo et al. 2021 ; McDowell et al. 2021 ). Indeed, in terms of the hallmark approaches taken to adaptation, those of incremental and transformational adaptation, are perhaps the two most prominent (Kates et al. 2012 ). Although, as many authors have noted, there is no fixed definition for transformative adaptation and its interpretation differs among different users and contexts (Fedele et al. 2019 ), its relevance and necessity are nevertheless widely recognized (Klein et al. 2019 ; Bentz et al. 2022 ). Such importance appears to lie in the need to move from business-as-usual or traditional incremental strategies to systemic commitments that better address the complex challenges linked to climate change risks through a shift in paradigms and values (Lonsdale et al. 2015 ). Lately, the success of adaptation, whether transformative or incremental, has become strongly interrelated to its effectiveness in reducing climate risks (Owen 2020 ; Chausson et al. 2020 ), with the feasibility of adaptation as an indication of potential barriers, limits or maladaptation (Singh et al. 2020 ; Thomas et al. 2021 ).
In the pursuit of achieving a synthetic picture of the overall landscape of adaptation, its characteristics, effectiveness and transformative potential, numerous systematic reviews and meta-analyses have emerged in the past decade (McDowell et al. 2014 , 2019b ; Berrang-Ford et al. 2015 , 2019 ; Berrang-Ford, Sietsma, et al., 2021 ). Berrang-Ford et al. 2021a combined traditional review methods with machine learning to take stock of empirical adaptation globally. Meanwhile, other reviews have focused on specific sub-topics within the adaptation literature, such as health (Berrang-Ford et al. 2021b ), equity (Araos et al. 2021 ), adaptation limits (Thomas et al. 2021 ), and government adaptation (Berrang-Ford et al. 2019 ). Systematic reviews of adaptation also exist for specific topological regions, including the Arctic (Canosa et al. 2020 ) and mountain areas (McDowell et al. 2014 , 2019b ; Terzi et al. 2019 ; Vij et al. 2021 ).
These reviews have proved extremely valuable to tracking adaptation progress, and some have played a key role in global assessments such as the IPCC (Berrang-Ford et al. 2021a ; Adler et al. 2022 ; O’Neill et al. 2022 ). Notwithstanding, they predominantly assess adaptation if evidence is reported in the academic literature. Technical and logistical challenges have been identified when attempting at systematically assessing adaptation practice from the grey literature in ways that are comparable and on pair with the academic evidence (Berrang-Ford et al. 2021a ). This is often because adaptation projects carried out in the public, NGO and private sectors are seldomly reported in peer-reviewed literature (McDowell et al. 2019b ; Berrang-Ford et al. 2021a ; Vij et al. 2021 ). In response, a number of portals have been developed over the years to track adaptation on the ground, such as Climate-Adapt of the European Environment Agency (Mattern and Jol 2018 ; Dubo et al. 2022 ), the Climate Change Knowledge Portal of the World Bank, and the Dutch adaptation web portal (Laudien et al. 2019 ). Facts and figures from these portals are starting to gain recognition by the scientific literature, and their usefulness is increasingly acknowledged (Laudien et al. 2019 ; Dubo et al. 2022 ; Jevne et al. 2023 ).
This study responds to the urgent need of shedding light on adaptation practice in mountains by compiling wide ranging facts and figures from a dedicated portal on adaptation solutions in mountain regions. It seeks to produce a comprehensive inventory of adaptation efforts taking place in mountains as part of realised and ongoing projects. The focus is placed on implemented adaptation solutions, where solutions are referred to as actual measures, approaches, or processes designed to adjust natural or human systems to current or anticipated climate-related impacts in ways that reduce climate risks and increase resilience (Haasnoot et al. 2020 ). Solutions were collected from the Adaptation at Altitude Solutions Portal (hereafter A@A Solution portal) (Adaptation at Altitude 2021 ), which was co-designed by scientists and practitioners in response to the increased needs of a more practice-oriented science of adaptation that takes into account local necessities and different knowledge systems (Muccione et al. 2019 ). We assessed 88 adaptation solutions initially featured in the A@A Solution portal, implemented across various mountain regions and countries by different organizations and project developers. We explored their characteristics, feasibility and transformative potential. By highlighting the importance of co-developing and maintaining an adaptation solution portal, we demonstrate how such an approach enriches our understanding of adaptation progress in mountains and contribute to broaden the landscape of systematic assessments ofadaptation.
The methodological approach used in this study was designed in the context of Adaptation at Altitude (hereafter A@A), launched in 2020. A@A aims to enhance the resilience and adaptive capacities of mountain communities (Adaptation at Altitude 2021 ). The programme addresses four main challenges of adaptation in mountains, namely: (1) data information and monitoring; (2) regional science-policy exchange and collaborative action; (3) knowledge generation and sharing; and (4) policy mainstreaming. To address challenge three, “knowledge generation and sharing”, an online survey was designed to systematically collect relevant information from mountain adaptation projects with the ultimate goal of building a live portal of adaptation solutions in mountains. To this end, the A@A Solution Portal collects, in one place, relevant information concerning numerous adaptation projects and their implementers around the world. The portal allows the sharing and exploring of past or ongoing tried-and-tested adaptation solutions in mountain regions. A schematic view on the methodological approach used in this study is given in Fig. 1 and explained in the next sub-sections.
Schematic overview of the methodological approach used in the paper from survey design to assessment of the solutions
The survey employed to populate the A@A solution portal was co-designed by the partner institutions of the programme and informed by a preparatory phase that included a user needs assessment, as well as a review of existing on-line climate adaptation platforms. The user needs assessment involved eleven semi-structured interviews and one on-line workshop with international actors engaged in the funding, evaluation, planning, management and/or implementation of climate adaptation activities in mountain regions. These stakeholders included representatives from A@A partners, the World Bank, Business for Nature, and lead authors of the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). More detailed information on the project and its partners can be found on the A@A website (Adaptation at Altitude 2021 ). The user needs consultation was done bottom-up and allowed participants to define the type of information most valuable to practitioners and developers of adaptation projects, as well technical gaps or shortcomings of existing platforms. In parallel, the review of on-line platforms providing climate adaptation solutions was also conducted. This review consisted of three main phases: screening, in-depth analysis of selected platforms, and gaps identification. From the 55 platforms screened, 20 were selected for in-depth analysis. This analysis revealed that more than half of the platforms (54%) showcase climate change adaptation (CCA) solutions primarily at the local scale, followed by mixed (23%, this category includes local, regional, national, international and global), national (15%) and regional (8%) level solutions. None of the analysed platforms specifically focussed on mountain regions, nor considered a comprehensive range of factors that enable or limit transformative potential. The results of the preparatory phase are described in (Scolobig A. et al. 2020 ). The final product of the preparatory phase was a survey with multiple choices and open-ended questions that served to populate the solution portal. The survey was co-developed in an iterative process involving A@A partners in eight review rounds. Along with the descriptive information, the survey collected significant supporting documentation, and the contact details of some of the principal actors involved in the planning and/or implementation processes.
An overview of the main information collected through the survey is given in Table 1 , while a copy of the survey can be found in the supplementary material. Project implementers fill in the survey through an electronic template. This process benefitted from the extensive media efforts of the A@A team that promoted the survey and ultimately the solution portal on websites of the partner institutions, Facebook, X (former Twitter) and LinkedIn, as well as in workshops, seminars, and conferences, mainly under the umbrella of the A@A programme. In addition to project implementers directly responding to the survey, the A@A team also actively collected information from project resources available online, in all cases iterating with project implementers to ensure accuracy of the information entered into the portal. Training resources for filling in the survey, such as a step-by-step guide, an example of a filled-in survey, and the inclusion of sample responses into the questionnaire were made available through the A@A website. To secure consistency and high quality of information, all completed surveys undergo a quality control evaluation, performed by the project team before the corresponding adaptation solution is published on the portal. At the time of writing this article, the A@A Solution Portal consisted of 88 solutions.
For the purpose of this study, we assessed the (1) general characteristics of the solutions, namely location, climate impact addressed, type of mountain ecosystem, sectors where the solution was implemented and type of solution, (2) their feasibility and effectiveness, and (3) their transformation potential. To measure feasibility, we followed a concept developed by Singh et al. 2020 where feasibility is understood as the potential for an adaptation solution to be implemented. We measured the contribution made to the implementation of the solutions in terms of knowledge, technology, political/legal, institutional and socio-cultural factors, to which we refer as “capacities”. Such list of factors was agreed upon using existing literature (Singh et al. 2020 ) and supplemented by the user needs consultations. The analogy of feasibility with capacity is related to the concept of adaptive capacity in adaptation science, which is the ability of a systems to prepare for, or respond to potential damages, and to take advantage of new opportunities by making the appropriate adjustments. The definition of each category is provided in Table 1 . We measured each category using a qualitative scoring from 0 (not present) and 1 (very low), to 5 (very high). To capture effectiveness, we focused on the outcomes of adaptation (Singh et al. 2020 ), both as risk reduction benefits and as more extensive benefits derived from adaptation as improvement in environmental, economic or socio-political conditions (Remling and Persson 2015 ; Sharifi 2021 ).
Regarding the transformation potential of adaptation solutions, this was measured using the four key dimensions for transformations developed by the World Bank (World Bank Group 2016 ). This choice is justified by the need to focus on an approach coming from an applied or practical perspective. In a nutshell, we measured four dimensions:
Relevance – does the solution address a major constraint or problem of critical importance to sustainable development in mountain regions?
Depth of change – does the solution cause or support fundamental change in a governance system or behaviour?
Scalability of change – could the solution be feasibly scaled-up and duplicated in other mountain regions?
Sustainability – does the solution demonstrate financial, economic, and environmental sustainability?
One key difference from more academic approaches such as those that measure transformations as speed (how fast adaptation is being implemented), scope (breadth of the measures in terms of both sectorial and spatial extent), and depth (represents the novelty of adaptation actions) (Termeer et al. 2017 ; Berrang-Ford et al. 2021a ), is that we allocated a greater emphasis on the potential for scaling up, rather than on the initial scale of the solution. This enabled the inclusion of small-scale solutions (e.g., community-based approaches) that may be only in the pilot phase but offer large potential for future replication and mainstreaming. An overview on the characteristics assessed, as well as proxies to measure feasibility, effectiveness and transformation (or transformative potential) is given in Table 1 .
For the data analysis, the information included in the solution portal was downloaded from the A@A Portal website and saved in an excel sheet. The dataset was subjected to a series of pre-processing steps to ensure its suitability for subsequent analysis. The dataset was structured into a Pandas dataframe object. The dataframe serves as a two-dimensional, size-mutable, and heterogeneous tabular data structure, providing a convenient and intuitive way to perform data manipulation and analysis (Pandas 2024 ). To facilitate analysis of categorical variables, we applied one-hot encoding, converting categorical attributes into a binary representation. Such transformation is essential for preparing categorical data for certain types of analysis that require numerical input. To analyse the solution description text, we first utilize the spaCy ( https://spacy.io/ ), which is an open-source natural language processing library specifically crafted for extracting information from text corpora. Subsequently, the term-frequency times inverse document frequency (TF-IDF) technique is employed to reducing the influence of frequently occurring words that lack informative value within the corpus (Leskovec 2014 ). TF-IDF serve diverse purposes, including facilitating the visualization of words via word clouds.
The capacities were scored on a five-point scale going from very low to very high. The score for each solution and its capacities was assessed by a minimum of 2 project members to check for consistencies and discussions were held until agreement was reached on the final score. The score was also triangulated with the qualitative description of the text on the corresponding capacity, which is also stored in the solution database.
At the time of analysis, the solution portal contained 88 discrete adaptation solutions. New solutions are being uploaded to the A@A Portal on an ongoing basis. The final dataset with the 88 solutions can be found in the supplementary material and the notebooks needed to reproduce all analysis and figures are available through the https://github.com/vmuccion/Adaptation-Altitude .
The first entry in the database alongside the unique title, is a description of the solution. Figure 2 displays a word cloud illustrating the prevalence of the words extracted from the description text. Notably, “water” is highlighted as the most prevalent word, followed by other key words such as “community”, “land”, “local”, and “capacity”. This pattern indicated a prevalence of community and local based measures, with water being the dominant aspect, not only in terms of sector, but also concerning the typology of solutions.
World cloud of most frequent single words obtained from the summary description of the solutions
The geographical distribution of solutions in Fig. 3 (top panel) shows that there is a considerable tendency in the portal towards specific regions such as North and Southwestern South America, East Africa, and the Hindukush Himalaya (HKH) region. Moreover, there is a handful of solutions in Europe and the Caucasus, but so far, none from North America or Oceania. This is because the solution portal was mainly an effort to collect solutions from the Global South, expressed through the stakeholder needs consultation. However, efforts are underway to have a more balanced geographical coverage that includes additional regions. When it comes to the impacts addressed (Fig. 3 bottom panel), a diversity can be observed in the majority of continents, except in Europe.
Top figure shows a choropleth map of the solutions per country. The bottom figure shows the proportion of climate impacts addressed per continent. Only continents having at least one solution or more are shown
The general characteristics of the solutions are shown in Fig. 4 . Across all solutions, drought emerges as the most common climate impact addressed (63), followed by flood (39), and almost in equal proportion, landslides, altered growing seasons, and heat stress. Wildfire is addressed by only 5 solutions. In addition to these main impacts, the portal retains information on secondary impacts as well. The open nature of this question resulted in greater diversity in terms of reported impacts. In this case, water stress is the most common secondary impact, followed by land degradation, and glacier lake outburst flood. Other secondary impacts include erosion, snow scarcity, and unseasonal frost. The distribution of solution types shows that education and awareness, as well as land use practice, are the most common solution types, followed by monitoring and engineering strategies. Finance solutions are the least common. The sectorial distribution is dominated by agriculture and water, reflecting the emphasis on addressing drought and flood. A similar distribution is seen amongst other sectors, namely human health and well-being, natural hazards, plans and policy, ecosystem, and biodiversity. Tourism and transport are the least covered sectors. Finally, there is a more proportional distribution in the ecosystem types, with a prevalence of agricultural land, forest and high alpine. Urban solutions represent the lowest percentage.
Summary of the main characteristics across all solutions, from top to bottom clockwise, in orange the number of solutions per climate impact addressed, in blue the number of solutions per mountain ecosystem type, in green the number of solutions per solution type and finally in pink the number of solutions per sector
To gain deeper insights into adaptation efforts—particularly the nature, location, and methodologies of implemented solutions—we analyzed the co-occurrence of selected pairs of characteristics. As depicted in Fig. 5 , this analysis focuses on the relationships between solution types and climate impacts (left panel), as well as between solution types and ecosystems (right panel). Notably, education and awareness initiatives, along with land use practices, emerge as the predominant strategies employed to address a wide array of impacts. This includes adapting to the effects of droughts and floods, which constitute the primary climate impacts documented within our portal. Our observations reveal that solutions emphasizing education and awareness are frequently implemented in response to these challenges, complemented by the adoption of land use practices and engineering solutions. However, wildfire mitigation efforts are relatively limited, represented by only five documented solutions, thus revealing a lack of discernible co-occurrence patterns. Moreover, when examining the ecosystems wherein these solutions are enacted, it becomes evident that education and awareness types, alongside land use practices, are prevalent across diverse ecosystem types, spanning from agricultural lands to lakes and rivers. Conversely, fewer solutions are observed in ecosystems such as meadows, peatlands, and urban mountain areas, resulting in a lack of notable co-occurrence patterns within these contexts.
The heatmap on the left side represents co-occurrence between solution types and climate impact addressed; the heatmap on the right side represents co-occurrence between solution types and ecosystem types. The numbers within each cell represent the observation counts in ascending order from light blue to dark blue
Presented here are the feasibility results assessed through the lenses of five capacity categories, scored on a qualitative scale ranging from very low to very high, as shown in Fig. 6 . As can be observed, many of the solutions exhibit very high capacity in all the categories. Knowledge capacities ensure that adaptation is informed from the outset by diverse knowledge types, including scientific, evidence based, and indigenous knowledge. Overall, political/legal and technology capacities were evaluated by solution providers as less crucial than knowledge, institutional, and socio-cultural capacities in enabling the implementation of the solutions. In contrast, providers gave high evaluations to the role played by socio-cultural and institutional capacities. However, it should be noted that approximately one quarter of solutions do not report results on one or more capacities. This gap in reporting complicates the determination of whether a specific capacity is relevant for that solution or not.
The figure shows the number of self-assessed solutions with respect to the five dimensions of capacity on a qualitative scale going from very low to very high. NA means that the dimension was either not assessed or was not relevant
In order to understand the effectiveness of solutions in delivering positive changes ex-post, we explored various categories of benefits. All solutions have benefits associated to them. Our observations indicate that the majority of solutions have resulted in environmental benefits (33), followed by climate risk reduction (32). Other key benefits include social (13), economic (6), and technological (1) benefits. No solution indicates political benefits (Fig. 7 ).
Number of solutions reporting some type of benefits after implementation
The last segment of the analysis focuses on the assessment of the transformative potential of solutions whereby transformation is assessed according to the indicators described in SM Fig. 1 . The file used to assess the transformative potential is uploaded as supplementary dataset. Figure 8 summarises the results, depicting the number of solutions addressing specific criteria measured by corresponding sets of indicators. As it can be observed, relevance is prevalent across almost all the solutions, except for a handful which either address only one sector or report no specific climate impacts. The depth of change also shows a similar behaviour, with most solutions showing evidence of innovation within their own context and addressing multiple SDGs and Sendai Targets. Further details on specific SDGs and Sendai Target, as well as on their relationship, is provided later in this section. Sustainability is reported in more than two thirds of the solutions, while only a few solutions provide evidence on the scalability of change. While we acknowledge the importance of tailoring adaptation solutions to local environmental, cultural, social and institutional contexts, under transformative adaptation there is an expectation to see learnings and a pathway forward as to how the basic fundamentals of the solution could be transferred to another community, village, district, country or region. Evidence of mainstreaming into wider policies and plans is reported in less than one third of the solutions, and approximately half of them offer evidence of overcoming barriers and successful replication.
Number of solutions for each indicator of transformative potential. A score of 1 is given for each of the indicators being present and 0 when there is no evidence of such. Indicators corresponding to the same dimension of transformations are grouped by colour to facilitate observations. The dimension is shown on top of each group of indicators
In line with the survey design and scope of the study, this analysis includes a review of the principal contributions that the solutions provided to the SDGs (United Nations, 2022). Likewise, the survey also sought to investigate evidence of supporting at least one of the 7 global targets set under the Sendai Framework for Disaster Risk Reduction. Observations indicate that most solutions address at least one SDG, while 18 solutions do not address any of the Sendai targets. Overall, all SDGs, except “life under water” (Fig. 9 ), and all of the Sendai targets (Fig. 10 ) are addressed by the solutions. Some solutions address more than one SDG or Sendai target. As it could be expected given its relevance on the matter of climate adaptation, the most common SDG addressed is Goal 13 (Climate Action), followed by Goal 15 (Life on Land), and Goal 1 (No Poverty). Goals 4 (Quality Education), 7 (Affordable and Clean Energy), and 16 (Peace, Justice and Strong Institutions) are the least frequent. In the case of Sendai Targets, target B, “Substantially reduce the number of affected people globally by 2030”, is addressed by almost 2/3 of the solutions. Target A, “Substantially reduce global disaster mortality by 2030”, is the least addressed target.
The figure shows the number of solutions addressing each of the 17 Sustainable Development Goals (SDGs). Details on the SDGs are provided on the right side of the figure
The figure shows the number of solutions addressing each of the 7 Sendai Targets. Details on the targets are provided on the right side of the figure
Documented adaptation efforts which are measurable and comparable are critical to track progress on the status of implementation (Magnan and Chalastani 2019 , Nalau 2021 ). Therefore, it is essential to assess adaptation experiences by systematically collecting and analysing information on implementation that is happening on the ground (McDowell 2019 ). To respond to this need and as testimony of increasing adaptation efforts, several adaptation portals have appeared in the past few years. These portals facilitate organized tracking of adaptation progress and are well suited for further analysis and assessments (Cebrián-Piqueras 2023 ). In this study, we analysed and assessed the recently implemented or ongoing adaptation solutions in mountain regions, that were collected from the Adaptation at Altitude Solution’s Portal.
The initial survey employed to populate the portal, was co-designed with a bottom-up process by experts and practitioners, this with the aim to capture the elements of adaptation which matter to both groups.
Our research results illustrate that drought (63) is largely the most targeted climate impact, followed by flood (39). This finding is corroborated by systematic reviews, and research articles consistently highlights drought as the primary climate impact targeted for adaptation, followed by flood, in mountain regions (Dubo et al. 2022 ; Wyss et al. 2022 ). Furthermore, the latest IPCC report also indicates that drought and flood pose key risks with the potential for severe consequences for mountain people and livelihoods and highlighted the significance and urgency of addressing water-related hazards in mountains (Adler et al. 2022 ). The prevalence and importance of water for mountains and adaptation are visible in the key words analysis of solutions summary description in Fig. 2 . Interestingly, it is observed that many of the solutions addressing water-related impacts prioritize the integration of watershed management practices. These practices have demonstrated their capacity to effectively tackle multiple concerns beyond climate impacts, including the improvement of water quality (Shin et al. 2023 ), the promotion of aquifer recharge (Bigdeli Nalbandan et al. 2023 ), and the enhancement of the natural linkages between upstream and downstream areas through transdisciplinary planning process (Cheng et al. 2017 ).
When examining the type of solutions, there is a prevalence of education and awareness focused solutions, followed by land use practices. These solutions although implemented to address the majority of climate impacts, appear to be commonly implemented to respond to impacts from floods and droughts (see Fig. 5 ). Evidence indicates that the implementation of this type of solutions is often accompanied by improvements in project acceptability and reduced risk of maladaptation (Nalau and Cobb 2022 ). This positive outcome is attributed to the fact that awareness is, in most cases, the result of community involvement (Oliver et al. 2023 ). The solutions showcased on the A@A Solutions Portal reveal a high involvement of local community groups and populations in project activities, well beyond the classical initial consultations. Remarkably, about 75% of solutions show inclusive decision making (see Fig. 8 ). However, despite the pivotal role of community participation, the exercise often faces a number of challenges and requires careful handling to prevent the reinforcement of social issues, such as gender inequality and class-based hierarchies (Nalau and Cobb 2022 ; Singh 2020 ).
Agriculture land and forests emerge as the main mountain ecosystems wherein solutions are reported, with agriculture and water being the main sectors within which solutions are mostly implemented. This further reflects the importance of tackling water-related impacts and risks for the management of critical sectors, given that mountains boast some of the highest proportions of water availability globally, as well as water withdrawal (Reader et al. 2023b ). The type of solutions implemented in these mountain ecosystems point at a prevalence of education and awareness and land use practices since, as already mentioned, these are by far the most used solutions. It is not surprising that land use practices are highly present in forest and agricultural land areas. However, while the dataset highlights a significant contribution of education and awareness as adaptation solutions in almost every typology of ecosystem, it paradoxically reveals a low impact on Sustainable Development Goal 4 (SDG 4) regarding quality education (Fig. 7 ). This discrepancy may stem from the underreporting of capacity-building and awareness-raising activities under the broad category of education. Additionally, it prompts consideration of whether the targets outlined in SDG 4 are perceived as exclusively related to conventional curriculum-based education, potentially overlooking non-traditional forms of educational initiatives such as those related to awareness raising or building capacity. McKenzie et al. ( 2024 ) have argued that indeed it is currently difficult to track progress on SDG4 in relation to climate change due to a lack of quality and appropriate indicators. Despite this discrepancy, the overall picture remains positive, with many Sustainable Development Goals (SDGs) and Sendai targets being addressed laterally within the solution portal, with only a few exceptions (Fig. 7 ). This observation aligns with the significant synergies underscored in the IPCC WG2 Cross-Chapter paper on Mountains (Adler et al. 2022 ). Based on the findings of our research, we have identified that several Sustainable Development Goals (SDGs) and Sendai targets are indeed addressed within the solution portal. This evidence counters previously highlighted gaps that acknowledged the limited evidence of implementation of international agendas in addressing disaster risk reduction and adaptation in mountainous regions (Adler et al. 2022 ; Alcántara-Ayala et al. 2022 ). By tracking evidence collected from empirical adaptation, we underscore here the imperative for sustained efforts to bridge the disparity between policy frameworks and their practical implementation on the ground.
Nuanced concepts such as feasibility, effectiveness, and transformative potential, were assessed by means of proxy indicators. In the case of feasibility, we examined the score of five main categories of capacity that were present in the project survey and that are analogous to the characterisation of feasibility according to existing literature (Singh et al. 2020 ). Although the results in Fig. 6 would point at high to very high capacity for many categories, we recognise that there is a high proportion of solutions which do not provide such information and cannot be assessed. There are nonetheless some noticeable patterns as for example, the fact that knowledge capacities score very high for more than half of solutions, whereas technological capacities show a more heterogeneous picture as enablers of solution implementation. This could be due to technology in mountain areas, being used in diverse ways, such as the development of high-resolution models that incorporate climate and socio-economic impacts on natural ecosystems, and on significant resources such as hydrological components (Immerzeel et al. 2020 ). At the same time, adaptation initiatives may rely on the formulation of structural and physical components (e.g., hard adaptation), addressing agriculture and food security, water management, and infrastructure, for example, through the creation of reservoirs and modern irrigation systems, water conservation techniques, and hazard management technologies such as early warning systems (Adler et al. 2022 ). However, in contrast, solutions which focus on education and awareness raising do not rely upon strong technical capacities from the onset, but rather aim to build these capacities through the lifetime of the project. A more pessimistic explanation for the medium to low scores could be the lack of appropriate technological know-how and technology transfer where it is most needed (Wang et al. 2020 ). This though would be at odds with the high score in the knowledge capacities, which can be reasonably associated with technological knowhow, among other dimensions of knowledge. The effectiveness also scores low in technical and political benefits, which might again indicate a persistence in the low technologic and political scores even after solutions are implemented. This last assertion would confirm the findings in McDowell et al. 2021 ; which cite limited technological know-how and political willingness as hindrances to the full realization of adaptation solutions in mountainous areas. In general, we can infer that solutions are being effective in reducing risks and improving environmental conditions and are benefitting from high knowledge capacities to enable implementation. Nevertheless, solutions do not seem to spur technological or political improvements, or such improvements are not relevant to the project scope, which suggests possible missed opportunities for important co-benefits. Analogous studies which performed systematic assessments of the adaptation literature in mountain regions have reported also environmental co-benefits but limited political or institutional positive spill over (Aggarwal et al. 2022 ).
To get a sense of the transformative potential of solutions, we explored transformations through the lenses of four criteria, namely relevance, depth of change, scalability of change, and sustainability. We see from the results in Fig. 8 that solutions are being implemented where they are most relevant, and that almost all of them cause or support fundamental change (depth of change). As most solutions are local or sub-national (see Fig. 3 ), it is plausible to infer that such depth of change happens more at the community level. However, the fact that upscaling is difficult to achieve poses questions concerning the identification of the enabling factors that eventually lead to upscaling. This is also supported by the finding that only a handful of solutions provide evidence of mainstreaming and replication. Berrang-Ford et al. ( 2021a ) confirmed this trend of limited scope of solutions in their global stocktake of human adaptation. Indeed, they reported that globally, adaptation solutions generally have a limited geographical extent and low levels of mainstreaming (Berrang-Ford et al. 2021a ). In part, this comes down to the typical short duration of adaptation projects (4–5 years) where mainstreaming becomes something of an afterthought towards the end of the project cycle rather than a goal in itself. Nonetheless, the reported success of the mountain solutions in terms of depth of change at local or sub-national level bodes well for future mainstreaming and upscaling, even if this is not occurring as rapidly as would be desired.
In essence, we can say that while the criteria of relevance and, to a geographically limited extent, depth of change, have largely been met, solutions had difficulties in demonstrating that their contribution to deliver large-scale impact by introducing new measures into the local policy frameworks or by replicating their actions in other locations. Research on social innovation identifies different types of upscaling that may be instrumental also for climate adaptation (Moore et al. 2015 ), namely, scale up (impacting laws and policies), scale out (increasing number of people or communities impacted by the solution), and scale deep (impacting cultural values and beliefs). Given the longer time frames needed, designing project with a second phase dedicated to mainstreaming and upscaling efforts would significantly increase the transformative potential of adaptation solutions in mountain regions.
The sustainability criteria are moderately met for our analysed solutions, and it is encouraging to see that inclusive decision-making processes and future proofing are being embedded in many of them. It is less clear though, whether long term plans are being integrated, and again, this is something that confirms the limited scalability and mainstreaming potential of solutions. Limited scalability, mainstreaming, and long-term planning could be all explained by an observed tendency in climate project decision making to leave planning and discussion around scaling up or replication until very late stages or following the closure of interventions (Jain and Bardhan 2023 ). Furthermore, the gap in the implementation of adaptation mainstreaming seems closely related to the lack of political commitment and mandate at the higher governmental levels (Runhaar et al. 2018 ).
Far from being all encompassing, the A@A Solution Portal misses yet the showcasing of other important mountain regions, possibly because of a bias in the initial scope of the survey and solicitation efforts, which were mainly geared towards international development and cooperation. Fortunately, efforts are underway to have a more geographically balanced display of solutions that will enhance learning between mountain regions in the global south and north. It is worth pointing out that the portal collected information not only from the project developers and implementers but also triangulated this information with project evaluation reports, which are usually developed by independent evaluation bodies and consultants. Typical mid-term or final project reports are normally based on a mix of interviews conducted with those involved in project implementation and projected beneficiaries. To minimise bias in reporting, the information was thoroughly screened for quality control by the independent team members from the A@A project. For example, project reports only seldomly involve any longer-term monitoring and evaluation of the solutions. Hence, effort was made during the quality control to ensure that statements around the foreseen long-term success and sustainability of the solutions was well-supported with concrete evidence that financial and technical plans were in place. Obvious difficulties exist for reaching out to an independent and representative sample of stakeholders, particularly ensuring representation of the most vulnerable or marginalised members of the communities. Therefore, the implementation of adaptation project design should from the beginning include more regular external evaluations and broader stakeholder engagement, whose views would equally constitute the body of independent evidence for ex-post project assessment (Wamsler et al. 2020 ; Oliver et al. 2023 ). In absence of such independent information, it is often difficult to get a sense of the progress for those who are the direct beneficiaries of these solutions and therefore such views cannot fully by captured in the remit of this solution portal. The second phase of the A@A project will attempt to fill this gap for selected solutions, by undertaking focus group meetings and interviews with benefactors and other stakeholders to gain ground level insights on the long-term effectiveness of the implemented solutions.
Another challenge of adaptation is the persistent lack of integration of concepts and terminology across different strains of literature, whether adaptation, vulnerability, or impact driven (Berrang-Ford et al. 2021a ). This has been identified as a persistent barrier to adaptation assessment. To this end we invoke here for a common adaptation taxonomy. Currently absent, such a taxonomy would require consensus within the broadest community, offering scholars and practitioners a detailed and common description of benefits, ecosystems, sectors, solutions, capacities, as well as other critical concepts. The survey conducted within this study presents intriguing entry points for such a taxonomy specific to mountain regions. For instance, it identifies solutions and their characteristics in mountains, including sectors, ecosystems, and solution types. Yet, further work is necessary to achieve a robust consensus.
Data and Jupyter notebooks for the analysis are all accessible through the following GitHub repository https://github.com/vmuccion/Adaptation-Altitude .
The notebooks are accessible through GitHub: https://github.com/vmuccion/Adaptation-Altitude .
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This research has been supported by the Adaptation at Altitude project, which is a project financed by the Swiss Agency for Development and Cooperation (SDC).
Open Access funding provided by Lib4RI – Library for the Research Institutes within the ETH Domain: Eawag, Empa, PSI & WSL. No funding was received to assist with the preparation of this manuscript. The authors also declare that they have no financial interests.
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Veruska Muccione
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Veruska Muccione, Julia Aguilera Rodriguez, Anna Scolobig, Markus Stoffel & Simon K. Allen
Department of Geography, University of Zurich, Zurich, Switzerland
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Anna Scolobig
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VM developed the concept for the paper and wrote every section of the manuscript. She did extensive data pre-processing and most of the data analysis. SKA had the initial ideas for such a paper and contributed in developing the methodology to assess the transformative potential together with JA. AS and JA were actively involved in the development of the methodology for the data collection and quality control. RW and JB hosted the portal database and provided VM with the raw dataset from the Adaptation at Altitude website. RW maintained the Adaptation at Altitude Portal together with JZ, OS, and SKA. Everyone contributed to edit and revise the paper. Correspondence and requests for materials should be addressed to [email protected].
Correspondence to Veruska Muccione .
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Muccione, V., Aguilera Rodriguez, J., Scolobig, A. et al. Trends in climate adaptation solutions for mountain regions. Mitig Adapt Strateg Glob Change 29 , 74 (2024). https://doi.org/10.1007/s11027-024-10168-8
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A theoretical framework guides the research process like a roadmap for the study, so you need to get this right. Theoretical framework 1,2 is the structure that supports and describes a theory. A theory is a set of interrelated concepts and definitions that present a systematic view of phenomena by describing the relationship among the variables for explaining these phenomena.
Theoretical Framework. Definition: Theoretical framework refers to a set of concepts, theories, ideas, and assumptions that serve as a foundation for understanding a particular phenomenon or problem. It provides a conceptual framework that helps researchers to design and conduct their research, as well as to analyze and interpret their findings.
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The theoretical framework is the structure that can hold or support a theory of a research study. The theoretical framework encompasses not just the theory, but the narrative explanation about how the researcher engages in using the theory and its underlying assumptions to investigate the research problem.
These different types of frameworks highlight different aspects in an educational setting, which influences the design of the study and the collection of data. ... Including a conceptual framework in a research study is important, but researchers often opt to include either a conceptual or a theoretical framework. Either may be adequate, but ...
A theoretical framework is a foundational review of existing theories that serves as a roadmap for developing the arguments you will use in your own work. Theories are developed by researchers to explain phenomena, draw connections, and make predictions. In a theoretical framework, you explain the existing theories that support your research ...
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The Importance of Research Frameworks. Researchers may draw on several elements to frame their research. Generally, a framework is regarded as "a set of ideas that you use when you are forming your decisions and judgements" 13 or "a system of rules, ideas, or beliefs that is used to plan or decide something." 14 Research frameworks may consist of a single formal theory or part thereof ...
A conceptual framework illustrates the expected relationship between your variables. It defines the relevant objectives for your research process and maps out how they come together to draw coherent conclusions. Tip. You should construct your conceptual framework before you begin collecting your data.
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Frameworks for Qualitative Research CHAPTER 05-Willis (Foundations)-45170.qxd 1/2/2007 3:15 PM Page 147. ... to standards of truth and falsity, that subject hypotheses (of whatever type) to test and thus potential disconfirmation, and on being open-minded about criticism" (pp. 86-87). "We need disciplined, competent inquiry to establish
A conceptual framework in research is used to understand a research problem and guide the development and analysis of the research. It serves as a roadmap to conceptualize and structure the work by providing an outline that connects different ideas, concepts, and theories within the field of study. A conceptual framework pictorially or verbally ...
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A theoretical framework is a conceptual model that provides a systematic and structured way of thinking about a research problem or question. It helps to identify key variables and the relationships between them and to guide the selection and interpretation of data. Theoretical frameworks draw on existing theories and research and can be used ...
A research framework provides an underlying structure or model to support our collective research efforts. Up until now, we've referenced, referred to and occasionally approached research as more of an amalgamated set of activities. But as we know, research comes in many different shapes and sizes, is variable in scope, and can be used to ...
2.2 Analytical framework of the survey. For the purpose of this study, we assessed the (1) general characteristics of the solutions, namely location, climate impact addressed, type of mountain ecosystem, sectors where the solution was implemented and type of solution, (2) their feasibility and effectiveness, and (3) their transformation potential.